diff --git a/core/src/main/java/org/apache/carbondata/core/datastore/page/ColumnPage.java b/core/src/main/java/org/apache/carbondata/core/datastore/page/ColumnPage.java index fa71681a0b0..680ee3f2fd5 100644 --- a/core/src/main/java/org/apache/carbondata/core/datastore/page/ColumnPage.java +++ b/core/src/main/java/org/apache/carbondata/core/datastore/page/ColumnPage.java @@ -445,6 +445,8 @@ public Object getData(int rowId) { return getInt(rowId); } else if (dataType == LONG) { return getLong(rowId); + } else if (dataType == DataTypes.FLOAT) { + return getFloat(rowId); } else if (dataType == DataTypes.DOUBLE) { return getDouble(rowId); } else if (DataTypes.isDecimal(dataType)) { @@ -560,6 +562,8 @@ private Object getNull(int rowId) { result = getInt(rowId); } else if (dataType == LONG) { result = getLong(rowId); + } else if (dataType == DataTypes.FLOAT) { + return getFloat(rowId); } else if (dataType == DataTypes.DOUBLE) { result = getDouble(rowId); } else if (DataTypes.isDecimal(dataType)) { diff --git a/core/src/main/java/org/apache/carbondata/core/datastore/page/ColumnPageValueConverter.java b/core/src/main/java/org/apache/carbondata/core/datastore/page/ColumnPageValueConverter.java index 96623678e87..dc6621d5a0f 100644 --- a/core/src/main/java/org/apache/carbondata/core/datastore/page/ColumnPageValueConverter.java +++ b/core/src/main/java/org/apache/carbondata/core/datastore/page/ColumnPageValueConverter.java @@ -43,6 +43,12 @@ public interface ColumnPageValueConverter { long decodeLong(int value); + float decodeFloat(byte value); + + float decodeFloat(short value); + + float decodeFloat(int value); + double decodeDouble(byte value); double decodeDouble(short value); diff --git a/core/src/main/java/org/apache/carbondata/core/datastore/page/LazyColumnPage.java b/core/src/main/java/org/apache/carbondata/core/datastore/page/LazyColumnPage.java index c90eb6b3ea2..2a3251df5a6 100644 --- a/core/src/main/java/org/apache/carbondata/core/datastore/page/LazyColumnPage.java +++ b/core/src/main/java/org/apache/carbondata/core/datastore/page/LazyColumnPage.java @@ -94,7 +94,22 @@ public double getDouble(int rowId) { @Override public float getFloat(int rowId) { - return (float) getDouble(rowId); + DataType dataType = columnPage.getDataType(); + if (dataType == DataTypes.BOOLEAN || dataType == DataTypes.BYTE) { + return converter.decodeFloat(columnPage.getByte(rowId)); + } else if (dataType == DataTypes.SHORT) { + return converter.decodeFloat(columnPage.getShort(rowId)); + } else if (dataType == DataTypes.SHORT_INT) { + return converter.decodeFloat(columnPage.getShortInt(rowId)); + } else if (dataType == DataTypes.INT) { + return converter.decodeFloat(columnPage.getInt(rowId)); + } else if (dataType == DataTypes.FLOAT) { + return columnPage.getFloat(rowId); + } else if (dataType == DataTypes.BINARY) { + return converter.decodeFloat(columnPage.getByte(rowId)); + } else { + throw new RuntimeException("internal error: " + this); + } } @Override diff --git a/core/src/main/java/org/apache/carbondata/core/datastore/page/encoding/ColumnPageEncoderMeta.java b/core/src/main/java/org/apache/carbondata/core/datastore/page/encoding/ColumnPageEncoderMeta.java index a1e37652d55..d80527f4233 100644 --- a/core/src/main/java/org/apache/carbondata/core/datastore/page/encoding/ColumnPageEncoderMeta.java +++ b/core/src/main/java/org/apache/carbondata/core/datastore/page/encoding/ColumnPageEncoderMeta.java @@ -247,6 +247,11 @@ private byte[] getValueAsBytes(Object value, DataType dataType) { b.putLong((long) value); b.flip(); return b.array(); + } else if (dataType == DataTypes.FLOAT) { + b = ByteBuffer.allocate(8); + b.putFloat((float) value); + b.flip(); + return b.array(); } else if (dataType == DataTypes.DOUBLE) { b = ByteBuffer.allocate(8); b.putDouble((double) value); diff --git a/core/src/main/java/org/apache/carbondata/core/datastore/page/encoding/DefaultEncodingFactory.java b/core/src/main/java/org/apache/carbondata/core/datastore/page/encoding/DefaultEncodingFactory.java index b43f5662ba5..ef0ba73674a 100644 --- a/core/src/main/java/org/apache/carbondata/core/datastore/page/encoding/DefaultEncodingFactory.java +++ b/core/src/main/java/org/apache/carbondata/core/datastore/page/encoding/DefaultEncodingFactory.java @@ -166,6 +166,8 @@ private static DataType fitMinMax(DataType dataType, Object max, Object min) { return fitLongMinMax((int) max, (int) min); } else if ((dataType == DataTypes.LONG) || (dataType == DataTypes.TIMESTAMP)) { return fitLongMinMax((long) max, (long) min); + } else if (dataType == DataTypes.FLOAT) { + return fitLongMinMax((long) (float) max, (long) (float) min); } else if (dataType == DataTypes.DOUBLE) { return fitLongMinMax((long) (double) max, (long) (double) min); } else { @@ -212,6 +214,8 @@ private static DataType fitDelta(DataType dataType, Object max, Object min) { value = (long) (short) max - (long) (short) min; } else if (dataType == DataTypes.INT) { value = (long) (int) max - (long) (int) min; + } else if (dataType == DataTypes.FLOAT) { + value = (long) (float) max - (long) (float) min; } else if (dataType == DataTypes.LONG || dataType == DataTypes.TIMESTAMP) { value = (long) max - (long) min; // The subtraction overflowed iff the operands have opposing signs @@ -329,6 +333,9 @@ private static ColumnPageCodec getColumnPageCodec(SimpleStatsResult stats, // If absMaxValue exceeds LONG.MAX_VALUE, then go for direct compression if ((Math.pow(10, decimalCount) * absMaxValue) > Long.MAX_VALUE) { return new DirectCompressCodec(DataTypes.DOUBLE); + } else if (srcDataType == DataTypes.FLOAT && + (Math.pow(10, decimalCount) * absMaxValue) > Integer.MAX_VALUE) { + return new DirectCompressCodec(DataTypes.FLOAT); } else { long max = (long) (Math.pow(10, decimalCount) * absMaxValue); DataType adaptiveDataType = fitLongMinMax(max, 0); diff --git a/core/src/main/java/org/apache/carbondata/core/datastore/page/encoding/adaptive/AdaptiveDeltaFloatingCodec.java b/core/src/main/java/org/apache/carbondata/core/datastore/page/encoding/adaptive/AdaptiveDeltaFloatingCodec.java index f9abd16b766..b5467c4d89e 100644 --- a/core/src/main/java/org/apache/carbondata/core/datastore/page/encoding/adaptive/AdaptiveDeltaFloatingCodec.java +++ b/core/src/main/java/org/apache/carbondata/core/datastore/page/encoding/adaptive/AdaptiveDeltaFloatingCodec.java @@ -53,6 +53,7 @@ public class AdaptiveDeltaFloatingCodec extends AdaptiveCodec { private Double factor; + private float floatFactor; private long max; public static ColumnPageCodec newInstance(DataType srcDataType, DataType targetDataType, @@ -65,6 +66,7 @@ public AdaptiveDeltaFloatingCodec(DataType srcDataType, DataType targetDataType, SimpleStatsResult stats, boolean isInvertedIndex) { super(srcDataType, targetDataType, stats, isInvertedIndex); this.factor = Math.pow(10, stats.getDecimalCount()); + floatFactor = factor.floatValue(); if (srcDataType == DataTypes.FLOAT) { this.max = (long) ((long) Math.pow(10, stats.getDecimalCount()) * ((float) stats.getMax())); @@ -185,13 +187,15 @@ public void encode(int rowId, long value) { @Override public void encode(int rowId, float value) { if (targetDataType.equals(DataTypes.BYTE)) { - encodedPage.putByte(rowId, (byte) (max - (value * factor))); + encodedPage.putByte(rowId, (byte) (max - (value * floatFactor))); } else if (targetDataType.equals(DataTypes.SHORT)) { - encodedPage.putShort(rowId, (short) (max - (value * factor))); + encodedPage.putShort(rowId, (short) (max - (value * floatFactor))); } else if (targetDataType.equals(DataTypes.SHORT_INT)) { - encodedPage.putShortInt(rowId, (int) (max - (value * factor))); + encodedPage.putShortInt(rowId, (int) (max - (value * floatFactor))); } else if (targetDataType.equals(DataTypes.INT)) { - encodedPage.putInt(rowId, (int) (max - (value * factor))); + encodedPage.putInt(rowId, (int) (max - (value * floatFactor))); + } else if (targetDataType.equals(DataTypes.FLOAT)) { + encodedPage.putFloat(rowId, value); } else { throw new RuntimeException("internal error: " + debugInfo()); } @@ -229,6 +233,21 @@ public long decodeLong(int value) { throw new RuntimeException("internal error: " + debugInfo()); } + @Override + public float decodeFloat(byte value) { + return (max - value) / floatFactor; + } + + @Override + public float decodeFloat(short value) { + return (max - value) / floatFactor; + } + + @Override + public float decodeFloat(int value) { + return (max - value) / floatFactor; + } + @Override public double decodeDouble(byte value) { return (max - value) / factor; @@ -265,7 +284,6 @@ public void decodeAndFillVector(byte[] pageData, ColumnVectorInfo vectorInfo, Bi int intSizeInBytes = DataTypes.INT.getSizeInBytes(); int longSizeInBytes = DataTypes.LONG.getSizeInBytes(); if (vectorDataType == DataTypes.FLOAT) { - float floatFactor = factor.floatValue(); if (pageDataType == DataTypes.BOOLEAN || pageDataType == DataTypes.BYTE) { for (int i = 0; i < pageSize; i++) { vector.putFloat(i, (max - pageData[i]) / floatFactor); diff --git a/core/src/main/java/org/apache/carbondata/core/datastore/page/encoding/adaptive/AdaptiveDeltaIntegralCodec.java b/core/src/main/java/org/apache/carbondata/core/datastore/page/encoding/adaptive/AdaptiveDeltaIntegralCodec.java index 29c8b7b8491..eab75e14c7c 100644 --- a/core/src/main/java/org/apache/carbondata/core/datastore/page/encoding/adaptive/AdaptiveDeltaIntegralCodec.java +++ b/core/src/main/java/org/apache/carbondata/core/datastore/page/encoding/adaptive/AdaptiveDeltaIntegralCodec.java @@ -69,6 +69,8 @@ public AdaptiveDeltaIntegralCodec(DataType srcDataType, DataType targetDataType, this.max = (int) stats.getMax(); } else if (srcDataType == DataTypes.LONG || srcDataType == DataTypes.TIMESTAMP) { this.max = (long) stats.getMax(); + } else if (srcDataType == DataTypes.FLOAT) { + this.max = (int) (float) stats.getMax(); } else if (srcDataType == DataTypes.DOUBLE) { this.max = (long) (double) stats.getMax(); } else if (DataTypes.isDecimal(srcDataType)) { @@ -236,8 +238,6 @@ public void encode(int rowId, float value) { encodedPage.putShortInt(rowId, (int) (max - value)); } else if (targetDataType == DataTypes.INT) { encodedPage.putInt(rowId, (int) (max - value)); - } else if (targetDataType == DataTypes.LONG) { - encodedPage.putLong(rowId, (long) (max - value)); } else { throw new RuntimeException("internal error"); } @@ -275,6 +275,21 @@ public long decodeLong(int value) { return max - value; } + @Override + public float decodeFloat(byte value) { + return max - value; + } + + @Override + public float decodeFloat(short value) { + return max - value; + } + + @Override + public float decodeFloat(int value) { + return max - value; + } + @Override public double decodeDouble(byte value) { return max - value; diff --git a/core/src/main/java/org/apache/carbondata/core/datastore/page/encoding/adaptive/AdaptiveFloatingCodec.java b/core/src/main/java/org/apache/carbondata/core/datastore/page/encoding/adaptive/AdaptiveFloatingCodec.java index c6a0c28ca8f..ecce286bf78 100644 --- a/core/src/main/java/org/apache/carbondata/core/datastore/page/encoding/adaptive/AdaptiveFloatingCodec.java +++ b/core/src/main/java/org/apache/carbondata/core/datastore/page/encoding/adaptive/AdaptiveFloatingCodec.java @@ -172,7 +172,19 @@ public void encode(int rowId, long value) { @Override public void encode(int rowId, float value) { - encode(rowId, (double) value); + if (targetDataType == DataTypes.BYTE) { + encodedPage.putByte(rowId, (byte) Math.round(value * floatFactor)); + } else if (targetDataType == DataTypes.SHORT) { + encodedPage.putShort(rowId, (short) Math.round(value * floatFactor)); + } else if (targetDataType == DataTypes.SHORT_INT) { + encodedPage.putShortInt(rowId, Math.round(value * floatFactor)); + } else if (targetDataType == DataTypes.INT) { + encodedPage.putInt(rowId, Math.round(value * floatFactor)); + } else if (targetDataType == DataTypes.FLOAT) { + encodedPage.putFloat(rowId, value); + } else { + throw new RuntimeException("internal error: " + debugInfo()); + } } @Override @@ -209,6 +221,21 @@ public long decodeLong(int value) { throw new RuntimeException("internal error: " + debugInfo()); } + @Override + public float decodeFloat(byte value) { + return value / floatFactor; + } + + @Override + public float decodeFloat(short value) { + return value / floatFactor; + } + + @Override + public float decodeFloat(int value) { + return value / floatFactor; + } + @Override public double decodeDouble(byte value) { return value / factor; diff --git a/core/src/main/java/org/apache/carbondata/core/datastore/page/encoding/adaptive/AdaptiveIntegralCodec.java b/core/src/main/java/org/apache/carbondata/core/datastore/page/encoding/adaptive/AdaptiveIntegralCodec.java index 7c8ff75aa38..c24678f5807 100644 --- a/core/src/main/java/org/apache/carbondata/core/datastore/page/encoding/adaptive/AdaptiveIntegralCodec.java +++ b/core/src/main/java/org/apache/carbondata/core/datastore/page/encoding/adaptive/AdaptiveIntegralCodec.java @@ -251,6 +251,21 @@ public long decodeLong(int value) { return value; } + @Override + public float decodeFloat(byte value) { + return value; + } + + @Override + public float decodeFloat(short value) { + return value; + } + + @Override + public float decodeFloat(int value) { + return value; + } + @Override public double decodeDouble(byte value) { return value; diff --git a/core/src/main/java/org/apache/carbondata/core/datastore/page/encoding/compress/DirectCompressCodec.java b/core/src/main/java/org/apache/carbondata/core/datastore/page/encoding/compress/DirectCompressCodec.java index 7b1fe1628d4..4e7bc8be2d7 100644 --- a/core/src/main/java/org/apache/carbondata/core/datastore/page/encoding/compress/DirectCompressCodec.java +++ b/core/src/main/java/org/apache/carbondata/core/datastore/page/encoding/compress/DirectCompressCodec.java @@ -215,6 +215,21 @@ public long decodeLong(int value) { return value; } + @Override + public float decodeFloat(byte value) { + return value; + } + + @Override + public float decodeFloat(short value) { + return value; + } + + @Override + public float decodeFloat(int value) { + return value; + } + @Override public double decodeDouble(byte value) { return value; diff --git a/core/src/main/java/org/apache/carbondata/core/scan/expression/ExpressionResult.java b/core/src/main/java/org/apache/carbondata/core/scan/expression/ExpressionResult.java index 03965366325..2e544dbfa9f 100644 --- a/core/src/main/java/org/apache/carbondata/core/scan/expression/ExpressionResult.java +++ b/core/src/main/java/org/apache/carbondata/core/scan/expression/ExpressionResult.java @@ -80,6 +80,7 @@ public Integer getInt() throws FilterIllegalMemberException { } else if (dataType == DataTypes.SHORT) { return ((Short) value).intValue(); } else if (dataType == DataTypes.INT || + dataType == DataTypes.FLOAT || dataType == DataTypes.DOUBLE) { if (value instanceof Double) { return ((Double) value).intValue(); @@ -87,6 +88,9 @@ public Integer getInt() throws FilterIllegalMemberException { if (value instanceof Long) { return ((Long) value).intValue(); } + if (value instanceof Float) { + return ((Float) value).intValue(); + } return (Integer) value; } else if (dataType == DataTypes.DATE) { if (value instanceof java.sql.Date) { @@ -129,9 +133,12 @@ public Short getShort() throws FilterIllegalMemberException { } } else if (dataType == DataTypes.SHORT || dataType == DataTypes.INT || + dataType == DataTypes.FLOAT || dataType == DataTypes.DOUBLE) { if (value instanceof Double) { return ((Double) value).shortValue(); + } else if (value instanceof Float) { + return ((Float) value).shortValue(); } else if (value instanceof Integer) { return ((Integer) value).shortValue(); } @@ -182,6 +189,54 @@ public String getString() throws FilterIllegalMemberException { } } + public Float getFloat() throws FilterIllegalMemberException { + if (value == null) { + return null; + } + try { + DataType dataType = this.getDataType(); + if (dataType == DataTypes.STRING) { + try { + return Float.parseFloat(value.toString()); + } catch (NumberFormatException e) { + throw new FilterIllegalMemberException(e); + } + } else if (dataType == DataTypes.SHORT) { + return ((Short) value).floatValue(); + } else if (dataType == DataTypes.INT) { + return ((Integer) value).floatValue(); + } else if (dataType == DataTypes.LONG) { + return ((Long) value).floatValue(); + } else if (dataType == DataTypes.FLOAT) { + return (Float) value; + } else if (dataType == DataTypes.DOUBLE) { + return ((Double) value).floatValue(); + } else if (dataType == DataTypes.DATE) { + if (value instanceof java.sql.Date) { + return (float) ((java.sql.Date) value).getTime(); + } else { + return (Float) value; + } + } else if (dataType == DataTypes.TIMESTAMP) { + if (value instanceof Timestamp) { + return (float) ((Timestamp) value).getTime(); + } else { + if (isLiteral) { + long l = (Long) value / 1000; + return (float) l; + } + return (Float) value; + } + } else { + throw new FilterIllegalMemberException( + "Cannot convert" + this.getDataType().getName() + " to float type value"); + } + } catch (ClassCastException e) { + throw new FilterIllegalMemberException( + "Cannot convert" + this.getDataType().getName() + " to Float type value"); + } + } + public Double getDouble() throws FilterIllegalMemberException { if (value == null) { return null; @@ -200,6 +255,8 @@ public Double getDouble() throws FilterIllegalMemberException { return ((Integer) value).doubleValue(); } else if (dataType == DataTypes.LONG) { return ((Long) value).doubleValue(); + } else if (dataType == DataTypes.FLOAT) { + return ((Float) value).doubleValue(); } else if (dataType == DataTypes.DOUBLE) { return (Double) value; } else if (dataType == DataTypes.DATE) { @@ -246,6 +303,8 @@ public Long getLong() throws FilterIllegalMemberException { return (Long) value; } else if (dataType == DataTypes.LONG) { return (Long) value; + } else if (dataType == DataTypes.FLOAT) { + return (Long) value; } else if (dataType == DataTypes.DOUBLE) { return (Long) value; } else if (dataType == DataTypes.DATE) { @@ -290,7 +349,8 @@ public BigDecimal getDecimal() throws FilterIllegalMemberException { return new BigDecimal((int) value); } else if (dataType == DataTypes.LONG) { return new BigDecimal((long) value); - } else if (dataType == DataTypes.DOUBLE || DataTypes.isDecimal(dataType)) { + } else if (dataType == DataTypes.FLOAT || dataType == DataTypes.DOUBLE || + DataTypes.isDecimal(dataType)) { return new BigDecimal(value.toString()); } else if (dataType == DataTypes.DATE) { if (value instanceof java.sql.Date) { @@ -343,7 +403,7 @@ public Long getTime() throws FilterIllegalMemberException { return ((Short) value).longValue(); } else if (dataType == DataTypes.INT || dataType == DataTypes.LONG) { return (Long) value; - } else if (dataType == DataTypes.DOUBLE) { + } else if (dataType == DataTypes.FLOAT || dataType == DataTypes.DOUBLE) { return (Long) value; } else if (dataType == DataTypes.DATE) { if (value instanceof java.sql.Date) { @@ -396,7 +456,7 @@ public Long getTimeAsMillisecond() throws FilterIllegalMemberException { return ((Short) value).longValue(); } else if (dataType == DataTypes.INT || dataType == DataTypes.LONG) { return (Long) value; - } else if (dataType == DataTypes.DOUBLE) { + } else if (dataType == DataTypes.FLOAT || dataType == DataTypes.DOUBLE) { return (Long) value; } else if (dataType == DataTypes.DATE) { if (value instanceof java.sql.Date) { @@ -527,6 +587,8 @@ public boolean equals(Object obj) { dataType == DataTypes.DATE || dataType == DataTypes.TIMESTAMP) { result = this.getLong().equals(objToCompare.getLong()); + } else if (dataType == DataTypes.FLOAT) { + result = this.getFloat().equals(objToCompare.getFloat()); } else if (dataType == DataTypes.DOUBLE) { result = this.getDouble().equals(objToCompare.getDouble()); } else if (DataTypes.isDecimal(dataType)) { @@ -550,6 +612,7 @@ public int compareTo(ExpressionResult o) { if (type == DataTypes.SHORT || type == DataTypes.INT || type == DataTypes.LONG || + type == DataTypes.FLOAT || type == DataTypes.DOUBLE) { Double d1 = this.getDouble(); Double d2 = o.getDouble(); diff --git a/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/EqualToExpression.java b/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/EqualToExpression.java index c3268401891..0034fdd744d 100644 --- a/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/EqualToExpression.java +++ b/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/EqualToExpression.java @@ -76,6 +76,8 @@ public ExpressionResult evaluate(RowIntf value) result = val1.getShort().equals(val2.getShort()); } else if (dataType == DataTypes.INT) { result = val1.getInt().equals(val2.getInt()); + } else if (dataType == DataTypes.FLOAT) { + result = FilterUtil.nanSafeEqualsFloats(val1.getFloat(), val2.getFloat()); } else if (dataType == DataTypes.DOUBLE) { result = FilterUtil.nanSafeEqualsDoubles(val1.getDouble(), val2.getDouble()); } else if (dataType == DataTypes.DATE) { diff --git a/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/GreaterThanEqualToExpression.java b/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/GreaterThanEqualToExpression.java index db677848191..9cdc9bdb9ec 100644 --- a/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/GreaterThanEqualToExpression.java +++ b/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/GreaterThanEqualToExpression.java @@ -58,6 +58,8 @@ public ExpressionResult evaluate(RowIntf value) result = elRes.getShort() >= (erRes.getShort()); } else if (dataType == DataTypes.INT) { result = elRes.getInt() >= (erRes.getInt()); + } else if (dataType == DataTypes.FLOAT) { + result = elRes.getFloat() >= (erRes.getFloat()); } else if (dataType == DataTypes.DOUBLE) { result = elRes.getDouble() >= (erRes.getDouble()); } else if (dataType == DataTypes.DATE) { diff --git a/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/GreaterThanExpression.java b/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/GreaterThanExpression.java index 31f1b862b50..82118c01b14 100644 --- a/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/GreaterThanExpression.java +++ b/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/GreaterThanExpression.java @@ -56,6 +56,8 @@ public ExpressionResult evaluate(RowIntf value) result = exprLeftRes.getBoolean().compareTo(exprRightRes.getBoolean()) > 0; } else if (dataType == DataTypes.STRING || dataType == DataTypes.VARCHAR) { result = exprLeftRes.getString().compareTo(exprRightRes.getString()) > 0; + } else if (dataType == DataTypes.FLOAT) { + result = exprLeftRes.getFloat() > (exprRightRes.getFloat()); } else if (dataType == DataTypes.DOUBLE) { result = exprLeftRes.getDouble() > (exprRightRes.getDouble()); } else if (dataType == DataTypes.SHORT) { diff --git a/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/InExpression.java b/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/InExpression.java index 681d8baef29..26defda06a5 100644 --- a/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/InExpression.java +++ b/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/InExpression.java @@ -63,6 +63,8 @@ public ExpressionResult evaluate(RowIntf value) val = new ExpressionResult(val.getDataType(), expressionResVal.getShort()); } else if (dataType == DataTypes.INT) { val = new ExpressionResult(val.getDataType(), expressionResVal.getInt()); + } else if (dataType == DataTypes.FLOAT) { + val = new ExpressionResult(val.getDataType(), expressionResVal.getFloat()); } else if (dataType == DataTypes.DOUBLE) { val = new ExpressionResult(val.getDataType(), expressionResVal.getDouble()); } else if (dataType == DataTypes.LONG) { diff --git a/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/LessThanEqualToExpression.java b/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/LessThanEqualToExpression.java index 53a689da122..2ec4878e8b8 100644 --- a/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/LessThanEqualToExpression.java +++ b/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/LessThanEqualToExpression.java @@ -58,6 +58,8 @@ public ExpressionResult evaluate(RowIntf value) result = elRes.getShort() <= (erRes.getShort()); } else if (dataType == DataTypes.INT) { result = elRes.getInt() <= (erRes.getInt()); + } else if (dataType == DataTypes.FLOAT) { + result = elRes.getFloat() <= (erRes.getFloat()); } else if (dataType == DataTypes.DOUBLE) { result = elRes.getDouble() <= (erRes.getDouble()); } else if (dataType == DataTypes.DATE) { diff --git a/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/LessThanExpression.java b/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/LessThanExpression.java index 671f1f1a06a..ea78b854d76 100644 --- a/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/LessThanExpression.java +++ b/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/LessThanExpression.java @@ -62,6 +62,8 @@ public ExpressionResult evaluate(RowIntf value) result = elRes.getShort() < (erRes.getShort()); } else if (dataType == DataTypes.INT) { result = elRes.getInt() < (erRes.getInt()); + } else if (dataType == DataTypes.FLOAT) { + result = elRes.getFloat() < (erRes.getFloat()); } else if (dataType == DataTypes.DOUBLE) { result = elRes.getDouble() < (erRes.getDouble()); } else if (dataType == DataTypes.DATE) { diff --git a/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/NotEqualsExpression.java b/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/NotEqualsExpression.java index 5f68d2513dd..805fb0cc9a5 100644 --- a/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/NotEqualsExpression.java +++ b/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/NotEqualsExpression.java @@ -72,6 +72,8 @@ public ExpressionResult evaluate(RowIntf value) result = val1.getShort().shortValue() != val2.getShort().shortValue(); } else if (dataType == DataTypes.INT) { result = val1.getInt().intValue() != val2.getInt().intValue(); + } else if (dataType == DataTypes.FLOAT) { + result = val1.getFloat().floatValue() != val2.getFloat().floatValue(); } else if (dataType == DataTypes.DOUBLE) { result = val1.getDouble().doubleValue() != val2.getDouble().doubleValue(); } else if (dataType == DataTypes.DATE) { diff --git a/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/NotInExpression.java b/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/NotInExpression.java index 5264b896db1..2677e793462 100644 --- a/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/NotInExpression.java +++ b/core/src/main/java/org/apache/carbondata/core/scan/expression/conditional/NotInExpression.java @@ -84,6 +84,8 @@ public ExpressionResult evaluate(RowIntf value) val = new ExpressionResult(val.getDataType(), exprResVal.getShort()); } else if (dataType == DataTypes.INT) { val = new ExpressionResult(val.getDataType(), exprResVal.getInt()); + } else if (dataType == DataTypes.FLOAT) { + val = new ExpressionResult(val.getDataType(), exprResVal.getFloat()); } else if (dataType == DataTypes.DOUBLE) { val = new ExpressionResult(val.getDataType(), exprResVal.getDouble()); } else if (dataType == DataTypes.DATE) { diff --git a/core/src/main/java/org/apache/carbondata/core/scan/filter/FilterUtil.java b/core/src/main/java/org/apache/carbondata/core/scan/filter/FilterUtil.java index 571eb2e3d1a..dbcd7b4a58b 100644 --- a/core/src/main/java/org/apache/carbondata/core/scan/filter/FilterUtil.java +++ b/core/src/main/java/org/apache/carbondata/core/scan/filter/FilterUtil.java @@ -833,6 +833,8 @@ public static int compareFilterKeyBasedOnDataType(String dictionaryVal, String m return Short.compare((Short.parseShort(dictionaryVal)), (Short.parseShort(memberVal))); } else if (dataType == DataTypes.INT) { return Integer.compare((Integer.parseInt(dictionaryVal)), (Integer.parseInt(memberVal))); + } else if (dataType == DataTypes.FLOAT) { + return Float.compare((Float.parseFloat(dictionaryVal)), (Float.parseFloat(memberVal))); } else if (dataType == DataTypes.DOUBLE) { return Double.compare((Double.parseDouble(dictionaryVal)), (Double.parseDouble(memberVal))); } else if (dataType == DataTypes.LONG) { @@ -890,6 +892,10 @@ public static void logError(Throwable e, boolean invalidRowsPresent) { } } + public static boolean nanSafeEqualsFloats(Float f1, Float f2) { + return (f1.floatValue() == f2.floatValue()) || (Float.isNaN(f1) && Float.isNaN(f2)); + } + /** * This method will compare double values for its equality and also it will preserve * the -0.0 and 0.0 equality as per == ,also preserve NaN equality check as per @@ -901,7 +907,6 @@ public static void logError(Throwable e, boolean invalidRowsPresent) { */ public static boolean nanSafeEqualsDoubles(Double d1, Double d2) { return (d1.doubleValue() == d2.doubleValue()) || (Double.isNaN(d1) && Double.isNaN(d2)); - } /** diff --git a/core/src/main/java/org/apache/carbondata/core/scan/filter/executer/RowLevelFilterExecutorImpl.java b/core/src/main/java/org/apache/carbondata/core/scan/filter/executer/RowLevelFilterExecutorImpl.java index 31add9adf7d..13449a5d17c 100644 --- a/core/src/main/java/org/apache/carbondata/core/scan/filter/executer/RowLevelFilterExecutorImpl.java +++ b/core/src/main/java/org/apache/carbondata/core/scan/filter/executer/RowLevelFilterExecutorImpl.java @@ -510,6 +510,8 @@ private void createRow(RawBlockletColumnChunks blockChunkHolder, RowIntf row, in msrType = DataTypes.LONG; } else if (DataTypes.isDecimal(dataType)) { msrType = DataTypes.createDefaultDecimalType(); + } else if (dataType == DataTypes.FLOAT) { + msrType = DataTypes.FLOAT; } else { msrType = DataTypes.DOUBLE; } diff --git a/core/src/main/java/org/apache/carbondata/core/util/ByteUtil.java b/core/src/main/java/org/apache/carbondata/core/util/ByteUtil.java index b9961d03e04..b2185f3ca48 100644 --- a/core/src/main/java/org/apache/carbondata/core/util/ByteUtil.java +++ b/core/src/main/java/org/apache/carbondata/core/util/ByteUtil.java @@ -572,6 +572,10 @@ public static byte[] toBytes(double val) { return toBytes(Double.doubleToLongBits(val)); } + public static float toFloat(byte[] value, int offset, int length) { + return Float.intBitsToFloat(toInt(value, offset, length)); + } + public static double toDouble(byte[] value, int offset, int length) { return Double.longBitsToDouble(toLong(value, offset, length)); } diff --git a/core/src/main/java/org/apache/carbondata/core/util/DataTypeUtil.java b/core/src/main/java/org/apache/carbondata/core/util/DataTypeUtil.java index 17d560169fd..5e3528899f2 100644 --- a/core/src/main/java/org/apache/carbondata/core/util/DataTypeUtil.java +++ b/core/src/main/java/org/apache/carbondata/core/util/DataTypeUtil.java @@ -435,6 +435,8 @@ public static Object getDataDataTypeForNoDictionaryColumn(String dimensionValue, return Integer.parseInt(dimensionValue); } else if (actualDataType == DataTypes.LONG) { return Long.parseLong(dimensionValue); + } else if (actualDataType == DataTypes.FLOAT) { + return Float.parseFloat(dimensionValue); } else if (actualDataType == DataTypes.DOUBLE) { return Double.parseDouble(dimensionValue); } else if (DataTypes.isDecimal(actualDataType)) { @@ -830,6 +832,12 @@ public static Object getDataBasedOnDataType(byte[] dataInBytes, CarbonDimension return null; } return Short.parseShort(data2); + } else if (dataType == DataTypes.FLOAT) { + String data3 = new String(dataInBytes, CarbonCommonConstants.DEFAULT_CHARSET_CLASS); + if (data3.isEmpty()) { + return null; + } + return Float.parseFloat(data3); } else if (dataType == DataTypes.DOUBLE) { String data3 = new String(dataInBytes, CarbonCommonConstants.DEFAULT_CHARSET_CLASS); if (data3.isEmpty()) { @@ -1008,6 +1016,9 @@ public static byte[] convertDataToBytesBasedOnDataType(String data, ColumnSchema parsedIntVal = Short.parseShort(data); return String.valueOf(parsedIntVal) .getBytes(Charset.forName(CarbonCommonConstants.DEFAULT_CHARSET)); + } else if (dataType == DataTypes.FLOAT) { + return String.valueOf(Float.parseFloat(data)) + .getBytes(Charset.forName(CarbonCommonConstants.DEFAULT_CHARSET)); } else if (dataType == DataTypes.DOUBLE) { return String.valueOf(Double.parseDouble(data)) .getBytes(Charset.forName(CarbonCommonConstants.DEFAULT_CHARSET)); diff --git a/core/src/test/java/org/apache/carbondata/core/metadata/converter/ThriftWrapperSchemaConverterImplTest.java b/core/src/test/java/org/apache/carbondata/core/metadata/converter/ThriftWrapperSchemaConverterImplTest.java index 236c3fc9763..161482c73d9 100644 --- a/core/src/test/java/org/apache/carbondata/core/metadata/converter/ThriftWrapperSchemaConverterImplTest.java +++ b/core/src/test/java/org/apache/carbondata/core/metadata/converter/ThriftWrapperSchemaConverterImplTest.java @@ -735,6 +735,74 @@ public List getSchema_evoluti assertEquals(thriftColumnSchema, actualResult); } + @Test + public void testFromWrapperToExternalColumnSchemaForFloatDatatype() { + org.apache.carbondata.format.ColumnSchema thriftColumnSchema = + new org.apache.carbondata.format.ColumnSchema(org.apache.carbondata.format.DataType.FLOAT, + "columnName", "1", true, encoders, true); + thriftColumnSchema.setSchemaOrdinal(1); + thriftColumnSchema.setAggregate_function(""); + + new MockUp() { + @Mock public List getEncodingList() { + return encodings; + } + + @Mock public int getSchemaOrdinal() { + return 1; + } + + @Mock public DataType getDataType() { + return DataTypes.FLOAT; + } + + @Mock public String getColumnName() { + return "columnName"; + } + + @Mock public String getColumnUniqueId() { + return "1"; + } + + @Mock public boolean isDimensionColumn() { + return true; + } + + @Mock public int getScale() { + return 1; + } + + @Mock public int getPrecision() { + return 1; + } + + @Mock public int getNumberOfChild() { + return 1; + } + + @Mock public byte[] getDefaultValue() { + return new byte[] { 1, 2 }; + } + + @Mock public Map getColumnProperties() { + return columnPropertyMap; + } + + @Mock public boolean isInvisible() { + return true; + } + + @Mock public String getColumnReferenceId() { + return "1"; + } + }; + + ColumnSchema wrapperColumnSchema = new ColumnSchema(); + org.apache.carbondata.format.ColumnSchema actualResult = + thriftWrapperSchemaConverter.fromWrapperToExternalColumnSchema(wrapperColumnSchema); + assertEquals(thriftColumnSchema, actualResult); + } + @Test public void testFromWrapperToExternalColumnSchemaForDoubleDatatype() { org.apache.carbondata.format.ColumnSchema thriftColumnSchema = new org.apache.carbondata.format.ColumnSchema(org.apache.carbondata.format.DataType.DOUBLE, diff --git a/core/src/test/java/org/apache/carbondata/core/metadata/schema/table/TableSchemaBuilderSuite.java b/core/src/test/java/org/apache/carbondata/core/metadata/schema/table/TableSchemaBuilderSuite.java index 2cbe69b2f9c..b7f5272e295 100644 --- a/core/src/test/java/org/apache/carbondata/core/metadata/schema/table/TableSchemaBuilderSuite.java +++ b/core/src/test/java/org/apache/carbondata/core/metadata/schema/table/TableSchemaBuilderSuite.java @@ -43,11 +43,13 @@ public void testBuilder() { builder.addColumn(new StructField("a", DataTypes.INT), new AtomicInteger(0), true, false); builder.setSortColumns(Arrays.asList(columnSchema)); builder.addColumn(new StructField("b", DataTypes.DOUBLE), new AtomicInteger(0), false, false); + builder.addColumn(new StructField("c", DataTypes.FLOAT), new AtomicInteger(0), false, false); TableSchema schema = builder.build(); - Assert.assertEquals(2, schema.getListOfColumns().size()); + Assert.assertEquals(3, schema.getListOfColumns().size()); List columns = schema.getListOfColumns(); Assert.assertEquals("a", columns.get(0).getColumnName()); Assert.assertEquals("b", columns.get(1).getColumnName()); + Assert.assertEquals("c", columns.get(2).getColumnName()); } @Test(expected = IllegalArgumentException.class) diff --git a/core/src/test/java/org/apache/carbondata/core/scan/expression/ExpressionResultTest.java b/core/src/test/java/org/apache/carbondata/core/scan/expression/ExpressionResultTest.java index 1d6447ff134..d105d065685 100644 --- a/core/src/test/java/org/apache/carbondata/core/scan/expression/ExpressionResultTest.java +++ b/core/src/test/java/org/apache/carbondata/core/scan/expression/ExpressionResultTest.java @@ -78,10 +78,12 @@ public void testGetIntForNumberFormatException() throws FilterIllegalMemberExcep assertEquals(expectedValue, actualValue); } - @Test(expected = FilterIllegalMemberException.class) - public void testGetIntForIllegalMemberException() throws FilterIllegalMemberException { - ExpressionResult expressionResultForInt = new ExpressionResult(DataTypes.FLOAT, 25.36); - expressionResultForInt.getInt(); + @Test + public void testGetIntForFloat() throws FilterIllegalMemberException { + ExpressionResult expressionResultForFloat = new ExpressionResult(DataTypes.FLOAT, 25.36); + int actualValue = expressionResultForFloat.getInt(); + int expectedValue = 25; + assertEquals(expectedValue, actualValue); } @Test(expected = FilterIllegalMemberException.class) public void testGetIntForClassCastException() @@ -121,10 +123,12 @@ public void testGetShortForNumberFormatException() throws FilterIllegalMemberExc assertEquals(expectedValue, actualValue); } - @Test(expected = FilterIllegalMemberException.class) - public void testGetShortForIllegalMemberException() throws FilterIllegalMemberException { - ExpressionResult expressionResultForInt = new ExpressionResult(DataTypes.FLOAT, 25.36); - expressionResultForInt.getShort(); + @Test + public void testGetShortForFloat() throws FilterIllegalMemberException { + ExpressionResult expressionResultForFloat = new ExpressionResult(DataTypes.FLOAT, 25.36); + short actualValue = expressionResultForFloat.getShort(); + short expectedValue = 25; + assertEquals(expectedValue, actualValue); } @Test(expected = FilterIllegalMemberException.class) @@ -182,10 +186,18 @@ public void testGetDoubleForNumberFormatException() throws FilterIllegalMemberEx assertEquals(expectedValue, actualValue, DELTA); } + @Test + public void testGetDoubleForFloat() throws FilterIllegalMemberException { + ExpressionResult expressionResultForFloat = new ExpressionResult(DataTypes.FLOAT, 25.36f); + double actualValue = expressionResultForFloat.getDouble(); + double expectedValue = 25.36; + assertEquals(expectedValue, actualValue, 1e-6); + } + @Test(expected = FilterIllegalMemberException.class) - public void testGetDoubleForIllegalMemberException() throws FilterIllegalMemberException { - ExpressionResult expressionResultForDouble = new ExpressionResult(DataTypes.FLOAT, 25.36); - expressionResultForDouble.getDouble(); + public void testGetFloatForClassCastException() throws FilterIllegalMemberException { + ExpressionResult expressionResultForFloat = new ExpressionResult(DataTypes.FLOAT, "STRING"); + expressionResultForFloat.getFloat(); } @Test(expected = FilterIllegalMemberException.class) @@ -261,10 +273,12 @@ public void testGetDecimalForNumberFormatException() throws FilterIllegalMemberE assertEquals(expectedValue, actualValue); } - @Test(expected = FilterIllegalMemberException.class) - public void testGetDecimalForIllegalMemberException() throws FilterIllegalMemberException { + @Test + public void testGetDecimalForFloat() throws FilterIllegalMemberException { ExpressionResult expressionResultForFloat = new ExpressionResult(DataTypes.FLOAT, 25.36); - expressionResultForFloat.getDecimal(); + BigDecimal actualValue = expressionResultForFloat.getDecimal(); + BigDecimal expectedValue = new BigDecimal("25.36"); + assertEquals(expectedValue, actualValue); } @Test(expected = FilterIllegalMemberException.class) @@ -365,6 +379,14 @@ public void testGetBooleanForIllegalMemberException() throws FilterIllegalMember assertEquals(expectedValue, actualValue); } + @Test + public void testHashCodeForFloatNul() { + ExpressionResult obj = new ExpressionResult(DataTypes.FLOAT, null); + int actualValue = obj.hashCode(); + int expectedValue = 31; + assertEquals(expectedValue, actualValue); + } + @Test public void testHashCodeForNul() { ExpressionResult obj = new ExpressionResult(DataTypes.DOUBLE, null); int actualValue = obj.hashCode(); @@ -409,6 +431,14 @@ public void testGetBooleanForIllegalMemberException() throws FilterIllegalMember assertFalse(result); } + @Test + public void testEqualsForFloat() { + ExpressionResult expressionResult = new ExpressionResult(DataTypes.FLOAT, 5.89); + ExpressionResult objToCompare = new ExpressionResult(DataTypes.FLOAT, 6.90); + boolean result = expressionResult.equals(objToCompare); + assertFalse(result); + } + @Test public void testEqualsForDouble() { ExpressionResult expressionResult = new ExpressionResult(DataTypes.DOUBLE, 5.89); ExpressionResult objToCompare = new ExpressionResult(DataTypes.DOUBLE, 6.90); @@ -416,6 +446,15 @@ public void testGetBooleanForIllegalMemberException() throws FilterIllegalMember assertFalse(result); } + @Test + public void testEqualsForFloatException() { + ExpressionResult expressionResult = + new ExpressionResult(DataTypes.DATE, "2016-11-07"); + ExpressionResult objToCompare = new ExpressionResult(DataTypes.FLOAT, "2016-11-06"); + boolean result = expressionResult.equals(objToCompare); + assertFalse(result); + } + @Test public void testEqualsForException() { ExpressionResult expressionResult = new ExpressionResult(DataTypes.TIMESTAMP, "2016-11-07 10:15:09"); diff --git a/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/EqualToExpressionUnitTest.java b/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/EqualToExpressionUnitTest.java index f2e82adc8e6..3f3dcad52e1 100644 --- a/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/EqualToExpressionUnitTest.java +++ b/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/EqualToExpressionUnitTest.java @@ -116,6 +116,27 @@ public class EqualToExpressionUnitTest { assertTrue(result.getBoolean()); } + @Test + public void testEvaluateForEqualToExpressionWithFloatDataType() + throws FilterUnsupportedException, FilterIllegalMemberException { + ColumnExpression right = new ColumnExpression("contact", DataTypes.FLOAT); + right.setColIndex(0); + equalToExpression = new EqualToExpression(right, right); + RowImpl value = new RowImpl(); + Float[] row = { 44f }; + Object[] objectRow = { row }; + value.setValues(objectRow); + + new MockUp() { + @Mock public Float getFloat() { + return 44f; + } + }; + + ExpressionResult result = equalToExpression.evaluate(value); + assertTrue(result.getBoolean()); + } + @Test public void testEvaluateForEqualToExpressionWithDoubleDataType() throws FilterUnsupportedException, FilterIllegalMemberException { ColumnExpression right = new ColumnExpression("contact", DataTypes.DOUBLE); diff --git a/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/GreaterThanEqualToExpressionUnitTest.java b/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/GreaterThanEqualToExpressionUnitTest.java index ef979652b4a..82feb82aaad 100644 --- a/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/GreaterThanEqualToExpressionUnitTest.java +++ b/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/GreaterThanEqualToExpressionUnitTest.java @@ -169,6 +169,59 @@ public class GreaterThanEqualToExpressionUnitTest { assertFalse(result.getBoolean()); } + @Test + public void testEvaluateForGreaterThanEqualToExpressionWithFloatDataType() + throws FilterUnsupportedException, FilterIllegalMemberException { + ColumnExpression right = new ColumnExpression("right_contact", DataTypes.FLOAT); + right.setColIndex(0); + ColumnExpression left = new ColumnExpression("left_contact", DataTypes.FLOAT); + left.setColIndex(1); + greaterThanEqualToExpression = new GreaterThanEqualToExpression(left, right); + RowImpl value = new RowImpl(); + Float[] row = { 44f }; + Float[] row1 = { 45f }; + Object[] objectRow = { row1, row }; + value.setValues(objectRow); + + new MockUp() { + @Mock public Float getFloat() { + return 45f; + } + }; + + ExpressionResult result = greaterThanEqualToExpression.evaluate(value); + assertTrue(result.getBoolean()); + } + + @Test + public void testEvaluateForGreaterThanEqualToExpressionWithFloatDataType1() + throws FilterUnsupportedException, FilterIllegalMemberException { + ColumnExpression right = new ColumnExpression("right_contact", DataTypes.FLOAT); + right.setColIndex(0); + ColumnExpression left = new ColumnExpression("left_contact", DataTypes.FLOAT); + left.setColIndex(1); + greaterThanEqualToExpression = new GreaterThanEqualToExpression(left, right); + RowImpl value = new RowImpl(); + Float[] row = { 44f }; + Float[] row1 = { 45f }; + Object[] objectRow = { row1, row }; + value.setValues(objectRow); + + new MockUp() { + boolean isFirst = true; + @Mock public Float getFloat() { + if (isFirst) { + isFirst = false; + return 44f; + } + return 45f; + } + }; + + ExpressionResult result = greaterThanEqualToExpression.evaluate(value); + assertFalse(result.getBoolean()); + } + @Test public void testEvaluateForGreaterThanEqualToExpressionWithDoubleDataType() throws FilterUnsupportedException, FilterIllegalMemberException { ColumnExpression right = new ColumnExpression("right_contact", DataTypes.DOUBLE); diff --git a/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/GreaterThanExpressionUnitTest.java b/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/GreaterThanExpressionUnitTest.java index 625b7b28a32..ab7a97cf108 100644 --- a/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/GreaterThanExpressionUnitTest.java +++ b/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/GreaterThanExpressionUnitTest.java @@ -141,6 +141,37 @@ public class GreaterThanExpressionUnitTest { } + @Test + public void testEvaluateForGreaterThanExpressionWithFloatDataType() + throws FilterUnsupportedException, FilterIllegalMemberException { + ColumnExpression right = new ColumnExpression("right_contact", DataTypes.FLOAT); + right.setColIndex(0); + ColumnExpression left = new ColumnExpression("left_contact", DataTypes.FLOAT); + left.setColIndex(1); + greaterThanExpression = new GreaterThanExpression(left, right); + RowImpl value = new RowImpl(); + Float[] row = { 44f }; + Float[] row1 = { 20f }; + Object[] objectRow = { row1, row }; + value.setValues(objectRow); + + new MockUp() { + Boolean returnMockFlag = true; + + @Mock public Float getFloat() { + if (returnMockFlag) { + returnMockFlag = false; + return 44f; + } else { + return 20f; + } + } + }; + + ExpressionResult result = greaterThanExpression.evaluate(value); + assertTrue(result.getBoolean()); + } + @Test public void testEvaluateForGreaterThanExpressionWithDoubleDataType() throws FilterUnsupportedException, FilterIllegalMemberException { ColumnExpression right = new ColumnExpression("right_contact", DataTypes.DOUBLE); diff --git a/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/InExpressionUnitTest.java b/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/InExpressionUnitTest.java index 4489d9fbdc4..deb7ca36815 100644 --- a/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/InExpressionUnitTest.java +++ b/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/InExpressionUnitTest.java @@ -122,6 +122,30 @@ public class InExpressionUnitTest { } + @Test + public void testEvaluateForInExpressionWithFloatDataType() + throws FilterUnsupportedException, FilterIllegalMemberException { + ColumnExpression left = new ColumnExpression("left_contact", DataTypes.FLOAT); + left.setColIndex(0); + ColumnExpression right = new ColumnExpression("right_contact", DataTypes.FLOAT); + right.setColIndex(1); + inExpression = new InExpression(left, right); + RowImpl value = new RowImpl(); + float row = 44521f; + float row1 = 44521f; + Object[] objectRow = { row, row1 }; + value.setValues(objectRow); + + new MockUp() { + @Mock public Float getFloat() { + return 44521f; + } + }; + + ExpressionResult result = inExpression.evaluate(value); + assertTrue(result.getBoolean()); + } + @Test public void testEvaluateForInExpressionWithDoubleDataType() throws FilterUnsupportedException, FilterIllegalMemberException { ColumnExpression left = new ColumnExpression("left_contact", DataTypes.DOUBLE); diff --git a/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/LessThanEqualToExpressionUnitTest.java b/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/LessThanEqualToExpressionUnitTest.java index b48fc6e2436..17a67f9690a 100644 --- a/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/LessThanEqualToExpressionUnitTest.java +++ b/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/LessThanEqualToExpressionUnitTest.java @@ -144,6 +144,37 @@ public class LessThanEqualToExpressionUnitTest { } + @Test + public void testEvaluateForLessThanEqualToExpressionWithFloatDataType() + throws FilterUnsupportedException, FilterIllegalMemberException { + ColumnExpression right = new ColumnExpression("right_contact", DataTypes.FLOAT); + right.setColIndex(0); + ColumnExpression left = new ColumnExpression("left_contact", DataTypes.FLOAT); + left.setColIndex(1); + lessThanEqualToExpression = new LessThanEqualToExpression(left, right); + RowImpl value = new RowImpl(); + Float[] row = { 4852.2f }; + Float[] row1 = { 4852.2f }; + Object[] objectRow = { row1, row }; + value.setValues(objectRow); + + new MockUp() { + Boolean returnMockFlag = true; + + @Mock public Float getFloat() { + if (returnMockFlag) { + returnMockFlag = false; + return 4852.2f; + } else { + return 4852.2f; + } + } + }; + + ExpressionResult result = lessThanEqualToExpression.evaluate(value); + assertTrue(result.getBoolean()); + } + @Test public void testEvaluateForLessThanEqualToExpressionWithDoubleDataType() throws FilterUnsupportedException, FilterIllegalMemberException { ColumnExpression right = new ColumnExpression("right_contact", DataTypes.DOUBLE); diff --git a/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/LessThanExpressionUnitTest.java b/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/LessThanExpressionUnitTest.java index 8206a6b7fe9..ff7c4e81915 100644 --- a/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/LessThanExpressionUnitTest.java +++ b/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/LessThanExpressionUnitTest.java @@ -172,6 +172,37 @@ public class LessThanExpressionUnitTest { } + @Test + public void testEvaluateForLessThanExpressionWithFloatDataType() + throws FilterUnsupportedException, FilterIllegalMemberException { + ColumnExpression right = new ColumnExpression("right_contact", DataTypes.FLOAT); + right.setColIndex(0); + ColumnExpression left = new ColumnExpression("left_contact", DataTypes.FLOAT); + left.setColIndex(1); + lessThanExpression = new LessThanExpression(left, right); + RowImpl value = new RowImpl(); + Float[] row = { 2087f }; + Float[] row1 = { 4454f }; + Object[] objectRow = { row1, row }; + value.setValues(objectRow); + + new MockUp() { + Boolean returnMockFlag = true; + + @Mock public Float getFloat() { + if (returnMockFlag) { + returnMockFlag = false; + return 2087f; + } else { + return 4454f; + } + } + }; + + ExpressionResult result = lessThanExpression.evaluate(value); + assertTrue(result.getBoolean()); + } + @Test public void testEvaluateForLessThanExpressionWithDoubleDataType() throws FilterUnsupportedException, FilterIllegalMemberException { ColumnExpression right = new ColumnExpression("right_contact", DataTypes.DOUBLE); diff --git a/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/NotEqualsExpressionUnitTest.java b/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/NotEqualsExpressionUnitTest.java index ece6d16c869..24472bd4d33 100644 --- a/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/NotEqualsExpressionUnitTest.java +++ b/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/NotEqualsExpressionUnitTest.java @@ -190,6 +190,37 @@ public class NotEqualsExpressionUnitTest { assertFalse(result.getBoolean()); } + @Test + public void testEvaluateForNotEqualsExpressionWithFloatDataType() + throws FilterUnsupportedException, FilterIllegalMemberException { + ColumnExpression right = new ColumnExpression("right_contact", DataTypes.FLOAT); + right.setColIndex(1); + ColumnExpression left = new ColumnExpression("left_contact", DataTypes.FLOAT); + left.setColIndex(0); + notEqualsExpression = new NotEqualsExpression(left, right); + RowImpl value = new RowImpl(); + Float[] row = { 445.2f }; + Float[] row1 = { 452.08f }; + Object[] objectRow = { row, row1 }; + value.setValues(objectRow); + + new MockUp() { + Boolean returnMockFlag = true; + + @Mock public Float getFloat() { + if (returnMockFlag) { + returnMockFlag = false; + return 445.2f; + } else { + return 452.08f; + } + } + }; + + ExpressionResult result = notEqualsExpression.evaluate(value); + assertTrue(result.getBoolean()); + } + @Test public void testEvaluateForNotEqualsExpressionWithDoubleDataType() throws FilterUnsupportedException, FilterIllegalMemberException { ColumnExpression right = new ColumnExpression("right_contact", DataTypes.DOUBLE); diff --git a/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/NotInExpressionUnitTest.java b/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/NotInExpressionUnitTest.java index ce06c662fb9..1bc8d4c951f 100644 --- a/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/NotInExpressionUnitTest.java +++ b/core/src/test/java/org/apache/carbondata/core/scan/expression/conditional/NotInExpressionUnitTest.java @@ -122,6 +122,60 @@ public class NotInExpressionUnitTest { } + @Test + public void testEvaluateForNotInExpressionWithFloatDataType() + throws FilterUnsupportedException, FilterIllegalMemberException { + ColumnExpression left = new ColumnExpression("left_contact", DataTypes.FLOAT); + left.setColIndex(0); + ColumnExpression right = new ColumnExpression("right_contact", DataTypes.FLOAT); + right.setColIndex(1); + notInExpression = new NotInExpression(left, right); + RowImpl value = new RowImpl(); + float row = 44521f; + float row1 = 44521.023f; + Object[] objectRow = { row, row1 }; + value.setValues(objectRow); + + new MockUp() { + @Mock public Float getFloat() { + return 44521.023f; + } + }; + + ExpressionResult result = notInExpression.evaluate(value); + assertTrue(result.getBoolean()); + } + + @Test + public void testEvaluateForNotInExpressionWithFloatDataType1() + throws FilterUnsupportedException, FilterIllegalMemberException { + ColumnExpression left = new ColumnExpression("left_contact", DataTypes.FLOAT); + left.setColIndex(0); + ColumnExpression right = new ColumnExpression("right_contact", DataTypes.FLOAT); + right.setColIndex(1); + notInExpression = new NotInExpression(left, right); + RowImpl value = new RowImpl(); + float row = 44521f; + float row1 = 44521.023f; + Object[] objectRow = { row, row1 }; + value.setValues(objectRow); + + new MockUp() { + @Mock public boolean isNull() { + return true; + } + }; + + new MockUp() { + @Mock public Float getFloat() { + return 44521.023f; + } + }; + + ExpressionResult result = notInExpression.evaluate(value); + assertFalse(result.getBoolean()); + } + @Test public void testEvaluateForNotInExpressionWithDoubleDataType() throws FilterUnsupportedException, FilterIllegalMemberException { ColumnExpression left = new ColumnExpression("left_contact", DataTypes.DOUBLE); diff --git a/core/src/test/java/org/apache/carbondata/core/scan/filter/FilterUtilTest.java b/core/src/test/java/org/apache/carbondata/core/scan/filter/FilterUtilTest.java index 94ae41976cc..7ce0bc73b12 100644 --- a/core/src/test/java/org/apache/carbondata/core/scan/filter/FilterUtilTest.java +++ b/core/src/test/java/org/apache/carbondata/core/scan/filter/FilterUtilTest.java @@ -105,6 +105,16 @@ public void testCheckIfLeftExpressionRequireEvaluationWithExpressionNotInstanceO assertEquals(expectedResult, actualResult); } + @Test + public void testCompareFilterKeyBasedOnDataTypeForFloatValue() { + String dictionaryVal = "1.90"; + String memberVal = "1.89"; + int actualResult = + FilterUtil.compareFilterKeyBasedOnDataType(dictionaryVal, memberVal, DataTypes.FLOAT); + int expectedResult = 1; + assertEquals(expectedResult, actualResult); + } + @Test public void testCompareFilterKeyBasedOnDataTypeForDoubleValue() { String dictionaryVal = "1.90"; String memberVal = "1.89"; diff --git a/core/src/test/java/org/apache/carbondata/core/util/DataTypeUtilTest.java b/core/src/test/java/org/apache/carbondata/core/util/DataTypeUtilTest.java index 855d1ec501a..13e70f94018 100644 --- a/core/src/test/java/org/apache/carbondata/core/util/DataTypeUtilTest.java +++ b/core/src/test/java/org/apache/carbondata/core/util/DataTypeUtilTest.java @@ -67,10 +67,12 @@ public class DataTypeUtilTest { Object resultLong = getMeasureValueBasedOnDataType("1", DataTypes.LONG, carbonMeasure.getScale(), carbonMeasure.getPrecision()); Object expectedLong = Long.valueOf(1); assertEquals(expectedLong, resultLong); + Object resultFloat = getMeasureValueBasedOnDataType("1", DataTypes.FLOAT, carbonMeasure.getScale(), carbonMeasure.getPrecision()); + Object expectedFloat = 1f; + assertEquals(expectedFloat, resultFloat); Object resultDefault = getMeasureValueBasedOnDataType("1", DataTypes.DOUBLE, carbonMeasure.getScale(), carbonMeasure.getPrecision()); Double expectedDefault = Double.valueOf(1); assertEquals(expectedDefault, resultDefault); - } @Test public void testNormalizeIntAndLongValues() throws NumberFormatException { diff --git a/docs/supported-data-types-in-carbondata.md b/docs/supported-data-types-in-carbondata.md index 4960453a172..d52919b4e45 100644 --- a/docs/supported-data-types-in-carbondata.md +++ b/docs/supported-data-types-in-carbondata.md @@ -28,7 +28,7 @@ * FLOAT * BYTE - **NOTE**: Float and Bytes are only supported for SDK and FileFormat. + **NOTE**: Bytes is only supported for SDK and FileFormat. * Date/Time Types * TIMESTAMP diff --git a/hadoop/src/main/java/org/apache/carbondata/hadoop/stream/StreamBlockletReader.java b/hadoop/src/main/java/org/apache/carbondata/hadoop/stream/StreamBlockletReader.java index 6d91e78d073..2bc538af8f0 100644 --- a/hadoop/src/main/java/org/apache/carbondata/hadoop/stream/StreamBlockletReader.java +++ b/hadoop/src/main/java/org/apache/carbondata/hadoop/stream/StreamBlockletReader.java @@ -236,6 +236,10 @@ public long readLong() { return v; } + public float readFloat() { + return Float.intBitsToFloat(readInt()); + } + public double readDouble() { return Double.longBitsToDouble(readLong()); } diff --git a/hadoop/src/main/java/org/apache/carbondata/hadoop/stream/StreamRecordReader.java b/hadoop/src/main/java/org/apache/carbondata/hadoop/stream/StreamRecordReader.java index 5b284e94f23..9014e1033bd 100644 --- a/hadoop/src/main/java/org/apache/carbondata/hadoop/stream/StreamRecordReader.java +++ b/hadoop/src/main/java/org/apache/carbondata/hadoop/stream/StreamRecordReader.java @@ -505,6 +505,18 @@ protected void readRowFromStream() { } else { input.skipBytes(8); } + } else if (dataType == DataTypes.FLOAT) { + if (isRequired[colCount]) { + float v = input.readFloat(); + if (isFilterRequired[colCount]) { + filterValues[filterMap[colCount]] = v; + } + if (isProjectionRequired[colCount]) { + outputValues[projectionMap[colCount]] = v; + } + } else { + input.skipBytes(4); + } } else if (dataType == DataTypes.DOUBLE) { if (isRequired[colCount]) { double v = input.readDouble(); @@ -586,6 +598,8 @@ private void readRawRowFromStream() { outputValues[colCount] = input.readInt(); } else if (dataType == DataTypes.LONG) { outputValues[colCount] = input.readLong(); + } else if (dataType == DataTypes.FLOAT) { + outputValues[colCount] = input.readFloat(); } else if (dataType == DataTypes.DOUBLE) { outputValues[colCount] = input.readDouble(); } else if (DataTypes.isDecimal(dataType)) { diff --git a/index/bloom/src/main/java/org/apache/carbondata/index/bloom/DataConvertUtil.java b/index/bloom/src/main/java/org/apache/carbondata/index/bloom/DataConvertUtil.java index 3fb4cabc043..327ff0c6ffd 100644 --- a/index/bloom/src/main/java/org/apache/carbondata/index/bloom/DataConvertUtil.java +++ b/index/bloom/src/main/java/org/apache/carbondata/index/bloom/DataConvertUtil.java @@ -63,6 +63,8 @@ public static Object getNullValueForMeasure(DataType dataType, int scale) { return 0; } else if (dataType == DataTypes.LONG || dataType == DataTypes.TIMESTAMP) { return 0L; + } else if (dataType == DataTypes.FLOAT) { + return 0.0f; } else if (dataType == DataTypes.DOUBLE) { return 0.0; } else if (DataTypes.isDecimal(dataType)) { diff --git a/integration/presto/src/main/prestosql/org/apache/carbondata/presto/CarbondataPageSource.java b/integration/presto/src/main/prestosql/org/apache/carbondata/presto/CarbondataPageSource.java index 18b384d3ea3..af80a0f14ba 100644 --- a/integration/presto/src/main/prestosql/org/apache/carbondata/presto/CarbondataPageSource.java +++ b/integration/presto/src/main/prestosql/org/apache/carbondata/presto/CarbondataPageSource.java @@ -163,7 +163,7 @@ private void initializeForRow() { for (ProjectionMeasure msr : queryMeasures) { DataType dataType = msr.getMeasure().getDataType(); if (dataType == DataTypes.BOOLEAN || dataType == DataTypes.SHORT || dataType == DataTypes.INT - || dataType == DataTypes.LONG) { + || dataType == DataTypes.LONG || dataType == DataTypes.FLOAT) { fields[msr.getOrdinal()] = new StructField(msr.getColumnName(), msr.getMeasure().getDataType()); } else if (DataTypes.isDecimal(dataType)) { diff --git a/integration/spark/src/main/java/org/apache/carbondata/converter/SparkDataTypeConverterImpl.java b/integration/spark/src/main/java/org/apache/carbondata/converter/SparkDataTypeConverterImpl.java index b0e78b2e61f..a778d8fb8b4 100644 --- a/integration/spark/src/main/java/org/apache/carbondata/converter/SparkDataTypeConverterImpl.java +++ b/integration/spark/src/main/java/org/apache/carbondata/converter/SparkDataTypeConverterImpl.java @@ -123,6 +123,8 @@ public static org.apache.spark.sql.types.DataType convertCarbonToSparkDataType( return DataTypes.IntegerType; } else if (carbonDataType == org.apache.carbondata.core.metadata.datatype.DataTypes.LONG) { return DataTypes.LongType; + } else if (carbonDataType == org.apache.carbondata.core.metadata.datatype.DataTypes.FLOAT) { + return DataTypes.FloatType; } else if (carbonDataType == org.apache.carbondata.core.metadata.datatype.DataTypes.DOUBLE) { return DataTypes.DoubleType; } else if (carbonDataType == org.apache.carbondata.core.metadata.datatype.DataTypes.BOOLEAN) { @@ -171,7 +173,8 @@ public Object[] convertCarbonSchemaToSparkSchema(CarbonColumn[] carbonColumns) { || dataType == org.apache.carbondata.core.metadata.datatype.DataTypes.INT || dataType == org.apache.carbondata.core.metadata.datatype.DataTypes.LONG || dataType == org.apache.carbondata.core.metadata.datatype.DataTypes.BINARY - || dataType == org.apache.carbondata.core.metadata.datatype.DataTypes.VARCHAR) { + || dataType == org.apache.carbondata.core.metadata.datatype.DataTypes.VARCHAR + || dataType == org.apache.carbondata.core.metadata.datatype.DataTypes.FLOAT) { fields[i] = new StructField(carbonColumn.getColName(), convertCarbonToSparkDataType(dataType), true, null); diff --git a/integration/spark/src/main/java/org/apache/carbondata/spark/util/Util.java b/integration/spark/src/main/java/org/apache/carbondata/spark/util/Util.java index 30c2b5f5381..ee60b4506d4 100644 --- a/integration/spark/src/main/java/org/apache/carbondata/spark/util/Util.java +++ b/integration/spark/src/main/java/org/apache/carbondata/spark/util/Util.java @@ -69,6 +69,8 @@ public static org.apache.spark.sql.types.DataType convertCarbonToSparkDataType( return DataTypes.IntegerType; } else if (carbonDataType == org.apache.carbondata.core.metadata.datatype.DataTypes.LONG) { return DataTypes.LongType; + } else if (carbonDataType == org.apache.carbondata.core.metadata.datatype.DataTypes.FLOAT) { + return DataTypes.FloatType; } else if (carbonDataType == org.apache.carbondata.core.metadata.datatype.DataTypes.DOUBLE) { return DataTypes.DoubleType; } else if (carbonDataType == org.apache.carbondata.core.metadata.datatype.DataTypes.BOOLEAN) { diff --git a/integration/spark/src/main/scala/org/apache/carbondata/spark/util/CarbonSparkUtil.scala b/integration/spark/src/main/scala/org/apache/carbondata/spark/util/CarbonSparkUtil.scala index 4508c14da66..375d6668084 100644 --- a/integration/spark/src/main/scala/org/apache/carbondata/spark/util/CarbonSparkUtil.scala +++ b/integration/spark/src/main/scala/org/apache/carbondata/spark/util/CarbonSparkUtil.scala @@ -176,7 +176,6 @@ object CarbonSparkUtil { def updateDataType(dataType: DataType): DataType = { dataType match { - case _: FloatType => DataTypes.DoubleType case struct: StructType => updateStruct(struct) case array: ArrayType => updateArray(array) case map: MapType => updateMap(map) diff --git a/integration/spark/src/main/scala/org/apache/carbondata/spark/util/DataTypeConverterUtil.scala b/integration/spark/src/main/scala/org/apache/carbondata/spark/util/DataTypeConverterUtil.scala index 001c3360f82..fa51e77e9a1 100644 --- a/integration/spark/src/main/scala/org/apache/carbondata/spark/util/DataTypeConverterUtil.scala +++ b/integration/spark/src/main/scala/org/apache/carbondata/spark/util/DataTypeConverterUtil.scala @@ -42,7 +42,7 @@ object DataTypeConverterUtil { case "bigint" => DataTypes.LONG case "numeric" => DataTypes.DOUBLE case "double" => DataTypes.DOUBLE - case "float" => DataTypes.DOUBLE + case "float" => DataTypes.FLOAT case "decimal" => DataTypes.createDefaultDecimalType case FIXED_DECIMAL(_, _) => DataTypes.createDefaultDecimalType case "timestamp" => DataTypes.TIMESTAMP @@ -67,7 +67,7 @@ object DataTypeConverterUtil { case "biginttype" => DataTypes.LONG case "numerictype" => DataTypes.DOUBLE case "doubletype" => DataTypes.DOUBLE - case "floattype" => DataTypes.DOUBLE + case "floattype" => DataTypes.FLOAT case "decimaltype" => DataTypes.createDefaultDecimalType case FIXED_DECIMALTYPE(_, _) => DataTypes.createDefaultDecimalType case "timestamptype" => DataTypes.TIMESTAMP @@ -159,6 +159,7 @@ object DataTypeConverterUtil { case "boolean" => ThriftDataType.BOOLEAN case "short" => ThriftDataType.SHORT case "long" | "bigint" => ThriftDataType.LONG + case "float" => ThriftDataType.FLOAT case "double" => ThriftDataType.DOUBLE case "decimal" => ThriftDataType.DECIMAL case "date" => ThriftDataType.DATE diff --git a/integration/spark/src/main/scala/org/apache/spark/sql/catalyst/CarbonDDLSqlParser.scala b/integration/spark/src/main/scala/org/apache/spark/sql/catalyst/CarbonDDLSqlParser.scala index a79865bade2..93fe40de1aa 100644 --- a/integration/spark/src/main/scala/org/apache/spark/sql/catalyst/CarbonDDLSqlParser.scala +++ b/integration/spark/src/main/scala/org/apache/spark/sql/catalyst/CarbonDDLSqlParser.scala @@ -273,7 +273,7 @@ abstract class CarbonDDLSqlParser extends AbstractCarbonSparkSQLParser { STRING ^^^ "string" |BOOLEAN ^^^ "boolean" | INTEGER ^^^ "integer" | TIMESTAMP ^^^ "timestamp" | NUMERIC ^^^ "numeric" | (LONG | BIGINT) ^^^ "bigint" | (SHORT | SMALLINT) ^^^ "smallint" | - INT ^^^ "int" | DOUBLE ^^^ "double" | FLOAT ^^^ "double" | decimalType | + INT ^^^ "int" | DOUBLE ^^^ "double" | FLOAT ^^^ "float" | decimalType | DATE ^^^ "date" | charType protected lazy val miscType = BINARY ^^^ "binary" diff --git a/integration/spark/src/main/scala/org/apache/spark/sql/catalyst/CarbonParserUtil.scala b/integration/spark/src/main/scala/org/apache/spark/sql/catalyst/CarbonParserUtil.scala index 3c4ed7b58e4..75dec1eb7e2 100644 --- a/integration/spark/src/main/scala/org/apache/spark/sql/catalyst/CarbonParserUtil.scala +++ b/integration/spark/src/main/scala/org/apache/spark/sql/catalyst/CarbonParserUtil.scala @@ -1067,7 +1067,7 @@ object CarbonParserUtil { case "integer" | "int" => field.copy(dataType = Some("Integer")) case "long" => field.copy(dataType = Some("Long")) case "double" => field.copy(dataType = Some("Double")) - case "float" => field.copy(dataType = Some("Double")) + case "float" => field.copy(dataType = Some("Float")) case "timestamp" => field.copy(dataType = Some("Timestamp")) case "date" => field.copy(dataType = Some("Date")) case "numeric" => field.copy(dataType = Some("Numeric")) diff --git a/integration/spark/src/main/scala/org/apache/spark/sql/execution/CastExpressionOptimization.scala b/integration/spark/src/main/scala/org/apache/spark/sql/execution/CastExpressionOptimization.scala index abda9e57495..71bdae72458 100644 --- a/integration/spark/src/main/scala/org/apache/spark/sql/execution/CastExpressionOptimization.scala +++ b/integration/spark/src/main/scala/org/apache/spark/sql/execution/CastExpressionOptimization.scala @@ -29,7 +29,7 @@ import org.apache.spark.sql.carbondata.execution.datasources.CarbonSparkDataSour import org.apache.spark.sql.catalyst.expressions.{Attribute, EmptyRow, EqualTo, GreaterThan, GreaterThanOrEqual, In, LessThan, LessThanOrEqual, Literal, Not} import org.apache.spark.sql.catalyst.expressions.{Expression => SparkExpression} import org.apache.spark.sql.optimizer.CarbonFilters.{transformExpression, translateColumn, translateLiteral} -import org.apache.spark.sql.types.{ArrayType, DateType, DoubleType, IntegerType, ShortType, StringType, TimestampType} +import org.apache.spark.sql.types.{ArrayType, DateType, DoubleType, FloatType, IntegerType, ShortType, StringType, TimestampType} import org.apache.spark.sql.types.{DataType => SparkDataType} import org.apache.carbondata.core.constants.CarbonCommonConstants @@ -112,11 +112,28 @@ object CastExpressionOptimization { def typeCastDoubleToIntList(list: Seq[SparkExpression]): Seq[SparkExpression] = { val tempList = new util.ArrayList[SparkExpression]() - list.foreach { value => - val output = value.asInstanceOf[Double].toInt - if (value.asInstanceOf[Double].toInt.equals(output)) { - tempList.add(output.asInstanceOf[SparkExpression]) - } + list map { + case Literal(value, _) => + val newValue = value.asInstanceOf[Double].toInt + if (value.asInstanceOf[Double].toInt.equals(newValue)) { + tempList.add(Literal(newValue)) + } + } + if (tempList.size() != list.size) { + list + } else { + tempList.asScala + } + } + + def typeCastDoubleToFloatList(list: Seq[SparkExpression]): Seq[SparkExpression] = { + val tempList = new util.ArrayList[SparkExpression]() + list map { + case Literal(value, _) => + val newValue = value.asInstanceOf[Double].toFloat + if (newValue.toString.equals(value.toString)) { + tempList.add(Literal(newValue)) + } } if (tempList.size() != list.size) { list @@ -152,6 +169,10 @@ object CastExpressionOptimization { * Input from Spark : cast (col as double) <> 'Double Literal' * Change to : Column <> 'Int value' * + * c) Left : Float Column Right : String Value + * Input from Spark : cast (col as double) <> 'Double Literal' + * Change to : Column <> 'Float value' + * * @param expr * @return */ @@ -173,6 +194,8 @@ object CastExpressionOptimization { updateFilterForInt(value, expr, attributeType) case _: ShortType if valueType.sameType(IntegerType) => updateFilterForShort(value, expr, attributeType) + case _: FloatType if valueType.sameType(DoubleType) => + updateFilterForFloat(value, expr, attributeType) case arr: ArrayType if !nonEqual => checkBinaryExpression(arr.elementType, value, valueType, nonEqual) case _ => Some(transformExpression(expr)) @@ -223,6 +246,9 @@ object CastExpressionOptimization { case _: ShortType if list.head.dataType.sameType(IntegerType) => checkInValueList(attribute.name, list, typeCastIntToShortList(list), attribute.dataType, hasNot) + case _: FloatType if list.head.dataType.sameType(DoubleType) => + checkInValueList(attribute.name, list, typeCastDoubleToFloatList(list), + attribute.dataType, hasNot) case _ => Some(transformExpression(expr)) } } @@ -295,6 +321,24 @@ object CastExpressionOptimization { } } + /** + * the method removes the cast for float type columns + * + * @param actualValue + * @param exp + * @return + */ + def updateFilterForFloat(actualValue: Any, + exp: SparkExpression, + dt: SparkDataType): Option[Expression] = { + val newValue = actualValue.asInstanceOf[Double].toFloat + if (newValue.toString.equals(actualValue.toString)) { + updateFilterBasedOnFilterType(exp, newValue, dt) + } else { + Some(transformExpression(exp)) + } + } + /** * * @param actualValue actual value of filter diff --git a/integration/spark/src/main/scala/org/apache/spark/sql/execution/command/management/CommonLoadUtils.scala b/integration/spark/src/main/scala/org/apache/spark/sql/execution/command/management/CommonLoadUtils.scala index 2cf94889663..06edde86f5f 100644 --- a/integration/spark/src/main/scala/org/apache/spark/sql/execution/command/management/CommonLoadUtils.scala +++ b/integration/spark/src/main/scala/org/apache/spark/sql/execution/command/management/CommonLoadUtils.scala @@ -756,12 +756,15 @@ object CommonLoadUtils { var timeStampIndex = scala.collection.mutable.Set[Int]() var dateIndex = scala.collection.mutable.Set[Int]() var doubleIndex = scala.collection.mutable.Set[Int]() + val floatIndex = scala.collection.mutable.Set[Int]() var i: Int = 0 for (col <- columnSchema) { if (col.getDataType == org.apache.carbondata.core.metadata.datatype.DataTypes.TIMESTAMP) { timeStampIndex += i } else if (col.getDataType == org.apache.carbondata.core.metadata.datatype.DataTypes.DATE) { dateIndex += i; + } else if (col.getDataType == org.apache.carbondata.core.metadata.datatype.DataTypes.FLOAT) { + floatIndex += i; } else if (col.getDataType == org.apache.carbondata.core.metadata.datatype.DataTypes.DOUBLE) { doubleIndex += i @@ -788,6 +791,14 @@ object CommonLoadUtils { internalRow.getLong(index) / TimeStampGranularityTypeValue.MILLIS_SECONDS.getValue) } } + var floatValue: Float = 0 + for (index <- doubleIndex) { + floatValue = internalRow.getFloat(index) + if (floatValue.isNaN || floatValue.isInfinite) { + // converter used to set null for NAN or infinite + internalRow.setNullAt(index) + } + } var doubleValue: Double = 0 for (index <- doubleIndex) { doubleValue = internalRow.getDouble(index) diff --git a/integration/spark/src/main/scala/org/apache/spark/sql/optimizer/CarbonFilters.scala b/integration/spark/src/main/scala/org/apache/spark/sql/optimizer/CarbonFilters.scala index 2dd8c234f05..e198df74501 100644 --- a/integration/spark/src/main/scala/org/apache/spark/sql/optimizer/CarbonFilters.scala +++ b/integration/spark/src/main/scala/org/apache/spark/sql/optimizer/CarbonFilters.scala @@ -109,10 +109,18 @@ object CarbonFilters { translateNotIn(a.name, list.map(e => e.eval(EmptyRow)), columnTypes) case In(a: Attribute, list) if list.forall(_.isInstanceOf[Literal]) => translateIn(a.name, list.map(e => e.eval(EmptyRow)), columnTypes) - case c@Not(In(Cast(_: Attribute, _, _), list)) if list.forall(_.isInstanceOf[Literal]) => - Some(transformExpression(c)) - case c@In(Cast(_: Attribute, _, _), list) if list.forall(_.isInstanceOf[Literal]) => - Some(transformExpression(c)) + case c@Not(In(Cast(a: Attribute, _, _), list)) if list.forall(_.isInstanceOf[Literal]) => + if (a.dataType == FloatType) { + CastExpressionOptimization.checkIfCastCanBeRemove(c) + } else { + Some(transformExpression(c)) + } + case c@In(Cast(a: Attribute, _, _), list) if list.forall(_.isInstanceOf[Literal]) => + if (a.dataType == FloatType) { + CastExpressionOptimization.checkIfCastCanBeRemove(c) + } else { + Some(transformExpression(c)) + } case InSet(a: Attribute, set) => translateIn(a.name, set.toSeq, columnTypes) case Not(InSet(a: Attribute, set)) => diff --git a/integration/spark/src/main/scala/org/apache/spark/sql/parser/CarbonSpark2SqlParser.scala b/integration/spark/src/main/scala/org/apache/spark/sql/parser/CarbonSpark2SqlParser.scala index 225c4df96dc..f7c3c2c022e 100644 --- a/integration/spark/src/main/scala/org/apache/spark/sql/parser/CarbonSpark2SqlParser.scala +++ b/integration/spark/src/main/scala/org/apache/spark/sql/parser/CarbonSpark2SqlParser.scala @@ -826,9 +826,9 @@ class CarbonSpark2SqlParser extends CarbonDDLSqlParser { // to tokenize and it wont be able to differentiate between whether its actual character or // token character. So just for parsing temporarily replace with !, then once field is // prepared, just replace the original name. - getScannerInput(dataType, columnComment, name.replaceAll("`", "!"), isExternal) + getScannerInput(dataType, columnComment, name.replaceAll("`", "!")) } else { - getScannerInput(dataType, columnComment, name, isExternal) + getScannerInput(dataType, columnComment, name) } var field: Field = anyFieldDef(new lexical.Scanner(scannerInput.toLowerCase)) match { @@ -844,7 +844,7 @@ class CarbonSpark2SqlParser extends CarbonDDLSqlParser { val actualName = indexesToReplace.foldLeft(field.column)((s, i) => s.updated(i .asInstanceOf[Int], '`')) field = field.copy(column = actualName, name = Some(actualName)) - scannerInput = getScannerInput(dataType, columnComment, name, isExternal) + scannerInput = getScannerInput(dataType, columnComment, name) } // the data type of the decimal type will be like decimal(10,0) // so checking the start of the string and taking the precision and scale. @@ -858,9 +858,6 @@ class CarbonSpark2SqlParser extends CarbonDDLSqlParser { if (field.dataType.getOrElse("").startsWith("char")) { field.dataType = Some("char") } - else if (field.dataType.getOrElse("").startsWith("float") && !isExternal) { - field.dataType = Some("double") - } field.rawSchema = scannerInput if (comment.isDefined) { field.columnComment = plainComment @@ -870,14 +867,8 @@ class CarbonSpark2SqlParser extends CarbonDDLSqlParser { def getScannerInput(dataType: DataType, columnComment: String, - columnName: String, - isExternal: Boolean): String = { - if (dataType.catalogString == "float" && !isExternal) { - '`' + columnName + '`' + " double" + columnComment - } else { - '`' + columnName + '`' + ' ' + dataType.catalogString + - columnComment - } + columnName: String) : String = { + '`' + columnName + '`' + ' ' + dataType.catalogString + columnComment } def getBucketFields( diff --git a/integration/spark/src/test/resources/complexdata3.csv b/integration/spark/src/test/resources/complexdata3.csv index 63cd44ba6b2..2f9da9cb443 100644 --- a/integration/spark/src/test/resources/complexdata3.csv +++ b/integration/spark/src/test/resources/complexdata3.csv @@ -1,10 +1,10 @@ -e01a1773-bd37-40be-a1de-d7e74837a281 (0551)96116063 886 0031 5 (0551)46819921 853 4 0 1568220618904 50 asp fk 2745000 1 0 0 0 0 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chin chin 0.64250153 mm 0.0 2744000 2742000 1568249840037 1568249838749 面发展，朝着实现全体人民共同富裕不断迈进”。 经过长期艰苦奋斗，我们党在实践中形成了先富带动后富、逐步实现共同富裕的规律性认识，推动人民生活质量和社会共享水平显著提升。粮票、布票、肉票、鱼票、油票、豆腐票、副食本、工业券等百姓生活曾经离不开的票证，已经进入了历史博物馆。忍饥挨饿、缺吃少穿、生活困顿这些几千年来困扰我国人民的问题总体上一去不复返了，中国人民迎来了从温饱不足到小康富裕的伟大飞跃。 （39）实现全体人民共同富裕的宏伟目标，最终靠的是发展。发展是基础，唯有发展才能满足人民对美好生活的热切向往。没有发展，没有扎扎实实的发展成果，共同富裕就无从谈起。 要毫不动摇坚持发展是硬道理、发展应该是科学发展和高质量发展的战略思想，举全民之力推进中国特色社会主义事业，不断把“蛋糕”做大。同时，还要在不断发展的基础上把促进社会公平正义的事情做好，把不断做大的“蛋糕”分好，让社会主义制度的优越性更加充分体现出来，让实现全体人民共同富裕在广大人民现实生活中更加充分展示出来。绝不能出现“富者累巨万，而贫者食糟糠”的现象。 实现全体人民共同富裕的目标需要一个很长的历史过程，是由一个一个阶段性目标逐步达成的。我国正处于并将长期处于社会主义初级阶段，我们不能做超越阶段的事情，但也不是说在逐步实现共同富裕方面就无所作为，而是要根据现有条件把能做的事情尽量做起来，积小胜为大胜，不断朝着全体人民共同富裕的目标前进。 4.群众路线是党的生命线和根本工作路线 （40）习近平总书记指出：“群众路线是我们党的生命线和根本工作路线，是我们党永葆青春活力和战斗力的重要传家宝。”不论过去、现在和将来，我们都要坚持一切为了群众，一切依靠群众，从群众中来，到群众中去，把党的正确主张变为群众的自觉行动，把群众路线贯彻到治国理政全部活动之中。 坚持群众路线，核心的问题是党要始终保持同人民群众的血肉联系，一刻也不脱离群众。密切联系群众，是党的性质和宗旨的体现。我们党是在同人民群众的密切联系中成长、发展、壮大起来的，是靠宣传群众、组织群众、依靠群众起家，从胜利走向胜利的。党的最大政治优势是密切联系群众，党执政后的最大危险是脱离群众。能否保持党同人民群众的血肉联系，决定着党的事业的成败。要从政治的高度深刻认识密切联系群众的重要性，任何时候任何情况下，与人民群众同呼吸共命运的立场不能变，全心全意为人民服务的宗旨不能忘，坚信群众是真正英雄的历史唯物主义观点不能丢。 （41）群众路线是我们党始终坚持的根本工作方法。党的领导工作的正确方法，就是将群众意见集中起来形成正确的决策，又到群众中宣传解释，将决策化为群众的行动，并在群众实践中检验这些决策是否正确。 坚持走群众路线，绝不是喊喊口号走走过场，而是要诚心诚意、实打实做。要善于通过提出并贯彻正确的理论和路线方针政策带领人民前进，善于从人民的实践创造和发展要求中完善政策主张，善于从群众中寻找解决问题的方案和办法，使作出的决策和决策的执行充分体现民心民意。深入研究新形势下群众工作的规律和特点，把党的优良传统和新技术新手段结合起来，学会通过网络走群众路线，提高做好群众工作的本领。 贯彻党的群众路线，“知”是基础、是前提，“行”是重点、是关键。必须以知促行、以行促知，做到知行合一，既解决认识提高问题，又解决行动自觉问题，使群众路线落地稳、扎根深，融入经济社会发展全过程，贯穿到党的全部工作中。 新华社合肥7月23日电 题：安徽70年：改革创新书写时代答卷 新华社记者王正忠、杨玉华、徐海涛、姜刚 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（1）安徽70年：改革创新书写时代答卷 这是安徽凤阳小岗村（2018年9月27日无人机拍摄）。 新华社记者 张端 摄 这是一方敢为人先的热土：开启农村改革巨幕的“大包干”、闯出个体经济之路的“傻子瓜子”、冲出重围高擎自主品牌大旗的奇瑞汽车……一次次书写时代传奇。 这是一片勇于革新的天地：税费改革、医药卫生体制改革、文化体制改革、全面创新改革试验……一次次引领风气之先。 这是一处创新喷涌的泉源：全球首颗量子通信卫星上天、智能语音技术站上全球制高点、世界最薄的玻璃问世、“人造太阳”正求解人类终极能源难题……一次次刷新进步的前沿。 从新中国成立时一个落后的农业省，到今天实现农业大省、工业大省、科技大省的“三级跳”，安徽以70年的精彩蝶变书写时代答卷，以改革创新的昂扬姿态挺进新时代。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（3）安徽70年：改革创新书写时代答卷 在安徽凤阳小岗村，农民驾驶收割机收割水稻（2018年9月27日摄）。 新华社记者 刘军喜 摄 昔日农业省今成“科创极” 八百里巢湖之滨，坐落着全国首处以创新为主题的场馆。其中“人造太阳”、强磁场装置、量子通信等高精尖的科技创新成果令八方来宾感到震撼。 安徽，曾长期是经济发展的洼地，不沿边、不靠海、工业基础薄弱。新中国成立初期，一产占比高达75%以上。70年沧桑巨变，到2018年，安徽的三产结构已变为8.8：46.1：45.1，农业占比退居十分之一以下，昔日的农业大省成为以家电、汽车、建材等为主的工业大省和以电子信息、平板显示、智能语音等领衔的科技产业大省。 巨变的背后，是安徽把科技创新作为转型升级的突破口。早在1995年安徽就提出实施“科教兴皖”战略，2008年启动合芜蚌自主创新综合试验区建设，实施创新驱动发展之路。2013年，安徽成为全国第二个开展创新型省份建设试点工作的地区，随后获批建设合芜蚌国家自主创新示范区和系统推进全面创新改革试验区。2017年初，合肥综合性国家科学中心获批。2018年，合肥滨湖科学城挂牌成立……安徽的创新名片越擦越亮。 多年的探索实践，安徽已形成从原始创新到技术开发、成果转化、企业孵化，再到新兴产业的“全创新链”，走出一条具有安徽特色的中西部创新发展之路。 在位于合肥北郊的合肥综合性国家科学中心聚变堆园区，聚变堆主机关键系统综合研究设施工程正在紧张建设。这个由安徽省政府主要领导率队答辩争取来的国家大科学装置，与之前建成的合肥“人造太阳”装置相呼应，使安徽成为世界一流的核聚变能源创新研究基地。瞄准前沿、前瞻布局、潜心投入，安徽已建成“同步辐射光源”“全超导托卡马克”“稳态强磁场”三大国际科技前沿科研平台，并在量子通信、智能语音等科研领域达到国际领跑或并跑水平。安徽的区域创新能力已连续7年跻身全国第一方阵。 贴近产业抓创新，集聚资源搞科研，不断破除体制机制障碍，让安徽驶入创新发展快车道。安徽省统计局局长吴劲松说，党的十八大以来，安徽的新兴产业增加值不断提升。 目前，安徽的战略性新兴产业对全省工业增长的贡献率已超三分之一。在合肥，形成了国内唯一拥有6代线、8.5代线和10.5代线的新型平板显示产业基地。在蚌埠，以蚌埠玻璃工业设计研究院为核心的新型玻璃产业，诞生了我国首条具有自主知识产权的8.5代TFT-LCD玻璃基板生产线，成功研发世界最薄的0.12毫米超薄触控玻璃；在芜湖，形成了以中电科钻石飞机为代表的通用航空产业集群，飞机交付量处于国内领先行列。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（6）安徽70年：改革创新书写时代答卷 观众在2018世界制造业大会上观看蚌埠玻璃工业设计研究院展出的柔性触控玻璃（2018年5月25日摄）。 新华社记者 张端 摄 改革不停步发展活力涌 经济因改革而活，发展因创新而兴。今年上半年，安徽的经济呈现出“加速跑、趋向优”的良好态势。生产总值增长8%，居全国第6位，规模以上工业增加值增速居第6位，城镇、农村常住居民人均可支配收入增速分别居第3位和第4位。 “速度位次靠前，发展质效提升，内生动力增强。”安徽省统计局副局长赵金宝将全省上半年经济运行的亮点概括为三句话。 今年7月12日，三只松鼠股份有限公司在深交所上市。创立7年来的这家企业，不断颠覆传统销售模式，年销售额从零增加到2018年的82亿元。“创新发展是企业前进的根本。”公司党委书记潘道伟说，上市能加速公司数字化供应链战略的推进，为消费者提供更好的产品和服务。 作为民营经济的试验田，如今的江淮大地仍是一片创新创业的热土，敢闯敢试的改革火种继续在这里播撒。现在的安徽，平均每天新登记注册企业925家，每天获授44件发明专利。 经济“逆势上扬”，靠的是改革，是服务，是创新。十八大以来，安徽着力推动“放管服”改革，首创权力、责任清单的“安徽模式”，让公共服务做“加法”、中介服务做“减法”。而群众办事“最多跑一次”“互联网+政务服务”等创举，正推动安徽迈向审批事项少、办事效率高、投资环境优、市场主体和人民群众获得感强的省份之一。 唯改革才有出路，改革要常讲常新。目前，安徽正持续将一系列新的改革推向深入： ——政务服务改革。努力将省级行政许可事项减到全国最少，企业开办时间压缩至全国最短……到今年底，目标实现80%个人事项全程网办，部分事项“秒批秒办”。 ——全面创新改革。加快建设合肥综合性国家科学中心，加紧布局一批世界前沿的大科学装置，下放科技成果处置权、收益权、使用权，探索实行首席科学家全权负责制……目标建成具有国际影响力的科技创新中心和产业创新中心。 ——农业农村改革。持续深化农村承包地“三权分置”改革，稳步推进农村集体产权制度改革……围绕处理好农民与土地关系主线，努力在农村改革上继续走在前列。 增进民生福祉，乃经济发展之根本目的。居住在集中连片贫困山区金寨县大湾村村民陈泽申前年主动要求摘掉“贫困帽”。得益于党和政府的扶贫政策，他家盖了新房，流转了山地，有了公益性岗位，每年收入三四万元。此前，他将扭转命运的期望全部寄托在“改变家居风水”上面。 为了兑现“小康路上一个都不能掉队”的庄严承诺，安徽把以人民为中心的理念贯穿到经济社会发展各个领域。在实施脱贫攻坚“十大工程”等举措作用下，全省贫困人口由2012年底的679.1万人下降到2018年底的49.9万人，年均减贫100万人以上；贫困发生率由12.6%下降到0.93%；22个贫困县先后“摘帽”。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（7）安徽70年：改革创新书写时代答卷 2017年9月29日，世界首条量子保密通信干线——“京沪干线”正式开通。科研人员在中国科学技术大学先进技术研究院量子通信“京沪干线”总控中心工作。 新华社记者 刘军喜 摄 融入长三角勇于当“尖兵” 早在7年前，位于安徽滁州市的中新苏滁高新技术产业开发区便开工建设，拉开了苏皖两省共建园区的大幕。此后，园区内产业实现深度对接、集群发展，这里成为安徽融入长三角开放合作的典范。 记者近日走进苏滁高新区看到，一排排标准化厂房内，一片热火朝天的生产场面；以新加坡理念建设的邻里中心内，百姓购物、办事功能一应俱全。“截至6月底，从长三角引进的项目数占74%。”该区管委会主任杨广兰说，该区去年经营收入突破100亿元，同比增长逾30%。 2016年5月，中央将安徽合肥、芜湖、马鞍山等8市纳入长三角规划，安徽正式从地理上的长江下游进入长三角世界级城市群。去年11月，长三角一体化发展上升为国家战略。 坐拥皖江水，面向太平洋。安徽迎来历史性的重大发展机遇。中国区域经济学会顾问程必定认为，长三角上升为国家战略对安徽是百年未有的机遇，这意味着，作为长三角一员，安徽将跻身配置全球资源的开放门户、迎来更高质量一体化发展的崭新阶段。 “以前想到外地注册一个公司，交材料、核准名称、领证件，一切顺利也得跑三趟，现在真是节省了很多成本。”去年11月，合肥与星途科技服务有限公司负责人池天水，在合肥高新区的办事窗口领到了由浙江杭州工商部门颁发的营业执照。这也是该区通过长三角“一网通办”业务平台，发出的首张跨省异地登记的营业执照。 致力于要素破壁、深度融入，安徽各地也在抢抓长三角一体化机遇，争当落实国家战略的排头兵。池州、铜陵等地立足区位优势，承接长三角的产业分工，迈向价值链更高端；芜湖市提出“等高对接”，在长三角一体化中找准自身优势、谋求互补发展。 取消长三角区域高速公路省界收费站，推进长三角地区异地就医直接结算，打造长三角“安全消费大市场”……今年5月22日在芜湖举行的2019年长三角地区三省一市主要领导座谈会上，沪苏浙皖相关部门签署了10个打破分割、加速一体化的协议。 “扬皖所长、主动作为。”安徽省委主要负责人表示，安徽是长三角的重要方面军，在区位、科技、产业、生态、能源等方面具有自身的独特优势。安徽未来将把准自身定位，扬创新之长，努力打造长三角科技创新的重要策源地；扬产业之长，努力打造长三角高质量发展的重要增长极；扬区位之长，努力打造长三角世界级城市群的重要副中心；扬生态之长，努力打造长三角生态文明建设的重要样板区，在国家战略下自觉行动当好尖兵！ 新华社广州7月23日电 题：高质量发展的粤式“色香味”——广东制造业一线见闻 新华社记者毛鑫、邓瑞璇、魏玉坤 手机扫描二维码下单，经过10道全自动工序，不到2分钟就可拿到专属机器人模型。在广东拓斯达科技股份有限公司展厅，扫码定制机器人的“神操作”，令人对智能制造新水准印象深刻。 在广东，依托持续的研发投入、技术改造和体制机制创新，制造业正乘势转型升级，走向数字化、自动化、智能化，这一过程犹如烹制一桌高质量发展粤式大餐，“色香味”俱全。 “智”造增“色”：机器换人迎来热潮 原本人头攒动、环境嘈杂的生产车间，逐渐由精密的高度自动化生产线代替。在广东，“智造力”正成为制造业企业标配。 单条生产线的工人数量从160人降到51人、生产换型时间从45分钟降到9分钟、品质控制点从6个增加到108个……美的南沙工厂通过使用全智能生产线，实现效率和品质的双重提升。 “工人少了，但产量在递增，成本效率品质方面改善明显。”美的集团副总裁兼CIO张小懿说，美的通过自动化、数字化的支撑，正在编织一张工业互联网。 美的发展的背后，是广东制造业不断修炼“内功”的火热实践。 走进拓斯达位于东莞的厂区，在工业机器人装配测试车间，近百台机器人排成5列，有节奏地进行出厂前的自动测试。不久后，它们将进入更多工厂的车间，成为生产的主力军。 “智能制造引发机器换人热潮，国内工业机器人市场需求快速增长。”拓斯达产品研发中心总经理徐必业说，未来将持续增加研发投入，重点布局工业传感器、标准工艺设备等领域。 2018年起，广东大力发展智能制造：投入130亿元实施两轮工业技改三年行动计划，惠及3万多家企业，推动3000家工业企业“上云上平台”，工业机器人产量占全国1/5，先进制造业和高技术制造业增加值占规模以上工业增加值比重分别达56.4%和31.5%，制造业提质增效进展明显。 创新添“香”：从要素驱动转向创新驱动 国内研发成功“3D结构光人脸识别技术”、识别速度从0.1秒降至0.001秒、商用跨镜追踪技术……过去4年，广州云从信息科技公司主攻技术研发，成长为国内人工智能行业的佼佼者。 耀眼的成绩离不开研发支撑。云从科技总经理林明都介绍，公司研发团队已达700余人，其中80%以上拥有硕士及以上学历。该团队瞄准“卡脖子”的技术难点潜心钻研，持续突破核心技术。 云从科技的发展，是广东制造业把技术创新摆在核心位置，实现从要素驱动向创新驱动转变的缩影。 近年，广东采取多项措施增加技术有效供给，先后出台“科创12条”等政策，把企业作为科技创新的主体，并着力加强基础研究和应用基础研究，为高质量发展赋能。 数据显示，2018年，广东研发经费支出超过2500亿元，PCT国际专利申请量约占全国一半，有效发明专利量达24.9万件，稳居全国前列。区域创新综合能力排名连续两年居全国第一。 创新“引力”效果渐显，投资加速向高端产业聚集已成趋势。目前，广东规模以上工业企业总量超5万家，在全国名列前茅。国家级高新技术企业达4.5万家，近两年翻了一倍多。巴斯夫、埃克森美孚、富士康、LG等一批投资过百亿美元的项目逐一落地。 政策调“味”：加强制度供给 优化营商环境 2009年，刚从清华大学博士毕业的佘振定，带着一份“人工皮肤”项目的商业计划书南下深圳创业。 接触多家风投机构无果后，是深圳清华大学研究院相中了这个项目，决定进行孵化，并许诺研发团队可以持有项目70%的股份。 这坚定了佘振定在深圳创业的决心，他创办了深圳兰度生物科技有限公司。 后来，该团队从牛跟腱里提取高纯度胶原，制成了可媲美真实皮肤的双层“人工皮肤”，一经上市便填补了国内空白。多 {} {"audioInfo":{"langDec":"chin","decSc":1.0,"nlpSc":0.0,"gender":"m","audioFiles":[{"decSc":1.0,"side":"c","gender":"m","bits":16,"langSc":0.0,"url":"http://10.5.3.85/heluAB.wav","vtl":135560,"codec":6,"channels":1,"rate":8000,"spkn":0,"name":"heluAB.wav","tl":211962}],"langSc":0.64250153,"lang":"chin"}} -f72ce5cb-2ea6-423b-8c1f-6dadfd6f52e7 (0551)73382297 853 0031 4 (0551)73382297 49 9 0 1568275177770 1559 asp fk 5821000 1 0 0 0 0 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chin chin 0.64250153 mm 0.0 5820000 5818000 1568249840060 1568249838792 位，充分调动人民积极性，始终是我们党立于不败之地的强大根基。 老百姓是天，老百姓是地。中国共产党之所以能够发展壮大，中国特色社会主义之所以能够不断前进，正是因为依靠了人民。谋划发展，最了解实际情况的，是人民群众；推动改革，最大的依靠力量，也是人民群众。改革开放在认识和实践上的每一次突破和发展，改革开放中每一个新生事物的产生和发展，改革开放每一个方面经验的创造和积累，无不来自亿万人民的实践和智慧。无论遇到任何困难和挑战，只要有人民支持和参与，就没有克服不了的困难，就没有越不过去的坎，就没有完成不了的任务。 人民群众有着无尽的智慧和力量，在人民面前，我们永远是小学生。必须充分尊重人民所表达的意愿、所创造的经验、所拥有的权利、所发挥的作用，自觉拜人民为师，向能者求教，向智者问策。要把政治智慧的增长、执政本领的增强、领导艺术的提高深深扎根于人民群众的实践沃土中，不断从人民群众中吸取营养和力量。始终与人民心心相印、与人民同甘共苦、与人民团结奋斗，使全体人民都满腔热情地投身到建设祖国的美好未来和创造自己的幸福生活中去。 （37）习近平总书记指出：“时代是出卷人，我们是答卷人，人民是阅卷人。” 人民是我们党的工作的最高裁决者和最终评判者。党的执政水平和执政成效都不是由自己说了算，必须而且只能由人民来评判，最终都要看人民是否真正得到了实惠，人民生活是否真正得到了改善，人民权益是否真正得到了保障。在新时代，我们面临的挑战和问题依然严峻复杂，党面临的“赶考”远未结束。要坚持把人民拥护不拥护、赞成不赞成、高兴不高兴、答应不答应作为衡量一切工作得失的根本标准，努力向历史、向人民交出新的更加优异的答卷。 3.朝着实现全体人民共同富裕不断迈进 （38）共同富裕，是马克思主义的一个基本目标。按照马克思、恩格斯的构想，共产主义社会将彻底消除阶级之间、城乡之间、脑力劳动和体力劳动之间的对立和差别，实行各尽所能、按需分配，真正实现社会共享、实现每个人自由而全面的发展。到那时，“生产将以所有的人富裕为目的”，“所有人共同享受大家创造出来的福利”。实现共同富裕，反映了社会主义的本质要求，体现了以人民为中心的根本立场。 我们党始终带领人民为创造美好生活、实现共同富裕而不懈奋斗。毛泽东同志在新中国成立之初就提出了我国发展富强的目标，指出“这个富，是共同的富，这个强，是共同的强，大家都有份”。邓小平同志多次强调共同富裕，指出“社会主义不是少数人富起来、大多数人穷，不是那个样子。社会主义最大的优越性就是共同富裕，这是体现社会主义本质的一个东西”。江泽民同志强调：“实现共同富裕是社会主义的根本原则和本质特征，绝不能动摇。”胡锦涛同志也要求“使全体人民共享改革发展成果，使全体人民朝着共同富裕的方向稳步前进”。 进入新时代，我们走上了创造美好生活、逐步实现全体人民共同富裕的新征程。习近平总书记指出，共同富裕是中国特色社会主义的根本原则，实现共同富裕是我们党的重要使命。他还强调，“我们追求的发展是造福人民的发展，我们追求的富裕是全体人民共同富裕”，要“让发展成果更多更公平惠及全体人民，不断促进人的全面发展，朝着实现全体人民共同富裕不断迈进”。 经过长期艰苦奋斗，我们党在实践中形成了先富带动后富、逐步实现共同富裕的规律性认识，推动人民生活质量和社会共享水平显著提升。粮票、布票、肉票、鱼票、油票、豆腐票、副食本、工业券等百姓生活曾经离不开的票证，已经进入了历史博物馆。忍饥挨饿、缺吃少穿、生活困顿这些几千年来困扰我国人民的问题总体上一去不复返了，中国人民迎来了从温饱不足到小康富裕的伟大飞跃。 （39）实现全体人民共同富裕的宏伟目标，最终靠的是发展。发展是基础，唯有发展才能满足人民对美好生活的热切向往。没有发展，没有扎扎实实的发展成果，共同富裕就无从谈起。 要毫不动摇坚持发展是硬道理、发展应该是科学发展和高质量发展的战略思想，举全民之力推进中国特色社会主义事业，不断把“蛋糕”做大。同时，还要在不断发展的基础上把促进社会公平正义的事情做好，把不断做大的“蛋糕”分好，让社会主义制度的优越性更加充分体现出来，让实现全体人民共同富裕在广大人民现实生活中更加充分展示出来。绝不能出现“富者累巨万，而贫者食糟糠”的现象。 实现全体人民共同富裕的目标需要一个很长的历史过程，是由一个一个阶段性目标逐步达成的。我国正处于并将长期处于社会主义初级阶段，我们不能做超越阶段的事情，但也不是说在逐步实现共同富裕方面就无所作为，而是要根据现有条件把能做的事情尽量做起来，积小胜为大胜，不断朝着全体人民共同富裕的目标前进。 4.群众路线是党的生命线和根本工作路线 （40）习近平总书记指出：“群众路线是我们党的生命线和根本工作路线，是我们党永葆青春活力和战斗力的重要传家宝。”不论过去、现在和将来，我们都要坚持一切为了群众，一切依靠群众，从群众中来，到群众中去，把党的正确主张变为群众的自觉行动，把群众路线贯彻到治国理政全部活动之中。 坚持群众路线，核心的问题是党要始终保持同人民群众的血肉联系，一刻也不脱离群众。密切联系群众，是党的性质和宗旨的体现。我们党是在同人民群众的密切联系中成长、发展、壮大起来的，是靠宣传群众、组织群众、依靠群众起家，从胜利走向胜利的。党的最大政治优势是密切联系群众，党执政后的最大危险是脱离群众。能否保持党同人民群众的血肉联系，决定着党的事业的成败。要从政治的高度深刻认识密切联系群众的重要性，任何时候任何情况下，与人民群众同呼吸共命运的立场不能变，全心全意为人民服务的宗旨不能忘，坚信群众是真正英雄的历史唯物主义观点不能丢。 （41）群众路线是我们党始终坚持的根本工作方法。党的领导工作的正确方法，就是将群众意见集中起来形成正确的决策，又到群众中宣传解释，将决策化为群众的行动，并在群众实践中检验这些决策是否正确。 坚持走群众路线，绝不是喊喊口号走走过场，而是要诚心诚意、实打实做。要善于通过提出并贯彻正确的理论和路线方针政策带领人民前进，善于从人民的实践创造和发展要求中完善政策主张，善于从群众中寻找解决问题的方案和办法，使作出的决策和决策的执行充分体现民心民意。深入研究新形势下群众工作的规律和特点，把党的优良传统和新技术新手段结合起来，学会通过网络走群众路线，提高做好群众工作的本领。 贯彻党的群众路线，“知”是基础、是前提，“行”是重点、是关键。必须以知促行、以行促知，做到知行合一，既解决认识提高问题，又解决行动自觉问题，使群众路线落地稳、扎根深，融入经济社会发展全过程，贯穿到党的全部工作中。 新华社合肥7月23日电 题：安徽70年：改革创新书写时代答卷 新华社记者王正忠、杨玉华、徐海涛、姜刚 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（1）安徽70年：改革创新书写时代答卷 这是安徽凤阳小岗村（2018年9月27日无人机拍摄）。 新华社记者 张端 摄 这是一方敢为人先的热土：开启农村改革巨幕的“大包干”、闯出个体经济之路的“傻子瓜子”、冲出重围高擎自主品牌大旗的奇瑞汽车……一次次书写时代传奇。 这是一片勇于革新的天地：税费改革、医药卫生体制改革、文化体制改革、全面创新改革试验……一次次引领风气之先。 这是一处创新喷涌的泉源：全球首颗量子通信卫星上天、智能语音技术站上全球制高点、世界最薄的玻璃问世、“人造太阳”正求解人类终极能源难题……一次次刷新进步的前沿。 从新中国成立时一个落后的农业省，到今天实现农业大省、工业大省、科技大省的“三级跳”，安徽以70年的精彩蝶变书写时代答卷，以改革创新的昂扬姿态挺进新时代。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（3）安徽70年：改革创新书写时代答卷 在安徽凤阳小岗村，农民驾驶收割机收割水稻（2018年9月27日摄）。 新华社记者 刘军喜 摄 昔日农业省今成“科创极” 八百里巢湖之滨，坐落着全国首处以创新为主题的场馆。其中“人造太阳”、强磁场装置、量子通信等高精尖的科技创新成果令八方来宾感到震撼。 安徽，曾长期是经济发展的洼地，不沿边、不靠海、工业基础薄弱。新中国成立初期，一产占比高达75%以上。70年沧桑巨变，到2018年，安徽的三产结构已变为8.8：46.1：45.1，农业占比退居十分之一以下，昔日的农业大省成为以家电、汽车、建材等为主的工业大省和以电子信息、平板显示、智能语音等领衔的科技产业大省。 巨变的背后，是安徽把科技创新作为转型升级的突破口。早在1995年安徽就提出实施“科教兴皖”战略，2008年启动合芜蚌自主创新综合试验区建设，实施创新驱动发展之路。2013年，安徽成为全国第二个开展创新型省份建设试点工作的地区，随后获批建设合芜蚌国家自主创新示范区和系统推进全面创新改革试验区。2017年初，合肥综合性国家科学中心获批。2018年，合肥滨湖科学城挂牌成立……安徽的创新名片越擦越亮。 多年的探索实践，安徽已形成从原始创新到技术开发、成果转化、企业孵化，再到新兴产业的“全创新链”，走出一条具有安徽特色的中西部创新发展之路。 在位于合肥北郊的合肥综合性国家科学中心聚变堆园区，聚变堆主机关键系统综合研究设施工程正在紧张建设。这个由安徽省政府主要领导率队答辩争取来的国家大科学装置，与之前建成的合肥“人造太阳”装置相呼应，使安徽成为世界一流的核聚变能源创新研究基地。瞄准前沿、前瞻布局、潜心投入，安徽已建成“同步辐射光源”“全超导托卡马克”“稳态强磁场”三大国际科技前沿科研平台，并在量子通信、智能语音等科研领域达到国际领跑或并跑水平。安徽的区域创新能力已连续7年跻身全国第一方阵。 贴近产业抓创新，集聚资源搞科研，不断破除体制机制障碍，让安徽驶入创新发展快车道。安徽省统计局局长吴劲松说，党的十八大以来，安徽的新兴产业增加值不断提升。 目前，安徽的战略性新兴产业对全省工业增长的贡献率已超三分之一。在合肥，形成了国内唯一拥有6代线、8.5代线和10.5代线的新型平板显示产业基地。在蚌埠，以蚌埠玻璃工业设计研究院为核心的新型玻璃产业，诞生了我国首条具有自主知识产权的8.5代TFT-LCD玻璃基板生产线，成功研发世界最薄的0.12毫米超薄触控玻璃；在芜湖，形成了以中电科钻石飞机为代表的通用航空产业集群，飞机交付量处于国内领先行列。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（6）安徽70年：改革创新书写时代答卷 观众在2018世界制造业大会上观看蚌埠玻璃工业设计研究院展出的柔性触控玻璃（2018年5月25日摄）。 新华社记者 张端 摄 改革不停步发展活力涌 经济因改革而活，发展因创新而兴。今年上半年，安徽的经济呈现出“加速跑、趋向优”的良好态势。生产总值增长8%，居全国第6位，规模以上工业增加值增速居第6位，城镇、农村常住居民人均可支配收入增速分别居第3位和第4位。 “速度位次靠前，发展质效提升，内生动力增强。”安徽省统计局副局长赵金宝将全省上半年经济运行的亮点概括为三句话。 今年7月12日，三只松鼠股份有限公司在深交所上市。创立7年来的这家企业，不断颠覆传统销售模式，年销售额从零增加到2018年的82亿元。“创新发展是企业前进的根本。”公司党委书记潘道伟说，上市能加速公司数字化供应链战略的推进，为消费者提供更好的产品和服务。 作为民营经济的试验田，如今的江淮大地仍是一片创新创业的热土，敢闯敢试的改革火种继续在这里播撒。现在的安徽，平均每天新登记注册企业925家，每天获授44件发明专利。 经济“逆势上扬”，靠的是改革，是服务，是创新。十八大以来，安徽着力推动“放管服”改革，首创权力、责任清单的“安徽模式”，让公共服务做“加法”、中介服务做“减法”。而群众办事“最多跑一次”“互联网+政务服务”等创举，正推动安徽迈向审批事项少、办事效率高、投资环境优、市场主体和人民群众获得感强的省份之一。 唯改革才有出路，改革要常讲常新。目前，安徽正持续将一系列新的改革推向深入： ——政务服务改革。努力将省级行政许可事项减到全国最少，企业开办时间压缩至全国最短……到今年底，目标实现80%个人事项全程网办，部分事项“秒批秒办”。 ——全面创新改革。加快建设合肥综合性国家科学中心，加紧布局一批世界前沿的大科学装置，下放科技成果处置权、收益权、使用权，探索实行首席科学家全权负责制……目标建成具有国际影响力的科技创新中心和产业创新中心。 ——农业农村改革。持续深化农村承包地“三权分置”改革，稳步推进农村集体产权制度改革……围绕处理好农民与土地关系主线，努力在农村改革上继续走在前列。 增进民生福祉，乃经济发展之根本目的。居住在集中连片贫困山区金寨县大湾村村民陈泽申前年主动要求摘掉“贫困帽”。得益于党和政府的扶贫政策，他家盖了新房，流转了山地，有了公益性岗位，每年收入三四万元。此前，他将扭转命运的期望全部寄托在“改变家居风水”上面。 为了兑现“小康路上一个都不能掉队”的庄严承诺，安徽把以人民为中心的理念贯穿到经济社会发展各个领域。在实施脱贫攻坚“十大工程”等举措作用下，全省贫困人口由2012年底的679.1万人下降到2018年底的49.9万人，年均减贫100万人以上；贫困发生率由12.6%下降到0.93%；22个贫困县先后“摘帽”。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（7）安徽70年：改革创新书写时代答卷 2017年9月29日，世界首条量子保密通信干线——“京沪干线”正式开通。科研人员在中国科学技术大学先进技术研究院量子通信“京沪干线”总控中心工作。 新华社记者 刘军喜 摄 融入长三角勇于当“尖兵” 早在7年前，位于安徽滁州市的中新苏滁高新技术产业开发区便开工建设，拉开了苏皖两省共建园区的大幕。此后，园区内产业实现深度对接、集群发展，这里成为安徽融入长三角开放合作的典范。 记者近日走进苏滁高新区看到，一排排标准化厂房内，一片热火朝天的生产场面；以新加坡理念建设的邻里中心内，百姓购物、办事功能一应俱全。“截至6月底，从长三角引进的项目数占74%。”该区管委会主任杨广兰说，该区去年经营收入突破100亿元，同比增长逾30%。 2016年5月，中央将安徽合肥、芜湖、马鞍山等8市纳入长三角规划，安徽正式从地理上的长江下游进入长三角世界级城市群。去年11月，长三角一体化发展上升为国家战略。 坐拥皖江水，面向太平洋。安徽迎来历史性的重大发展机遇。中国区域经济学会顾问程必定认为，长三角上升为国家战略对安徽是百年未有的机遇，这意味着，作为长三角一员，安徽将跻身配置全球资源的开放门户、迎来更高质量一体化发展的崭新阶段。 “以前想到外地注册一个公司，交材料、核准名称、领证件，一切顺利也得跑三趟，现在真是节省了很多成本。”去年11月，合肥与星途科技服务有限公司负责人池天水，在合肥高新区的办事窗口领到了由浙江杭州工商部门颁发的营业执照。这也是该区通过长三角“一网通办”业务平台，发出的首张跨省异地登记的营业执照。 致力于要素破壁、深度融入，安徽各地也在抢抓长三角一体化机遇，争当落实国家战略的排头兵。池州、铜陵等地立足区位优势，承接长三角的产业分工，迈向价值链更高端；芜湖市提出“等高对接”，在长三角一体化中找准自身优势、谋求互补发展。 取消长三角区域高速公路省界收费站，推进长三角地区异地就医直接结算，打造长三角“安全消费大市场”……今年5月22日在芜湖举行的2019年长三角地区三省一市主要领导座谈会上，沪苏浙皖相关部门签署了10个打破分割、加速一体化的协议。 “扬皖所长、主动作为。”安徽省委主要负责人表示，安徽是长三角的重要方面军，在区位、科技、产业、生态、能源等方面具有自身的独特优势。安徽未来将把准自身定位，扬创新之长，努力打造长三角科技创新的重要策源地；扬产业之长，努力打造长三角高质量发展的重要增长极；扬区位之长，努力打造长三角世界级城市群的重要副中心；扬生态之长，努力打造长三角生态文明建设的重要样板区，在国家战略下自觉行动当好尖兵！ 新华社广州7月23日电 题：高质量发展的粤式“色香味”——广东制造业一线见闻 新华社记者毛鑫、邓瑞璇、魏玉坤 手机扫描二维码下单，经过10道全自动工序，不到2分钟就可拿到专属机器人模型。在广东拓斯达科技股份有限公司展厅，扫码定制机器人的“神操作”，令人对智能制造新水准印象深刻。 在广东，依托持续的研发投入、技术改造和体制机制创新，制造业正乘势转型升级，走向数字化、自动化、智能化，这一过程犹如烹制一桌高质量发展粤式大餐，“色香味”俱全。 “智”造增“色”：机器换人迎来热潮 原本人头攒动、环境嘈杂的生产车间，逐渐由精密的高度自动化生产线代替。在广东，“智造力”正成为制造业企业标配。 单条生产线的工人数量从160人降到51人、生产换型时间从45分钟降到9分钟、品质控制点从6个增加到108个……美的南沙工厂通过使用全智能生产线，实现效率和品质的双重提升。 “工人少了，但产量在递增，成本效率品质方面改善明显。”美的集团副总裁兼CIO张小懿说，美的通过自动化、数字化的支撑，正在编织一张工业互联网。 美的发展的背后，是广东制造业不断修炼“内功”的火热实践。 走进拓斯达位于东莞的厂区，在工业机器人装配测试车间，近百台机器人排成5列，有节奏地进行出厂前的自动测试。不久后，它们将进入更多工厂的车间，成为生产的主力军。 “智能制造引发机器换人热潮，国内工业机器人市场需求快速增长。”拓斯达产品研发中心总经理徐必业说，未来将持续增加研发投入，重点布局工业传感器、标准工艺设备等领域。 2018年起，广东大力发展智能制造：投入130亿元实施两轮工业技改三年行动计划，惠及3万多家企业，推动3000家工业企业“上云上平台”，工业机器人产量占全国1/5，先进制造业和高技术制造业增加值占规模以上工业增加值比重分别达56.4%和31.5%，制造业提质增效进展明显。 创新添“香”：从要素驱动转向创新驱动 国内研发成功“3D结构光人脸识别技术”、识别速度从0.1秒降至0.001秒、商用跨镜追踪技术……过去4年，广州云从信息科技公司主攻技术研发，成长为国内人工智能行业的佼佼者。 耀眼的成绩离不开研发支撑。云从科技总经理林明都介绍，公司研发团队已达700余人，其中80%以上拥有硕士及以上学历。该团队瞄准“卡脖子”的技术难点潜心钻研，持续突破核心技术。 云从科技的发展，是广东制造业把技术创新摆在核心位置，实现从要素驱动向创新驱动转变的缩影。 近年，广东采取多项措施增加技术有效供给，先后出台“科创12条”等政策，把企业作为科技创新的主体，并着力加强基础研究和应用基础研究，为高质量发展赋能。 数据显示，2018年，广东研发经费支出超过2500亿元，PCT国际专利申请量约占全国一半，有效发明专利量达24.9万件，稳居全国前列。区域创新综合能力排名连续两年居全国第一。 创新“引力”效果渐显，投资加速向高端产业聚集已成趋势。目前，广东规模以上工业企业总量超5万家，在全国名列前茅。国家级高新技术企业达4.5万家，近两年翻了一倍多。巴斯夫、埃克森美孚、富士康、LG等一批投资过百亿美元的项目逐一落地。 政策调“味”：加强制度供给 优化营商环境 2009年，刚从清华大学博士毕业的佘振定，带着一份“人工皮肤”项目的商业计划书南下深圳创业。 接触多家风投机构无果后，是深圳清华大学研究院相中了这个项目，决定进行孵化，并许诺研发团队可以持有项目70%的股份。 这坚定了佘振定在深圳创业的决心，他创办了深圳兰度生物科技有限公司。 后来，该团队从牛跟腱里提取高纯度胶原，制成了可媲美真实皮肤的双层“人工皮肤”，一经上市便填补了国内空白。多轮融资后，公司市值也翻了几番，达到15亿元。 “只有真正把市场作为配置创新资源的关键要素，才更有利于激发成果转化的积极性。”深圳清华大学研究院常务副院长刘伟强说。截至2018年末，该研究院已累计孵化企业2500多家，培养上市公司21家。 近年来，广东大力支持实体经济发展，以制度创新“扫清”制造业“绊脚石”，力争政府“不缺席”“不包揽”，市场“挑大梁”“说了算”。 实行个税补贴等优惠政策吸引海内外高端人才，推行“零跑动”、“秒批”、注册易等改革举措方便企业办事……一系列涉企惠企政策不断出台，持续释放各类市场主体活力。 截至2018年末，广东个体工商户和企业数量达1146万户，其中企业492万户，均稳居全国第一。其中，主营业务收入超百亿、千亿元的企业分别达276家、29家，进入世界500强的企业13家。这是2018年8月1日无人机拍摄的位于山西省朔州市的安太堡露天煤矿复垦区。新华社记者 曹阳 摄 新华社太原7月22日电 题：三晋大地起苍黄——新中国成立70年山西发展巡礼 新华社记者于振海、梁晓飞、王井怀 太行之西，黄河之东，表里山河，资源富集。 新中国成立70年来，勤劳智慧的三晋儿女在党的领导下，发扬太行精神、吕梁精神、右玉精神，在15万平方公里的土地上，建设起共和国能源重化工基地。如今，又担当起新时代能源革命排头兵重任，展开决战贫困攻坚战，朝着高质量发展之路、向着全面建成小康社会目标阔步前进。 山西，正以全新姿态挺进新时代。 当好“能源革命排头兵” 山西潞安集团石圪节煤矿曾以“艰苦奋斗、勤俭办矿”精神闻名全国。3年前，关闭了矿井，转型发展油用牡丹。前不久，其生产的牡丹籽油出口到了韩国。 石圪节“退煤种花”的同时，千里之外的千万吨级矿井同忻煤矿建成了山西首个智能化工作面，只用在地面控制室里，轻轻一点鼠标，地层深处的“乌金”就会滚滚不息地涌上地面。 新中国成立70年来，山西累计产煤190多亿吨，运往全国的煤炭130多亿吨。 山西因煤而名，也因煤而困。粗放的采矿业，经年累月，导致生态破坏、环境污染、矿难频发，经济发展患上了“煤炭依赖症”；多年来，始终走不出“煤炭市场好时，无暇转型；市场不好，无力转型”的怪圈。 关键时刻，新一届党中央为山西把脉，开出两剂良方：资源型经济转型综合配套改革和供给侧结构性改革。山西省委省政府深化“供改”与“综改”，明确了“能源革命排头兵”“资源型经济转型示范区”“内陆地区对外开放新高地”的目标定位。山西，开始走上高质量发展新征程。 尽管煤价持续高位运行，山西仍坚决淘汰落后产能，3年来关闭88座煤矿，退出落后产能8841万吨，煤炭去产能规模全国第一。煤矿数量由新中国成立后最多的4万多座，降到了1000座以内，先进产能占比提升到57%。 如今，山西1天开采的煤，相当于1949年全年的煤炭产量。通过科技创新，煤炭从燃料变原料，变身为高端润滑油、高端蜡、化妆品。 新型综合能源异军突起。风电、光伏装机总容量突破2000万千瓦，新能源装机容量进入全国前5。煤层气抽采利用量占全国的90%以上。晋电外送能力超过3800万千瓦，越来越多的清洁电力输往全国。 大同建设“氢都”，吕梁培育“数谷”，晋城发展“光谷”，阳泉打造“纳谷”……一批老煤城贴上新名片。晋能集团转型清洁能源，晋煤集团主攻燃气产业，潞安集团整合现代煤化工，阳煤集团发力煤机制造……多家老煤企密集调整主业。 全省上下推动国企改革、开发区改革、电力体制改革、医疗体制改革、营商环境改革等，一些关键领域改革迈向全国第一方阵，发展态势焕然一新。 山西率先启动企业投资项目承诺制改革，用一纸承诺代替“一箩筐材料”，企业自行办理的审批事项压减80%，项目从立项到开工时间平均缩短一半以上。 开发区成为山西转型发展的主战场。2017年山西批准新设开发区15家，2018年新设24家，今年已新设立11家。各开发区围绕先进装备制造、新能源汽车、节能环保、大数据、新材料、现代煤化工等精准招商。 今年5月，山西锦波生物医药股份有限公司与中国科学院、复旦大学共同发布人源Ⅲ型胶原蛋白，这是山西转型综改示范区催生的最新科技成果。去年一年，这里新增高新技术企业110家，初步形成电子信息装备、装备制造及汽车、生物医药及食品、新材料及加工等4个千亿级产业集群。 甩掉贫苦穷困的帽子 “人说山西好风光，地肥水美五谷香……”一曲《人说山西好风光》，唱出了山西人的家乡情。 山西，也是英雄、光荣的土地。在中国革命斗争中，留下了数不尽的光荣故事。新中国成立以来，这里孕育出了西沟精神、大寨精神、锡崖沟精神、大泉山精神…… 然而，巍巍太行，遍布干石山；绵绵吕梁，满是残垣沟壑。因土地瘠薄、干旱缺水，全国14个集中连片特困地区，太行、吕梁就占了两个，山西58个贫困县分布其间。 党的十八大以来，山西打响声势浩大、前所未有的脱贫攻坚战。 2017年9月，家住吕梁山深处、年过七旬的刘福有、杨娥子老两口，卖掉了家里的两头大犍牛，搬进了岢岚县广惠园移民新村。只有6户人家共13人的赵家洼村在民政部门销了号，拉开了山西决战深度贫困的序幕。 刘福有没花一分钱，就搬进了新楼房，后续扶持政策随之跟进。 很快，山西从7993个贫困村中，确定了3350个需要整村搬迁的深度贫困村，以“打不赢脱贫攻坚战，就对不起这块红色土地”的态度和决心，采取超常举措，啃深度贫困“硬骨头”。 到今年5月底，3350个深度贫困村已有3084个完成整体搬迁，1502个集中安置点已竣工1493个。 数万名扶贫干部奔走在太行吕梁山上。长治市扶贫办主任段志岗上任3年，跑了全市933个贫困村的一半以上。“时间紧任务重，群众眼里看着，心里盼着，不使劲不行啊。”他说。 扶贫干部们从太行精神、吕梁精神中汲取力量。在革命老区武乡县，楼则峪村第一书记闫小波进村后，挨家挨户走，一家一家问，连老太太爱看什么电视节目都记在心里。他带领村民发展电商，把当地小米卖出了好价钱。离任时，村民给他塞上自己做的鞋垫，表达对他的感激和不舍。 今年，山西将确保最后17个贫困县全部“摘帽”，22万贫困人口脱贫，确保2020年同全国一道进入全面小康社会。 绘就新的“山西好风光” 汾河，是山西最著名的河流。 党的十八大以来，山西把生态文明建设摆在更加突出地位，以环境保护倒逼经济高质量转型，积极回应人民群众的生态关切，让人们能望见星空、看见绿山、闻到花香。 山西实施大水网建设，启动占全省总面积72%的七河流域治理工程，省政府每周公布汾河流域治污情况，确保到明年全面消除劣Ⅴ类断面。太原、临汾等汾河河段再现“一川清流，两岸锦绣”。 水的命脉在山，山的灵魂在树。在支离破碎的黄土高原，有的地方“种树比养孩子还难”，生存条件恶劣。 在吕梁山区的永和县，永和关、乾坤湾等临近黄河的陡峭石质山上，现在已是绿油油一片。当地生态防护林从新中国成立初的不足5000亩，增加到现在的34万亩。 在太行山深处的壶关县，一道长达5000公里、全部由石头垒成的森林防护墙蔚为壮观，当地森林覆盖率从1978年的5%增至50%以上。 在毛乌素沙漠边缘的右玉县，新中国成立以来，历任县委书记一任接着一任干，林木绿化率达到55%，创造出让荒漠变绿洲的“右玉精神”。 几代人披荆斩棘，换来今日绿水青山。山西省森林覆盖率从新中国成立初期的2.4%提升至20.5%。全国生态气象公报显示，近20年来，山西植被生态质量改善位居全国前列。 山西曾是“千泉之省”，但由于地下水超采严重，尤其是采煤采矿破坏地下水系，造成19处岩溶大泉大部分衰减，6处断流干涸或流量锐减。去年以来，50座涉及泉域重点保护范围的煤矿，6座被列为去产能关闭矿井，2座被注销采矿证，42座办理了采矿证变更手续。 山西地下水位逐年回升。“晋祠三绝”之一的难老泉已断流25年，地下水水位距泉水出水口最高时有9层楼高，如今只差1.61米，难老泉复流在望。 山西也是蓝天保卫战的主战场。“宁可牺牲点GDP，也要把环保指标提上去。”山西以壮士断腕的勇气，向污染开刀。山西省生态环境厅数据显示，2018年山西二氧化硫、PM2.5、一氧化碳等主要污染物浓度较2013年分别下降49.23%、28.57%、36.17%。 如今，山青、水绿、空气好的山西，吸引了八方游客，旅游产业正成为山西新的经济增长极。黄河、长城、太行3条旅游公路开工建设，100个3A级乡村旅游示范村挂牌。去年，山西接待旅游人数达到5.6亿人次，旅游总收入进入全国前十。 华夏古文明，山西好风光——新时代3700万三晋儿女，为故乡骄傲！参考消息网7月25日报道外媒称，美国商务部长罗斯7月23日对彭博电视表示，特朗普政府计划在未来几周内处理美国科技企业提出的向被列入“黑名单”的华为技术有限公司出售产品的豁免申请。 路透社7月23日报道，“我们会迅速处理这些申请，”罗斯接受采访时表示，“目前已经收到35家公司提出的50多份申请，我们预计在未来几周内会有结果。” 当被追问及是否会在两周内有结果时，罗斯说，“我说的是在未来几周内。” 罗斯发表上述言论的前一天，美国总统特朗普在白宫举行的会议上向几家大型科技公司高管表示，对于美国企业提出的向华为销售产品的请求，政府会“及时”做出决定。 罗斯说，决定是否给予公司豁免是一个跨部门的程序，涉及美国商务部、国务院、国防部和能源部。 “这不是件简单的事情，但总的来说，我们遵循的原则是……从国家安全的角度来看，那些不敏感的东西，我们会以非常支持的态度来对待，”罗斯说，“那些敏感的东西则另当别论，我们必须非常、非常谨慎地处理。”据外媒报道称，印度尼西亚气象和地球物理局（BMKG）近日的监测报告指出，印度尼西亚目前很多地方正在遭遇干旱的影响，比如印度尼西亚的爪哇、峇厘和努沙登加拉大部分的地区已经正在干旱的肆虐之下，而面临长期严重旱灾的威胁，不少地区已经出现了比较严重的水资源匮乏状况。 而来自印度尼西亚气象和地球物理局的气象专家指出，除了这几个地区，印尼还有更多地区例如北亚齐、廖省、占碑，南苏门答腊、朋姑鲁，南苏拉威西南部、西/东加里曼丹和巴布亚南部等地也都面临着长期干旱的可能性。对于印度尼西亚这个热带雨林气候的热带国家来说，这样的状况对于该国的农业和居民生活可能带来明显影响。 事实上，东南亚地区的干旱并非只限于印度尼西亚，从今年春季开始，东南亚地区的泰国、菲律宾等国也先后遭遇了持续干旱的影响。这一系列的异常情况值得我们警惕——印度尼西亚等地的干旱天气或许意味着，厄尔尼诺的威力仍然停留在地球大气层中，还并未出现明显减弱！ 此前，一系列分析数据显示，从2018年秋冬季发展而来的厄尔尼诺已经出现了减弱态势，比如在6月下旬中下旬以来，热带中东太平洋上的关键区水温出现了明显降温，并且持续低于厄尔尼诺阈值（大于等于0.5摄氏度），这种情况被认为是厄尔尼诺即将减弱的征兆。 而来自美国海洋大气管理局（NOAA）的监测数据则指出，太平洋深层的海水偏暖已经减弱，这将不能再给热带中东太平洋地区的表层海水提供暖水，因此太平洋厄尔尼诺的减弱已是大势所趋。其预测认为，厄尔尼诺将在北半球的夏季（6-8月）间逐渐减弱，并且将可能在今年秋冬季转为中性状态。 尽管厄尔尼诺的减弱已是大势所趋，但此前厄尔尼诺对于大气层的影响似乎还并未减弱，比如印度尼西亚等地的干旱正是厄尔尼诺的证明——在厄尔尼诺期间，因为热带中东太平洋水温偏暖，将使得热带中东太平洋上有更多上升气流成云致雨，而西太平洋印度尼西亚等地则转为下沉气流，变为干旱少雨的状态。因此印度尼西亚等地的干旱提醒我们，太平洋地区的厄尔尼诺仍在持续捣乱，我国接下来还得小心它带来的极端天气！近日，据媒体报道，日本和韩国之间的贸易冲突已经彻底失控，最近美国的这两大盟友终于大打出手，先是日本在半导体材料上对韩国进行限制出口，随后两国摩擦越来越严重，两国甚至已放话要搞死对方，大火已烧到国防领域，有消息称两国将重新评估《军事情报保护协定》，而这将影响美军的东北亚军事联盟，就算特朗普劝阻也没用了，外界对此表示：两大盟友在敏感时刻突然摊牌，表明美国的影响力和领导力大幅下降，美国真的要头疼了。 图为在独岛对峙的日韩军舰 这一轮的日韩冲突起源于日本对于某些历史问题的处理态度刺激了敏感而自卑的韩国，韩国对日本的某些激烈举动则引发了日本对韩国高新技术企业的全面制裁。现在两国之间的冲突已经趋于白热化，韩国民间的反日呼声很高，抗议活动甚至波及了在韩的日本外交官。 在民间的各种小打小闹弄得一塌糊涂的时候，韩国高层也准备给日本以颜色。据韩国军方高层人士透露，韩方很可能会谋求“修改”日韩之间的《军事情报保护协定》。这份旨在共享日韩之间机密情报的协定是在美国框架下签署的，象征日韩“和解”并一起被纳入美国的亚太战略之中。如果韩国改动该协定成功，将动摇美国在亚太地区的立足基础。 图为首尔的抗议游行现场 尽管日本与韩国摩擦不断，但是在靠拢美国方面，双方的立场出奇的一致。由于国土狭小，国力贫弱，只有依赖域外大国的力量才能获得生存与发展的机会。由于二战结束之后的国际格局，韩国与日本便乘上了美国的顺风车，得以发展起来。通过在美国的亚太战略当中扮演前进基地与桥头堡的角色，日韩都收到了相当不错的报酬。由于体量不大，通过美国订单与承接产业转移，两国的经济成功起飞，成为了有声有色的发达国家。 韩国打破与日本的默契，寻求日韩决裂的道路，实际上是试图倒逼美国进场仲裁，利用目前被日本制裁打到无力还手的局面营造受害者形象，争取美国做出有利于自己的判决。不过，这一动摇美国在亚太地区实力根基的行为能否得到美国的谅解，目前还是个未知数。为了达成自己微不足道的一个目的而去砸掉老大家里的饭锅，怎么看都不会是个明智的决定，虽然美国的实力已经不如从前，整治一个不听话的小弟想来也不太困难。 图为韩国抗议者与防暴警察冲突 长期以来，韩国一直表现出一种极端自负又极端自卑的复杂。一直在试图摆脱宗主国可怕的文化影响力的同时，韩国却根本无法摆脱自己深受影响的既成事实，形成了极端敏感乃至有些神经质的集体性格。而过敏的后果，一般情况下都是成为笑柄。根据俄罗斯塔斯社7月24日最新报道称，就在近日，中方新一轮的国防白皮书已经发表。在这其中我们将对俄罗斯的军事关系，列在同等重要的位置，对美国的军事关系已经列居第二位，此举等于把最后一层窗户纸捅破。要知道在过去很长一段时间之内，中方跟美方虽说在很多时候观点都处于对立面，但两国在军事领域的合作，还是要远远大于对抗的。在此前我们发表的所有国防白皮书中，都把对美国的军事关系列在头等重要的位置。而近10年以来，随着中方在海空军事力量等领域全面崛起，美国人也开始将中方看成他们的头等威胁，在各个领域疯狂对我们进行打压。 面对美国人咄咄逼人的霸权主义行径，中俄这两个爱好和平的国家，唯有联起手来才能对付得了五角大楼的四处扩张。我们可以看出，如今中方在与俄罗斯的军事合作领域，已经上升到了一个很深的层次。举个例子来说，2018年俄罗斯在远东地区举行了代号为“东方-2018”的大规模战略性演习。在过去，这种演习只有俄罗斯和白俄罗斯可以派遣兵力参加，属于绝密等级。然而在“东方-2018”演习之中，我们整整动用了3000人，以及数百辆坦克装甲车辆的庞大兵力深入俄罗斯内陆，足见两国两军的战略互信等级之高。 就在近日，中俄两国的5架大型军用飞机，飞抵了韩国独岛周边的防空识别区。其中俄罗斯的一 {} {"audioInfo":{"langDec":"chin","decSc":1.0,"nlpSc":0.0,"gender":"m","audioFiles":[{"decSc":1.0,"side":"c","gender":"m","bits":16,"langSc":0.0,"url":"http://10.5.3.85/heluAB.wav","vtl":135560,"codec":6,"channels":1,"rate":8000,"spkn":0,"name":"heluAB.wav","tl":211962}],"langSc":0.64250153,"lang":"chin"}} -e282ecb5-9be8-4a0e-8faf-d10e535ab877 13396633307 49 0031 9 13918448986 1 7 0 1568260253193 1150 asp fk 3884000 1 0 0 0 0 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chin chin 0.64250153 ff 0.0 3883000 3881000 1568249840083 1568249838869 共产党人的奋斗精神，永远保持对人民的赤子之心，始终把人民利益摆在至高无上的地位，始终同人民想在一起、干在一起，以人民忧乐为忧乐，以人民甘苦为甘苦，努力为人民创造更美好、更幸福的生活。 2.依靠人民创造历史伟业 （35）人民是历史的创造者，人民是真正的英雄。离开了人民，我们就会一事无成。只有坚持历史唯物主义这一基本原理，才能把握历史前进的基本规律；只有按历史规律办事，才能无往而不胜。 勤劳勇敢的中国人民是中华民族生生不息、发展壮大的脊梁。回望上下五千年，波澜壮阔的中华民族发展史是中国人民书写的，博大精深的中华文明是中国人民创造的，历久弥新的中华民族精神是中国人民培育的，中华民族迎来了从站起来、富起来到强起来的伟大飞跃是中国人民奋斗出来的。必须深刻认识人民群众是历史发展和社会进步的主体力量，紧紧依靠人民创造历史伟业。 （36）坚持人民主体地位，充分调动人民积极性，始终是我们党立于不败之地的强大根基。 老百姓是天，老百姓是地。中国共产党之所以能够发展壮大，中国特色社会主义之所以能够不断前进，正是因为依靠了人民。谋划发展，最了解实际情况的，是人民群众；推动改革，最大的依靠力量，也是人民群众。改革开放在认识和实践上的每一次突破和发展，改革开放中每一个新生事物的产生和发展，改革开放每一个方面经验的创造和积累，无不来自亿万人民的实践和智慧。无论遇到任何困难和挑战，只要有人民支持和参与，就没有克服不了的困难，就没有越不过去的坎，就没有完成不了的任务。 人民群众有着无尽的智慧和力量，在人民面前，我们永远是小学生。必须充分尊重人民所表达的意愿、所创造的经验、所拥有的权利、所发挥的作用，自觉拜人民为师，向能者求教，向智者问策。要把政治智慧的增长、执政本领的增强、领导艺术的提高深深扎根于人民群众的实践沃土中，不断从人民群众中吸取营养和力量。始终与人民心心相印、与人民同甘共苦、与人民团结奋斗，使全体人民都满腔热情地投身到建设祖国的美好未来和创造自己的幸福生活中去。 （37）习近平总书记指出：“时代是出卷人，我们是答卷人，人民是阅卷人。” 人民是我们党的工作的最高裁决者和最终评判者。党的执政水平和执政成效都不是由自己说了算，必须而且只能由人民来评判，最终都要看人民是否真正得到了实惠，人民生活是否真正得到了改善，人民权益是否真正得到了保障。在新时代，我们面临的挑战和问题依然严峻复杂，党面临的“赶考”远未结束。要坚持把人民拥护不拥护、赞成不赞成、高兴不高兴、答应不答应作为衡量一切工作得失的根本标准，努力向历史、向人民交出新的更加优异的答卷。 3.朝着实现全体人民共同富裕不断迈进 （38）共同富裕，是马克思主义的一个基本目标。按照马克思、恩格斯的构想，共产主义社会将彻底消除阶级之间、城乡之间、脑力劳动和体力劳动之间的对立和差别，实行各尽所能、按需分配，真正实现社会共享、实现每个人自由而全面的发展。到那时，“生产将以所有的人富裕为目的”，“所有人共同享受大家创造出来的福利”。实现共同富裕，反映了社会主义的本质要求，体现了以人民为中心的根本立场。 我们党始终带领人民为创造美好生活、实现共同富裕而不懈奋斗。毛泽东同志在新中国成立之初就提出了我国发展富强的目标，指出“这个富，是共同的富，这个强，是共同的强，大家都有份”。邓小平同志多次强调共同富裕，指出“社会主义不是少数人富起来、大多数人穷，不是那个样子。社会主义最大的优越性就是共同富裕，这是体现社会主义本质的一个东西”。江泽民同志强调：“实现共同富裕是社会主义的根本原则和本质特征，绝不能动摇。”胡锦涛同志也要求“使全体人民共享改革发展成果，使全体人民朝着共同富裕的方向稳步前进”。 进入新时代，我们走上了创造美好生活、逐步实现全体人民共同富裕的新征程。习近平总书记指出，共同富裕是中国特色社会主义的根本原则，实现共同富裕是我们党的重要使命。他还强调，“我们追求的发展是造福人民的发展，我们追求的富裕是全体人民共同富裕”，要“让发展成果更多更公平惠及全体人民，不断促进人的全面发展，朝着实现全体人民共同富裕不断迈进”。 经过长期艰苦奋斗，我们党在实践中形成了先富带动后富、逐步实现共同富裕的规律性认识，推动人民生活质量和社会共享水平显著提升。粮票、布票、肉票、鱼票、油票、豆腐票、副食本、工业券等百姓生活曾经离不开的票证，已经进入了历史博物馆。忍饥挨饿、缺吃少穿、生活困顿这些几千年来困扰我国人民的问题总体上一去不复返了，中国人民迎来了从温饱不足到小康富裕的伟大飞跃。 （39）实现全体人民共同富裕的宏伟目标，最终靠的是发展。发展是基础，唯有发展才能满足人民对美好生活的热切向往。没有发展，没有扎扎实实的发展成果，共同富裕就无从谈起。 要毫不动摇坚持发展是硬道理、发展应该是科学发展和高质量发展的战略思想，举全民之力推进中国特色社会主义事业，不断把“蛋糕”做大。同时，还要在不断发展的基础上把促进社会公平正义的事情做好，把不断做大的“蛋糕”分好，让社会主义制度的优越性更加充分体现出来，让实现全体人民共同富裕在广大人民现实生活中更加充分展示出来。绝不能出现“富者累巨万，而贫者食糟糠”的现象。 实现全体人民共同富裕的目标需要一个很长的历史过程，是由一个一个阶段性目标逐步达成的。我国正处于并将长期处于社会主义初级阶段，我们不能做超越阶段的事情，但也不是说在逐步实现共同富裕方面就无所作为，而是要根据现有条件把能做的事情尽量做起来，积小胜为大胜，不断朝着全体人民共同富裕的目标前进。 4.群众路线是党的生命线和根本工作路线 （40）习近平总书记指出：“群众路线是我们党的生命线和根本工作路线，是我们党永葆青春活力和战斗力的重要传家宝。”不论过去、现在和将来，我们都要坚持一切为了群众，一切依靠群众，从群众中来，到群众中去，把党的正确主张变为群众的自觉行动，把群众路线贯彻到治国理政全部活动之中。 坚持群众路线，核心的问题是党要始终保持同人民群众的血肉联系，一刻也不脱离群众。密切联系群众，是党的性质和宗旨的体现。我们党是在同人民群众的密切联系中成长、发展、壮大起来的，是靠宣传群众、组织群众、依靠群众起家，从胜利走向胜利的。党的最大政治优势是密切联系群众，党执政后的最大危险是脱离群众。能否保持党同人民群众的血肉联系，决定着党的事业的成败。要从政治的高度深刻认识密切联系群众的重要性，任何时候任何情况下，与人民群众同呼吸共命运的立场不能变，全心全意为人民服务的宗旨不能忘，坚信群众是真正英雄的历史唯物主义观点不能丢。 （41）群众路线是我们党始终坚持的根本工作方法。党的领导工作的正确方法，就是将群众意见集中起来形成正确的决策，又到群众中宣传解释，将决策化为群众的行动，并在群众实践中检验这些决策是否正确。 坚持走群众路线，绝不是喊喊口号走走过场，而是要诚心诚意、实打实做。要善于通过提出并贯彻正确的理论和路线方针政策带领人民前进，善于从人民的实践创造和发展要求中完善政策主张，善于从群众中寻找解决问题的方案和办法，使作出的决策和决策的执行充分体现民心民意。深入研究新形势下群众工作的规律和特点，把党的优良传统和新技术新手段结合起来，学会通过网络走群众路线，提高做好群众工作的本领。 贯彻党的群众路线，“知”是基础、是前提，“行”是重点、是关键。必须以知促行、以行促知，做到知行合一，既解决认识提高问题，又解决行动自觉问题，使群众路线落地稳、扎根深，融入经济社会发展全过程，贯穿到党的全部工作中。 新华社合肥7月23日电 题：安徽70年：改革创新书写时代答卷 新华社记者王正忠、杨玉华、徐海涛、姜刚 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（1）安徽70年：改革创新书写时代答卷 这是安徽凤阳小岗村（2018年9月27日无人机拍摄）。 新华社记者 张端 摄 这是一方敢为人先的热土：开启农村改革巨幕的“大包干”、闯出个体经济之路的“傻子瓜子”、冲出重围高擎自主品牌大旗的奇瑞汽车……一次次书写时代传奇。 这是一片勇于革新的天地：税费改革、医药卫生体制改革、文化体制改革、全面创新改革试验……一次次引领风气之先。 这是一处创新喷涌的泉源：全球首颗量子通信卫星上天、智能语音技术站上全球制高点、世界最薄的玻璃问世、“人造太阳”正求解人类终极能源难题……一次次刷新进步的前沿。 从新中国成立时一个落后的农业省，到今天实现农业大省、工业大省、科技大省的“三级跳”，安徽以70年的精彩蝶变书写时代答卷，以改革创新的昂扬姿态挺进新时代。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（3）安徽70年：改革创新书写时代答卷 在安徽凤阳小岗村，农民驾驶收割机收割水稻（2018年9月27日摄）。 新华社记者 刘军喜 摄 昔日农业省今成“科创极” 八百里巢湖之滨，坐落着全国首处以创新为主题的场馆。其中“人造太阳”、强磁场装置、量子通信等高精尖的科技创新成果令八方来宾感到震撼。 安徽，曾长期是经济发展的洼地，不沿边、不靠海、工业基础薄弱。新中国成立初期，一产占比高达75%以上。70年沧桑巨变，到2018年，安徽的三产结构已变为8.8：46.1：45.1，农业占比退居十分之一以下，昔日的农业大省成为以家电、汽车、建材等为主的工业大省和以电子信息、平板显示、智能语音等领衔的科技产业大省。 巨变的背后，是安徽把科技创新作为转型升级的突破口。早在1995年安徽就提出实施“科教兴皖”战略，2008年启动合芜蚌自主创新综合试验区建设，实施创新驱动发展之路。2013年，安徽成为全国第二个开展创新型省份建设试点工作的地区，随后获批建设合芜蚌国家自主创新示范区和系统推进全面创新改革试验区。2017年初，合肥综合性国家科学中心获批。2018年，合肥滨湖科学城挂牌成立……安徽的创新名片越擦越亮。 多年的探索实践，安徽已形成从原始创新到技术开发、成果转化、企业孵化，再到新兴产业的“全创新链”，走出一条具有安徽特色的中西部创新发展之路。 在位于合肥北郊的合肥综合性国家科学中心聚变堆园区，聚变堆主机关键系统综合研究设施工程正在紧张建设。这个由安徽省政府主要领导率队答辩争取来的国家大科学装置，与之前建成的合肥“人造太阳”装置相呼应，使安徽成为世界一流的核聚变能源创新研究基地。瞄准前沿、前瞻布局、潜心投入，安徽已建成“同步辐射光源”“全超导托卡马克”“稳态强磁场”三大国际科技前沿科研平台，并在量子通信、智能语音等科研领域达到国际领跑或并跑水平。安徽的区域创新能力已连续7年跻身全国第一方阵。 贴近产业抓创新，集聚资源搞科研，不断破除体制机制障碍，让安徽驶入创新发展快车道。安徽省统计局局长吴劲松说，党的十八大以来，安徽的新兴产业增加值不断提升。 目前，安徽的战略性新兴产业对全省工业增长的贡献率已超三分之一。在合肥，形成了国内唯一拥有6代线、8.5代线和10.5代线的新型平板显示产业基地。在蚌埠，以蚌埠玻璃工业设计研究院为核心的新型玻璃产业，诞生了我国首条具有自主知识产权的8.5代TFT-LCD玻璃基板生产线，成功研发世界最薄的0.12毫米超薄触控玻璃；在芜湖，形成了以中电科钻石飞机为代表的通用航空产业集群，飞机交付量处于国内领先行列。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（6）安徽70年：改革创新书写时代答卷 观众在2018世界制造业大会上观看蚌埠玻璃工业设计研究院展出的柔性触控玻璃（2018年5月25日摄）。 新华社记者 张端 摄 改革不停步发展活力涌 经济因改革而活，发展因创新而兴。今年上半年，安徽的经济呈现出“加速跑、趋向优”的良好态势。生产总值增长8%，居全国第6位，规模以上工业增加值增速居第6位，城镇、农村常住居民人均可支配收入增速分别居第3位和第4位。 “速度位次靠前，发展质效提升，内生动力增强。”安徽省统计局副局长赵金宝将全省上半年经济运行的亮点概括为三句话。 今年7月12日，三只松鼠股份有限公司在深交所上市。创立7年来的这家企业，不断颠覆传统销售模式，年销售额从零增加到2018年的82亿元。“创新发展是企业前进的根本。”公司党委书记潘道伟说，上市能加速公司数字化供应链战略的推进，为消费者提供更好的产品和服务。 作为民营经济的试验田，如今的江淮大地仍是一片创新创业的热土，敢闯敢试的改革火种继续在这里播撒。现在的安徽，平均每天新登记注册企业925家，每天获授44件发明专利。 经济“逆势上扬”，靠的是改革，是服务，是创新。十八大以来，安徽着力推动“放管服”改革，首创权力、责任清单的“安徽模式”，让公共服务做“加法”、中介服务做“减法”。而群众办事“最多跑一次”“互联网+政务服务”等创举，正推动安徽迈向审批事项少、办事效率高、投资环境优、市场主体和人民群众获得感强的省份之一。 唯改革才有出路，改革要常讲常新。目前，安徽正持续将一系列新的改革推向深入： ——政务服务改革。努力将省级行政许可事项减到全国最少，企业开办时间压缩至全国最短……到今年底，目标实现80%个人事项全程网办，部分事项“秒批秒办”。 ——全面创新改革。加快建设合肥综合性国家科学中心，加紧布局一批世界前沿的大科学装置，下放科技成果处置权、收益权、使用权，探索实行首席科学家全权负责制……目标建成具有国际影响力的科技创新中心和产业创新中心。 ——农业农村改革。持续深化农村承包地“三权分置”改革，稳步推进农村集体产权制度改革……围绕处理好农民与土地关系主线，努力在农村改革上继续走在前列。 增进民生福祉，乃经济发展之根本目的。居住在集中连片贫困山区金寨县大湾村村民陈泽申前年主动要求摘掉“贫困帽”。得益于党和政府的扶贫政策，他家盖了新房，流转了山地，有了公益性岗位，每年收入三四万元。此前，他将扭转命运的期望全部寄托在“改变家居风水”上面。 为了兑现“小康路上一个都不能掉队”的庄严承诺，安徽把以人民为中心的理念贯穿到经济社会发展各个领域。在实施脱贫攻坚“十大工程”等举措作用下，全省贫困人口由2012年底的679.1万人下降到2018年底的49.9万人，年均减贫100万人以上；贫困发生率由12.6%下降到0.93%；22个贫困县先后“摘帽”。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（7）安徽70年：改革创新书写时代答卷 2017年9月29日，世界首条量子保密通信干线——“京沪干线”正式开通。科研人员在中国科学技术大学先进技术研究院量子通信“京沪干线”总控中心工作。 新华社记者 刘军喜 摄 融入长三角勇于当“尖兵” 早在7年前，位于安徽滁州市的中新苏滁高新技术产业开发区便开工建设，拉开了苏皖两省共建园区的大幕。此后，园区内产业实现深度对接、集群发展，这里成为安徽融入长三角开放合作的典范。 记者近日走进苏滁高新区看到，一排排标准化厂房内，一片热火朝天的生产场面；以新加坡理念建设的邻里中心内，百姓购物、办事功能一应俱全。“截至6月底，从长三角引进的项目数占74%。”该区管委会主任杨广兰说，该区去年经营收入突破100亿元，同比增长逾30%。 2016年5月，中央将安徽合肥、芜湖、马鞍山等8市纳入长三角规划，安徽正式从地理上的长江下游进入长三角世界级城市群。去年11月，长三角一体化发展上升为国家战略。 坐拥皖江水，面向太平洋。安徽迎来历史性的重大发展机遇。中国区域经济学会顾问程必定认为，长三角上升为国家战略对安徽是百年未有的机遇，这意味着，作为长三角一员，安徽将跻身配置全球资源的开放门户、迎来更高质量一体化发展的崭新阶段。 “以前想到外地注册一个公司，交材料、核准名称、领证件，一切顺利也得跑三趟，现在真是节省了很多成本。”去年11月，合肥与星途科技服务有限公司负责人池天水，在合肥高新区的办事窗口领到了由浙江杭州工商部门颁发的营业执照。这也是该区通过长三角“一网通办”业务平台，发出的首张跨省异地登记的营业执照。 致力于要素破壁、深度融入，安徽各地也在抢抓长三角一体化机遇，争当落实国家战略的排头兵。池州、铜陵等地立足区位优势，承接长三角的产业分工，迈向价值链更高端；芜湖市提出“等高对接”，在长三角一体化中找准自身优势、谋求互补发展。 取消长三角区域高速公路省界收费站，推进长三角地区异地就医直接结算，打造长三角“安全消费大市场”……今年5月22日在芜湖举行的2019年长三角地区三省一市主要领导座谈会上，沪苏浙皖相关部门签署了10个打破分割、加速一体化的协议。 “扬皖所长、主动作为。”安徽省委主要负责人表示，安徽是长三角的重要方面军，在区位、科技、产业、生态、能源等方面具有自身的独特优势。安徽未来将把准自身定位，扬创新之长，努力打造长三角科技创新的重要策源地；扬产业之长，努力打造长三角高质量发展的重要增长极；扬区位之长，努力打造长三角世界级城市群的重要副中心；扬生态之长，努力打造长三角生态文明建设的重要样板区，在国家战略下自觉行动当好尖兵！ 新华社广州7月23日电 题：高质量发展的粤式“色香味”——广东制造业一线见闻 新华社记者毛鑫、邓瑞璇、魏玉坤 手机扫描二维码下单，经过10道全自动工序，不到2分钟就可拿到专属机器人模型。在广东拓斯达科技股份有限公司展厅，扫码定制机器人的“神操作”，令人对智能制造新水准印象深刻。 在广东，依托持续的研发投入、技术改造和体制机制创新，制造业正乘势转型升级，走向数字化、自动化、智能化，这一过程犹如烹制一桌高质量发展粤式大餐，“色香味”俱全。 “智”造增“色”：机器换人迎来热潮 原本人头攒动、环境嘈杂的生产车间，逐渐由精密的高度自动化生产线代替。在广东，“智造力”正成为制造业企业标配。 单条生产线的工人数量从160人降到51人、生产换型时间从45分钟降到9分钟、品质控制点从6个增加到108个……美的南沙工厂通过使用全智能生产线，实现效率和品质的双重提升。 “工人少了，但产量在递增，成本效率品质方面改善明显。”美的集团副总裁兼CIO张小懿说，美的通过自动化、数字化的支撑，正在编织一张工业互联网。 美的发展的背后，是广东制造业不断修炼“内功”的火热实践。 走进拓斯达位于东莞的厂区，在工业机器人装配测试车间，近百台机器人排成5列，有节奏地进行出厂前的自动测试。不久后，它们将进入更多工厂的车间，成为生产的主力军。 “智能制造引发机器换人热潮，国内工业机器人市场需求快速增长。”拓斯达产品研发中心总经理徐必业说，未来将持续增加研发投入，重点布局工业传感器、标准工艺设备等领域。 2018年起，广东大力发展智能制造：投入130亿元实施两轮工业技改三年行动计划，惠及3万多家企业，推动3000家工业企业“上云上平台”，工业机器人产量占全国1/5，先进制造业和高技术制造业增加值占规模以上工业增加值比重分别达56.4%和31.5%，制造业提质增效进展明显。 创新添“香”：从要素驱动转向创新驱动 国内研发成功“3D结构光人脸识别技术”、识别速度从0.1秒降至0.001秒、商用跨镜追踪技术……过去4年，广州云从信息科技公司主攻技术研发，成长为国内人工智能行业的佼佼者。 耀眼的成绩离不开研发支撑。云从科技总经理林明都介绍，公司研发团队已达700余人，其中80%以上拥有硕士及以上学历。该团队瞄准“卡脖子”的技术难点潜心钻研，持续突破核心技术。 云从科技的发展，是广东制造业把技术创新摆在核心位置，实现从要素驱动向创新驱动转变的缩影。 近年，广东采取多项措施增加技术有效供给，先后出台“科创12条”等政策，把企业作为科技创新的主体，并着力加强基础研究和应用基础研究，为高质量发展赋能。 数据显示，2018年，广东研发经费支出超过2500亿元，PCT国际专利申请量约占全国一半，有效发明专利量达24.9万件，稳居全国前列。区域创新综合能力排名连续两年居全国第一。 创新“引力”效果渐显，投资加速向高端产业聚集已成趋势。目前，广东规模以上工业企业总量超5万家，在全国名列前茅。国家级高新技术企业达4.5万家，近两年翻了一倍多。巴斯夫、埃克森美孚、富士康、LG等一批投资过百亿美元的项目逐一落地。 政策调“味”：加强制度供给 优化营商环境 2009年，刚从清华大学博士毕业的佘振定，带着一份“人工皮肤”项目的商业计划书南下深圳创业。 接触多家风投机构无果后，是深圳清华大学研究院相中了这个项目，决定进行孵化，并许诺研发团队可以持有项目70%的股份。 这坚定了佘振定在深圳创业的决心，他创办了深圳兰度生物科技有限公司。 后来，该团队从牛跟腱里提取高纯度胶原，制成了可媲美真实皮肤的双层“人工皮肤”，一经上市便填补了国内空白。多轮融资后，公司市值也翻了几番，达到15亿元。 “只有真正把市场作为配置创新资源的关键要素，才更有利于激发成果转化的积极性。”深圳清华大学研究院常务副院长刘伟强说。截至2018年末，该研究院已累计孵化企业2500多家，培养上市公司21家。 近年来，广东大力支持实体经济发展，以制度创新“扫清”制造业“绊脚石”，力争政府“不缺席”“不包揽”，市场“挑大梁”“说了算”。 实行个税补贴等优惠政策吸引海内外高端人才，推行“零跑动”、“秒批”、注册易等改革举措方便企业办事……一系列涉企惠企政策不断出台，持续释放各类市场主体活力。 截至2018年末，广东个体工商户和企业数量达1146万户，其中企业492万户，均稳居全国第一。其中，主营业务收入超百亿、千亿元的企业分别达276家、29家，进入世界500强的企业13家。这是2018年8月1日无人机拍摄的位于山西省朔州市的安太堡露天煤矿复垦区。新华社记者 曹阳 摄 新华社太原7月22日电 题：三晋大地起苍黄——新中国成立70年山西发展巡礼 新华社记者于振海、梁晓飞、王井怀 太行之西，黄河之东，表里山河，资源富集。 新中国成立70年来，勤劳智慧的三晋儿女在党的领导下，发扬太行精神、吕梁精神、右玉精神，在15万平方公里的土地上，建设起共和国能源重化工基地。如今，又担当起新时代能源革命排头兵重任，展开决战贫困攻坚战，朝着高质量发展之路、向着全面建成小康社会目标阔步前进。 山西，正以全新姿态挺进新时代。 当好“能源革命排头兵” 山西潞安集团石圪节煤矿曾以“艰苦奋斗、勤俭办矿”精神闻名全国。3年前，关闭了矿井，转型发展油用牡丹。前不久，其生产的牡丹籽油出口到了韩国。 石圪节“退煤种花”的同时，千里之外的千万吨级矿井同忻煤矿建成了山西首个智能化工作面，只用在地面控制室里，轻轻一点鼠标，地层深处的“乌金”就会滚滚不息地涌上地面。 新中国成立70年来，山西累计产煤190多亿吨，运往全国的煤炭130多亿吨。 山西因煤而名，也因煤而困。粗放的采矿业，经年累月，导致生态破坏、环境污染、矿难频发，经济发展患上了“煤炭依赖症”；多年来，始终走不出“煤炭市场好时，无暇转型；市场不好，无力转型”的怪圈。 关键时刻，新一届党中央为山西把脉，开出两剂良方：资源型经济转型综合配套改革和供给侧结构性改革。山西省委省政府深化“供改”与“综改”，明确了“能源革命排头兵”“资源型经济转型示范区”“内陆地区对外开放新高地”的目标定位。山西，开始走上高质量发展新征程。 尽管煤价持续高位运行，山西仍坚决淘汰落后产能，3年来关闭88座煤矿，退出落后产能8841万吨，煤炭去产能规模全国第一。煤矿数量由新中国成立后最多的4万多座，降到了1000座以内，先进产能占比提升到57%。 如今，山 {} {"audioInfo":{"langDec":"chin","decSc":1.0,"nlpSc":0.0,"gender":"m","audioFiles":[{"decSc":1.0,"side":"c","gender":"m","bits":16,"langSc":0.0,"url":"http://10.5.3.85/heluAB.wav","vtl":135560,"codec":6,"channels":1,"rate":8000,"spkn":0,"name":"heluAB.wav","tl":211962}],"langSc":0.64250153,"lang":"chin"}} -01e36a06-b4fd-4638-862c-2785f9e4331b 13924865616 82 0031 0 0086(021)60080162 82 6 0 1568293725356 2108 asp fk 3152000 1 0 0 0 0 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chin chin 0.64250153 ff 0.0 3151000 3149000 1568249840107 1568249838883 ……一次次引领风气之先。 这是一处创新喷涌的泉源：全球首颗量子通信卫星上天、智能语音技术站上全球制高点、世界最薄的玻璃问世、“人造太阳”正求解人类终极能源难题……一次次刷新进步的前沿。 从新中国成立时一个落后的农业省，到今天实现农业大省、工业大省、科技大省的“三级跳”，安徽以70年的精彩蝶变书写时代答卷，以改革创新的昂扬姿态挺进新时代。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（3）安徽70年：改革创新书写时代答卷 在安徽凤阳小岗村，农民驾驶收割机收割水稻（2018年9月27日摄）。 新华社记者 刘军喜 摄 昔日农业省今成“科创极” 八百里巢湖之滨，坐落着全国首处以创新为主题的场馆。其中“人造太阳”、强磁场装置、量子通信等高精尖的科技创新成果令八方来宾感到震撼。 安徽，曾长期是经济发展的洼地，不沿边、不靠海、工业基础薄弱。新中国成立初期，一产占比高达75%以上。70年沧桑巨变，到2018年，安徽的三产结构已变为8.8：46.1：45.1，农业占比退居十分之一以下，昔日的农业大省成为以家电、汽车、建材等为主的工业大省和以电子信息、平板显示、智能语音等领衔的科技产业大省。 巨变的背后，是安徽把科技创新作为转型升级的突破口。早在1995年安徽就提出实施“科教兴皖”战略，2008年启动合芜蚌自主创新综合试验区建设，实施创新驱动发展之路。2013年，安徽成为全国第二个开展创新型省份建设试点工作的地区，随后获批建设合芜蚌国家自主创新示范区和系统推进全面创新改革试验区。2017年初，合肥综合性国家科学中心获批。2018年，合肥滨湖科学城挂牌成立……安徽的创新名片越擦越亮。 多年的探索实践，安徽已形成从原始创新到技术开发、成果转化、企业孵化，再到新兴产业的“全创新链”，走出一条具有安徽特色的中西部创新发展之路。 在位于合肥北郊的合肥综合性国家科学中心聚变堆园区，聚变堆主机关键系统综合研究设施工程正在紧张建设。这个由安徽省政府主要领导率队答辩争取来的国家大科学装置，与之前建成的合肥“人造太阳”装置相呼应，使安徽成为世界一流的核聚变能源创新研究基地。瞄准前沿、前瞻布局、潜心投入，安徽已建成“同步辐射光源”“全超导托卡马克”“稳态强磁场”三大国际科技前沿科研平台，并在量子通信、智能语音等科研领域达到国际领跑或并跑水平。安徽的区域创新能力已连续7年跻身全国第一方阵。 贴近产业抓创新，集聚资源搞科研，不断破除体制机制障碍，让安徽驶入创新发展快车道。安徽省统计局局长吴劲松说，党的十八大以来，安徽的新兴产业增加值不断提升。 目前，安徽的战略性新兴产业对全省工业增长的贡献率已超三分之一。在合肥，形成了国内唯一拥有6代线、8.5代线和10.5代线的新型平板显示产业基地。在蚌埠，以蚌埠玻璃工业设计研究院为核心的新型玻璃产业，诞生了我国首条具有自主知识产权的8.5代TFT-LCD玻璃基板生产线，成功研发世界最薄的0.12毫米超薄触控玻璃；在芜湖，形成了以中电科钻石飞机为代表的通用航空产业集群，飞机交付量处于国内领先行列。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（6）安徽70年：改革创新书写时代答卷 观众在2018世界制造业大会上观看蚌埠玻璃工业设计研究院展出的柔性触控玻璃（2018年5月25日摄）。 新华社记者 张端 摄 改革不停步发展活力涌 经济因改革而活，发展因创新而兴。今年上半年，安徽的经济呈现出“加速跑、趋向优”的良好态势。生产总值增长8%，居全国第6位，规模以上工业增加值增速居第6位，城镇、农村常住居民人均可支配收入增速分别居第3位和第4位。 “速度位次靠前，发展质效提升，内生动力增强。”安徽省统计局副局长赵金宝将全省上半年经济运行的亮点概括为三句话。 今年7月12日，三只松鼠股份有限公司在深交所上市。创立7年来的这家企业，不断颠覆传统销售模式，年销售额从零增加到2018年的82亿元。“创新发展是企业前进的根本。”公司党委书记潘道伟说，上市能加速公司数字化供应链战略的推进，为消费者提供更好的产品和服务。 作为民营经济的试验田，如今的江淮大地仍是一片创新创业的热土，敢闯敢试的改革火种继续在这里播撒。现在的安徽，平均每天新登记注册企业925家，每天获授44件发明专利。 经济“逆势上扬”，靠的是改革，是服务，是创新。十八大以来，安徽着力推动“放管服”改革，首创权力、责任清单的“安徽模式”，让公共服务做“加法”、中介服务做“减法”。而群众办事“最多跑一次”“互联网+政务服务”等创举，正推动安徽迈向审批事项少、办事效率高、投资环境优、市场主体和人民群众获得感强的省份之一。 唯改革才有出路，改革要常讲常新。目前，安徽正持续将一系列新的改革推向深入： ——政务服务改革。努力将省级行政许可事项减到全国最少，企业开办时间压缩至全国最短……到今年底，目标实现80%个人事项全程网办，部分事项“秒批秒办”。 ——全面创新改革。加快建设合肥综合性国家科学中心，加紧布局一批世界前沿的大科学装置，下放科技成果处置权、收益权、使用权，探索实行首席科学家全权负责制……目标建成具有国际影响力的科技创新中心和产业创新中心。 ——农业农村改革。持续深化农村承包地“三权分置”改革，稳步推进农村集体产权制度改革……围绕处理好农民与土地关系主线，努力在农村改革上继续走在前列。 增进民生福祉，乃经济发展之根本目的。居住在集中连片贫困山区金寨县大湾村村民陈泽申前年主动要求摘掉“贫困帽”。得益于党和政府的扶贫政策，他家盖了新房，流转了山地，有了公益性岗位，每年收入三四万元。此前，他将扭转命运的期望全部寄托在“改变家居风水”上面。 为了兑现“小康路上一个都不能掉队”的庄严承诺，安徽把以人民为中心的理念贯穿到经济社会发展各个领域。在实施脱贫攻坚“十大工程”等举措作用下，全省贫困人口由2012年底的679.1万人下降到2018年底的49.9万人，年均减贫100万人以上；贫困发生率由12.6%下降到0.93%；22个贫困县先后“摘帽”。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（7）安徽70年：改革创新书写时代答卷 2017年9月29日，世界首条量子保密通信干线——“京沪干线”正式开通。科研人员在中国科学技术大学先进技术研究院量子通信“京沪干线”总控中心工作。 新华社记者 刘军喜 摄 融入长三角勇于当“尖兵” 早在7年前，位于安徽滁州市的中新苏滁高新技术产业开发区便开工建设，拉开了苏皖两省共建园区的大幕。此后，园区内产业实现深度对接、集群发展，这里成为安徽融入长三角开放合作的典范。 记者近日走进苏滁高新区看到，一排排标准化厂房内，一片热火朝天的生产场面；以新加坡理念建设的邻里中心内，百姓购物、办事功能一应俱全。“截至6月底，从长三角引进的项目数占74%。”该区管委会主任杨广兰说，该区去年经营收入突破100亿元，同比增长逾30%。 2016年5月，中央将安徽合肥、芜湖、马鞍山等8市纳入长三角规划，安徽正式从地理上的长江下游进入长三角世界级城市群。去年11月，长三角一体化发展上升为国家战略。 坐拥皖江水，面向太平洋。安徽迎来历史性的重大发展机遇。中国区域经济学会顾问程必定认为，长三角上升为国家战略对安徽是百年未有的机遇，这意味着，作为长三角一员，安徽将跻身配置全球资源的开放门户、迎来更高质量一体化发展的崭新阶段。 “以前想到外地注册一个公司，交材料、核准名称、领证件，一切顺利也得跑三趟，现在真是节省了很多成本。”去年11月，合肥与星途科技服务有限公司负责人池天水，在合肥高新区的办事窗口领到了由浙江杭州工商部门颁发的营业执照。这也是该区通过长三角“一网通办”业务平台，发出的首张跨省异地登记的营业执照。 致力于要素破壁、深度融入，安徽各地也在抢抓长三角一体化机遇，争当落实国家战略的排头兵。池州、铜陵等地立足区位优势，承接长三角的产业分工，迈向价值链更高端；芜湖市提出“等高对接”，在长三角一体化中找准自身优势、谋求互补发展。 取消长三角区域高速公路省界收费站，推进长三角地区异地就医直接结算，打造长三角“安全消费大市场”……今年5月22日在芜湖举行的2019年长三角地区三省一市主要领导座谈会上，沪苏浙皖相关部门签署了10个打破分割、加速一体化的协议。 “扬皖所长、主动作为。”安徽省委主要负责人表示，安徽是长三角的重要方面军，在区位、科技、产业、生态、能源等方面具有自身的独特优势。安徽未来将把准自身定位，扬创新之长，努力打造长三角科技创新的重要策源地；扬产业之长，努力打造长三角高质量发展的重要增长极；扬区位之长，努力打造长三角世界级城市群的重要副中心；扬生态之长，努力打造长三角生态文明建设的重要样板区，在国家战略下自觉行动当好尖兵！ 新华社广州7月23日电 题：高质量发展的粤式“色香味”——广东制造业一线见闻 新华社记者毛鑫、邓瑞璇、魏玉坤 手机扫描二维码下单，经过10道全自动工序，不到2分钟就可拿到专属机器人模型。在广东拓斯达科技股份有限公司展厅，扫码定制机器人的“神操作”，令人对智能制造新水准印象深刻。 在广东，依托持续的研发投入、技术改造和体制机制创新，制造业正乘势转型升级，走向数字化、自动化、智能化，这一过程犹如烹制一桌高质量发展粤式大餐，“色香味”俱全。 “智”造增“色”：机器换人迎来热潮 原本人头攒动、环境嘈杂的生产车间，逐渐由精密的高度自动化生产线代替。在广东，“智造力”正成为制造业企业标配。 单条生产线的工人数量从160人降到51人、生产换型时间从45分钟降到9分钟、品质控制点从6个增加到108个……美的南沙工厂通过使用全智能生产线，实现效率和品质的双重提升。 “工人少了，但产量在递增，成本效率品质方面改善明显。”美的集团副总裁兼CIO张小懿说，美的通过自动化、数字化的支撑，正在编织一张工业互联网。 美的发展的背后，是广东制造业不断修炼“内功”的火热实践。 走进拓斯达位于东莞的厂区，在工业机器人装配测试车间，近百台机器人排成5列，有节奏地进行出厂前的自动测试。不久后，它们将进入更多工厂的车间，成为生产的主力军。 “智能制造引发机器换人热潮，国内工业机器人市场需求快速增长。”拓斯达产品研发中心总经理徐必业说，未来将持续增加研发投入，重点布局工业传感器、标准工艺设备等领域。 2018年起，广东大力发展智能制造：投入130亿元实施两轮工业技改三年行动计划，惠及3万多家企业，推动3000家工业企业“上云上平台”，工业机器人产量占全国1/5，先进制造业和高技术制造业增加值占规模以上工业增加值比重分别达56.4%和31.5%，制造业提质增效进展明显。 创新添“香”：从要素驱动转向创新驱动 国内研发成功“3D结构光人脸识别技术”、识别速度从0.1秒降至0.001秒、商用跨镜追踪技术……过去4年，广州云从信息科技公司主攻技术研发，成长为国内人工智能行业的佼佼者。 耀眼的成绩离不开研发支撑。云从科技总经理林明都介绍，公司研发团队已达700余人，其中80%以上拥有硕士及以上学历。该团队瞄准“卡脖子”的技术难点潜心钻研，持续突破核心技术。 云从科技的发展，是广东制造业把技术创新摆在核心位置，实现从要素驱动向创新驱动转变的缩影。 近年，广东采取多项措施增加技术有效供给，先后出台“科创12条”等政策，把企业作为科技创新的主体，并着力加强基础研究和应用基础研究，为高质量发展赋能。 数据显示，2018年，广东研发经费支出超过2500亿元，PCT国际专利申请量约占全国一半，有效发明专利量达24.9万件，稳居全国前列。区域创新综合能力排名连续两年居全国第一。 创新“引力”效果渐显，投资加速向高端产业聚集已成趋势。目前，广东规模以上工业企业总量超5万家，在全国名列前茅。国家级高新技术企业达4.5万家，近两年翻了一倍多。巴斯夫、埃克森美孚、富士康、LG等一批投资过百亿美元的项目逐一落地。 政策调“味”：加强制度供给 优化营商环境 2009年，刚从清华大学博士毕业的佘振定，带着一份“人工皮肤”项目的商业计划书南下深圳创业。 接触多家风投机构无果后，是深圳清华大学研究院相中了这个项目，决定进行孵化，并许诺研发团队可以持有项目70%的股份。 这坚定了佘振定在深圳创业的决心，他创办了深圳兰度生物科技有限公司。 后来，该团队从牛跟腱里提取高纯度胶原，制成了可媲美真实皮肤的双层“人工皮肤”，一经上市便填补了国内空白。多轮融资后，公司市值也翻了几番，达到15亿元。 “只有真正把市场作为配置创新资源的关键要素，才更有利于激发成果转化的积极性。”深圳清华大学研究院常务副院长刘伟强说。截至2018年末，该研究院已累计孵化企业2500多家，培养上市公司21家。 近年来，广东大力支持实体经济发展，以制度创新“扫清”制造业“绊脚石”，力争政府“不缺席”“不包揽”，市场“挑大梁”“说了算”。 实行个税补贴等优惠政策吸引海内外高端人才，推行“零跑动”、“秒批”、注册易等改革举措方便企业办事……一系列涉企惠企政策不断出台，持续释放各类市场主体活力。 截至2018年末，广东个体工商户和企业数量达1146万户，其中企业492万户，均稳居全国第一。其中，主营业务收入超百亿、千亿元的企业分别达276家、29家，进入世界500强的企业13家。这是2018年8月1日无人机拍摄的位于山西省朔州市的安太堡露天煤矿复垦区。新华社记者 曹阳 摄 新华社太原7月22日电 题：三晋大地起苍黄——新中国成立70年山西发展巡礼 新华社记者于振海、梁晓飞、王井怀 太行之西，黄河之东，表里山河，资源富集。 新中国成立70年来，勤劳智慧的三晋儿女在党的领导下，发扬太行精神、吕梁精神、右玉精神，在15万平方公里的土地上，建设起共和国能源重化工基地。如今，又担当起新时代能源革命排头兵重任，展开决战贫困攻坚战，朝着高质量发展之路、向着全面建成小康社会目标阔步前进。 山西，正以全新姿态挺进新时代。 当好“能源革命排头兵” 山西潞安集团石圪节煤矿曾以“艰苦奋斗、勤俭办矿”精神闻名全国。3年前，关闭了矿井，转型发展油用牡丹。前不久，其生产的牡丹籽油出口到了韩国。 石圪节“退煤种花”的同时，千里之外的千万吨级矿井同忻煤矿建成了山西首个智能化工作面，只用在地面控制室里，轻轻一点鼠标，地层深处的“乌金”就会滚滚不息地涌上地面。 新中国成立70年来，山西累计产煤190多亿吨，运往全国的煤炭130多亿吨。 山西因煤而名，也因煤而困。粗放的采矿业，经年累月，导致生态破坏、环境污染、矿难频发，经济发展患上了“煤炭依赖症”；多年来，始终走不出“煤炭市场好时，无暇转型；市场不好，无力转型”的怪圈。 关键时刻，新一届党中央为山西把脉，开出两剂良方：资源型经济转型综合配套改革和供给侧结构性改革。山西省委省政府深化“供改”与“综改”，明确了“能源革命排头兵”“资源型经济转型示范区”“内陆地区对外开放新高地”的目标定位。山西，开始走上高质量发展新征程。 尽管煤价持续高位运行，山西仍坚决淘汰落后产能，3年来关闭88座煤矿，退出落后产能8841万吨，煤炭去产能规模全国第一。煤矿数量由新中国成立后最多的4万多座，降到了1000座以内，先进产能占比提升到57%。 如今，山西1天开采的煤，相当于1949年全年的煤炭产量。通过科技创新，煤炭从燃料变原料，变身为高端润滑油、高端蜡、化妆品。 新型综合能源异军突起。风电、光伏装机总容量突破2000万千瓦，新能源装机容量进入全国前5。煤层气抽采利用量占全国的90%以上。晋电外送能力超过3800万千瓦，越来越多的清洁电力输往全国。 大同建设“氢都”，吕梁培育“数谷”，晋城发展“光谷”，阳泉打造“纳谷”……一批老煤城贴上新名片。晋能集团转型清洁能源，晋煤集团主攻燃气产业，潞安集团整合现代煤化工，阳煤集团发力煤机制造……多家老煤企密集调整主业。 全省上下推动国企改革、开发区改革、电力体制改革、医疗体制改革、营商环境改革等，一些关键领域改革迈向全国第一方阵，发展态势焕然一新。 山西率先启动企业投资项目承诺制改革，用一纸承诺代替“一箩筐材料”，企业自行办理的审批事项压减80%，项目从立项到开工时间平均缩短一半以上。 开发区成为山西转型发展的主战场。2017年山西批准新设开发区15家，2018年新设24家，今年已新设立11家。各开发区围绕先进装备制造、新能源汽车、节能环保、大数据、新材料、现代煤化工等精准招商。 今年5月，山西锦波生物医药股份有限公司与中国科学院、复旦大学共同发布人源Ⅲ型胶原蛋白，这是山西转型综改示范区催生的最新科技成果。去年一年，这里新增高新技术企业110家，初步形成电子信息装备、装备制造及汽车、生物医药及食品、新材料及加工等4个千亿级产业集群。 甩掉贫苦穷困的帽子 “人说山西好风光，地肥水美五谷香……”一曲《人说山西好风光》，唱出了山西人的家乡情。 山西，也是英雄、光荣的土地。在中国革命斗争中，留下了数不尽的光荣故事。新中国成立以来，这里孕育出了西沟精神、大寨精神、锡崖沟精神、大泉山精神…… 然而，巍巍太行，遍布干石山；绵绵吕梁，满是残垣沟壑。因土地瘠薄、干旱缺水，全国14个集中连片特困地区，太行、吕梁就占了两个，山西58个贫困县分布其间。 党的十八大以来，山西打响声势浩大、前所未有的脱贫攻坚战。 2017年9月，家住吕梁山深处、年过七旬的刘福有、杨娥子老两口，卖掉了家里的两头大犍牛，搬进了岢岚县广惠园移民新村。只有6户人家共13人的赵家洼村在民政部门销了号，拉开了山西决战深度贫困的序幕。 刘福有没花一分钱，就搬进了新楼房，后续扶持政策随之跟进。 很快，山西从7993个贫困村中，确定了3350个需要整村搬迁的深度贫困村，以“打不赢脱贫攻坚战，就对不起这块红色土地”的态度和决心，采取超常举措，啃深度贫困“硬骨头”。 到今年5月底，3350个深度贫困村已有3084个完成整体搬迁，1502个集中安置点已竣工1493个。 数万名扶贫干部奔走在太行吕梁山上。长治市扶贫办主任段志岗上任3年，跑了全市933个贫困村的一半以上。“时间紧任务重，群众眼里看着，心里盼着，不使劲不行啊。”他说。 扶贫干部们从太行精神、吕梁精神中汲取力量。在革命老区武乡县，楼则峪村第一书记闫小波进村后，挨家挨户走，一家一家问，连老太太爱看什么电视节目都记在心里。他带领村民发展电商，把当地小米卖出了好价钱。离任时，村民给他塞上自己做的鞋垫，表达对他的感激和不舍。 今年，山西将确保最后17个贫困县全部“摘帽”，22万贫困人口脱贫，确保2020年同全国一道进入全面小康社会。 绘就新的“山西好风光” 汾河，是山西最著名的河流。 党的十八大以来，山西把生态文明建设摆在更加突出地位，以环境保护倒逼经济高质量转型，积极回应人民群众的生态关切，让人们能望见星空、看见绿山、闻到花香。 山西实施大水网建设，启动占全省总面积72%的七河流域 {} {"audioInfo":{"langDec":"chin","decSc":1.0,"nlpSc":0.0,"gender":"m","audioFiles":[{"decSc":1.0,"side":"c","gender":"m","bits":16,"langSc":0.0,"url":"http://10.5.3.85/heluAB.wav","vtl":135560,"codec":6,"channels":1,"rate":8000,"spkn":0,"name":"heluAB.wav","tl":211962}],"langSc":0.64250153,"lang":"chin"}} -a451790d-42f8-48e5-88f4-ba21118e63e6 13326037312 81 0031 8 (0551)17198025 852 2 0 1568294179731 2116 asp fk 1127000 1 0 0 0 0 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chin chin 0.64250153 ff 0.0 1126000 1124000 1568249840130 1568249839006 卷 新华社记者王正忠、杨玉华、徐海涛、姜刚 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（1）安徽70年：改革创新书写时代答卷 这是安徽凤阳小岗村（2018年9月27日无人机拍摄）。 新华社记者 张端 摄 这是一方敢为人先的热土：开启农村改革巨幕的“大包干”、闯出个体经济之路的“傻子瓜子”、冲出重围高擎自主品牌大旗的奇瑞汽车……一次次书写时代传奇。 这是一片勇于革新的天地：税费改革、医药卫生体制改革、文化体制改革、全面创新改革试验……一次次引领风气之先。 这是一处创新喷涌的泉源：全球首颗量子通信卫星上天、智能语音技术站上全球制高点、世界最薄的玻璃问世、“人造太阳”正求解人类终极能源难题……一次次刷新进步的前沿。 从新中国成立时一个落后的农业省，到今天实现农业大省、工业大省、科技大省的“三级跳”，安徽以70年的精彩蝶变书写时代答卷，以改革创新的昂扬姿态挺进新时代。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（3）安徽70年：改革创新书写时代答卷 在安徽凤阳小岗村，农民驾驶收割机收割水稻（2018年9月27日摄）。 新华社记者 刘军喜 摄 昔日农业省今成“科创极” 八百里巢湖之滨，坐落着全国首处以创新为主题的场馆。其中“人造太阳”、强磁场装置、量子通信等高精尖的科技创新成果令八方来宾感到震撼。 安徽，曾长期是经济发展的洼地，不沿边、不靠海、工业基础薄弱。新中国成立初期，一产占比高达75%以上。70年沧桑巨变，到2018年，安徽的三产结构已变为8.8：46.1：45.1，农业占比退居十分之一以下，昔日的农业大省成为以家电、汽车、建材等为主的工业大省和以电子信息、平板显示、智能语音等领衔的科技产业大省。 巨变的背后，是安徽把科技创新作为转型升级的突破口。早在1995年安徽就提出实施“科教兴皖”战略，2008年启动合芜蚌自主创新综合试验区建设，实施创新驱动发展之路。2013年，安徽成为全国第二个开展创新型省份建设试点工作的地区，随后获批建设合芜蚌国家自主创新示范区和系统推进全面创新改革试验区。2017年初，合肥综合性国家科学中心获批。2018年，合肥滨湖科学城挂牌成立……安徽的创新名片越擦越亮。 多年的探索实践，安徽已形成从原始创新到技术开发、成果转化、企业孵化，再到新兴产业的“全创新链”，走出一条具有安徽特色的中西部创新发展之路。 在位于合肥北郊的合肥综合性国家科学中心聚变堆园区，聚变堆主机关键系统综合研究设施工程正在紧张建设。这个由安徽省政府主要领导率队答辩争取来的国家大科学装置，与之前建成的合肥“人造太阳”装置相呼应，使安徽成为世界一流的核聚变能源创新研究基地。瞄准前沿、前瞻布局、潜心投入，安徽已建成“同步辐射光源”“全超导托卡马克”“稳态强磁场”三大国际科技前沿科研平台，并在量子通信、智能语音等科研领域达到国际领跑或并跑水平。安徽的区域创新能力已连续7年跻身全国第一方阵。 贴近产业抓创新，集聚资源搞科研，不断破除体制机制障碍，让安徽驶入创新发展快车道。安徽省统计局局长吴劲松说，党的十八大以来，安徽的新兴产业增加值不断提升。 目前，安徽的战略性新兴产业对全省工业增长的贡献率已超三分之一。在合肥，形成了国内唯一拥有6代线、8.5代线和10.5代线的新型平板显示产业基地。在蚌埠，以蚌埠玻璃工业设计研究院为核心的新型玻璃产业，诞生了我国首条具有自主知识产权的8.5代TFT-LCD玻璃基板生产线，成功研发世界最薄的0.12毫米超薄触控玻璃；在芜湖，形成了以中电科钻石飞机为代表的通用航空产业集群，飞机交付量处于国内领先行列。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（6）安徽70年：改革创新书写时代答卷 观众在2018世界制造业大会上观看蚌埠玻璃工业设计研究院展出的柔性触控玻璃（2018年5月25日摄）。 新华社记者 张端 摄 改革不停步发展活力涌 经济因改革而活，发展因创新而兴。今年上半年，安徽的经济呈现出“加速跑、趋向优”的良好态势。生产总值增长8%，居全国第6位，规模以上工业增加值增速居第6位，城镇、农村常住居民人均可支配收入增速分别居第3位和第4位。 “速度位次靠前，发展质效提升，内生动力增强。”安徽省统计局副局长赵金宝将全省上半年经济运行的亮点概括为三句话。 今年7月12日，三只松鼠股份有限公司在深交所上市。创立7年来的这家企业，不断颠覆传统销售模式，年销售额从零增加到2018年的82亿元。“创新发展是企业前进的根本。”公司党委书记潘道伟说，上市能加速公司数字化供应链战略的推进，为消费者提供更好的产品和服务。 作为民营经济的试验田，如今的江淮大地仍是一片创新创业的热土，敢闯敢试的改革火种继续在这里播撒。现在的安徽，平均每天新登记注册企业925家，每天获授44件发明专利。 经济“逆势上扬”，靠的是改革，是服务，是创新。十八大以来，安徽着力推动“放管服”改革，首创权力、责任清单的“安徽模式”，让公共服务做“加法”、中介服务做“减法”。而群众办事“最多跑一次”“互联网+政务服务”等创举，正推动安徽迈向审批事项少、办事效率高、投资环境优、市场主体和人民群众获得感强的省份之一。 唯改革才有出路，改革要常讲常新。目前，安徽正持续将一系列新的改革推向深入： ——政务服务改革。努力将省级行政许可事项减到全国最少，企业开办时间压缩至全国最短……到今年底，目标实现80%个人事项全程网办，部分事项“秒批秒办”。 ——全面创新改革。加快建设合肥综合性国家科学中心，加紧布局一批世界前沿的大科学装置，下放科技成果处置权、收益权、使用权，探索实行首席科学家全权负责制……目标建成具有国际影响力的科技创新中心和产业创新中心。 ——农业农村改革。持续深化农村承包地“三权分置”改革，稳步推进农村集体产权制度改革……围绕处理好农民与土地关系主线，努力在农村改革上继续走在前列。 增进民生福祉，乃经济发展之根本目的。居住在集中连片贫困山区金寨县大湾村村民陈泽申前年主动要求摘掉“贫困帽”。得益于党和政府的扶贫政策，他家盖了新房，流转了山地，有了公益性岗位，每年收入三四万元。此前，他将扭转命运的期望全部寄托在“改变家居风水”上面。 为了兑现“小康路上一个都不能掉队”的庄严承诺，安徽把以人民为中心的理念贯穿到经济社会发展各个领域。在实施脱贫攻坚“十大工程”等举措作用下，全省贫困人口由2012年底的679.1万人下降到2018年底的49.9万人，年均减贫100万人以上；贫困发生率由12.6%下降到0.93%；22个贫困县先后“摘帽”。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（7）安徽70年：改革创新书写时代答卷 2017年9月29日，世界首条量子 {} 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chin chin 0.64250153 mm 0.0 2042000 2040000 1568249840153 1568249839068 、芜湖、马鞍山等8市纳入长三角规划，安徽正式从地理上的长江下游进入长三角世界级城市群。去年11月，长三角一体化发展上升为国家战略。 坐拥皖江水，面向太平洋。安徽迎来历史性的重大发展机遇。中国区域经济学会顾问程必定认为，长三角上升为国家战略对安徽是百年未有的机遇，这意味着，作为长三角一员，安徽将跻身配置全球资源的开放门户、迎来更高质量一体化发展的崭新阶段。 “以前想到外地注册一个公司，交材料、核准名称、领证件，一切顺利也得跑三趟，现在真是节省了很多成本。”去年11月，合肥与星途科技服务有限公司负责人池天水，在合肥高新区的办事窗口领到了由浙江杭州工商部门颁发的营业执照。这也是该区通过长三角“一网通办”业务平台，发出的首张跨省异地登记的营业执照。 致力于要素破壁、深度融入，安徽各地也在抢抓长三角一体化机遇，争当落实国家战略的排头兵。池州、铜陵等地立足区位优势，承接长三角的产业分工，迈向价值链更高端；芜湖市提出“等高对接”，在长三角一体化中找准自身优势、谋求互补发展。 取消长三角区域高速公路省界收费站，推进长三角地区异地就医直接结算，打造长三角“安全消费大市场”……今年5月22日在芜湖举行的2019年长三角地区三省一市主要领导座谈会上，沪苏浙皖相关部门签署了10个打破分割、加速一体化的协议。 “扬皖所长、主动作为。”安徽省委主要负责人表示，安徽是长三角的重要方面军，在区位、科技、产业、生态、能源等方面具有自身的独特优势。安徽未来将把准自身定位，扬创新之长，努力打造长三角科技创新的重要策源地；扬产业之长，努力打造长三角高质量发展的重要增长极；扬区位之长，努力打造长三角世界级城市群的重要副中心；扬生态之长，努力打造长三角生态文明建设的重要样板区，在国家战略下自觉行动当好尖兵！ 新华社广州7月23日电 题：高质量发展的粤式“色香味”——广东制造业一线见闻 新华社记者毛鑫、邓瑞璇、魏玉坤 手机扫描二维码下单，经过10道全自动工序，不到2分钟就可拿到专属机器人模型。在广东拓斯达科技股份有限公司展厅，扫码定制机器人的“神操作”，令人对智能制造新水准印象深刻。 在广东，依托持续的研发投入、技术改造和体制机制创新，制造业正乘势转型升级，走向数字化、自动化、智能化，这一过程犹如烹制一桌高质量发展粤式大餐，“色香味”俱全。 “智”造增“色”：机器换人迎来热潮 原本人头攒动、环境嘈杂的生产车间，逐渐由精密的高度自动化生产线代替。在广东，“智造力”正成为制造业企业标配。 单条生产线的工人数量从160人降到51人、生产换型时间从45分钟降到9分钟、品质控制点从6个增加到108个……美的南沙工厂通过使用全智能生产线，实现效率和品质的双重提升。 “工人少了，但产量在递增，成本效率品质方面改善明显。”美的集团副总裁兼CIO张小懿说，美的通过自动化、数字化的支撑，正在编织一张工业互联网。 美的发展的背后，是广东制造业不断修炼“内功”的火热实践。 走进拓斯达位于东莞的厂区，在工业机器人装配测试车间，近百台机器人排成5列，有节奏地进行出厂前的自动测试。不久后，它们将进入更多工厂的车间，成为生产的主力军。 “智能制造引发机器换人热潮，国内工业机器人市场需求快速增长。”拓斯达产品研发中心总经理徐必业说，未来将持续增加研发投入，重点布局工业传感器、标准工艺设备等领域。 2018年起，广东大力发展智能制造：投入130亿元实施两轮工业技改三年行动计划，惠及3万多家企业，推动3000家工业企业“上云上平台”，工业机器人产量占全国1/5，先进制造业和高技术制造业增加值占规模以上工业增加值比重分别达56.4%和31.5%，制造业提质增效进展明显。 创新添“香”：从要素驱动转向创新驱动 国内研发成功“3D结构光人脸识别技术”、识别速度从0.1秒降至0.001秒、商用跨镜追踪技术……过去4年，广州云从信息科技公司主攻技术研发，成长为国内人工智能行业的佼佼者。 耀眼的成绩离不开研发支撑。云从科技总经理林明都介绍，公司研发团队已达700余人，其中80%以上拥有硕士及以上学历。该团队瞄准“卡脖子”的技术难点潜心钻研，持续突破核心技术。 云从科技的发展，是广东制造业把技术创新摆在核心位置，实现从要素驱动向创新驱动转变的缩影。 近年，广东采取多项措施增加技术有效供给，先后出台“科创12条”等政策，把企业作为科技创新的主体，并着力加强基础研究和应用基础研究，为高质量发展赋能。 数据显示，2018年，广东研发经费支出超过2500亿元，PCT国际专利申请量约占全国一半，有效发明专利量达24.9万件，稳居全国前列。区域创新综合能力排名连续两年居全国第一。 创新“引力”效果渐显，投资加速向高端产业聚集已成趋势。目前，广东规模以上工业企业总量超5万家，在全国名列前茅。国家级高新技术企业达4.5万家，近两年翻了一倍多。巴斯夫、埃克森美孚、富士康、LG等一批投资过百亿美元的项目逐一落地。 政策调“味”：加强制度供给 优化营商环境 2009年，刚从清华大学博士毕业的佘振定，带着一份“人工皮肤”项目的商业计划书南下深圳创业。 接触多家风投机构无果后，是深圳清华大学研究院相中了这个项目，决定进行孵化，并许诺研发团队可以持有项目70%的股份。 这坚定了佘振定在深圳创业的决心，他创办了深圳兰度生物科技有限公司。 后来，该团队从牛跟腱里提取高纯度胶原，制成了可媲美真实皮肤的双层“人工皮肤”，一经上市便填补了国内空白。多轮融资后，公司市值也翻了几番，达到15亿元。 “只有真正把市场作为配置创新资源的关键要素，才更有利于激发成果转化的积极性。”深圳清华大学研究院常务副院长刘伟强说。截至2018年末，该研究院已累计孵化企业2500多家，培养上市公司21家。 近年来，广东大力支持实体经济发展，以制度创新“扫清”制造业“绊脚石”，力争政府“不缺席”“不包揽”，市场“挑大梁”“说了算”。 实行个税补贴等优惠政策吸引海内外高端人才，推行“零跑动”、“秒批”、注册易等改革举措方便企业办事……一系列涉企惠企政策不断出台，持续释放各类市场主体活力。 截至2018年末，广东个体工商户和企业数量达1146万户，其中企业492万户，均稳居全国第一。其中，主营业务收入超百亿、千亿元的企业分别达276家、29家，进入世界500强的企业13家。这是2018年8月1日无人机拍摄的位于山西省朔州市的安太堡露天煤矿复垦区。新华社记者 曹阳 摄 新华社太原7月22日电 题：三晋大地起苍黄——新中国成立70年山西发展巡礼 新华社记者于振海、梁晓飞、王井怀 太行之西，黄河之东，表里山河，资源富集。 新中国成立70年来，勤劳智慧的三晋儿女在党的领导下，发扬太行精神、吕梁精神、右玉精神，在15万平方公里的土地上，建设起共和国能源重化工基地。如今，又担当起新时代能源革命排头兵重任，展开决战贫困攻坚战，朝着高质量发展之路、向着全面建成小康社会目标阔步前进。 山西，正以全新姿态挺进新时代。 当好“能源革命排头兵” 山西潞安集团石圪节煤矿曾以“艰苦奋斗、勤俭办矿”精神闻名全国。3年前，关闭了矿井，转型发展油用牡丹。前不久，其生产的牡丹籽油出口到了韩国。 石圪节“退煤种花”的同时，千里之外的千万吨级矿井同忻煤矿建成了山西首个智能化工作面，只用在地面控制室里，轻轻一点鼠标，地层深处的“乌金”就会滚滚不息地涌上地面。 新中国成立70年来，山西累计产煤190多亿吨，运往全国的煤炭130多亿吨。 山西因煤而名，也因煤而困。粗放的采矿业，经年累月，导致生态破坏、环境污染、矿难频发，经济发展患上了“煤炭依赖症”；多年来，始终走不出“煤炭市场好时，无暇转型；市场不好，无力转型”的怪圈。 关键时刻，新一届党中央为山西把脉，开出两剂良方：资源型经济转型综合配套改革和供给侧结构性改革。山西省委省政府深化“供改”与“综改”，明确了“能源革命排头兵”“资源型经济转型示范区”“内陆地区对外开放新高地”的目标定位。山西，开始走上高质量发展新征程。 尽管煤价持续高位运行，山西仍坚决淘汰落后产能，3年来关闭88座煤矿，退出落后产能8841万吨，煤炭去产能规模全国第一。煤矿数量由新中国成立后最多的4万多座，降到了1000座以内，先进产能占比提升到57%。 如今，山西1天开采的煤，相当于1949年全年的煤炭产量。通过科技创新，煤炭从燃料变原料，变身为高端润滑油、高端蜡、化妆品。 新型综合能源异军突起。风电、光伏装机总容量突破2000万千瓦，新能源装机容量进入全国前5。煤层气抽采利用量占全国的90%以上。晋电外送能力超过3800万千瓦，越来越多的清洁电力输往全国。 大同建设“氢都”，吕梁培育“数谷”，晋城发展“光谷”，阳泉打造“纳谷”……一批老煤城贴上新名片。晋能集团转型清洁能源，晋煤集团主攻燃气产业，潞安集团整合现代煤化工，阳煤集团发力煤机制造……多家老煤企密集调整主业。 全省上下推动国企改革、开发区改革、电力体制改革、医疗体制改革、营商环境改革等，一些关键领域改革迈向全国第一方阵，发展态势焕然一新。 山西率先启动企业投资项目承诺制改革，用一纸承诺代替“一箩筐材料”，企业自行办理的审批事项压减80%，项目从立项到开工时间平均缩短一半以上。 开发区成为山西转型发展的主战场。2017年山西批准新设开发区15家，2018年新设24家，今年已新设立11家。各开发区围绕先进装备制造、新能源汽车、节能环保、大数据、新材料、现代煤化工等精准招商。 今年5月，山西锦波生物医药股份有限公司与中国科学院、复旦大学共同发布人源Ⅲ型胶原蛋白，这是山西转型综改示范区催生的最新科技成果。去年一年，这里新增高新技术企业110家，初步形成电子信息装备、装备制造及汽车、生物医药及食品、新材料及加工等4个千亿级产业集群。 甩掉贫苦穷困的帽子 “人说山西好风光，地肥水美五谷香……”一曲《人说山西好风光》，唱出了山西人的家乡情。 山西，也是英雄、光荣的土地。在中国革命斗争中，留下了数不尽的光荣故事。新中国成立以来，这里孕育出了西沟精神、大寨精神、锡崖沟精神、大泉山精神…… 然而，巍巍太行，遍布干石山；绵绵吕梁，满是残垣沟壑。因土地瘠薄、干旱缺水，全国14个集中连片特困地区，太行、吕梁就占了两个，山西58个贫困县分布其间。 党的十八大以来，山西打响声势浩大、前所未有的脱贫攻坚战。 2017年9月，家住吕梁山深处、年过七旬的刘福有、杨娥子老两口，卖掉了家里的两头大犍牛，搬进了岢岚县广惠园移民新村。只有6户人家共13人的赵家洼村在民政部门销了号，拉开了山西决战深度贫困的序幕。 刘福有没花一分钱，就搬进了新楼房，后续扶持政策随之跟进。 很快，山西从7993个贫困村中，确定了3350个需要整村搬迁的深度贫困村，以“打不赢脱贫攻坚战，就对不起这块红色土地”的态度和决心，采取超常举措，啃深度贫困“硬骨头”。 到今年5月底，3350个深度贫困村已有3084个完成整体搬迁，1502个集中安置点已竣工1493个。 数万名扶贫干部奔走在太行吕梁山上。长治市扶贫办主任段志岗上任3年，跑了全市933个贫困村的一半以上。“时间紧任务重，群众眼里看着，心里盼着，不使劲不行啊。”他说。 扶贫干部们从太行精神、吕梁精神中汲取力量。在革命老区武乡县，楼则峪村第一书记闫小波进村后，挨家挨户走，一家一家问，连老太太爱看什么电视节目都记在心里。他带领村民发展电商，把当地小米卖出了好价钱。离任时，村民给他塞上自己做的鞋垫，表达对他的感激和不舍。 今年，山西将确保最后17个贫困县全部“摘帽”，22万贫困人口脱贫，确保2020年同全国一道进入全面小康社会。 绘就新的“山西好风光” 汾河，是山西最著名的河流。 党的十八大以来，山西把生态文明建设摆在更加突出地位，以环境保护倒逼经济高质量转型，积极回应人民群众的生态关切，让人们能望见星空、看见绿山、闻到花香。 山西实施大水网建设，启动占全省总面积72%的七河流域治理工程，省政府每周公布汾河流域治污情况，确保到明年全面消除劣Ⅴ类断面。太原、临汾等汾河河段再现“一川清流，两岸锦绣”。 水的命脉在山，山的灵魂在树。在支离破碎的黄土高原，有的地方“种树比养孩子还难”，生存条件恶劣。 在吕梁山区的永和县，永和关、乾 {} {"audioInfo":{"langDec":"chin","decSc":1.0,"nlpSc":0.0,"gender":"m","audioFiles":[{"decSc":1.0,"side":"c","gender":"m","bits":16,"langSc":0.0,"url":"http://10.5.3.85/heluAB.wav","vtl":135560,"codec":6,"channels":1,"rate":8000,"spkn":0,"name":"heluAB.wav","tl":211962}],"langSc":0.64250153,"lang":"chin"}} -c2eabec9-b8a7-405b-80d9-e73692d586f4 0086(021)77426829 81 0031 8 13326037312 44 0 0 1568252700180 945 asp fk 943000 1 0 0 0 0 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chin chin 0.64250153 ff 0.0 942000 940000 1568249840177 1568249839114 好生活、实现共同富裕而不懈奋斗。毛泽东同志在新中国成立之初就提出了我国发展富强的目标，指出“这个富，是共同的富，这个强，是共同的强，大家都有份”。邓小平同志多次强调共同富裕，指出“社会主义不是少数人富起来、大多数人穷，不是那个样子。社会主义最大的优越性就是共同富裕，这是体现社会主义本质的一个东西”。江泽民同志强调：“实现共同富裕是社会主义的根本原则和本质特征，绝不能动摇。”胡锦涛同志也要求“使全体人民共享改革发展成果，使全体人民朝着共同富裕的方向稳步前进”。 进入新时代，我们走上了创造美好生活、逐步实现全体人民共同富裕的新征程。习近平总书记指出，共同富裕是中国特色社会主义的根本原则，实现共同富裕是我们党的重要使命。他还强调，“我们追求的发展是造福人民的发展，我们追求的富裕是全体人民共同富裕”，要“让发展成果更多更公平惠及全体人民，不断促进人的全面发展，朝着实现全体人民共同富裕不断迈进”。 经过长期艰苦奋斗，我们党在实践中形成了先富带动后富、逐步实现共同富裕的规律性认识，推动人民生活质量和社会共享水平显著提升。粮票、布票、肉票、鱼票、油票、豆腐票、副食本、工业券等百姓生活曾经离不开的票证，已经进入了历史博物馆。忍饥挨饿、缺吃少穿、生活困顿这些几千年来困扰我国人民的问题总体上一去不复返了，中国人民迎来了从温饱不足到小康富裕的伟大飞跃。 （39）实现全体人民共同富裕的宏伟目标，最终靠的是发展。发展是基础，唯有发展才能满足人民对美好生活的热切向往。没有发展，没有扎扎实实的发展成果，共同富裕就无从谈起。 要毫不动摇坚持发展是硬道理、发展应该是科学发展和高质量发展的战略思想，举全民之力推进中国特色社会主义事业，不断把“蛋糕”做大。同时，还要在不断发展的基础上把促进社会公平正义的事情做好，把不断做大的“蛋糕”分好，让社会主义制度的优越性更加充分体现出来，让实现全体人民共同富裕在广大人民现实生活中更加充分展示出来。绝不能出现“富者累巨万，而贫者食糟糠”的现象。 实现全体人民共同富裕的目标需要一个很长的历史过程，是由一个一个阶段性目标逐步达成的。我国正处于并将长期处于社会主义初级阶段，我们不能做超越阶段的事情，但也不是说在逐步实现共同富裕方面就无所作为，而是要根据现有条件把能做的事情尽量做起来，积小胜为大胜，不断朝着全体人民共同富裕的目标前进。 4.群众路线是党的生命线和根本工作路线 （40）习近平总书记指出：“群众路线是我们党的生命线和根本工作路线，是我们党永葆青春活力和战斗力的重要传家宝。”不论过去、现在和将来，我们都要坚持一切为了群众，一切依靠群众，从群众中来，到群众中去，把党的正确主张变为群众的自觉行动，把群众路线贯彻到治国理政全部活动之中。 坚持群众路线，核心的问题是党要始终保持同人民群众的血肉联系，一刻也不脱离群众。密切联系群众，是党的性质和宗旨的体现。我们党是在同人民群众的密切联系中成长、发展、壮大起来的，是靠宣传群众、组织群众、依靠群众起家，从胜利走向胜利的。党的最大政治优势是密切联系群众，党执政后的最大危险是脱离群众。能否保持党同人民群众的血肉联系，决定着党的事业的成败。要从政治的高度深刻认识密切联系群众的重要性，任何时候任何情况下，与人民群众同呼吸共命运的立场不能变，全心全意为人民服务的宗旨不能忘，坚信群众是真正英雄的历史唯物主义观点不能丢。 （41）群众路线是我们党始终坚持的根本工作方法。党的领导工作的正确方法，就是将群众意见集中起来形成正确的决策，又到群众中宣传解释，将决策化为群众的行动，并在群众实践中检验这些决策是否正确。 坚持走群众路线，绝不是喊喊口号走走过场，而是要诚心诚意、实打实做。要善于通过提出并贯彻正确的理论和路线方针政策带领人民前进，善于从人民的实践创造和发展要求中完善政策主张，善于从群众中寻找解决问题的方案和办法，使作出的决策和决策的执行充分体现民心民意。深入研究新形势下群众工作的规律和特点，把党的优良传统和新技术新手段结合起来，学会通过网络走群众路线，提高做好群众工作的本领。 贯彻党的群众路线，“知”是基础、是前提，“行”是重点、是关键。必须以知促行、以行促知，做到知行合一，既解决认识提高问题，又解决行动自觉问题，使群众路线落地稳、扎根深，融入经济社会发展全过程，贯穿到党的全部工作中。 新华社合肥7月23日电 题：安徽70年：改革创新书写时代答卷 新华社记者王正忠、杨玉华、徐海涛、姜刚 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（1）安徽70年：改革创新书写时代答卷 这是安徽凤阳小岗村（2018年9月27日无人机拍摄）。 新华社记者 张端 摄 这是一方敢为人先的热土：开启农村改革巨幕的“大包干”、闯出个体经济之路的“傻子瓜子”、冲出重围高擎自主品牌大旗的奇瑞汽车……一次次书写时代传奇。 这是一片勇于革新的天地：税费改革、医药卫生体制改革、文化体制改革、全面创新改革试验……一次次引领风气之先。 这是一处创新喷涌的泉源：全球首颗量子通信卫星上天、智能语音技术站上全球制高点、世界最薄的玻璃问世、“人造太阳”正求解人类终极能源难题……一次次刷新进步的前沿。 从新中国成立时一个落后的农业省，到今天实现农业大省、工业大省、科技大省的“三级跳”，安徽以70年的精彩蝶变书写时代答卷，以改革创新的昂扬姿态挺进新时代。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（3）安徽70年：改革创新书写时代答卷 在安徽凤阳小岗村，农民驾驶收割机收割水稻（2018年9月27日摄）。 新华社记者 刘军喜 摄 昔日农业省今成“科创极” 八百里巢湖之滨，坐 {} {"audioInfo":{"langDec":"chin","decSc":1.0,"nlpSc":0.0,"gender":"m","audioFiles":[{"decSc":1.0,"side":"c","gender":"m","bits":16,"langSc":0.0,"url":"http://10.5.3.85/heluAB.wav","vtl":135560,"codec":6,"channels":1,"rate":8000,"spkn":0,"name":"heluAB.wav","tl":211962}],"langSc":0.64250153,"lang":"chin"}} -04a548aa-a103-4ffd-b72c-81b6cb2ea420 0086(021)77426829 82 0031 2 13924865616 33 0 0 1568249850352 857 asp fk 2450000 1 0 0 0 0 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cant cant 0.64250153 mm 0.0 2449000 2447000 1568249840221 1568249839126 兴、答应不答应作为衡量一切工作得失的根本标准，努力向历史、向人民交出新的更加优异的答卷。 3.朝着实现全体人民共同富裕不断迈进 （38）共同富裕，是马克思主义的一个基本目标。按照马克思、恩格斯的构想，共产主义社会将彻底消除阶级之间、城乡之间、脑力劳动和体力劳动之间的对立和差别，实行各尽所能、按需分配，真正实现社会共享、实现每个人自由而全面的发展。到那时，“生产将以所有的人富裕为目的”，“所有人共同享受大家创造出来的福利”。实现共同富裕，反映了社会主义的本质要求，体现了以人民为中心的根本立场。 我们党始终带领人民为创造美好生活、实现共同富裕而不懈奋斗。毛泽东同志在新中国成立之初就提出了我国发展富强的目标，指出“这个富，是共同的富，这个强，是共同的强，大家都有份”。邓小平同志多次强调共同富裕，指出“社会主义不是少数人富起来、大多数人穷，不是那个样子。社会主义最大的优越性就是共同富裕，这是体现社会主义本质的一个东西”。江泽民同志强调：“实现共同富裕是社会主义的根本原则和本质特征，绝不能动摇。”胡锦涛同志也要求“使全体人民共享改革发展成果，使全体人民朝着共同富裕的方向稳步前进”。 进入新时代，我们走上了创造美好生活、逐步实现全体人民共同富裕的新征程。习近平总书记指出，共同富裕是中国特色社会主义的根本原则，实现共同富裕是我们党的重要使命。他还强调，“我们追求的发展是造福人民的发展，我们追求的富裕是全体人民共同富裕”，要“让发展成果更多更公平惠及全体人民，不断促进人的全面发展，朝着实现全体人民共同富裕不断迈进”。 经过长期艰苦奋斗，我们党在实践中形成了先富带动后富、逐步实现共同富裕的规律性认识，推动人民生活质量和社会共享水平显著提升。粮票、布票、肉票、鱼票、油票、豆腐票、副食本、工业券等百姓生活曾经离不开的票证，已经进入了历史博物馆。忍饥挨饿、缺吃少穿、生活困顿这些几千年来困扰我国人民的问题总体上一去不复返了，中国人民迎来了从温饱不足到小康富裕的伟大飞跃。 （39）实现全体人民共同富裕的宏伟目标，最终靠的是发展。发展是基础，唯有发展才能满足人民对美好生活的热切向往。没有发展，没有扎扎实实的发展成果，共同富裕就无从谈起。 要毫不动摇坚持发展是硬道理、发展应该是科学发展和高质量发展的战略思想，举全民之力推进中国特色社会主义事业，不断把“蛋糕”做大。同时，还要在不断发展的基础上把促进社会公平正义的事情做好，把不断做大的“蛋糕”分好，让社会主义制度的优越性更加充分体现出来，让实现全体人民共同富裕在广大人民现实生活中更加充分展示出来。绝不能出现“富者累巨万，而贫者食糟糠”的现象。 实现全体人民共同富裕的目标需要一个很长的历史过程，是由一个一个阶段性目标逐步达成的。我国正处于并将长期处于社会主义初级阶段，我们不能做超越阶段的事情，但也不是说在逐步实现共同富裕方面就无所作为，而是要根据现有条件把能做的事情尽量做起来，积小胜为大胜，不断朝着全体人民共同富裕的目标前进。 4.群众路线是党的生命线和根本工作路线 （40）习近平总书记指出：“群众路线是我们党的生命线和根本工作路线，是我们党永葆青春活力和战斗力的重要传家宝。”不论过去、现在和将来，我们都要坚持一切为了群众，一切依靠群众，从群众中来，到群众中去，把党的正确主张变为群众的自觉行动，把群众路线贯彻到治国理政全部活动之中。 坚持群众路线，核心的问题是党要始终保持同人民群众的血肉联系，一刻也不脱离群众。密切联系群众，是党的性质和宗旨的体现。我们党是在同人民群众的密切联系中成长、发展、壮大起来的，是靠宣传群众、组织群众、依靠群众起家，从胜利走向胜利的。党的最大政治优势是密切联系群众，党执政后的最大危险是脱离群众。能否保持党同人民群众的血肉联系，决定着党的事业的成败。要从政治的高度深刻认识密切联系群众的重要性，任何时候任何情况下，与人民群众同呼吸共命运的立场不能变，全心全意为人民服务的宗旨不能忘，坚信群众是真正英雄的历史唯物主义观点不能丢。 （41）群众路线是我们党始终坚持的根本工作方法。党的领导工作的正确方法，就是将群众意见集中起来形成正确的决策，又到群众中宣传解释，将决策化为群众的行动，并在群众实践中检验这些决策是否正确。 坚持走群众路线，绝不是喊喊口号走走过场，而是要诚心诚意、实打实做。要善于通过提出并贯彻正确的理论和路线方针政策带领人民前进，善于从人民的实践创造和发展要求中完善政策主张，善于从群众中寻找解决问题的方案和办法，使作出的决策和决策的执行充分体现民心民意。深入研究新形势下群众工作的规律和特点，把党的优良传统和新技术新手段结合起来，学会通过网络走群众路线，提高做好群众工作的本领。 贯彻党的群众路线，“知”是基础、是前提，“行”是重点、是关键。必须以知促行、以行促知，做到知行合一，既解决认识提高问题，又解决行动自觉问题，使群众路线落地稳、扎根深，融入经济社会发展全过程，贯穿到党的全部工作中。 新华社合肥7月23日电 题：安徽70年：改革创新书写时代答卷 新华社记者王正忠、杨玉华、徐海涛、姜刚 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（1）安徽70年：改革创新书写时代答卷 这是安徽凤阳小岗村（2018年9月27日无人机拍摄）。 新华社记者 张端 摄 这是一方敢为人先的热土：开启农村改革巨幕的“大包干”、闯出个体经济之路的“傻子瓜子”、冲出重围高擎自主品牌大旗的奇瑞汽车……一次次书写时代传奇。 这是一片勇于革新的天地：税费改革、医药卫生体制改革、文化体制改革、全面创新改革试验……一次次引领风气之先。 这是一处创新喷涌的泉源：全球首颗量子通信卫星上天、智能语音技术站上全球制高点、世界最薄的玻璃问世、“人造太阳”正求解人类终极能源难题……一次次刷新进步的前沿。 从新中国成立时一个落后的农业省，到今天实现农业大省、工业大省、科技大省的“三级跳”，安徽以70年的精彩蝶变书写时代答卷，以改革创新的昂扬姿态挺进新时代。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（3）安徽70年：改革创新书写时代答卷 在安徽凤阳小岗村，农民驾驶收割机收割水稻（2018年9月27日摄）。 新华社记者 刘军喜 摄 昔日农业省今成“科创极” 八百里巢湖之滨，坐落着全国首处以创新为主题的场馆。其中“人造太阳”、强磁场装置、量子通信等高精尖的科技创新成果令八方来宾感到震撼。 安徽，曾长期是经济发展的洼地，不沿边、不靠海、工业基础薄弱。新中国成立初期，一产占比高达75%以上。70年沧桑巨变，到2018年，安徽的三产结构已变为8.8：46.1：45.1，农业占比退居十分之一以下，昔日的农业大省成为以家电、汽车、建材等为主的工业大省和以电子信息、平板显示、智能语音等领衔的科技产业大省。 巨变的背后，是安徽把科技创新作为转型升级的突破口。早在1995年安徽就提出实施“科教兴皖”战略，2008年启动合芜蚌自主创新综合试验区建设，实施创新驱动发展之路。2013年，安徽成为全国第二个开展创新型省份建设试点工作的地区，随后获批建设合芜蚌国家自主创新示范区和系统推进全面创新改革试验区。2017年初，合肥综合性国家科学中心获批。2018年，合肥滨湖科学城挂牌成立……安徽的创新名片越擦越亮。 多年的探索实践，安徽已形成从原始创新到技术开发、成果转化、企业孵化，再到新兴产业的“全创新链”，走出一条具有安徽特色的中西部创新发展之路。 在位于合肥北郊的合肥综合性国家科学中心聚变堆园区，聚变堆主机关键系统综合研究设施工程正在紧张建设。这个由安徽省政府主要领导率队答辩争取来的国家大科学装置，与之前建成的合肥“人造太阳”装置相呼应，使安徽成为世界一流的核聚变能源创新研究基地。瞄准前沿、前瞻布局、潜心投入，安徽已建成“同步辐射光源”“全超导托卡马克”“稳态强磁场”三大国际科技前沿科研平台，并在量子通信、智能语音等科研领域达到国际领跑或并跑水平。安徽的区域创新能力已连续7年跻身全国第一方阵。 贴近产业抓创新，集聚资源搞科研，不断破除体制机制障碍，让安徽驶入创新发展快车道。安徽省统计局局长吴劲松说，党的十八大以来，安徽的新兴产业增加值不断提升。 目前，安徽的战略性新兴产业对全省工业增长的贡献率已超三分之一。在合肥，形成了国内唯一拥有6代线、8.5代线和10.5代线的新型平板显示产业基地。在蚌埠，以蚌埠玻璃工业设计研究院为核心的新型玻璃产业，诞生了我国首条具有自主知识产权的8.5代TFT-LCD玻璃基板生产线，成功研发世界最薄的0.12毫米超薄触控玻璃；在芜湖，形成了以中电科钻石飞机为代表的通用航空产业集群，飞机交付量处于国内领先行列。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（6）安徽70年：改革创新书写时代答卷 观众在2018世界制造业大会上观看蚌埠玻璃工业设计研究院展出的柔性触控玻璃（2018年5月25日摄）。 新华社记者 张端 摄 改革不停步发展活力涌 经济因改革而活，发展因创新而兴。今年上半年，安徽的经济呈现出“加速跑、趋向优”的良好态势。生产总值增长8%，居全国第6位，规模以上工业增加值增速居第6位，城镇、农村常住居民人均可支配收入增速分别居第3位和第4位。 “速度位次靠前，发展质效提升，内生动力增强。”安徽省统计局副局长赵金宝将全省上半年经济运行的亮点概括为三句话。 今年7月12日，三只松鼠股份有限公司在深交所上市。创立7年来的这家企业，不断颠覆传统销售模式，年销售额从零增加到2018年的82亿元。“创新发展是企业前进的根本。”公司党委书记潘道伟说，上市能加速公司数字化供应链战略的推进，为消费者提供更好的产品和服务。 作为民营经济的试验田，如今的江淮大地仍是一片创新创业的热土，敢闯敢试的改革火种继续在这里播撒。现在的安徽，平均每天新登记注册企业925家，每天获授44件发明专利。 经济“逆势上扬”，靠的是改革，是服务，是创新。十八大以来，安徽着力推动“放管服”改革，首创权力、责任清单的“安徽模式”，让公共服务做“加法”、中介服务做“减法”。而群众办事“最多跑一次”“互联网+政务服务”等创举，正推动安徽迈向审批事项少、办事效率高、投资环境优、市场主体和人民群众获得感强的省份之一。 唯改革才有出路，改革要常讲常新。目前，安徽正持续将一系列新的改革推向深入： ——政务服务改革。努力将省级行政许可事项减到全国最少，企业开办时间压缩至全国最短……到今年底，目标实现80%个人事项全程网办，部分事项“秒批秒办”。 ——全面创新改革。加快建设合肥综合性国家科学中心，加紧布局一批世界前沿的大科学装置，下放科技成果处置权、收益权、使用权，探索实行首席科学家全权负责制……目标建成具有国际影响力的科技创新中心和产业创新中心。 ——农业农村改革。持续深化农村承包地“三权分置”改革，稳步推进农村集体产权制度改革……围绕处理好农民与土地关系主线，努力在农村改革上继续走在前列。 增进民生福祉，乃经济发展之根本目的。居住在集中连片贫困山区金寨县大湾村村民陈泽申前年主动要求摘掉“贫困帽”。得益于党和政府的扶贫政策，他家盖了新房，流转了山地，有了公益性岗位，每年收入三四万元。此前，他将扭转命运的期望全部寄托在“改变家居风水”上面。 为了兑现“小康路上一个都不能掉队”的庄严承诺，安徽把以人民为中心的理念贯穿到经济社会发展各个领域。在实施脱贫攻坚“十大工程”等举措作用下，全省贫困人口由2012年底的679.1万人下降到2018年底的49.9万人，年均减贫100万人以上；贫困发生率由12.6%下降到0.93%；22个贫困县先后“摘帽”。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（7）安徽70年：改革创新书写时代答卷 2017年9月29日，世界首条量子保密通信干线——“京沪干线”正式开通。科研人员在中国科学技术大学先进技术研究院量子通信“京沪干线”总控中心工作。 新华社记者 刘军喜 摄 融入长三角勇于当“尖兵” 早在7年前，位于安徽滁州市的中新苏滁高新技术产业开发区便开工建设，拉开了苏皖两省共建园区的大幕。此后，园区内产业实现深度对接、集群发展，这里成为安徽融入长三角开放合作的典范。 记者近日走进苏滁高新区看到，一排排标准化厂房内，一片热火朝天的生产场面；以新加坡理念建设的邻里中心内，百姓购物、办事功能一应俱全。“截至6月底，从长三角引进的项目数占74%。”该区管委会主任杨广兰说，该区去年经营收入突破100亿元，同比增长逾30%。 2016年5月，中央将安徽合肥、芜湖、马鞍山等8市纳入长三角规划，安徽正式从地理上的长江下游进入长三角世界级城市群。去年11月，长三角一体化发展上升为国家战略。 坐拥皖江水，面向太平洋。安徽迎来历史性的重大发展机遇。中国区域经济学会顾问程必定认为，长三角上升为国家战略对安徽是百年未有的机遇，这意味着，作为长三角一员，安徽将跻身配置全球资源的开放门户、迎来更高质量一体化发展的崭新阶段。 “以前想到外地注册一个公司，交材料、核准名称、领证件，一切顺利也得跑三趟，现在真是节省了很多成本。”去年11月，合肥与星途科技服务有限公司负责人池天水，在合肥高新区的办事窗口领到了由浙江杭州工商部门颁发的营业执照。这也是该区通过长三角“一网通办”业务平台，发出的首张跨省异地登记的营业执照。 致力于要素破壁、深度融入，安徽各地也在抢抓长三角一体化机遇，争当落实国家战略的排头兵。池州、铜陵等地立足区位优势，承接长三角的产业分工，迈向价值链更高端；芜湖市提出“等高对接”，在长三角一体化中找准自身优势、谋求互补发展。 取消长三角区域高速公路省界收费站，推进长三角地区异地就医直接结算，打造长三角“安全消费大市场”……今年5月22日在芜湖举行的2019年长三角地区三省一市主要领导座谈会上，沪苏浙皖相关部门签署了10个打破分割、加速一体化的协议。 “扬皖所长、主动作为。”安徽省委主要负责人表示，安徽是长三角的重要方面军，在区位、科技、产业、生态、能源等方面具有自身的独特优势。安徽未来将把准自身定位，扬创新之长，努力打造长三角科技创新的重要策源地；扬产业之长，努力打造长三角高质量发展的重要增长极；扬区位之长，努力打造长三角世界级城市群的重要副中心；扬生态之长，努力打造长三角生态文明建设的重要样板区，在国家战略下自觉行动当好尖兵！ 新华社广州7月23日电 题：高质量发展的粤式“色香味”——广东制造业一线见闻 新华社记者毛鑫、邓瑞璇、魏玉坤 手机扫描二维码下单，经过10道全自动工序，不到2分钟就可拿到专属机器人模型。在广东拓斯达科技股份有限公司展厅，扫码定制机器人的“神操作”，令人对智能制造新水准印象深刻。 在广东， {} {"audioInfo":{"langDec":"chin","decSc":1.0,"nlpSc":0.0,"gender":"m","audioFiles":[{"decSc":1.0,"side":"c","gender":"m","bits":16,"langSc":0.0,"url":"http://10.5.3.85/heluAB.wav","vtl":135560,"codec":6,"channels":1,"rate":8000,"spkn":0,"name":"heluAB.wav","tl":211962}],"langSc":0.64250153,"lang":"chin"}} -45c0ded1-c608-4a49-981d-faf720442a59 13378149447 49 0031 8 13376907227 1 5 0 1568289879606 2004 asp fk 3686000 1 0 0 0 0 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chin chin 0.64250153 fm 0.0 3685000 3683000 1568249840233 1568249839132 中心工作。 新华社记者 刘军喜 摄 融入长三角勇于当“尖兵” 早在7年前，位于安徽滁州市的中新苏滁高新技术产业开发区便开工建设，拉开了苏皖两省共建园区的大幕。此后，园区内产业实现深度对接、集群发展，这里成为安徽融入长三角开放合作的典范。 记者近日走进苏滁高新区看到，一排排标准化厂房内，一片热火朝天的生产场面；以新加坡理念建设的邻里中心内，百姓购物、办事功能一应俱全。“截至6月底，从长三角引进的项目数占74%。”该区管委会主任杨广兰说，该区去年经营收入突破100亿元，同比增长逾30%。 2016年5月，中央将安徽合肥、芜湖、马鞍山等8市纳入长三角规划，安徽正式从地理上的长江下游进入长三角世界级城市群。去年11月，长三角一体化发展上升为国家战略。 坐拥皖江水，面向太平洋。安徽迎来历史性的重大发展机遇。中国区域经济学会顾问程必定认为，长三角上升为国家战略对安徽是百年未有的机遇，这意味着，作为长三角一员，安徽将跻身配置全球资源的开放门户、迎来更高质量一体化发展的崭新阶段。 “以前想到外地注册一个公司，交材料、核准名称、领证件，一切顺利也得跑三趟，现在真是节省了很多成本。”去年11月，合肥与星途科技服务有限公司负责人池天水，在合肥高新区的办事窗口领到了由浙江杭州工商部门颁发的营业执照。这也是该区通过长三角“一网通办”业务平台，发出的首张跨省异地登记的营业执照。 致力于要素破壁、深度融入，安徽各地也在抢抓长三角一体化机遇，争当落实国家战略的排头兵。池州、铜陵等地立足区位优势，承接长三角的产业分工，迈向价值链更高端；芜湖市提出“等高对接”，在长三角一体化中找准自身优势、谋求互补发展。 取消长三角区域高速公路省界收费站，推进长三角地区异地就医直接结算，打造长三角“安全消费大市场”……今年5月22日在芜湖举行的2019年长三角地区三省一市主要领导座谈会上，沪苏浙皖相关部门签署了10个打破分割、加速一体化的协议。 “扬皖所长、主动作为。”安徽省委主要负责人表示，安徽是长三角的重要方面军，在区位、科技、产业、生态、能源等方面具有自身的独特优势。安徽未来将把准自身定位，扬创新之长，努力打造长三角科技创新的重要策源地；扬产业之长，努力打造长三角高质量发展的重要增长极；扬区位之长，努力打造长三角世界级城市群的重要副中心；扬生态之长，努力打造长三角生态文明建设的重要样板区，在国家战略下自觉行动当好尖兵！ 新华社广州7月23日电 题：高质量发展的粤式“色香味”——广东制造业一线见闻 新华社记者毛鑫、邓瑞璇、魏玉坤 手机扫描二维码下单，经过10道全自动工序，不到2分钟就可拿到专属机器人模型。在广东拓斯达科技股份有限公司展厅，扫码定制机器人的“神操作”，令人对智能制造新水准印象深刻。 在广东，依托持续的研发投入、技术改造和体制机制创新，制造业正乘势转型升级，走向数字化、自动化、智能化，这一过程犹如烹制一桌高质量发展粤式大餐，“色香味”俱全。 “智”造增“色”：机器换人迎来热潮 原本人头攒动、环境嘈杂的生产车间，逐渐由精密的高度自动化生产线代替。在广东，“智造力”正成为制造业企业标配。 单条生产线的工人数量从160人降到51人、生产换型时间从45分钟降到9分钟、品质控制点从6个增加到108个……美的南沙工厂通过使用全智能生产线，实现效率和品质的双重提升。 “工人少了，但产量在递增，成本效率品质方面改善明显。”美的集团副总裁兼CIO张小懿说，美的通过自动化、数字化的支撑，正在编织一张工业互联网。 美的发展的背后，是广东制造业不断修炼“内功”的火热实践。 走进拓斯达位于东莞的厂区，在工业机器人装配测试车间，近百台机器人排成5列，有节奏地进行出厂前的自动测试。不久后，它们将进入更多工厂的车间，成为生产的主力军。 “智能制造引发机器换人热潮，国内工业机器人市场需求快速增长。”拓斯达产品研发中心总经理徐必业说，未来将持续增加研发投入，重点布局工业传感器、标准工艺设备等领域。 2018年起，广东大力发展智能制造：投入130亿元实施两轮工业技改三年行动计划，惠及3万多家企业，推动3000家工业企业“上云上平台”，工业机器人产量占全国1/5，先进制造业和高技术制造业增加值占规模以上工业增加值比重分别达56.4%和31.5%，制造业提质增效进展明显。 创新添“香”：从要素驱动转向创新驱动 国内研发成功“3D结构光人脸识别技术”、识别速度从0.1秒降至0.001秒、商用跨镜追踪技术……过去4年，广州云从信息科技公司主攻技术研发，成长为国内人工智能行业的佼佼者。 耀眼的成绩离不开研发支撑。云从科技总经理林明都介绍，公司研发团队已达700余人，其中80%以上拥有硕士及以上学历。该团队瞄准“卡脖子”的技术难点潜心钻研，持续突破核心技术。 云从科技的发展，是广东制造业把技术创新摆在核心位置，实现从要素驱动向创新驱动转变的缩影。 近年，广东采取多项措施增加技术有效供给，先后出台“科创12条”等政策，把企业作为科技创新的主体，并着力加强基础研究和应用基础研究，为高质量发展赋能。 数据显示，2018年，广东研发经费支出超过2500亿元，PCT国际专利申请量约占全国一半，有效发明专利量达24.9万件，稳居全国前列。区域创新综合能力排名连续两年居全国第一。 创新“引力”效果渐显，投资加速向高端产业聚集已成趋势。目前，广东规模以上工业企业总量超5万家，在全国名列前茅。国家级高新技术企业达4.5万家，近两年翻了一倍多。巴斯夫、埃克森美孚、富士康、LG等一批投资过百亿美元的项目逐一落地。 政策调“味”：加强制度供给 优化营商环境 2009年，刚从清华大学博士毕业的佘振定，带着一份“人工皮肤”项目的商业计划书南下深圳创业。 接触多家风投机构无果后，是深圳清华大学研究院相中了这个项目，决定进行孵化，并许诺研发团队可以持有项目70%的股份。 这坚定了佘振定在深圳创业的决心，他创办了深圳兰度生物科技有限公司。 后来，该团队从牛跟腱里提取高纯度胶原，制成了可媲美真实皮肤的双层“人工皮肤”，一经上市便填补了国内空白。多轮融资后，公司市值也翻了几番，达到15亿元。 “只有真正把市场作为配置创新资源的关键要素，才更有利于激发成果转化的积极性。”深圳清华大学研究院常务副院长刘伟强说。截至2018年末，该研究院已累计孵化企业2500多家，培养上市公司21家。 近年来，广东大力支持实体经济发展，以制度创新“扫清”制造业“绊脚石”，力争政府“不缺席”“不包揽”，市场“挑大梁”“说了算”。 实行个税补贴等优惠政策吸引海内外高端人才，推行“零跑动”、“秒批”、注册易等改革举措方便企业办事……一系列涉企惠企政策不断出台，持续释放各类市场主体活力。 截至2018年末，广东个体工商户和企业数量达1146万户，其中企业492万户，均稳居全国第一。其中，主营业务收入超百亿、千亿元的企业分别达276家、29家，进入世界500强的企业13家。这是2018年8月1日无人机拍摄的位于山西省朔州市的安太堡露天煤矿复垦区。新华社记者 曹阳 摄 新华社太原7月22日电 题：三晋大地起苍黄——新中国成立70年山西发展巡礼 新华社记者于振海、梁晓飞、王井怀 太行之西，黄河之东，表里山河，资源富集。 新中国成立70年来，勤劳智慧的三晋儿女在党的领导下，发扬太行精神、吕梁精神、右玉精神，在15万平方公里的土地上，建设起共和国能源重化工基地。如今，又担当起新时代能源革命排头兵重任，展开决战贫困攻坚战，朝着高质量发展之路、向着全面建成小康社会目标阔步前进。 山西，正以全新姿态挺进新时代。 当好“能源革命排头兵” 山西潞安集团石圪节煤矿曾以“艰苦奋斗、勤俭办矿”精神闻名全国。3年前，关闭了矿井，转型发展油用牡丹。前不久，其生产的牡丹籽油出口到了韩国。 石圪节“退煤种花”的同时，千里之外的千万吨级矿井同忻煤矿建成了山西首个智能化工作面，只用在地面控制室里，轻轻一点鼠标，地层深处的“乌金”就会滚滚不息地涌上地面。 新中国成立70年来，山西累计产煤190多亿吨，运往全国的煤炭130多亿吨。 山西因煤而名，也因煤而困。粗放的采矿业，经年累月，导致生态破坏、环境污染、矿难频发，经济发展患上了“煤炭依赖症”；多年来，始终走不出“煤炭市场好时，无暇转型；市场不好，无力转型”的怪圈。 关键时刻，新一届党中央为山西把脉，开出两剂良方：资源型经济转型综合配套改革和供给侧结构性改革。山西省委省政府深化“供改”与“综改”，明确了“能源革命排头兵”“资源型经济转型示范区”“内陆地区对外开放新高地”的目标定位。山西，开始走上高质量发展新征程。 尽管煤价持续高位运行，山西仍坚决淘汰落后产能，3年来关闭88座煤矿，退出落后产能8841万吨，煤炭去产能规模全国第一。煤矿数量由新中国成立后最多的4万多座，降到了1000座以内，先进产能占比提升到57%。 如今，山西1天开采的煤，相当于1949年全年的煤炭产量。通过科技创新，煤炭从燃料变原料，变身为高端润滑油、高端蜡、化妆品。 新型综合能源异军突起。风电、光伏装机总容量突破2000万千瓦，新能源装机容量进入全国前5。煤层气抽采利用量占全国的90%以上。晋电外送能力超过3800万千瓦，越来越多的清洁电力输往全国。 大同建设“氢都”，吕梁培育“数谷”，晋城发展“光谷”，阳泉打造“纳谷”……一批老煤城贴上新名片。晋能集团转型清洁能源，晋煤集团主攻燃气产业，潞安集团整合现代煤化工，阳煤集团发力煤机制造……多家老煤企密集调整主业。 全省上下推动国企改革、开发区改革、电力体制改革、医疗体制改革、营商环境改革等，一些关键领域改革迈向全国第一方阵，发展态势焕然一新。 山西率先启动企业投资项目承诺制改革，用一纸承诺代替“一箩筐材料”，企业自行办理的审批事项压减80%，项目从立项到开工时间平均缩短一半以上。 开发区成为山西转型发展的主战场。2017年山西批准新设开发区15家，2018年新设24家，今年已新设立11家。各开发区围绕先进装备制造、新能源汽车、节能环保、大数据、新材料、现代煤化工等精准招商。 今年5月，山西锦波生物医药股份有限公司与中国科学院、复旦大学共同发布人源Ⅲ型胶原蛋白，这是山西转型综改示范区催生的最新科技成果。去年一年，这里新增高新技术企业110家，初步形成电子信息装备、装备制造及汽车、生物医药及食品、新材料及加工等4个千亿级产业集群。 甩掉贫苦穷困的帽子 “人说山西好风光，地肥水美五谷香……”一曲《人说山西好风光》，唱出了山西人的家乡情。 山西，也是英雄、光荣的土地。在中国革命斗争中，留下了数不尽的光荣故事。新中国成立以来，这里孕育出了西沟精神、大寨精神、锡崖沟精神、大泉山精神…… 然而，巍巍太行，遍布干石山；绵绵吕梁，满是残垣沟壑。因土地瘠薄、干旱缺水，全国14个集中连片特困地区，太行、吕梁就占了两个，山西58个贫困县分布其间。 党的十八大以来，山西打响声势浩大、前所未有的脱贫攻坚战。 2017年9月，家住吕梁山深处、年过七旬的刘福有、杨娥子老两口，卖掉了家里的两头大犍牛，搬进了岢岚县广惠园移民新村。只有6户人家共13人的赵家洼村在民政部门销了号，拉开了山西决战深度贫困的序幕。 刘福有没花一分钱，就搬进了新楼房，后续扶持政策随之跟进。 很快，山西从7993个贫困村中，确定了3350个需要整村搬迁的深度贫困村，以“打不赢脱贫攻坚战，就对不起这块红色土地”的态度和决心，采取超常举措，啃深度贫困“硬骨头”。 到今年5月底，3350个深度贫困村已有3084个完成整体搬迁，1502个集中安置点已竣工1493个。 数万名扶贫干部奔走在太行吕梁山上。长治市扶贫办主任段志岗上任3年，跑了全市933个贫困村的一半以上。“时间紧任务重，群众眼里看着，心里盼着，不使劲不行啊。”他说。 扶贫干部们从太行精神、吕梁精神中汲取力量。在革命老区武乡县，楼则峪村第一书记闫小波进村后，挨家挨户走，一家一家问，连老太太爱看什么电视节目都记在心里。他带领村民发展电商，把当地小米卖出了好价钱。离任时，村民给他塞上自己做的鞋垫，表达对他的感激和不舍。 今年，山西将确保最后17个贫困县全部“摘帽”，22万贫困人口脱贫，确保2020年同全国一道进入全面小康社会。 绘就新的“山西好风光” 汾河，是山西最著名的河流。 党的十八大以来，山西把生态文明建设摆在更加突出地位，以环境保护倒逼经济高质量转型，积极回应人民群众的生态关切，让人们能望见星空、看见绿山、闻到花香。 山西实施大水网建设，启动占全省总面积72%的七河流域治理工程，省政府每周公布汾河流域治污情况，确保到明年全面消除劣Ⅴ类断面。太原、临汾等汾河河段再现“一川清流，两岸锦绣”。 水的命脉在山，山的灵魂在树。在支离破碎的黄土高原，有的地方“种树比养孩子还难”，生存条件恶劣。 在吕梁山区的永和县，永和关、乾坤湾等临近黄河的陡峭石质山上，现在已是绿油油一片。当地生态防护林从新中国成立初的不足5000亩，增加到现在的34万亩。 在太行山深处的壶关县，一道长达5000公里、全部由石头垒成的森林防护墙蔚为壮观，当地森林覆盖率从1978年的5%增至50%以上。 在毛乌素沙漠边缘的右玉县，新中国成立以来，历任县委书记一任接着一任干，林木绿化率达到55%，创造出让荒漠变绿洲的“右玉精神”。 几代人披荆斩棘，换来今日绿水青山。山西省森林覆盖率从新中国成立初期的2.4%提升至20.5%。全国生态气象公报显示，近20年来，山西植被生态质量改善位居全国前列。 山西曾是“千泉之省”，但由于地下水超采严重，尤其是采煤采矿破坏地下水系，造成19处岩溶大泉大部分衰减，6处断流干涸或流量锐减。去年以来，50座涉及泉域重点保护范围的煤矿，6座被列为去产能关闭矿井，2座被注销采矿证，42座办理了采矿证变更手续。 山西地下水位逐年回升。“晋祠三绝”之一的难老泉已断流25年，地下水水位距泉水出水口最高时有9层楼高，如今只差1.61米，难老泉复流在望。 山西也是蓝天保卫战的主战场。“宁可牺牲点GDP，也要把环保指标提上去。”山西以壮士断腕的勇气，向污染开刀。山西省生态环境厅数据显示，2018年山西二氧化硫、PM2.5、一氧化碳等主要污染物浓度较2013年分别下降49.23%、28.57%、36.17%。 如今，山青、水绿、空气好的山西，吸引了八方游客，旅游产业正成为山西新的经济增长极。黄河、长城、太行3条旅游公路开工建设，100个3A级乡村旅游示范村挂牌。去年，山西接待旅游人数达到5.6亿人次，旅游总收入进入全国前十。 华夏古文明，山西好风光——新时代3700万三晋儿女，为故乡骄傲！参考消息网7月25日报道外媒称，美国商务部长罗斯7月23日对彭博电视表示，特朗普政府计划在未来几周内处理美国科技企业提出的向被列入“黑名单”的华为技术有限公司出售产品的豁免申请。 路透社7月23日报道，“我们会迅速处理这些申请，”罗斯接受采访时表示，“目前已经收到35家公司提出的50多份申请，我们预计在未来几周内会有结果。” 当被追问及是否会在两周内有结果时，罗斯说，“我说的是在未来几周内。” 罗斯发表上述言论的前一天，美国总统特朗普在白宫举行的会议上向几家大型科技公司高管表示，对于美国企业提出的向华为销售产品的请求，政府会“及时”做出决定。 罗斯说，决定是否给予公司豁免是一个跨部门的程序，涉及美国商务部、国务院、国防部和能源部。 “这不是件简单的事情，但总的来说，我们遵循的原则是……从国家安全的角度来看，那些不敏感的东西，我们会以非常支持的态度来对待，”罗斯说，“那些敏感的东西则另当别论，我们必须非常、非常谨慎地处理。”据外媒报道称，印度尼西亚气象和地球物理局（BMKG）近日的监测报告指出，印度尼西亚目前很多地方正在遭遇干旱的影响，比如印度尼西亚的爪哇、峇厘和努沙登加拉大部分的地区已经正在干旱的肆虐之下，而面临长期严重旱灾的威胁，不少地区已经出现了比较严重的水资源匮乏状况。 而来自印度尼西亚气象和地球物理局的气象专家指出，除了这几个地区，印尼还有更多地区例如北亚齐、廖省、占碑，南苏门答腊、朋姑鲁，南苏拉威西南部、西/东加里曼丹和巴布亚南部等地也都面临着长期干旱的可能性。对于印度尼西亚这个热带雨林气候的热带国家来说，这样的状况对于该国的农业和居民生活可能带来明显影响。 事实上，东南亚地区的干旱并非只限于印度尼西亚，从今年春季开始，东南亚地区的泰国、菲律宾等国也先后遭遇了持续干旱的影响。这一系列的异常情况值得我们警惕——印度尼西亚等地的干旱天气或许意味着，厄尔尼诺的威力仍然停留在地球大气层中，还并未出现明显减弱！ 此前，一系列分析数据显示，从2018年秋冬季发展而来的厄尔尼诺已经出现了减弱态势，比如在6月下旬中下旬以来，热带中东太平洋上的关键区水温出现了明显降温，并且持续低于厄尔尼诺阈值（大于等于0.5摄氏度），这种情况被认为是厄尔尼诺即将减弱的征兆。 而来自美国海洋大气管理局（NOAA）的监测数据则指出，太平洋深层的海水偏暖已经减弱，这将不能再给热带中东太平洋地区的表层海水提供暖水，因此太平洋厄尔尼诺的减弱已是大势所趋。其预测认为，厄尔尼诺将在北半球的夏季（6-8月）间逐渐减弱，并且将可能在今年秋冬季转为中性状态。 尽管厄尔尼诺的减弱已是大势所趋，但此前厄尔尼诺对于大气层的影响似乎还并未减弱，比如印度尼西亚等地的干旱正是厄尔尼诺的证明——在厄尔尼诺期间，因为热带中东太平洋水温偏暖，将使得热带中东太平洋上有更多上升气流成云致雨，而西太平洋印度尼西亚等地则转为下沉气流，变为干旱少雨的状态。因此印度尼西亚等地的干旱提醒我们，太平洋地区的厄尔尼诺仍在持续捣乱，我国接下来还得小心它带来的极端天气！近日，据媒体报道，日本和韩国之间的贸易冲突已经彻底失控，最近美国的这两大盟友终于大打出手，先是日本在半导体材料上对韩国进行限制出口，随后两国摩擦越来越严重，两国甚至已放话要搞死对方，大火已烧到国防领域，有消息称两国将重新评估《军事情报保护协定》，而这将影响美军的东北亚军事联盟，就算特朗普劝阻也没用了，外界对此表示：两大盟友在敏感时刻突然摊牌，表明美国的影响力和领导力大幅下降，美国真的要头疼了。 图为在独岛对峙的日韩军舰 这一轮的日韩冲突起源于日本对于某些历史问题的处理态度刺激了敏感而自卑的韩国，韩国对日本的某些激烈举动则引发了日本对韩国高新技术企业的全面制裁。现在两国之间的冲突已经趋于白热化，韩国民间的反日呼声很高，抗议活动甚至波及了在韩的日本外交官。 在民间的各种小打小闹弄得一塌糊涂的时候，韩国高层也准备给日本以颜色。据韩国军方高层人士透露，韩方很可能会谋求“修改”日韩之间的《军事情报保护协定》。这份旨在共享日韩之间机密情报的协定是在美国框架下签署的，象征日韩“和解”并一起被纳入美国的亚太战略之中。如果韩国改动该协定成功，将动摇美国在亚太地区的立足基础。 图为首尔的抗议游行现场 尽管日本与韩国摩擦不断，但是在靠拢美国方面，双方的立场出奇的一致。由于国土狭小，国力贫弱，只有依赖域外大国的力量才能获得生存与发展的机会。由于二战结束之后的国际格局，韩国与日本便乘上了美国的顺风车，得以发展起来。通过在美国的亚太战略当中扮演前进基地与桥头堡的角色，日韩都收到了相当不错的报酬。由于体量不大，通过美国订单与承接产业转移，两国的经济成功起飞，成为了有声有色的发达国家。 韩国打破与日本的默契，寻求日韩决裂的道路，实际上是试图倒逼美国进场仲裁，利用目前被日本制裁打到无力还手的局面营造受害者形象，争取美国做出有利于自己的判决。不过，这一动摇美国在亚太地区实力根基的行为能否得到美国的谅解，目前还是个未知数。为了达成自己微不足道的一个目的而去砸掉老大家里的饭锅，怎么看都不会是个明智的决定，虽然美国的实力已经不如从前，整治一个不听话的小弟想来也不太困难。 图为韩国抗议者与防暴警察冲突 长期以来，韩国一直表现出一种极端自负又极端自卑的复杂。一直在试图摆脱宗主国可怕的文化影响力的同时，韩国却根本无法摆脱自己深受影响的既成事实，形成了极端敏感乃至有些神经质的集体性格。而过敏的后果，一般情况下都是成为笑柄。根据俄罗斯塔斯社7月24日最新报道称，就在近日，中方新一轮的国防白皮书已经发表。在这其中我们将对俄罗斯的军事关系，列在同等重要的位置，对美国的军事关系已经列居第二位，此举等于把最后一层窗户纸捅破。要知道在过去很长一段时间之内，中方跟美方虽说在很多时候观点都处于对立面，但两国在军事领域的合作，还是要远远大于对抗的。在此前我们发表的所有国防白皮书中，都把对美国的军事关系列在头等重要的位置。而近10年以来，随着中方在海空军事力量等领域全面崛起，美国人也开始将中方看成他们的头等威胁，在各个领域疯狂对我们进行打压。 面对美国人咄咄逼人的霸权主义行径，中俄这两个爱好和平的国家，唯有联起手来才能对付得了五角大楼的四处扩张。我们可以看出，如今中方在与俄罗斯的军事合作领域，已经上升到了一个很深的层次。举个例子来说，2018年俄罗斯在远东地区举行了代号为“东方-2018”的大规模战略性演习。在过去，这种演习只有俄罗斯和白俄罗斯可以派遣兵力参加，属于绝密等级。然而在“东方-2018”演习之中，我们整整动用了3000人，以及数百辆坦克装甲车辆的庞大兵力深入俄罗斯内陆，足见两国两军的战略互信等级之高。 就在近日，中俄两国的5架大型军用飞机，飞抵了韩国独岛周边的防空识别区。其中俄罗斯的一架A-50预警机，甚至不慎闯入了韩国领空，引发对方战斗机开火。仅仅从国际关系角度来看，俄罗斯此举是侵犯韩国领空的行为。然而透过现象看本质，此次中俄两国预警机和轰炸机一起集结在日本海海域，是过去从未发生过的。对此外界普遍猜测，中俄两国是在演练在冲突爆发之时，如何封锁日本海。在过去半年多的时间之内，美国一边联合多家网络巨头公司，对我方进行猛烈打压；另一方面在俄罗斯的西部边境以及叙利亚，疯狂压缩莫斯科的生存空间。一系列举动都将中俄关系推得越来越近。 如今我国在国防白皮书中，已经不继续遮掩了。众所周知的世界上三个军事力量最强的国家，一旦其中两个结合起来对 {} {"audioInfo":{"langDec":"chin","decSc":1.0,"nlpSc":0.0,"gender":"m","audioFiles":[{"decSc":1.0,"side":"c","gender":"m","bits":16,"langSc":0.0,"url":"http://10.5.3.85/heluAB.wav","vtl":135560,"codec":6,"channels":1,"rate":8000,"spkn":0,"name":"heluAB.wav","tl":211962}],"langSc":0.64250153,"lang":"chin"}} -cff43f86-ae81-4bbc-90dd-b7de39bdda1b 0086(021)77426829 82 0031 2 0086(021)60080162 886 6 0 1568230183633 329 asp fk 1615000 1 0 0 0 0 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chin chin 0.64250153 fm 0.0 1614000 1612000 1568249840256 1568249839152 展质效提升，内生动力增强。”安徽省统计局副局长赵金宝将全省上半年经济运行的亮点概括为三句话。 今年7月12日，三只松鼠股份有限公司在深交所上市。创立7年来的这家企业，不断颠覆传统销售模式，年销售额从零增加到2018年的82亿元。“创新发展是企业前进的根本。”公司党委书记潘道伟说，上市能加速公司数字化供应链战略的推进，为消费者提供更好的产品和服务。 作为民营经济的试验田，如今的江淮大地仍是一片创新创业的热土，敢闯敢试的改革火种继续在这里播撒。现在的安徽，平均每天新登记注册企业925家，每天获授44件发明专利。 经济“逆势上扬”，靠的是改革，是服务，是创新。十八大以来，安徽着力推动“放管服”改革，首创权力、责任清单的“安徽模式”，让公共服务做“加法”、中介服务做“减法”。而群众办事“最多跑一次”“互联网+政务服务”等创举，正推动安徽迈向审批事项少、办事效率高、投资环境优、市场主体和人民群众获得感强的省份之一。 唯改革才有出路，改革要常讲常新。目前，安徽正持续将一系列新的改革推向深入： ——政务服务改革。努力将省级行政许可事项减到全国最少，企业开办时间压缩至全国最短……到今年底，目标实现80%个人事项全程网办，部分事项“秒批秒办”。 ——全面创新改革。加快建设合肥综合性国家科学中心，加紧布局一批世界前沿的大科学装置，下放科技成果处置权、收益权、使用权，探索实行首席科学家全权负责制……目标建成具有国际影响力的科技创新中心和产业创新中心。 ——农业农村改革。持续深化农村承包地“三权分置”改革，稳步推进农村集体产权制度改革……围绕处理好农民与土地关系主线，努力在农村改革上继续走在前列。 增进民生福祉，乃经济发展之根本目的。居住在集中连片贫困山区金寨县大湾村村民陈泽申前年主动要求摘掉“贫困帽”。得益于党和政府的扶贫政策，他家盖了新房，流转了山地，有了公益性岗位，每年收入三四万元。此前，他将扭转命运的期望全部寄托在“改变家居风水”上面。 为了兑现“小康路上一个都不能掉队”的庄严承诺，安徽把以人民为中心的理念贯穿到经济社会发展各个领域。在实施脱贫攻坚“十大工程”等举措作用下，全省贫困人口由2012年底的679.1万人下降到2018年底的49.9万人，年均减贫100万人以上；贫困发生率由12.6%下降到0.93%；22个贫困县先后“摘帽”。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（7）安徽70年：改革创新书写时代答卷 2017年9月29日，世界首条量子保密通信干线——“京沪干线”正式开通。科研人员在中国科学技术大学先进技术研究院量子通信“京沪干线”总控中心工作。 新华社记者 刘军喜 摄 融入长三角勇于当“尖兵” 早在7年前，位于安徽滁州市的中新苏滁高新技术产业开发区便开工建设，拉开了苏皖两省共建园区的大幕。此后，园区内产业实现深度对接、集群发展，这里成为安徽融入长三角开放合作的典范。 记者近日走进苏滁高新区看到，一排排标准化厂房内，一片热火朝天的生产场面；以新加坡理念建设的邻里中心内，百姓购物、办事功能一应俱全。“截至6月底，从长三角引进的项目数占74%。”该区管委会主任杨广兰说，该区去年经营收入突破100亿元，同比增长逾30%。 2016年5月，中央将安徽合肥、芜湖、马鞍山等8市纳入长三角规划，安徽正式从地理上的长江下游进入长三角世界级城市群。去年11月，长三角一体化发展上升为国家战略。 坐拥皖江水，面向太平洋。安徽迎来历史性的重大发展机遇。中国区域经济学会顾问程必定认为，长三角上升为国家战略对安徽是百年未有的机遇，这意味着，作为长三角一员，安徽将跻身配置全球资源的开放门户、迎来更高质量一体化发展的崭新阶段。 “以前想到外地注册一个公司，交材料、核准名称、领证件，一切顺利也得跑三趟，现在真是节省了很多成本。”去年11月，合肥与星途科技服务有限公司负责人池天水，在合肥高新区的办事窗口领到了由浙江杭州工商部门颁发的营业执照。这也是该区通过长三角“一网通办”业务平台，发出的首张跨省异地登记的营业执照。 致力于要素破壁、深度融入，安徽各地也在抢抓长三角一体化机遇，争当落实国家战略的排头兵。池州、铜陵等地立足区位优势，承接长三角的产业分工，迈向价值链更高端；芜湖市提出“等高对接”，在长三角一体化中找准自身优势、谋求互补发展。 取消长三角区域高速公路省界收费站，推进长三角地区异地就医直接结算，打造长三角“安全消费大市场”……今年5月22日在芜湖举行的2019年长三角地区三省一市主要领导座谈会上，沪苏浙皖相关部门签署了10个打破分割、加速一体化的协议。 “扬皖所长、主动作为。”安徽省委主要负责人表示，安徽是长三角的重要方面军，在区位、科技、产业、生态、能源等方面具有自身的独特优势。安徽未来将把准自身定位，扬创新之长，努力打造长三角科技创新的重要策源地；扬产业之长，努力打造长三角高质量发展的重要增长极；扬区位之长，努力打造长三角世界级城市群的重要副中心；扬生态之长，努力打造长三角生态文明建设的重要样板区，在国家战略下自觉行动当好尖兵！ 新华社广州7月23日电 题：高质量发展的粤式“色香味”——广东制造业一线见闻 新华社记者毛鑫、邓瑞璇、魏玉坤 手机扫描二维码下单，经过10道全自动工序，不到2分钟就可拿到专属机器人模型。在广东拓斯达科技股份有限公司展厅，扫码定制机器人的“神操作”，令人对智能制造新水准印象深刻。 在广东，依托持续的研发投入、技术改造和体制机制创新，制造业正乘势转型升级，走向数字化、自动化、智能化，这一过程犹如烹制一桌高质量发展粤式大餐，“色香味”俱全。 “智”造增“色”：机器换人迎来热潮 原本人头攒动、环境嘈杂的生产车间，逐渐由精密的高度自动化生产线代替。在广东，“智造力”正成为制造业企业标配。 单条生产线的工人数量从160人降到51人、生产换型时间从45分钟降到9分钟、品质控制点从6个增加到108个……美的南沙工厂通过使用全智能生产线，实现效率和品质的双重提升。 “工人少了，但产量在递增，成本效率品质方面改善明显。”美的集团副总裁兼CIO张小懿说，美的通过自动化、数字化的支撑，正在编织一张工业互联网。 美的发展的背后，是广东制造业不断修炼“内功”的火热实践。 走进拓斯达位于东莞的厂区，在工业机器人装配测试车间，近百台机器人排成5列，有节奏地进行出厂前的自动测试。不久后，它们将进入更多工厂的车间，成为生产的主力军。 “智能制造引发机器换人热潮，国内工业机器人市场需求快速增长。”拓斯达产品研发中心总经理徐必业说，未来将持续增加研发投入，重点布局工业传感器、标准工艺设备等领域。 2018年起，广东大力发展智能制造：投入130亿元实施两轮工业技改三年行动计划，惠及3万多家企业，推动3000家工业企业“上云上平台”，工业机器人产量占全国1/5，先进制造业和高技术制造业增加值占规模以上工业增加值比重分别达56.4%和31.5%，制造业提质增效进展明显。 创新添“香”：从要素驱动转向创新驱动 国内研发成功“3D结构光人脸识别技术”、识别速度从0.1秒降至0.001秒、商用跨镜追踪技术……过去4年，广州云从信息科技公司主攻技术研发，成长为国内人工智能行业的佼佼者。 耀眼的成绩离不开研发支撑。云从科技总经理林明都介绍，公司研发团队已达700余人，其中80%以上拥有硕士及以上学历。该团队瞄准“卡脖子”的技术难点潜心钻研，持续突破核心技术。 云从科技的发展，是广东制造业把技术创新摆在核心位置，实现从要素驱动向创新驱动转变的缩影。 近年，广东采取多项措施增加技术有效供给，先后出台“科创12条”等政策，把企业作为科技创新的主体，并着力加强基础研究和应用基础研究，为高质量发展赋能。 数据显示，2018年，广东研发经费支出超过2500亿元，PCT国际专利申请量约占全国一半，有效发明专利量达24.9万件，稳居全国前列。区域创新综合能力排名连续两年居全国第一。 创新“引力”效果渐显，投资加速向高端产业聚集已成趋势。目前，广东规模以上工业企业总量超5万家，在全国名列前茅。国家级高新技术企业达4.5万家，近两年翻了一倍多。巴斯夫、埃克森美孚、富士康、LG等一批投资过百亿美元的项目逐一落地。 政策调“味”：加强制度供给 优化营商环境 2009年，刚从清华大学博士毕业的佘振定，带着一份“人工皮肤”项目的商业计划书南下深圳创业。 接触多家风投机构无果后，是深圳清华大学研究院相中了这个项目，决定进行孵化，并许诺研发团队可以持有项目70%的股份。 这坚定了佘振定在深圳创业的决心，他创办了深圳兰度生物科技有限公司。 后来，该团队从牛跟腱里提取高纯度胶原，制成了可媲美真实皮肤的双层“人工皮肤”，一经上市便填补了国内空白。多轮融资后，公司市值也翻了几番，达到15亿元。 “只有真正把市场作为配置创新资源的关键要素，才更有利于激发成果转化的积极性。”深圳清华大学研究院常务副院长刘伟强说。截至2018年末，该研究院已累计孵化企业2500多家，培养上市公司21家。 近年来，广东大力支持实体经济发展，以制度创新“扫清”制造业“绊脚石”，力争政府“不缺席”“不包揽”，市场“挑大梁”“说了算”。 实行个税补贴等优惠政策吸引海内外高端人才，推行“零跑动”、“秒批”、注册易等改革举措方便企业办事……一系列涉企惠企政策不断出台，持续释放各类市场主体活力。 截至2018年末，广东个体工商户和企业数量达1146万户，其中企业492万户，均稳居全国第一。其中，主营业务收入超百亿、千亿元的企业分别达276家、29家，进入世界500强的企业13家。这是2018年8月1日无人机拍摄的位于山西省朔州 {} {"audioInfo":{"langDec":"chin","decSc":1.0,"nlpSc":0.0,"gender":"m","audioFiles":[{"decSc":1.0,"side":"c","gender":"m","bits":16,"langSc":0.0,"url":"http://10.5.3.85/heluAB.wav","vtl":135560,"codec":6,"channels":1,"rate":8000,"spkn":0,"name":"heluAB.wav","tl":211962}],"langSc":0.64250153,"lang":"chin"}} +e01a1773-bd37-40be-a1de-d7e74837a281 (0551)96116063 886 0031 5 (0551)46819921 853 4 0 1568220618904 50 asp fk 2745000 1 0 0 0 0 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chin chin 0.64250153 mm 0.0 2744000 2742000 1568249840037 1568249838749 面发展，朝着实现全体人民共同富裕不断迈进”。 经过长期艰苦奋斗，我们党在实践中形成了先富带动后富、逐步实现共同富裕的规律性认识，推动人民生活质量和社会共享水平显著提升。粮票、布票、肉票、鱼票、油票、豆腐票、副食本、工业券等百姓生活曾经离不开的票证，已经进入了历史博物馆。忍饥挨饿、缺吃少穿、生活困顿这些几千年来困扰我国人民的问题总体上一去不复返了，中国人民迎来了从温饱不足到小康富裕的伟大飞跃。 （39）实现全体人民共同富裕的宏伟目标，最终靠的是发展。发展是基础，唯有发展才能满足人民对美好生活的热切向往。没有发展，没有扎扎实实的发展成果，共同富裕就无从谈起。 要毫不动摇坚持发展是硬道理、发展应该是科学发展和高质量发展的战略思想，举全民之力推进中国特色社会主义事业，不断把“蛋糕”做大。同时，还要在不断发展的基础上把促进社会公平正义的事情做好，把不断做大的“蛋糕”分好，让社会主义制度的优越性更加充分体现出来，让实现全体人民共同富裕在广大人民现实生活中更加充分展示出来。绝不能出现“富者累巨万，而贫者食糟糠”的现象。 实现全体人民共同富裕的目标需要一个很长的历史过程，是由一个一个阶段性目标逐步达成的。我国正处于并将长期处于社会主义初级阶段，我们不能做超越阶段的事情，但也不是说在逐步实现共同富裕方面就无所作为，而是要根据现有条件把能做的事情尽量做起来，积小胜为大胜，不断朝着全体人民共同富裕的目标前进。 4.群众路线是党的生命线和根本工作路线 （40）习近平总书记指出：“群众路线是我们党的生命线和根本工作路线，是我们党永葆青春活力和战斗力的重要传家宝。”不论过去、现在和将来，我们都要坚持一切为了群众，一切依靠群众，从群众中来，到群众中去，把党的正确主张变为群众的自觉行动，把群众路线贯彻到治国理政全部活动之中。 坚持群众路线，核心的问题是党要始终保持同人民群众的血肉联系，一刻也不脱离群众。密切联系群众，是党的性质和宗旨的体现。我们党是在同人民群众的密切联系中成长、发展、壮大起来的，是靠宣传群众、组织群众、依靠群众起家，从胜利走向胜利的。党的最大政治优势是密切联系群众，党执政后的最大危险是脱离群众。能否保持党同人民群众的血肉联系，决定着党的事业的成败。要从政治的高度深刻认识密切联系群众的重要性，任何时候任何情况下，与人民群众同呼吸共命运的立场不能变，全心全意为人民服务的宗旨不能忘，坚信群众是真正英雄的历史唯物主义观点不能丢。 （41）群众路线是我们党始终坚持的根本工作方法。党的领导工作的正确方法，就是将群众意见集中起来形成正确的决策，又到群众中宣传解释，将决策化为群众的行动，并在群众实践中检验这些决策是否正确。 坚持走群众路线，绝不是喊喊口号走走过场，而是要诚心诚意、实打实做。要善于通过提出并贯彻正确的理论和路线方针政策带领人民前进，善于从人民的实践创造和发展要求中完善政策主张，善于从群众中寻找解决问题的方案和办法，使作出的决策和决策的执行充分体现民心民意。深入研究新形势下群众工作的规律和特点，把党的优良传统和新技术新手段结合起来，学会通过网络走群众路线，提高做好群众工作的本领。 贯彻党的群众路线，“知”是基础、是前提，“行”是重点、是关键。必须以知促行、以行促知，做到知行合一，既解决认识提高问题，又解决行动自觉问题，使群众路线落地稳、扎根深，融入经济社会发展全过程，贯穿到党的全部工作中。 新华社合肥7月23日电 题：安徽70年：改革创新书写时代答卷 新华社记者王正忠、杨玉华、徐海涛、姜刚 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（1）安徽70年：改革创新书写时代答卷 这是安徽凤阳小岗村（2018年9月27日无人机拍摄）。 新华社记者 张端 摄 这是一方敢为人先的热土：开启农村改革巨幕的“大包干”、闯出个体经济之路的“傻子瓜子”、冲出重围高擎自主品牌大旗的奇瑞汽车……一次次书写时代传奇。 这是一片勇于革新的天地：税费改革、医药卫生体制改革、文化体制改革、全面创新改革试验……一次次引领风气之先。 这是一处创新喷涌的泉源：全球首颗量子通信卫星上天、智能语音技术站上全球制高点、世界最薄的玻璃问世、“人造太阳”正求解人类终极能源难题……一次次刷新进步的前沿。 从新中国成立时一个落后的农业省，到今天实现农业大省、工业大省、科技大省的“三级跳”，安徽以70年的精彩蝶变书写时代答卷，以改革创新的昂扬姿态挺进新时代。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（3）安徽70年：改革创新书写时代答卷 在安徽凤阳小岗村，农民驾驶收割机收割水稻（2018年9月27日摄）。 新华社记者 刘军喜 摄 昔日农业省今成“科创极” 八百里巢湖之滨，坐落着全国首处以创新为主题的场馆。其中“人造太阳”、强磁场装置、量子通信等高精尖的科技创新成果令八方来宾感到震撼。 安徽，曾长期是经济发展的洼地，不沿边、不靠海、工业基础薄弱。新中国成立初期，一产占比高达75%以上。70年沧桑巨变，到2018年，安徽的三产结构已变为8.8：46.1：45.1，农业占比退居十分之一以下，昔日的农业大省成为以家电、汽车、建材等为主的工业大省和以电子信息、平板显示、智能语音等领衔的科技产业大省。 巨变的背后，是安徽把科技创新作为转型升级的突破口。早在1995年安徽就提出实施“科教兴皖”战略，2008年启动合芜蚌自主创新综合试验区建设，实施创新驱动发展之路。2013年，安徽成为全国第二个开展创新型省份建设试点工作的地区，随后获批建设合芜蚌国家自主创新示范区和系统推进全面创新改革试验区。2017年初，合肥综合性国家科学中心获批。2018年，合肥滨湖科学城挂牌成立……安徽的创新名片越擦越亮。 多年的探索实践，安徽已形成从原始创新到技术开发、成果转化、企业孵化，再到新兴产业的“全创新链”，走出一条具有安徽特色的中西部创新发展之路。 在位于合肥北郊的合肥综合性国家科学中心聚变堆园区，聚变堆主机关键系统综合研究设施工程正在紧张建设。这个由安徽省政府主要领导率队答辩争取来的国家大科学装置，与之前建成的合肥“人造太阳”装置相呼应，使安徽成为世界一流的核聚变能源创新研究基地。瞄准前沿、前瞻布局、潜心投入，安徽已建成“同步辐射光源”“全超导托卡马克”“稳态强磁场”三大国际科技前沿科研平台，并在量子通信、智能语音等科研领域达到国际领跑或并跑水平。安徽的区域创新能力已连续7年跻身全国第一方阵。 贴近产业抓创新，集聚资源搞科研，不断破除体制机制障碍，让安徽驶入创新发展快车道。安徽省统计局局长吴劲松说，党的十八大以来，安徽的新兴产业增加值不断提升。 目前，安徽的战略性新兴产业对全省工业增长的贡献率已超三分之一。在合肥，形成了国内唯一拥有6代线、8.5代线和10.5代线的新型平板显示产业基地。在蚌埠，以蚌埠玻璃工业设计研究院为核心的新型玻璃产业，诞生了我国首条具有自主知识产权的8.5代TFT-LCD玻璃基板生产线，成功研发世界最薄的0.12毫米超薄触控玻璃；在芜湖，形成了以中电科钻石飞机为代表的通用航空产业集群，飞机交付量处于国内领先行列。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（6）安徽70年：改革创新书写时代答卷 观众在2018世界制造业大会上观看蚌埠玻璃工业设计研究院展出的柔性触控玻璃（2018年5月25日摄）。 新华社记者 张端 摄 改革不停步发展活力涌 经济因改革而活，发展因创新而兴。今年上半年，安徽的经济呈现出“加速跑、趋向优”的良好态势。生产总值增长8%，居全国第6位，规模以上工业增加值增速居第6位，城镇、农村常住居民人均可支配收入增速分别居第3位和第4位。 “速度位次靠前，发展质效提升，内生动力增强。”安徽省统计局副局长赵金宝将全省上半年经济运行的亮点概括为三句话。 今年7月12日，三只松鼠股份有限公司在深交所上市。创立7年来的这家企业，不断颠覆传统销售模式，年销售额从零增加到2018年的82亿元。“创新发展是企业前进的根本。”公司党委书记潘道伟说，上市能加速公司数字化供应链战略的推进，为消费者提供更好的产品和服务。 作为民营经济的试验田，如今的江淮大地仍是一片创新创业的热土，敢闯敢试的改革火种继续在这里播撒。现在的安徽，平均每天新登记注册企业925家，每天获授44件发明专利。 经济“逆势上扬”，靠的是改革，是服务，是创新。十八大以来，安徽着力推动“放管服”改革，首创权力、责任清单的“安徽模式”，让公共服务做“加法”、中介服务做“减法”。而群众办事“最多跑一次”“互联网+政务服务”等创举，正推动安徽迈向审批事项少、办事效率高、投资环境优、市场主体和人民群众获得感强的省份之一。 唯改革才有出路，改革要常讲常新。目前，安徽正持续将一系列新的改革推向深入： ——政务服务改革。努力将省级行政许可事项减到全国最少，企业开办时间压缩至全国最短……到今年底，目标实现80%个人事项全程网办，部分事项“秒批秒办”。 ——全面创新改革。加快建设合肥综合性国家科学中心，加紧布局一批世界前沿的大科学装置，下放科技成果处置权、收益权、使用权，探索实行首席科学家全权负责制……目标建成具有国际影响力的科技创新中心和产业创新中心。 ——农业农村改革。持续深化农村承包地“三权分置”改革，稳步推进农村集体产权制度改革……围绕处理好农民与土地关系主线，努力在农村改革上继续走在前列。 增进民生福祉，乃经济发展之根本目的。居住在集中连片贫困山区金寨县大湾村村民陈泽申前年主动要求摘掉“贫困帽”。得益于党和政府的扶贫政策，他家盖了新房，流转了山地，有了公益性岗位，每年收入三四万元。此前，他将扭转命运的期望全部寄托在“改变家居风水”上面。 为了兑现“小康路上一个都不能掉队”的庄严承诺，安徽把以人民为中心的理念贯穿到经济社会发展各个领域。在实施脱贫攻坚“十大工程”等举措作用下，全省贫困人口由2012年底的679.1万人下降到2018年底的49.9万人，年均减贫100万人以上；贫困发生率由12.6%下降到0.93%；22个贫困县先后“摘帽”。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（7）安徽70年：改革创新书写时代答卷 2017年9月29日，世界首条量子保密通信干线——“京沪干线”正式开通。科研人员在中国科学技术大学先进技术研究院量子通信“京沪干线”总控中心工作。 新华社记者 刘军喜 摄 融入长三角勇于当“尖兵” 早在7年前，位于安徽滁州市的中新苏滁高新技术产业开发区便开工建设，拉开了苏皖两省共建园区的大幕。此后，园区内产业实现深度对接、集群发展，这里成为安徽融入长三角开放合作的典范。 记者近日走进苏滁高新区看到，一排排标准化厂房内，一片热火朝天的生产场面；以新加坡理念建设的邻里中心内，百姓购物、办事功能一应俱全。“截至6月底，从长三角引进的项目数占74%。”该区管委会主任杨广兰说，该区去年经营收入突破100亿元，同比增长逾30%。 2016年5月，中央将安徽合肥、芜湖、马鞍山等8市纳入长三角规划，安徽正式从地理上的长江下游进入长三角世界级城市群。去年11月，长三角一体化发展上升为国家战略。 坐拥皖江水，面向太平洋。安徽迎来历史性的重大发展机遇。中国区域经济学会顾问程必定认为，长三角上升为国家战略对安徽是百年未有的机遇，这意味着，作为长三角一员，安徽将跻身配置全球资源的开放门户、迎来更高质量一体化发展的崭新阶段。 “以前想到外地注册一个公司，交材料、核准名称、领证件，一切顺利也得跑三趟，现在真是节省了很多成本。”去年11月，合肥与星途科技服务有限公司负责人池天水，在合肥高新区的办事窗口领到了由浙江杭州工商部门颁发的营业执照。这也是该区通过长三角“一网通办”业务平台，发出的首张跨省异地登记的营业执照。 致力于要素破壁、深度融入，安徽各地也在抢抓长三角一体化机遇，争当落实国家战略的排头兵。池州、铜陵等地立足区位优势，承接长三角的产业分工，迈向价值链更高端；芜湖市提出“等高对接”，在长三角一体化中找准自身优势、谋求互补发展。 取消长三角区域高速公路省界收费站，推进长三角地区异地就医直接结算，打造长三角“安全消费大市场”……今年5月22日在芜湖举行的2019年长三角地区三省一市主要领导座谈会上，沪苏浙皖相关部门签署了10个打破分割、加速一体化的协议。 “扬皖所长、主动作为。”安徽省委主要负责人表示，安徽是长三角的重要方面军，在区位、科技、产业、生态、能源等方面具有自身的独特优势。安徽未来将把准自身定位，扬创新之长，努力打造长三角科技创新的重要策源地；扬产业之长，努力打造长三角高质量发展的重要增长极；扬区位之长，努力打造长三角世界级城市群的重要副中心；扬生态之长，努力打造长三角生态文明建设的重要样板区，在国家战略下自觉行动当好尖兵！ 新华社广州7月23日电 题：高质量发展的粤式“色香味”——广东制造业一线见闻 新华社记者毛鑫、邓瑞璇、魏玉坤 手机扫描二维码下单，经过10道全自动工序，不到2分钟就可拿到专属机器人模型。在广东拓斯达科技股份有限公司展厅，扫码定制机器人的“神操作”，令人对智能制造新水准印象深刻。 在广东，依托持续的研发投入、技术改造和体制机制创新，制造业正乘势转型升级，走向数字化、自动化、智能化，这一过程犹如烹制一桌高质量发展粤式大餐，“色香味”俱全。 “智”造增“色”：机器换人迎来热潮 原本人头攒动、环境嘈杂的生产车间，逐渐由精密的高度自动化生产线代替。在广东，“智造力”正成为制造业企业标配。 单条生产线的工人数量从160人降到51人、生产换型时间从45分钟降到9分钟、品质控制点从6个增加到108个……美的南沙工厂通过使用全智能生产线，实现效率和品质的双重提升。 “工人少了，但产量在递增，成本效率品质方面改善明显。”美的集团副总裁兼CIO张小懿说，美的通过自动化、数字化的支撑，正在编织一张工业互联网。 美的发展的背后，是广东制造业不断修炼“内功”的火热实践。 走进拓斯达位于东莞的厂区，在工业机器人装配测试车间，近百台机器人排成5列，有节奏地进行出厂前的自动测试。不久后，它们将进入更多工厂的车间，成为生产的主力军。 “智能制造引发机器换人热潮，国内工业机器人市场需求快速增长。”拓斯达产品研发中心总经理徐必业说，未来将持续增加研发投入，重点布局工业传感器、标准工艺设备等领域。 2018年起，广东大力发展智能制造：投入130亿元实施两轮工业技改三年行动计划，惠及3万多家企业，推动3000家工业企业“上云上平台”，工业机器人产量占全国1/5，先进制造业和高技术制造业增加值占规模以上工业增加值比重分别达56.4%和31.5%，制造业提质增效进展明显。 创新添“香”：从要素驱动转向创新驱动 国内研发成功“3D结构光人脸识别技术”、识别速度从0.1秒降至0.001秒、商用跨镜追踪技术……过去4年，广州云从信息科技公司主攻技术研发，成长为国内人工智能行业的佼佼者。 耀眼的成绩离不开研发支撑。云从科技总经理林明都介绍，公司研发团队已达700余人，其中80%以上拥有硕士及以上学历。该团队瞄准“卡脖子”的技术难点潜心钻研，持续突破核心技术。 云从科技的发展，是广东制造业把技术创新摆在核心位置，实现从要素驱动向创新驱动转变的缩影。 近年，广东采取多项措施增加技术有效供给，先后出台“科创12条”等政策，把企业作为科技创新的主体，并着力加强基础研究和应用基础研究，为高质量发展赋能。 数据显示，2018年，广东研发经费支出超过2500亿元，PCT国际专利申请量约占全国一半，有效发明专利量达24.9万件，稳居全国前列。区域创新综合能力排名连续两年居全国第一。 创新“引力”效果渐显，投资加速向高端产业聚集已成趋势。目前，广东规模以上工业企业总量超5万家，在全国名列前茅。国家级高新技术企业达4.5万家，近两年翻了一倍多。巴斯夫、埃克森美孚、富士康、LG等一批投资过百亿美元的项目逐一落地。 政策调“味”：加强制度供给 优化营商环境 2009年，刚从清华大学博士毕业的佘振定，带着一份“人工皮肤”项目的商业计划书南下深圳创业。 接触多家风投机构无果后，是深圳清华大学研究院相中了这个项目，决定进行孵化，并许诺研发团队可以持有项目70%的股份。 这坚定了佘振定在深圳创业的决心，他创办了深圳兰度生物科技有限公司。 后来，该团队从牛跟腱里提取高纯度胶原，制成了可媲美真实皮肤的双层“人工皮肤”，一经上市便填补了国内空白。多 {} {"audioInfo":{"langDec":"chin","decSc":1.0,"nlpSc":0.0,"gender":"m","audioFiles":[{"decSc":1.0,"side":"c","gender":"m","bits":16,"langSc":0.0,"url":"http://10.5.3.85/heluAB.wav","vtl":135560,"codec":6,"channels":1,"rate":8000,"spkn":0,"name":"heluAB.wav","tl":211962}],"langSc":0.64250153,"lang":"chin"}} +f72ce5cb-2ea6-423b-8c1f-6dadfd6f52e7 (0551)73382297 853 0031 4 (0551)73382297 49 9 0 1568275177770 1559 asp fk 5821000 1 0 0 0 0 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chin chin 0.64250153 mm 0.0 5820000 5818000 1568249840060 1568249838792 位，充分调动人民积极性，始终是我们党立于不败之地的强大根基。 老百姓是天，老百姓是地。中国共产党之所以能够发展壮大，中国特色社会主义之所以能够不断前进，正是因为依靠了人民。谋划发展，最了解实际情况的，是人民群众；推动改革，最大的依靠力量，也是人民群众。改革开放在认识和实践上的每一次突破和发展，改革开放中每一个新生事物的产生和发展，改革开放每一个方面经验的创造和积累，无不来自亿万人民的实践和智慧。无论遇到任何困难和挑战，只要有人民支持和参与，就没有克服不了的困难，就没有越不过去的坎，就没有完成不了的任务。 人民群众有着无尽的智慧和力量，在人民面前，我们永远是小学生。必须充分尊重人民所表达的意愿、所创造的经验、所拥有的权利、所发挥的作用，自觉拜人民为师，向能者求教，向智者问策。要把政治智慧的增长、执政本领的增强、领导艺术的提高深深扎根于人民群众的实践沃土中，不断从人民群众中吸取营养和力量。始终与人民心心相印、与人民同甘共苦、与人民团结奋斗，使全体人民都满腔热情地投身到建设祖国的美好未来和创造自己的幸福生活中去。 （37）习近平总书记指出：“时代是出卷人，我们是答卷人，人民是阅卷人。” 人民是我们党的工作的最高裁决者和最终评判者。党的执政水平和执政成效都不是由自己说了算，必须而且只能由人民来评判，最终都要看人民是否真正得到了实惠，人民生活是否真正得到了改善，人民权益是否真正得到了保障。在新时代，我们面临的挑战和问题依然严峻复杂，党面临的“赶考”远未结束。要坚持把人民拥护不拥护、赞成不赞成、高兴不高兴、答应不答应作为衡量一切工作得失的根本标准，努力向历史、向人民交出新的更加优异的答卷。 3.朝着实现全体人民共同富裕不断迈进 （38）共同富裕，是马克思主义的一个基本目标。按照马克思、恩格斯的构想，共产主义社会将彻底消除阶级之间、城乡之间、脑力劳动和体力劳动之间的对立和差别，实行各尽所能、按需分配，真正实现社会共享、实现每个人自由而全面的发展。到那时，“生产将以所有的人富裕为目的”，“所有人共同享受大家创造出来的福利”。实现共同富裕，反映了社会主义的本质要求，体现了以人民为中心的根本立场。 我们党始终带领人民为创造美好生活、实现共同富裕而不懈奋斗。毛泽东同志在新中国成立之初就提出了我国发展富强的目标，指出“这个富，是共同的富，这个强，是共同的强，大家都有份”。邓小平同志多次强调共同富裕，指出“社会主义不是少数人富起来、大多数人穷，不是那个样子。社会主义最大的优越性就是共同富裕，这是体现社会主义本质的一个东西”。江泽民同志强调：“实现共同富裕是社会主义的根本原则和本质特征，绝不能动摇。”胡锦涛同志也要求“使全体人民共享改革发展成果，使全体人民朝着共同富裕的方向稳步前进”。 进入新时代，我们走上了创造美好生活、逐步实现全体人民共同富裕的新征程。习近平总书记指出，共同富裕是中国特色社会主义的根本原则，实现共同富裕是我们党的重要使命。他还强调，“我们追求的发展是造福人民的发展，我们追求的富裕是全体人民共同富裕”，要“让发展成果更多更公平惠及全体人民，不断促进人的全面发展，朝着实现全体人民共同富裕不断迈进”。 经过长期艰苦奋斗，我们党在实践中形成了先富带动后富、逐步实现共同富裕的规律性认识，推动人民生活质量和社会共享水平显著提升。粮票、布票、肉票、鱼票、油票、豆腐票、副食本、工业券等百姓生活曾经离不开的票证，已经进入了历史博物馆。忍饥挨饿、缺吃少穿、生活困顿这些几千年来困扰我国人民的问题总体上一去不复返了，中国人民迎来了从温饱不足到小康富裕的伟大飞跃。 （39）实现全体人民共同富裕的宏伟目标，最终靠的是发展。发展是基础，唯有发展才能满足人民对美好生活的热切向往。没有发展，没有扎扎实实的发展成果，共同富裕就无从谈起。 要毫不动摇坚持发展是硬道理、发展应该是科学发展和高质量发展的战略思想，举全民之力推进中国特色社会主义事业，不断把“蛋糕”做大。同时，还要在不断发展的基础上把促进社会公平正义的事情做好，把不断做大的“蛋糕”分好，让社会主义制度的优越性更加充分体现出来，让实现全体人民共同富裕在广大人民现实生活中更加充分展示出来。绝不能出现“富者累巨万，而贫者食糟糠”的现象。 实现全体人民共同富裕的目标需要一个很长的历史过程，是由一个一个阶段性目标逐步达成的。我国正处于并将长期处于社会主义初级阶段，我们不能做超越阶段的事情，但也不是说在逐步实现共同富裕方面就无所作为，而是要根据现有条件把能做的事情尽量做起来，积小胜为大胜，不断朝着全体人民共同富裕的目标前进。 4.群众路线是党的生命线和根本工作路线 （40）习近平总书记指出：“群众路线是我们党的生命线和根本工作路线，是我们党永葆青春活力和战斗力的重要传家宝。”不论过去、现在和将来，我们都要坚持一切为了群众，一切依靠群众，从群众中来，到群众中去，把党的正确主张变为群众的自觉行动，把群众路线贯彻到治国理政全部活动之中。 坚持群众路线，核心的问题是党要始终保持同人民群众的血肉联系，一刻也不脱离群众。密切联系群众，是党的性质和宗旨的体现。我们党是在同人民群众的密切联系中成长、发展、壮大起来的，是靠宣传群众、组织群众、依靠群众起家，从胜利走向胜利的。党的最大政治优势是密切联系群众，党执政后的最大危险是脱离群众。能否保持党同人民群众的血肉联系，决定着党的事业的成败。要从政治的高度深刻认识密切联系群众的重要性，任何时候任何情况下，与人民群众同呼吸共命运的立场不能变，全心全意为人民服务的宗旨不能忘，坚信群众是真正英雄的历史唯物主义观点不能丢。 （41）群众路线是我们党始终坚持的根本工作方法。党的领导工作的正确方法，就是将群众意见集中起来形成正确的决策，又到群众中宣传解释，将决策化为群众的行动，并在群众实践中检验这些决策是否正确。 坚持走群众路线，绝不是喊喊口号走走过场，而是要诚心诚意、实打实做。要善于通过提出并贯彻正确的理论和路线方针政策带领人民前进，善于从人民的实践创造和发展要求中完善政策主张，善于从群众中寻找解决问题的方案和办法，使作出的决策和决策的执行充分体现民心民意。深入研究新形势下群众工作的规律和特点，把党的优良传统和新技术新手段结合起来，学会通过网络走群众路线，提高做好群众工作的本领。 贯彻党的群众路线，“知”是基础、是前提，“行”是重点、是关键。必须以知促行、以行促知，做到知行合一，既解决认识提高问题，又解决行动自觉问题，使群众路线落地稳、扎根深，融入经济社会发展全过程，贯穿到党的全部工作中。 新华社合肥7月23日电 题：安徽70年：改革创新书写时代答卷 新华社记者王正忠、杨玉华、徐海涛、姜刚 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（1）安徽70年：改革创新书写时代答卷 这是安徽凤阳小岗村（2018年9月27日无人机拍摄）。 新华社记者 张端 摄 这是一方敢为人先的热土：开启农村改革巨幕的“大包干”、闯出个体经济之路的“傻子瓜子”、冲出重围高擎自主品牌大旗的奇瑞汽车……一次次书写时代传奇。 这是一片勇于革新的天地：税费改革、医药卫生体制改革、文化体制改革、全面创新改革试验……一次次引领风气之先。 这是一处创新喷涌的泉源：全球首颗量子通信卫星上天、智能语音技术站上全球制高点、世界最薄的玻璃问世、“人造太阳”正求解人类终极能源难题……一次次刷新进步的前沿。 从新中国成立时一个落后的农业省，到今天实现农业大省、工业大省、科技大省的“三级跳”，安徽以70年的精彩蝶变书写时代答卷，以改革创新的昂扬姿态挺进新时代。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（3）安徽70年：改革创新书写时代答卷 在安徽凤阳小岗村，农民驾驶收割机收割水稻（2018年9月27日摄）。 新华社记者 刘军喜 摄 昔日农业省今成“科创极” 八百里巢湖之滨，坐落着全国首处以创新为主题的场馆。其中“人造太阳”、强磁场装置、量子通信等高精尖的科技创新成果令八方来宾感到震撼。 安徽，曾长期是经济发展的洼地，不沿边、不靠海、工业基础薄弱。新中国成立初期，一产占比高达75%以上。70年沧桑巨变，到2018年，安徽的三产结构已变为8.8：46.1：45.1，农业占比退居十分之一以下，昔日的农业大省成为以家电、汽车、建材等为主的工业大省和以电子信息、平板显示、智能语音等领衔的科技产业大省。 巨变的背后，是安徽把科技创新作为转型升级的突破口。早在1995年安徽就提出实施“科教兴皖”战略，2008年启动合芜蚌自主创新综合试验区建设，实施创新驱动发展之路。2013年，安徽成为全国第二个开展创新型省份建设试点工作的地区，随后获批建设合芜蚌国家自主创新示范区和系统推进全面创新改革试验区。2017年初，合肥综合性国家科学中心获批。2018年，合肥滨湖科学城挂牌成立……安徽的创新名片越擦越亮。 多年的探索实践，安徽已形成从原始创新到技术开发、成果转化、企业孵化，再到新兴产业的“全创新链”，走出一条具有安徽特色的中西部创新发展之路。 在位于合肥北郊的合肥综合性国家科学中心聚变堆园区，聚变堆主机关键系统综合研究设施工程正在紧张建设。这个由安徽省政府主要领导率队答辩争取来的国家大科学装置，与之前建成的合肥“人造太阳”装置相呼应，使安徽成为世界一流的核聚变能源创新研究基地。瞄准前沿、前瞻布局、潜心投入，安徽已建成“同步辐射光源”“全超导托卡马克”“稳态强磁场”三大国际科技前沿科研平台，并在量子通信、智能语音等科研领域达到国际领跑或并跑水平。安徽的区域创新能力已连续7年跻身全国第一方阵。 贴近产业抓创新，集聚资源搞科研，不断破除体制机制障碍，让安徽驶入创新发展快车道。安徽省统计局局长吴劲松说，党的十八大以来，安徽的新兴产业增加值不断提升。 目前，安徽的战略性新兴产业对全省工业增长的贡献率已超三分之一。在合肥，形成了国内唯一拥有6代线、8.5代线和10.5代线的新型平板显示产业基地。在蚌埠，以蚌埠玻璃工业设计研究院为核心的新型玻璃产业，诞生了我国首条具有自主知识产权的8.5代TFT-LCD玻璃基板生产线，成功研发世界最薄的0.12毫米超薄触控玻璃；在芜湖，形成了以中电科钻石飞机为代表的通用航空产业集群，飞机交付量处于国内领先行列。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（6）安徽70年：改革创新书写时代答卷 观众在2018世界制造业大会上观看蚌埠玻璃工业设计研究院展出的柔性触控玻璃（2018年5月25日摄）。 新华社记者 张端 摄 改革不停步发展活力涌 经济因改革而活，发展因创新而兴。今年上半年，安徽的经济呈现出“加速跑、趋向优”的良好态势。生产总值增长8%，居全国第6位，规模以上工业增加值增速居第6位，城镇、农村常住居民人均可支配收入增速分别居第3位和第4位。 “速度位次靠前，发展质效提升，内生动力增强。”安徽省统计局副局长赵金宝将全省上半年经济运行的亮点概括为三句话。 今年7月12日，三只松鼠股份有限公司在深交所上市。创立7年来的这家企业，不断颠覆传统销售模式，年销售额从零增加到2018年的82亿元。“创新发展是企业前进的根本。”公司党委书记潘道伟说，上市能加速公司数字化供应链战略的推进，为消费者提供更好的产品和服务。 作为民营经济的试验田，如今的江淮大地仍是一片创新创业的热土，敢闯敢试的改革火种继续在这里播撒。现在的安徽，平均每天新登记注册企业925家，每天获授44件发明专利。 经济“逆势上扬”，靠的是改革，是服务，是创新。十八大以来，安徽着力推动“放管服”改革，首创权力、责任清单的“安徽模式”，让公共服务做“加法”、中介服务做“减法”。而群众办事“最多跑一次”“互联网+政务服务”等创举，正推动安徽迈向审批事项少、办事效率高、投资环境优、市场主体和人民群众获得感强的省份之一。 唯改革才有出路，改革要常讲常新。目前，安徽正持续将一系列新的改革推向深入： ——政务服务改革。努力将省级行政许可事项减到全国最少，企业开办时间压缩至全国最短……到今年底，目标实现80%个人事项全程网办，部分事项“秒批秒办”。 ——全面创新改革。加快建设合肥综合性国家科学中心，加紧布局一批世界前沿的大科学装置，下放科技成果处置权、收益权、使用权，探索实行首席科学家全权负责制……目标建成具有国际影响力的科技创新中心和产业创新中心。 ——农业农村改革。持续深化农村承包地“三权分置”改革，稳步推进农村集体产权制度改革……围绕处理好农民与土地关系主线，努力在农村改革上继续走在前列。 增进民生福祉，乃经济发展之根本目的。居住在集中连片贫困山区金寨县大湾村村民陈泽申前年主动要求摘掉“贫困帽”。得益于党和政府的扶贫政策，他家盖了新房，流转了山地，有了公益性岗位，每年收入三四万元。此前，他将扭转命运的期望全部寄托在“改变家居风水”上面。 为了兑现“小康路上一个都不能掉队”的庄严承诺，安徽把以人民为中心的理念贯穿到经济社会发展各个领域。在实施脱贫攻坚“十大工程”等举措作用下，全省贫困人口由2012年底的679.1万人下降到2018年底的49.9万人，年均减贫100万人以上；贫困发生率由12.6%下降到0.93%；22个贫困县先后“摘帽”。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（7）安徽70年：改革创新书写时代答卷 2017年9月29日，世界首条量子保密通信干线——“京沪干线”正式开通。科研人员在中国科学技术大学先进技术研究院量子通信“京沪干线”总控中心工作。 新华社记者 刘军喜 摄 融入长三角勇于当“尖兵” 早在7年前，位于安徽滁州市的中新苏滁高新技术产业开发区便开工建设，拉开了苏皖两省共建园区的大幕。此后，园区内产业实现深度对接、集群发展，这里成为安徽融入长三角开放合作的典范。 记者近日走进苏滁高新区看到，一排排标准化厂房内，一片热火朝天的生产场面；以新加坡理念建设的邻里中心内，百姓购物、办事功能一应俱全。“截至6月底，从长三角引进的项目数占74%。”该区管委会主任杨广兰说，该区去年经营收入突破100亿元，同比增长逾30%。 2016年5月，中央将安徽合肥、芜湖、马鞍山等8市纳入长三角规划，安徽正式从地理上的长江下游进入长三角世界级城市群。去年11月，长三角一体化发展上升为国家战略。 坐拥皖江水，面向太平洋。安徽迎来历史性的重大发展机遇。中国区域经济学会顾问程必定认为，长三角上升为国家战略对安徽是百年未有的机遇，这意味着，作为长三角一员，安徽将跻身配置全球资源的开放门户、迎来更高质量一体化发展的崭新阶段。 “以前想到外地注册一个公司，交材料、核准名称、领证件，一切顺利也得跑三趟，现在真是节省了很多成本。”去年11月，合肥与星途科技服务有限公司负责人池天水，在合肥高新区的办事窗口领到了由浙江杭州工商部门颁发的营业执照。这也是该区通过长三角“一网通办”业务平台，发出的首张跨省异地登记的营业执照。 致力于要素破壁、深度融入，安徽各地也在抢抓长三角一体化机遇，争当落实国家战略的排头兵。池州、铜陵等地立足区位优势，承接长三角的产业分工，迈向价值链更高端；芜湖市提出“等高对接”，在长三角一体化中找准自身优势、谋求互补发展。 取消长三角区域高速公路省界收费站，推进长三角地区异地就医直接结算，打造长三角“安全消费大市场”……今年5月22日在芜湖举行的2019年长三角地区三省一市主要领导座谈会上，沪苏浙皖相关部门签署了10个打破分割、加速一体化的协议。 “扬皖所长、主动作为。”安徽省委主要负责人表示，安徽是长三角的重要方面军，在区位、科技、产业、生态、能源等方面具有自身的独特优势。安徽未来将把准自身定位，扬创新之长，努力打造长三角科技创新的重要策源地；扬产业之长，努力打造长三角高质量发展的重要增长极；扬区位之长，努力打造长三角世界级城市群的重要副中心；扬生态之长，努力打造长三角生态文明建设的重要样板区，在国家战略下自觉行动当好尖兵！ 新华社广州7月23日电 题：高质量发展的粤式“色香味”——广东制造业一线见闻 新华社记者毛鑫、邓瑞璇、魏玉坤 手机扫描二维码下单，经过10道全自动工序，不到2分钟就可拿到专属机器人模型。在广东拓斯达科技股份有限公司展厅，扫码定制机器人的“神操作”，令人对智能制造新水准印象深刻。 在广东，依托持续的研发投入、技术改造和体制机制创新，制造业正乘势转型升级，走向数字化、自动化、智能化，这一过程犹如烹制一桌高质量发展粤式大餐，“色香味”俱全。 “智”造增“色”：机器换人迎来热潮 原本人头攒动、环境嘈杂的生产车间，逐渐由精密的高度自动化生产线代替。在广东，“智造力”正成为制造业企业标配。 单条生产线的工人数量从160人降到51人、生产换型时间从45分钟降到9分钟、品质控制点从6个增加到108个……美的南沙工厂通过使用全智能生产线，实现效率和品质的双重提升。 “工人少了，但产量在递增，成本效率品质方面改善明显。”美的集团副总裁兼CIO张小懿说，美的通过自动化、数字化的支撑，正在编织一张工业互联网。 美的发展的背后，是广东制造业不断修炼“内功”的火热实践。 走进拓斯达位于东莞的厂区，在工业机器人装配测试车间，近百台机器人排成5列，有节奏地进行出厂前的自动测试。不久后，它们将进入更多工厂的车间，成为生产的主力军。 “智能制造引发机器换人热潮，国内工业机器人市场需求快速增长。”拓斯达产品研发中心总经理徐必业说，未来将持续增加研发投入，重点布局工业传感器、标准工艺设备等领域。 2018年起，广东大力发展智能制造：投入130亿元实施两轮工业技改三年行动计划，惠及3万多家企业，推动3000家工业企业“上云上平台”，工业机器人产量占全国1/5，先进制造业和高技术制造业增加值占规模以上工业增加值比重分别达56.4%和31.5%，制造业提质增效进展明显。 创新添“香”：从要素驱动转向创新驱动 国内研发成功“3D结构光人脸识别技术”、识别速度从0.1秒降至0.001秒、商用跨镜追踪技术……过去4年，广州云从信息科技公司主攻技术研发，成长为国内人工智能行业的佼佼者。 耀眼的成绩离不开研发支撑。云从科技总经理林明都介绍，公司研发团队已达700余人，其中80%以上拥有硕士及以上学历。该团队瞄准“卡脖子”的技术难点潜心钻研，持续突破核心技术。 云从科技的发展，是广东制造业把技术创新摆在核心位置，实现从要素驱动向创新驱动转变的缩影。 近年，广东采取多项措施增加技术有效供给，先后出台“科创12条”等政策，把企业作为科技创新的主体，并着力加强基础研究和应用基础研究，为高质量发展赋能。 数据显示，2018年，广东研发经费支出超过2500亿元，PCT国际专利申请量约占全国一半，有效发明专利量达24.9万件，稳居全国前列。区域创新综合能力排名连续两年居全国第一。 创新“引力”效果渐显，投资加速向高端产业聚集已成趋势。目前，广东规模以上工业企业总量超5万家，在全国名列前茅。国家级高新技术企业达4.5万家，近两年翻了一倍多。巴斯夫、埃克森美孚、富士康、LG等一批投资过百亿美元的项目逐一落地。 政策调“味”：加强制度供给 优化营商环境 2009年，刚从清华大学博士毕业的佘振定，带着一份“人工皮肤”项目的商业计划书南下深圳创业。 接触多家风投机构无果后，是深圳清华大学研究院相中了这个项目，决定进行孵化，并许诺研发团队可以持有项目70%的股份。 这坚定了佘振定在深圳创业的决心，他创办了深圳兰度生物科技有限公司。 后来，该团队从牛跟腱里提取高纯度胶原，制成了可媲美真实皮肤的双层“人工皮肤”，一经上市便填补了国内空白。多轮融资后，公司市值也翻了几番，达到15亿元。 “只有真正把市场作为配置创新资源的关键要素，才更有利于激发成果转化的积极性。”深圳清华大学研究院常务副院长刘伟强说。截至2018年末，该研究院已累计孵化企业2500多家，培养上市公司21家。 近年来，广东大力支持实体经济发展，以制度创新“扫清”制造业“绊脚石”，力争政府“不缺席”“不包揽”，市场“挑大梁”“说了算”。 实行个税补贴等优惠政策吸引海内外高端人才，推行“零跑动”、“秒批”、注册易等改革举措方便企业办事……一系列涉企惠企政策不断出台，持续释放各类市场主体活力。 截至2018年末，广东个体工商户和企业数量达1146万户，其中企业492万户，均稳居全国第一。其中，主营业务收入超百亿、千亿元的企业分别达276家、29家，进入世界500强的企业13家。这是2018年8月1日无人机拍摄的位于山西省朔州市的安太堡露天煤矿复垦区。新华社记者 曹阳 摄 新华社太原7月22日电 题：三晋大地起苍黄——新中国成立70年山西发展巡礼 新华社记者于振海、梁晓飞、王井怀 太行之西，黄河之东，表里山河，资源富集。 新中国成立70年来，勤劳智慧的三晋儿女在党的领导下，发扬太行精神、吕梁精神、右玉精神，在15万平方公里的土地上，建设起共和国能源重化工基地。如今，又担当起新时代能源革命排头兵重任，展开决战贫困攻坚战，朝着高质量发展之路、向着全面建成小康社会目标阔步前进。 山西，正以全新姿态挺进新时代。 当好“能源革命排头兵” 山西潞安集团石圪节煤矿曾以“艰苦奋斗、勤俭办矿”精神闻名全国。3年前，关闭了矿井，转型发展油用牡丹。前不久，其生产的牡丹籽油出口到了韩国。 石圪节“退煤种花”的同时，千里之外的千万吨级矿井同忻煤矿建成了山西首个智能化工作面，只用在地面控制室里，轻轻一点鼠标，地层深处的“乌金”就会滚滚不息地涌上地面。 新中国成立70年来，山西累计产煤190多亿吨，运往全国的煤炭130多亿吨。 山西因煤而名，也因煤而困。粗放的采矿业，经年累月，导致生态破坏、环境污染、矿难频发，经济发展患上了“煤炭依赖症”；多年来，始终走不出“煤炭市场好时，无暇转型；市场不好，无力转型”的怪圈。 关键时刻，新一届党中央为山西把脉，开出两剂良方：资源型经济转型综合配套改革和供给侧结构性改革。山西省委省政府深化“供改”与“综改”，明确了“能源革命排头兵”“资源型经济转型示范区”“内陆地区对外开放新高地”的目标定位。山西，开始走上高质量发展新征程。 尽管煤价持续高位运行，山西仍坚决淘汰落后产能，3年来关闭88座煤矿，退出落后产能8841万吨，煤炭去产能规模全国第一。煤矿数量由新中国成立后最多的4万多座，降到了1000座以内，先进产能占比提升到57%。 如今，山西1天开采的煤，相当于1949年全年的煤炭产量。通过科技创新，煤炭从燃料变原料，变身为高端润滑油、高端蜡、化妆品。 新型综合能源异军突起。风电、光伏装机总容量突破2000万千瓦，新能源装机容量进入全国前5。煤层气抽采利用量占全国的90%以上。晋电外送能力超过3800万千瓦，越来越多的清洁电力输往全国。 大同建设“氢都”，吕梁培育“数谷”，晋城发展“光谷”，阳泉打造“纳谷”……一批老煤城贴上新名片。晋能集团转型清洁能源，晋煤集团主攻燃气产业，潞安集团整合现代煤化工，阳煤集团发力煤机制造……多家老煤企密集调整主业。 全省上下推动国企改革、开发区改革、电力体制改革、医疗体制改革、营商环境改革等，一些关键领域改革迈向全国第一方阵，发展态势焕然一新。 山西率先启动企业投资项目承诺制改革，用一纸承诺代替“一箩筐材料”，企业自行办理的审批事项压减80%，项目从立项到开工时间平均缩短一半以上。 开发区成为山西转型发展的主战场。2017年山西批准新设开发区15家，2018年新设24家，今年已新设立11家。各开发区围绕先进装备制造、新能源汽车、节能环保、大数据、新材料、现代煤化工等精准招商。 今年5月，山西锦波生物医药股份有限公司与中国科学院、复旦大学共同发布人源Ⅲ型胶原蛋白，这是山西转型综改示范区催生的最新科技成果。去年一年，这里新增高新技术企业110家，初步形成电子信息装备、装备制造及汽车、生物医药及食品、新材料及加工等4个千亿级产业集群。 甩掉贫苦穷困的帽子 “人说山西好风光，地肥水美五谷香……”一曲《人说山西好风光》，唱出了山西人的家乡情。 山西，也是英雄、光荣的土地。在中国革命斗争中，留下了数不尽的光荣故事。新中国成立以来，这里孕育出了西沟精神、大寨精神、锡崖沟精神、大泉山精神…… 然而，巍巍太行，遍布干石山；绵绵吕梁，满是残垣沟壑。因土地瘠薄、干旱缺水，全国14个集中连片特困地区，太行、吕梁就占了两个，山西58个贫困县分布其间。 党的十八大以来，山西打响声势浩大、前所未有的脱贫攻坚战。 2017年9月，家住吕梁山深处、年过七旬的刘福有、杨娥子老两口，卖掉了家里的两头大犍牛，搬进了岢岚县广惠园移民新村。只有6户人家共13人的赵家洼村在民政部门销了号，拉开了山西决战深度贫困的序幕。 刘福有没花一分钱，就搬进了新楼房，后续扶持政策随之跟进。 很快，山西从7993个贫困村中，确定了3350个需要整村搬迁的深度贫困村，以“打不赢脱贫攻坚战，就对不起这块红色土地”的态度和决心，采取超常举措，啃深度贫困“硬骨头”。 到今年5月底，3350个深度贫困村已有3084个完成整体搬迁，1502个集中安置点已竣工1493个。 数万名扶贫干部奔走在太行吕梁山上。长治市扶贫办主任段志岗上任3年，跑了全市933个贫困村的一半以上。“时间紧任务重，群众眼里看着，心里盼着，不使劲不行啊。”他说。 扶贫干部们从太行精神、吕梁精神中汲取力量。在革命老区武乡县，楼则峪村第一书记闫小波进村后，挨家挨户走，一家一家问，连老太太爱看什么电视节目都记在心里。他带领村民发展电商，把当地小米卖出了好价钱。离任时，村民给他塞上自己做的鞋垫，表达对他的感激和不舍。 今年，山西将确保最后17个贫困县全部“摘帽”，22万贫困人口脱贫，确保2020年同全国一道进入全面小康社会。 绘就新的“山西好风光” 汾河，是山西最著名的河流。 党的十八大以来，山西把生态文明建设摆在更加突出地位，以环境保护倒逼经济高质量转型，积极回应人民群众的生态关切，让人们能望见星空、看见绿山、闻到花香。 山西实施大水网建设，启动占全省总面积72%的七河流域治理工程，省政府每周公布汾河流域治污情况，确保到明年全面消除劣Ⅴ类断面。太原、临汾等汾河河段再现“一川清流，两岸锦绣”。 水的命脉在山，山的灵魂在树。在支离破碎的黄土高原，有的地方“种树比养孩子还难”，生存条件恶劣。 在吕梁山区的永和县，永和关、乾坤湾等临近黄河的陡峭石质山上，现在已是绿油油一片。当地生态防护林从新中国成立初的不足5000亩，增加到现在的34万亩。 在太行山深处的壶关县，一道长达5000公里、全部由石头垒成的森林防护墙蔚为壮观，当地森林覆盖率从1978年的5%增至50%以上。 在毛乌素沙漠边缘的右玉县，新中国成立以来，历任县委书记一任接着一任干，林木绿化率达到55%，创造出让荒漠变绿洲的“右玉精神”。 几代人披荆斩棘，换来今日绿水青山。山西省森林覆盖率从新中国成立初期的2.4%提升至20.5%。全国生态气象公报显示，近20年来，山西植被生态质量改善位居全国前列。 山西曾是“千泉之省”，但由于地下水超采严重，尤其是采煤采矿破坏地下水系，造成19处岩溶大泉大部分衰减，6处断流干涸或流量锐减。去年以来，50座涉及泉域重点保护范围的煤矿，6座被列为去产能关闭矿井，2座被注销采矿证，42座办理了采矿证变更手续。 山西地下水位逐年回升。“晋祠三绝”之一的难老泉已断流25年，地下水水位距泉水出水口最高时有9层楼高，如今只差1.61米，难老泉复流在望。 山西也是蓝天保卫战的主战场。“宁可牺牲点GDP，也要把环保指标提上去。”山西以壮士断腕的勇气，向污染开刀。山西省生态环境厅数据显示，2018年山西二氧化硫、PM2.5、一氧化碳等主要污染物浓度较2013年分别下降49.23%、28.57%、36.17%。 如今，山青、水绿、空气好的山西，吸引了八方游客，旅游产业正成为山西新的经济增长极。黄河、长城、太行3条旅游公路开工建设，100个3A级乡村旅游示范村挂牌。去年，山西接待旅游人数达到5.6亿人次，旅游总收入进入全国前十。 华夏古文明，山西好风光——新时代3700万三晋儿女，为故乡骄傲！参考消息网7月25日报道外媒称，美国商务部长罗斯7月23日对彭博电视表示，特朗普政府计划在未来几周内处理美国科技企业提出的向被列入“黑名单”的华为技术有限公司出售产品的豁免申请。 路透社7月23日报道，“我们会迅速处理这些申请，”罗斯接受采访时表示，“目前已经收到35家公司提出的50多份申请，我们预计在未来几周内会有结果。” 当被追问及是否会在两周内有结果时，罗斯说，“我说的是在未来几周内。” 罗斯发表上述言论的前一天，美国总统特朗普在白宫举行的会议上向几家大型科技公司高管表示，对于美国企业提出的向华为销售产品的请求，政府会“及时”做出决定。 罗斯说，决定是否给予公司豁免是一个跨部门的程序，涉及美国商务部、国务院、国防部和能源部。 “这不是件简单的事情，但总的来说，我们遵循的原则是……从国家安全的角度来看，那些不敏感的东西，我们会以非常支持的态度来对待，”罗斯说，“那些敏感的东西则另当别论，我们必须非常、非常谨慎地处理。”据外媒报道称，印度尼西亚气象和地球物理局（BMKG）近日的监测报告指出，印度尼西亚目前很多地方正在遭遇干旱的影响，比如印度尼西亚的爪哇、峇厘和努沙登加拉大部分的地区已经正在干旱的肆虐之下，而面临长期严重旱灾的威胁，不少地区已经出现了比较严重的水资源匮乏状况。 而来自印度尼西亚气象和地球物理局的气象专家指出，除了这几个地区，印尼还有更多地区例如北亚齐、廖省、占碑，南苏门答腊、朋姑鲁，南苏拉威西南部、西/东加里曼丹和巴布亚南部等地也都面临着长期干旱的可能性。对于印度尼西亚这个热带雨林气候的热带国家来说，这样的状况对于该国的农业和居民生活可能带来明显影响。 事实上，东南亚地区的干旱并非只限于印度尼西亚，从今年春季开始，东南亚地区的泰国、菲律宾等国也先后遭遇了持续干旱的影响。这一系列的异常情况值得我们警惕——印度尼西亚等地的干旱天气或许意味着，厄尔尼诺的威力仍然停留在地球大气层中，还并未出现明显减弱！ 此前，一系列分析数据显示，从2018年秋冬季发展而来的厄尔尼诺已经出现了减弱态势，比如在6月下旬中下旬以来，热带中东太平洋上的关键区水温出现了明显降温，并且持续低于厄尔尼诺阈值（大于等于0.5摄氏度），这种情况被认为是厄尔尼诺即将减弱的征兆。 而来自美国海洋大气管理局（NOAA）的监测数据则指出，太平洋深层的海水偏暖已经减弱，这将不能再给热带中东太平洋地区的表层海水提供暖水，因此太平洋厄尔尼诺的减弱已是大势所趋。其预测认为，厄尔尼诺将在北半球的夏季（6-8月）间逐渐减弱，并且将可能在今年秋冬季转为中性状态。 尽管厄尔尼诺的减弱已是大势所趋，但此前厄尔尼诺对于大气层的影响似乎还并未减弱，比如印度尼西亚等地的干旱正是厄尔尼诺的证明——在厄尔尼诺期间，因为热带中东太平洋水温偏暖，将使得热带中东太平洋上有更多上升气流成云致雨，而西太平洋印度尼西亚等地则转为下沉气流，变为干旱少雨的状态。因此印度尼西亚等地的干旱提醒我们，太平洋地区的厄尔尼诺仍在持续捣乱，我国接下来还得小心它带来的极端天气！近日，据媒体报道，日本和韩国之间的贸易冲突已经彻底失控，最近美国的这两大盟友终于大打出手，先是日本在半导体材料上对韩国进行限制出口，随后两国摩擦越来越严重，两国甚至已放话要搞死对方，大火已烧到国防领域，有消息称两国将重新评估《军事情报保护协定》，而这将影响美军的东北亚军事联盟，就算特朗普劝阻也没用了，外界对此表示：两大盟友在敏感时刻突然摊牌，表明美国的影响力和领导力大幅下降，美国真的要头疼了。 图为在独岛对峙的日韩军舰 这一轮的日韩冲突起源于日本对于某些历史问题的处理态度刺激了敏感而自卑的韩国，韩国对日本的某些激烈举动则引发了日本对韩国高新技术企业的全面制裁。现在两国之间的冲突已经趋于白热化，韩国民间的反日呼声很高，抗议活动甚至波及了在韩的日本外交官。 在民间的各种小打小闹弄得一塌糊涂的时候，韩国高层也准备给日本以颜色。据韩国军方高层人士透露，韩方很可能会谋求“修改”日韩之间的《军事情报保护协定》。这份旨在共享日韩之间机密情报的协定是在美国框架下签署的，象征日韩“和解”并一起被纳入美国的亚太战略之中。如果韩国改动该协定成功，将动摇美国在亚太地区的立足基础。 图为首尔的抗议游行现场 尽管日本与韩国摩擦不断，但是在靠拢美国方面，双方的立场出奇的一致。由于国土狭小，国力贫弱，只有依赖域外大国的力量才能获得生存与发展的机会。由于二战结束之后的国际格局，韩国与日本便乘上了美国的顺风车，得以发展起来。通过在美国的亚太战略当中扮演前进基地与桥头堡的角色，日韩都收到了相当不错的报酬。由于体量不大，通过美国订单与承接产业转移，两国的经济成功起飞，成为了有声有色的发达国家。 韩国打破与日本的默契，寻求日韩决裂的道路，实际上是试图倒逼美国进场仲裁，利用目前被日本制裁打到无力还手的局面营造受害者形象，争取美国做出有利于自己的判决。不过，这一动摇美国在亚太地区实力根基的行为能否得到美国的谅解，目前还是个未知数。为了达成自己微不足道的一个目的而去砸掉老大家里的饭锅，怎么看都不会是个明智的决定，虽然美国的实力已经不如从前，整治一个不听话的小弟想来也不太困难。 图为韩国抗议者与防暴警察冲突 长期以来，韩国一直表现出一种极端自负又极端自卑的复杂。一直在试图摆脱宗主国可怕的文化影响力的同时，韩国却根本无法摆脱自己深受影响的既成事实，形成了极端敏感乃至有些神经质的集体性格。而过敏的后果，一般情况下都是成为笑柄。根据俄罗斯塔斯社7月24日最新报道称，就在近日，中方新一轮的国防白皮书已经发表。在这其中我们将对俄罗斯的军事关系，列在同等重要的位置，对美国的军事关系已经列居第二位，此举等于把最后一层窗户纸捅破。要知道在过去很长一段时间之内，中方跟美方虽说在很多时候观点都处于对立面，但两国在军事领域的合作，还是要远远大于对抗的。在此前我们发表的所有国防白皮书中，都把对美国的军事关系列在头等重要的位置。而近10年以来，随着中方在海空军事力量等领域全面崛起，美国人也开始将中方看成他们的头等威胁，在各个领域疯狂对我们进行打压。 面对美国人咄咄逼人的霸权主义行径，中俄这两个爱好和平的国家，唯有联起手来才能对付得了五角大楼的四处扩张。我们可以看出，如今中方在与俄罗斯的军事合作领域，已经上升到了一个很深的层次。举个例子来说，2018年俄罗斯在远东地区举行了代号为“东方-2018”的大规模战略性演习。在过去，这种演习只有俄罗斯和白俄罗斯可以派遣兵力参加，属于绝密等级。然而在“东方-2018”演习之中，我们整整动用了3000人，以及数百辆坦克装甲车辆的庞大兵力深入俄罗斯内陆，足见两国两军的战略互信等级之高。 就在近日，中俄两国的5架大型军用飞机，飞抵了韩国独岛周边的防空识别区。其中俄罗斯的一 {} {"audioInfo":{"langDec":"chin","decSc":1.0,"nlpSc":0.0,"gender":"m","audioFiles":[{"decSc":1.0,"side":"c","gender":"m","bits":16,"langSc":0.0,"url":"http://10.5.3.85/heluAB.wav","vtl":135560,"codec":6,"channels":1,"rate":8000,"spkn":0,"name":"heluAB.wav","tl":211962}],"langSc":0.64250153,"lang":"chin"}} +e282ecb5-9be8-4a0e-8faf-d10e535ab877 13396633307 49 0031 9 13918448986 1 7 0 1568260253193 1150 asp fk 3884000 1 0 0 0 0 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chin chin 0.64250153 ff 0.0 3883000 3881000 1568249840083 1568249838869 共产党人的奋斗精神，永远保持对人民的赤子之心，始终把人民利益摆在至高无上的地位，始终同人民想在一起、干在一起，以人民忧乐为忧乐，以人民甘苦为甘苦，努力为人民创造更美好、更幸福的生活。 2.依靠人民创造历史伟业 （35）人民是历史的创造者，人民是真正的英雄。离开了人民，我们就会一事无成。只有坚持历史唯物主义这一基本原理，才能把握历史前进的基本规律；只有按历史规律办事，才能无往而不胜。 勤劳勇敢的中国人民是中华民族生生不息、发展壮大的脊梁。回望上下五千年，波澜壮阔的中华民族发展史是中国人民书写的，博大精深的中华文明是中国人民创造的，历久弥新的中华民族精神是中国人民培育的，中华民族迎来了从站起来、富起来到强起来的伟大飞跃是中国人民奋斗出来的。必须深刻认识人民群众是历史发展和社会进步的主体力量，紧紧依靠人民创造历史伟业。 （36）坚持人民主体地位，充分调动人民积极性，始终是我们党立于不败之地的强大根基。 老百姓是天，老百姓是地。中国共产党之所以能够发展壮大，中国特色社会主义之所以能够不断前进，正是因为依靠了人民。谋划发展，最了解实际情况的，是人民群众；推动改革，最大的依靠力量，也是人民群众。改革开放在认识和实践上的每一次突破和发展，改革开放中每一个新生事物的产生和发展，改革开放每一个方面经验的创造和积累，无不来自亿万人民的实践和智慧。无论遇到任何困难和挑战，只要有人民支持和参与，就没有克服不了的困难，就没有越不过去的坎，就没有完成不了的任务。 人民群众有着无尽的智慧和力量，在人民面前，我们永远是小学生。必须充分尊重人民所表达的意愿、所创造的经验、所拥有的权利、所发挥的作用，自觉拜人民为师，向能者求教，向智者问策。要把政治智慧的增长、执政本领的增强、领导艺术的提高深深扎根于人民群众的实践沃土中，不断从人民群众中吸取营养和力量。始终与人民心心相印、与人民同甘共苦、与人民团结奋斗，使全体人民都满腔热情地投身到建设祖国的美好未来和创造自己的幸福生活中去。 （37）习近平总书记指出：“时代是出卷人，我们是答卷人，人民是阅卷人。” 人民是我们党的工作的最高裁决者和最终评判者。党的执政水平和执政成效都不是由自己说了算，必须而且只能由人民来评判，最终都要看人民是否真正得到了实惠，人民生活是否真正得到了改善，人民权益是否真正得到了保障。在新时代，我们面临的挑战和问题依然严峻复杂，党面临的“赶考”远未结束。要坚持把人民拥护不拥护、赞成不赞成、高兴不高兴、答应不答应作为衡量一切工作得失的根本标准，努力向历史、向人民交出新的更加优异的答卷。 3.朝着实现全体人民共同富裕不断迈进 （38）共同富裕，是马克思主义的一个基本目标。按照马克思、恩格斯的构想，共产主义社会将彻底消除阶级之间、城乡之间、脑力劳动和体力劳动之间的对立和差别，实行各尽所能、按需分配，真正实现社会共享、实现每个人自由而全面的发展。到那时，“生产将以所有的人富裕为目的”，“所有人共同享受大家创造出来的福利”。实现共同富裕，反映了社会主义的本质要求，体现了以人民为中心的根本立场。 我们党始终带领人民为创造美好生活、实现共同富裕而不懈奋斗。毛泽东同志在新中国成立之初就提出了我国发展富强的目标，指出“这个富，是共同的富，这个强，是共同的强，大家都有份”。邓小平同志多次强调共同富裕，指出“社会主义不是少数人富起来、大多数人穷，不是那个样子。社会主义最大的优越性就是共同富裕，这是体现社会主义本质的一个东西”。江泽民同志强调：“实现共同富裕是社会主义的根本原则和本质特征，绝不能动摇。”胡锦涛同志也要求“使全体人民共享改革发展成果，使全体人民朝着共同富裕的方向稳步前进”。 进入新时代，我们走上了创造美好生活、逐步实现全体人民共同富裕的新征程。习近平总书记指出，共同富裕是中国特色社会主义的根本原则，实现共同富裕是我们党的重要使命。他还强调，“我们追求的发展是造福人民的发展，我们追求的富裕是全体人民共同富裕”，要“让发展成果更多更公平惠及全体人民，不断促进人的全面发展，朝着实现全体人民共同富裕不断迈进”。 经过长期艰苦奋斗，我们党在实践中形成了先富带动后富、逐步实现共同富裕的规律性认识，推动人民生活质量和社会共享水平显著提升。粮票、布票、肉票、鱼票、油票、豆腐票、副食本、工业券等百姓生活曾经离不开的票证，已经进入了历史博物馆。忍饥挨饿、缺吃少穿、生活困顿这些几千年来困扰我国人民的问题总体上一去不复返了，中国人民迎来了从温饱不足到小康富裕的伟大飞跃。 （39）实现全体人民共同富裕的宏伟目标，最终靠的是发展。发展是基础，唯有发展才能满足人民对美好生活的热切向往。没有发展，没有扎扎实实的发展成果，共同富裕就无从谈起。 要毫不动摇坚持发展是硬道理、发展应该是科学发展和高质量发展的战略思想，举全民之力推进中国特色社会主义事业，不断把“蛋糕”做大。同时，还要在不断发展的基础上把促进社会公平正义的事情做好，把不断做大的“蛋糕”分好，让社会主义制度的优越性更加充分体现出来，让实现全体人民共同富裕在广大人民现实生活中更加充分展示出来。绝不能出现“富者累巨万，而贫者食糟糠”的现象。 实现全体人民共同富裕的目标需要一个很长的历史过程，是由一个一个阶段性目标逐步达成的。我国正处于并将长期处于社会主义初级阶段，我们不能做超越阶段的事情，但也不是说在逐步实现共同富裕方面就无所作为，而是要根据现有条件把能做的事情尽量做起来，积小胜为大胜，不断朝着全体人民共同富裕的目标前进。 4.群众路线是党的生命线和根本工作路线 （40）习近平总书记指出：“群众路线是我们党的生命线和根本工作路线，是我们党永葆青春活力和战斗力的重要传家宝。”不论过去、现在和将来，我们都要坚持一切为了群众，一切依靠群众，从群众中来，到群众中去，把党的正确主张变为群众的自觉行动，把群众路线贯彻到治国理政全部活动之中。 坚持群众路线，核心的问题是党要始终保持同人民群众的血肉联系，一刻也不脱离群众。密切联系群众，是党的性质和宗旨的体现。我们党是在同人民群众的密切联系中成长、发展、壮大起来的，是靠宣传群众、组织群众、依靠群众起家，从胜利走向胜利的。党的最大政治优势是密切联系群众，党执政后的最大危险是脱离群众。能否保持党同人民群众的血肉联系，决定着党的事业的成败。要从政治的高度深刻认识密切联系群众的重要性，任何时候任何情况下，与人民群众同呼吸共命运的立场不能变，全心全意为人民服务的宗旨不能忘，坚信群众是真正英雄的历史唯物主义观点不能丢。 （41）群众路线是我们党始终坚持的根本工作方法。党的领导工作的正确方法，就是将群众意见集中起来形成正确的决策，又到群众中宣传解释，将决策化为群众的行动，并在群众实践中检验这些决策是否正确。 坚持走群众路线，绝不是喊喊口号走走过场，而是要诚心诚意、实打实做。要善于通过提出并贯彻正确的理论和路线方针政策带领人民前进，善于从人民的实践创造和发展要求中完善政策主张，善于从群众中寻找解决问题的方案和办法，使作出的决策和决策的执行充分体现民心民意。深入研究新形势下群众工作的规律和特点，把党的优良传统和新技术新手段结合起来，学会通过网络走群众路线，提高做好群众工作的本领。 贯彻党的群众路线，“知”是基础、是前提，“行”是重点、是关键。必须以知促行、以行促知，做到知行合一，既解决认识提高问题，又解决行动自觉问题，使群众路线落地稳、扎根深，融入经济社会发展全过程，贯穿到党的全部工作中。 新华社合肥7月23日电 题：安徽70年：改革创新书写时代答卷 新华社记者王正忠、杨玉华、徐海涛、姜刚 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（1）安徽70年：改革创新书写时代答卷 这是安徽凤阳小岗村（2018年9月27日无人机拍摄）。 新华社记者 张端 摄 这是一方敢为人先的热土：开启农村改革巨幕的“大包干”、闯出个体经济之路的“傻子瓜子”、冲出重围高擎自主品牌大旗的奇瑞汽车……一次次书写时代传奇。 这是一片勇于革新的天地：税费改革、医药卫生体制改革、文化体制改革、全面创新改革试验……一次次引领风气之先。 这是一处创新喷涌的泉源：全球首颗量子通信卫星上天、智能语音技术站上全球制高点、世界最薄的玻璃问世、“人造太阳”正求解人类终极能源难题……一次次刷新进步的前沿。 从新中国成立时一个落后的农业省，到今天实现农业大省、工业大省、科技大省的“三级跳”，安徽以70年的精彩蝶变书写时代答卷，以改革创新的昂扬姿态挺进新时代。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（3）安徽70年：改革创新书写时代答卷 在安徽凤阳小岗村，农民驾驶收割机收割水稻（2018年9月27日摄）。 新华社记者 刘军喜 摄 昔日农业省今成“科创极” 八百里巢湖之滨，坐落着全国首处以创新为主题的场馆。其中“人造太阳”、强磁场装置、量子通信等高精尖的科技创新成果令八方来宾感到震撼。 安徽，曾长期是经济发展的洼地，不沿边、不靠海、工业基础薄弱。新中国成立初期，一产占比高达75%以上。70年沧桑巨变，到2018年，安徽的三产结构已变为8.8：46.1：45.1，农业占比退居十分之一以下，昔日的农业大省成为以家电、汽车、建材等为主的工业大省和以电子信息、平板显示、智能语音等领衔的科技产业大省。 巨变的背后，是安徽把科技创新作为转型升级的突破口。早在1995年安徽就提出实施“科教兴皖”战略，2008年启动合芜蚌自主创新综合试验区建设，实施创新驱动发展之路。2013年，安徽成为全国第二个开展创新型省份建设试点工作的地区，随后获批建设合芜蚌国家自主创新示范区和系统推进全面创新改革试验区。2017年初，合肥综合性国家科学中心获批。2018年，合肥滨湖科学城挂牌成立……安徽的创新名片越擦越亮。 多年的探索实践，安徽已形成从原始创新到技术开发、成果转化、企业孵化，再到新兴产业的“全创新链”，走出一条具有安徽特色的中西部创新发展之路。 在位于合肥北郊的合肥综合性国家科学中心聚变堆园区，聚变堆主机关键系统综合研究设施工程正在紧张建设。这个由安徽省政府主要领导率队答辩争取来的国家大科学装置，与之前建成的合肥“人造太阳”装置相呼应，使安徽成为世界一流的核聚变能源创新研究基地。瞄准前沿、前瞻布局、潜心投入，安徽已建成“同步辐射光源”“全超导托卡马克”“稳态强磁场”三大国际科技前沿科研平台，并在量子通信、智能语音等科研领域达到国际领跑或并跑水平。安徽的区域创新能力已连续7年跻身全国第一方阵。 贴近产业抓创新，集聚资源搞科研，不断破除体制机制障碍，让安徽驶入创新发展快车道。安徽省统计局局长吴劲松说，党的十八大以来，安徽的新兴产业增加值不断提升。 目前，安徽的战略性新兴产业对全省工业增长的贡献率已超三分之一。在合肥，形成了国内唯一拥有6代线、8.5代线和10.5代线的新型平板显示产业基地。在蚌埠，以蚌埠玻璃工业设计研究院为核心的新型玻璃产业，诞生了我国首条具有自主知识产权的8.5代TFT-LCD玻璃基板生产线，成功研发世界最薄的0.12毫米超薄触控玻璃；在芜湖，形成了以中电科钻石飞机为代表的通用航空产业集群，飞机交付量处于国内领先行列。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（6）安徽70年：改革创新书写时代答卷 观众在2018世界制造业大会上观看蚌埠玻璃工业设计研究院展出的柔性触控玻璃（2018年5月25日摄）。 新华社记者 张端 摄 改革不停步发展活力涌 经济因改革而活，发展因创新而兴。今年上半年，安徽的经济呈现出“加速跑、趋向优”的良好态势。生产总值增长8%，居全国第6位，规模以上工业增加值增速居第6位，城镇、农村常住居民人均可支配收入增速分别居第3位和第4位。 “速度位次靠前，发展质效提升，内生动力增强。”安徽省统计局副局长赵金宝将全省上半年经济运行的亮点概括为三句话。 今年7月12日，三只松鼠股份有限公司在深交所上市。创立7年来的这家企业，不断颠覆传统销售模式，年销售额从零增加到2018年的82亿元。“创新发展是企业前进的根本。”公司党委书记潘道伟说，上市能加速公司数字化供应链战略的推进，为消费者提供更好的产品和服务。 作为民营经济的试验田，如今的江淮大地仍是一片创新创业的热土，敢闯敢试的改革火种继续在这里播撒。现在的安徽，平均每天新登记注册企业925家，每天获授44件发明专利。 经济“逆势上扬”，靠的是改革，是服务，是创新。十八大以来，安徽着力推动“放管服”改革，首创权力、责任清单的“安徽模式”，让公共服务做“加法”、中介服务做“减法”。而群众办事“最多跑一次”“互联网+政务服务”等创举，正推动安徽迈向审批事项少、办事效率高、投资环境优、市场主体和人民群众获得感强的省份之一。 唯改革才有出路，改革要常讲常新。目前，安徽正持续将一系列新的改革推向深入： ——政务服务改革。努力将省级行政许可事项减到全国最少，企业开办时间压缩至全国最短……到今年底，目标实现80%个人事项全程网办，部分事项“秒批秒办”。 ——全面创新改革。加快建设合肥综合性国家科学中心，加紧布局一批世界前沿的大科学装置，下放科技成果处置权、收益权、使用权，探索实行首席科学家全权负责制……目标建成具有国际影响力的科技创新中心和产业创新中心。 ——农业农村改革。持续深化农村承包地“三权分置”改革，稳步推进农村集体产权制度改革……围绕处理好农民与土地关系主线，努力在农村改革上继续走在前列。 增进民生福祉，乃经济发展之根本目的。居住在集中连片贫困山区金寨县大湾村村民陈泽申前年主动要求摘掉“贫困帽”。得益于党和政府的扶贫政策，他家盖了新房，流转了山地，有了公益性岗位，每年收入三四万元。此前，他将扭转命运的期望全部寄托在“改变家居风水”上面。 为了兑现“小康路上一个都不能掉队”的庄严承诺，安徽把以人民为中心的理念贯穿到经济社会发展各个领域。在实施脱贫攻坚“十大工程”等举措作用下，全省贫困人口由2012年底的679.1万人下降到2018年底的49.9万人，年均减贫100万人以上；贫困发生率由12.6%下降到0.93%；22个贫困县先后“摘帽”。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（7）安徽70年：改革创新书写时代答卷 2017年9月29日，世界首条量子保密通信干线——“京沪干线”正式开通。科研人员在中国科学技术大学先进技术研究院量子通信“京沪干线”总控中心工作。 新华社记者 刘军喜 摄 融入长三角勇于当“尖兵” 早在7年前，位于安徽滁州市的中新苏滁高新技术产业开发区便开工建设，拉开了苏皖两省共建园区的大幕。此后，园区内产业实现深度对接、集群发展，这里成为安徽融入长三角开放合作的典范。 记者近日走进苏滁高新区看到，一排排标准化厂房内，一片热火朝天的生产场面；以新加坡理念建设的邻里中心内，百姓购物、办事功能一应俱全。“截至6月底，从长三角引进的项目数占74%。”该区管委会主任杨广兰说，该区去年经营收入突破100亿元，同比增长逾30%。 2016年5月，中央将安徽合肥、芜湖、马鞍山等8市纳入长三角规划，安徽正式从地理上的长江下游进入长三角世界级城市群。去年11月，长三角一体化发展上升为国家战略。 坐拥皖江水，面向太平洋。安徽迎来历史性的重大发展机遇。中国区域经济学会顾问程必定认为，长三角上升为国家战略对安徽是百年未有的机遇，这意味着，作为长三角一员，安徽将跻身配置全球资源的开放门户、迎来更高质量一体化发展的崭新阶段。 “以前想到外地注册一个公司，交材料、核准名称、领证件，一切顺利也得跑三趟，现在真是节省了很多成本。”去年11月，合肥与星途科技服务有限公司负责人池天水，在合肥高新区的办事窗口领到了由浙江杭州工商部门颁发的营业执照。这也是该区通过长三角“一网通办”业务平台，发出的首张跨省异地登记的营业执照。 致力于要素破壁、深度融入，安徽各地也在抢抓长三角一体化机遇，争当落实国家战略的排头兵。池州、铜陵等地立足区位优势，承接长三角的产业分工，迈向价值链更高端；芜湖市提出“等高对接”，在长三角一体化中找准自身优势、谋求互补发展。 取消长三角区域高速公路省界收费站，推进长三角地区异地就医直接结算，打造长三角“安全消费大市场”……今年5月22日在芜湖举行的2019年长三角地区三省一市主要领导座谈会上，沪苏浙皖相关部门签署了10个打破分割、加速一体化的协议。 “扬皖所长、主动作为。”安徽省委主要负责人表示，安徽是长三角的重要方面军，在区位、科技、产业、生态、能源等方面具有自身的独特优势。安徽未来将把准自身定位，扬创新之长，努力打造长三角科技创新的重要策源地；扬产业之长，努力打造长三角高质量发展的重要增长极；扬区位之长，努力打造长三角世界级城市群的重要副中心；扬生态之长，努力打造长三角生态文明建设的重要样板区，在国家战略下自觉行动当好尖兵！ 新华社广州7月23日电 题：高质量发展的粤式“色香味”——广东制造业一线见闻 新华社记者毛鑫、邓瑞璇、魏玉坤 手机扫描二维码下单，经过10道全自动工序，不到2分钟就可拿到专属机器人模型。在广东拓斯达科技股份有限公司展厅，扫码定制机器人的“神操作”，令人对智能制造新水准印象深刻。 在广东，依托持续的研发投入、技术改造和体制机制创新，制造业正乘势转型升级，走向数字化、自动化、智能化，这一过程犹如烹制一桌高质量发展粤式大餐，“色香味”俱全。 “智”造增“色”：机器换人迎来热潮 原本人头攒动、环境嘈杂的生产车间，逐渐由精密的高度自动化生产线代替。在广东，“智造力”正成为制造业企业标配。 单条生产线的工人数量从160人降到51人、生产换型时间从45分钟降到9分钟、品质控制点从6个增加到108个……美的南沙工厂通过使用全智能生产线，实现效率和品质的双重提升。 “工人少了，但产量在递增，成本效率品质方面改善明显。”美的集团副总裁兼CIO张小懿说，美的通过自动化、数字化的支撑，正在编织一张工业互联网。 美的发展的背后，是广东制造业不断修炼“内功”的火热实践。 走进拓斯达位于东莞的厂区，在工业机器人装配测试车间，近百台机器人排成5列，有节奏地进行出厂前的自动测试。不久后，它们将进入更多工厂的车间，成为生产的主力军。 “智能制造引发机器换人热潮，国内工业机器人市场需求快速增长。”拓斯达产品研发中心总经理徐必业说，未来将持续增加研发投入，重点布局工业传感器、标准工艺设备等领域。 2018年起，广东大力发展智能制造：投入130亿元实施两轮工业技改三年行动计划，惠及3万多家企业，推动3000家工业企业“上云上平台”，工业机器人产量占全国1/5，先进制造业和高技术制造业增加值占规模以上工业增加值比重分别达56.4%和31.5%，制造业提质增效进展明显。 创新添“香”：从要素驱动转向创新驱动 国内研发成功“3D结构光人脸识别技术”、识别速度从0.1秒降至0.001秒、商用跨镜追踪技术……过去4年，广州云从信息科技公司主攻技术研发，成长为国内人工智能行业的佼佼者。 耀眼的成绩离不开研发支撑。云从科技总经理林明都介绍，公司研发团队已达700余人，其中80%以上拥有硕士及以上学历。该团队瞄准“卡脖子”的技术难点潜心钻研，持续突破核心技术。 云从科技的发展，是广东制造业把技术创新摆在核心位置，实现从要素驱动向创新驱动转变的缩影。 近年，广东采取多项措施增加技术有效供给，先后出台“科创12条”等政策，把企业作为科技创新的主体，并着力加强基础研究和应用基础研究，为高质量发展赋能。 数据显示，2018年，广东研发经费支出超过2500亿元，PCT国际专利申请量约占全国一半，有效发明专利量达24.9万件，稳居全国前列。区域创新综合能力排名连续两年居全国第一。 创新“引力”效果渐显，投资加速向高端产业聚集已成趋势。目前，广东规模以上工业企业总量超5万家，在全国名列前茅。国家级高新技术企业达4.5万家，近两年翻了一倍多。巴斯夫、埃克森美孚、富士康、LG等一批投资过百亿美元的项目逐一落地。 政策调“味”：加强制度供给 优化营商环境 2009年，刚从清华大学博士毕业的佘振定，带着一份“人工皮肤”项目的商业计划书南下深圳创业。 接触多家风投机构无果后，是深圳清华大学研究院相中了这个项目，决定进行孵化，并许诺研发团队可以持有项目70%的股份。 这坚定了佘振定在深圳创业的决心，他创办了深圳兰度生物科技有限公司。 后来，该团队从牛跟腱里提取高纯度胶原，制成了可媲美真实皮肤的双层“人工皮肤”，一经上市便填补了国内空白。多轮融资后，公司市值也翻了几番，达到15亿元。 “只有真正把市场作为配置创新资源的关键要素，才更有利于激发成果转化的积极性。”深圳清华大学研究院常务副院长刘伟强说。截至2018年末，该研究院已累计孵化企业2500多家，培养上市公司21家。 近年来，广东大力支持实体经济发展，以制度创新“扫清”制造业“绊脚石”，力争政府“不缺席”“不包揽”，市场“挑大梁”“说了算”。 实行个税补贴等优惠政策吸引海内外高端人才，推行“零跑动”、“秒批”、注册易等改革举措方便企业办事……一系列涉企惠企政策不断出台，持续释放各类市场主体活力。 截至2018年末，广东个体工商户和企业数量达1146万户，其中企业492万户，均稳居全国第一。其中，主营业务收入超百亿、千亿元的企业分别达276家、29家，进入世界500强的企业13家。这是2018年8月1日无人机拍摄的位于山西省朔州市的安太堡露天煤矿复垦区。新华社记者 曹阳 摄 新华社太原7月22日电 题：三晋大地起苍黄——新中国成立70年山西发展巡礼 新华社记者于振海、梁晓飞、王井怀 太行之西，黄河之东，表里山河，资源富集。 新中国成立70年来，勤劳智慧的三晋儿女在党的领导下，发扬太行精神、吕梁精神、右玉精神，在15万平方公里的土地上，建设起共和国能源重化工基地。如今，又担当起新时代能源革命排头兵重任，展开决战贫困攻坚战，朝着高质量发展之路、向着全面建成小康社会目标阔步前进。 山西，正以全新姿态挺进新时代。 当好“能源革命排头兵” 山西潞安集团石圪节煤矿曾以“艰苦奋斗、勤俭办矿”精神闻名全国。3年前，关闭了矿井，转型发展油用牡丹。前不久，其生产的牡丹籽油出口到了韩国。 石圪节“退煤种花”的同时，千里之外的千万吨级矿井同忻煤矿建成了山西首个智能化工作面，只用在地面控制室里，轻轻一点鼠标，地层深处的“乌金”就会滚滚不息地涌上地面。 新中国成立70年来，山西累计产煤190多亿吨，运往全国的煤炭130多亿吨。 山西因煤而名，也因煤而困。粗放的采矿业，经年累月，导致生态破坏、环境污染、矿难频发，经济发展患上了“煤炭依赖症”；多年来，始终走不出“煤炭市场好时，无暇转型；市场不好，无力转型”的怪圈。 关键时刻，新一届党中央为山西把脉，开出两剂良方：资源型经济转型综合配套改革和供给侧结构性改革。山西省委省政府深化“供改”与“综改”，明确了“能源革命排头兵”“资源型经济转型示范区”“内陆地区对外开放新高地”的目标定位。山西，开始走上高质量发展新征程。 尽管煤价持续高位运行，山西仍坚决淘汰落后产能，3年来关闭88座煤矿，退出落后产能8841万吨，煤炭去产能规模全国第一。煤矿数量由新中国成立后最多的4万多座，降到了1000座以内，先进产能占比提升到57%。 如今，山 {} {"audioInfo":{"langDec":"chin","decSc":1.0,"nlpSc":0.0,"gender":"m","audioFiles":[{"decSc":1.0,"side":"c","gender":"m","bits":16,"langSc":0.0,"url":"http://10.5.3.85/heluAB.wav","vtl":135560,"codec":6,"channels":1,"rate":8000,"spkn":0,"name":"heluAB.wav","tl":211962}],"langSc":0.64250153,"lang":"chin"}} +01e36a06-b4fd-4638-862c-2785f9e4331b 13924865616 82 0031 0 0086(021)60080162 82 6 0 1568293725356 2108 asp fk 3152000 1 0 0 0 0 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chin chin 0.64250153 ff 0.0 3151000 3149000 1568249840107 1568249838883 ……一次次引领风气之先。 这是一处创新喷涌的泉源：全球首颗量子通信卫星上天、智能语音技术站上全球制高点、世界最薄的玻璃问世、“人造太阳”正求解人类终极能源难题……一次次刷新进步的前沿。 从新中国成立时一个落后的农业省，到今天实现农业大省、工业大省、科技大省的“三级跳”，安徽以70年的精彩蝶变书写时代答卷，以改革创新的昂扬姿态挺进新时代。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（3）安徽70年：改革创新书写时代答卷 在安徽凤阳小岗村，农民驾驶收割机收割水稻（2018年9月27日摄）。 新华社记者 刘军喜 摄 昔日农业省今成“科创极” 八百里巢湖之滨，坐落着全国首处以创新为主题的场馆。其中“人造太阳”、强磁场装置、量子通信等高精尖的科技创新成果令八方来宾感到震撼。 安徽，曾长期是经济发展的洼地，不沿边、不靠海、工业基础薄弱。新中国成立初期，一产占比高达75%以上。70年沧桑巨变，到2018年，安徽的三产结构已变为8.8：46.1：45.1，农业占比退居十分之一以下，昔日的农业大省成为以家电、汽车、建材等为主的工业大省和以电子信息、平板显示、智能语音等领衔的科技产业大省。 巨变的背后，是安徽把科技创新作为转型升级的突破口。早在1995年安徽就提出实施“科教兴皖”战略，2008年启动合芜蚌自主创新综合试验区建设，实施创新驱动发展之路。2013年，安徽成为全国第二个开展创新型省份建设试点工作的地区，随后获批建设合芜蚌国家自主创新示范区和系统推进全面创新改革试验区。2017年初，合肥综合性国家科学中心获批。2018年，合肥滨湖科学城挂牌成立……安徽的创新名片越擦越亮。 多年的探索实践，安徽已形成从原始创新到技术开发、成果转化、企业孵化，再到新兴产业的“全创新链”，走出一条具有安徽特色的中西部创新发展之路。 在位于合肥北郊的合肥综合性国家科学中心聚变堆园区，聚变堆主机关键系统综合研究设施工程正在紧张建设。这个由安徽省政府主要领导率队答辩争取来的国家大科学装置，与之前建成的合肥“人造太阳”装置相呼应，使安徽成为世界一流的核聚变能源创新研究基地。瞄准前沿、前瞻布局、潜心投入，安徽已建成“同步辐射光源”“全超导托卡马克”“稳态强磁场”三大国际科技前沿科研平台，并在量子通信、智能语音等科研领域达到国际领跑或并跑水平。安徽的区域创新能力已连续7年跻身全国第一方阵。 贴近产业抓创新，集聚资源搞科研，不断破除体制机制障碍，让安徽驶入创新发展快车道。安徽省统计局局长吴劲松说，党的十八大以来，安徽的新兴产业增加值不断提升。 目前，安徽的战略性新兴产业对全省工业增长的贡献率已超三分之一。在合肥，形成了国内唯一拥有6代线、8.5代线和10.5代线的新型平板显示产业基地。在蚌埠，以蚌埠玻璃工业设计研究院为核心的新型玻璃产业，诞生了我国首条具有自主知识产权的8.5代TFT-LCD玻璃基板生产线，成功研发世界最薄的0.12毫米超薄触控玻璃；在芜湖，形成了以中电科钻石飞机为代表的通用航空产业集群，飞机交付量处于国内领先行列。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（6）安徽70年：改革创新书写时代答卷 观众在2018世界制造业大会上观看蚌埠玻璃工业设计研究院展出的柔性触控玻璃（2018年5月25日摄）。 新华社记者 张端 摄 改革不停步发展活力涌 经济因改革而活，发展因创新而兴。今年上半年，安徽的经济呈现出“加速跑、趋向优”的良好态势。生产总值增长8%，居全国第6位，规模以上工业增加值增速居第6位，城镇、农村常住居民人均可支配收入增速分别居第3位和第4位。 “速度位次靠前，发展质效提升，内生动力增强。”安徽省统计局副局长赵金宝将全省上半年经济运行的亮点概括为三句话。 今年7月12日，三只松鼠股份有限公司在深交所上市。创立7年来的这家企业，不断颠覆传统销售模式，年销售额从零增加到2018年的82亿元。“创新发展是企业前进的根本。”公司党委书记潘道伟说，上市能加速公司数字化供应链战略的推进，为消费者提供更好的产品和服务。 作为民营经济的试验田，如今的江淮大地仍是一片创新创业的热土，敢闯敢试的改革火种继续在这里播撒。现在的安徽，平均每天新登记注册企业925家，每天获授44件发明专利。 经济“逆势上扬”，靠的是改革，是服务，是创新。十八大以来，安徽着力推动“放管服”改革，首创权力、责任清单的“安徽模式”，让公共服务做“加法”、中介服务做“减法”。而群众办事“最多跑一次”“互联网+政务服务”等创举，正推动安徽迈向审批事项少、办事效率高、投资环境优、市场主体和人民群众获得感强的省份之一。 唯改革才有出路，改革要常讲常新。目前，安徽正持续将一系列新的改革推向深入： ——政务服务改革。努力将省级行政许可事项减到全国最少，企业开办时间压缩至全国最短……到今年底，目标实现80%个人事项全程网办，部分事项“秒批秒办”。 ——全面创新改革。加快建设合肥综合性国家科学中心，加紧布局一批世界前沿的大科学装置，下放科技成果处置权、收益权、使用权，探索实行首席科学家全权负责制……目标建成具有国际影响力的科技创新中心和产业创新中心。 ——农业农村改革。持续深化农村承包地“三权分置”改革，稳步推进农村集体产权制度改革……围绕处理好农民与土地关系主线，努力在农村改革上继续走在前列。 增进民生福祉，乃经济发展之根本目的。居住在集中连片贫困山区金寨县大湾村村民陈泽申前年主动要求摘掉“贫困帽”。得益于党和政府的扶贫政策，他家盖了新房，流转了山地，有了公益性岗位，每年收入三四万元。此前，他将扭转命运的期望全部寄托在“改变家居风水”上面。 为了兑现“小康路上一个都不能掉队”的庄严承诺，安徽把以人民为中心的理念贯穿到经济社会发展各个领域。在实施脱贫攻坚“十大工程”等举措作用下，全省贫困人口由2012年底的679.1万人下降到2018年底的49.9万人，年均减贫100万人以上；贫困发生率由12.6%下降到0.93%；22个贫困县先后“摘帽”。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（7）安徽70年：改革创新书写时代答卷 2017年9月29日，世界首条量子保密通信干线——“京沪干线”正式开通。科研人员在中国科学技术大学先进技术研究院量子通信“京沪干线”总控中心工作。 新华社记者 刘军喜 摄 融入长三角勇于当“尖兵” 早在7年前，位于安徽滁州市的中新苏滁高新技术产业开发区便开工建设，拉开了苏皖两省共建园区的大幕。此后，园区内产业实现深度对接、集群发展，这里成为安徽融入长三角开放合作的典范。 记者近日走进苏滁高新区看到，一排排标准化厂房内，一片热火朝天的生产场面；以新加坡理念建设的邻里中心内，百姓购物、办事功能一应俱全。“截至6月底，从长三角引进的项目数占74%。”该区管委会主任杨广兰说，该区去年经营收入突破100亿元，同比增长逾30%。 2016年5月，中央将安徽合肥、芜湖、马鞍山等8市纳入长三角规划，安徽正式从地理上的长江下游进入长三角世界级城市群。去年11月，长三角一体化发展上升为国家战略。 坐拥皖江水，面向太平洋。安徽迎来历史性的重大发展机遇。中国区域经济学会顾问程必定认为，长三角上升为国家战略对安徽是百年未有的机遇，这意味着，作为长三角一员，安徽将跻身配置全球资源的开放门户、迎来更高质量一体化发展的崭新阶段。 “以前想到外地注册一个公司，交材料、核准名称、领证件，一切顺利也得跑三趟，现在真是节省了很多成本。”去年11月，合肥与星途科技服务有限公司负责人池天水，在合肥高新区的办事窗口领到了由浙江杭州工商部门颁发的营业执照。这也是该区通过长三角“一网通办”业务平台，发出的首张跨省异地登记的营业执照。 致力于要素破壁、深度融入，安徽各地也在抢抓长三角一体化机遇，争当落实国家战略的排头兵。池州、铜陵等地立足区位优势，承接长三角的产业分工，迈向价值链更高端；芜湖市提出“等高对接”，在长三角一体化中找准自身优势、谋求互补发展。 取消长三角区域高速公路省界收费站，推进长三角地区异地就医直接结算，打造长三角“安全消费大市场”……今年5月22日在芜湖举行的2019年长三角地区三省一市主要领导座谈会上，沪苏浙皖相关部门签署了10个打破分割、加速一体化的协议。 “扬皖所长、主动作为。”安徽省委主要负责人表示，安徽是长三角的重要方面军，在区位、科技、产业、生态、能源等方面具有自身的独特优势。安徽未来将把准自身定位，扬创新之长，努力打造长三角科技创新的重要策源地；扬产业之长，努力打造长三角高质量发展的重要增长极；扬区位之长，努力打造长三角世界级城市群的重要副中心；扬生态之长，努力打造长三角生态文明建设的重要样板区，在国家战略下自觉行动当好尖兵！ 新华社广州7月23日电 题：高质量发展的粤式“色香味”——广东制造业一线见闻 新华社记者毛鑫、邓瑞璇、魏玉坤 手机扫描二维码下单，经过10道全自动工序，不到2分钟就可拿到专属机器人模型。在广东拓斯达科技股份有限公司展厅，扫码定制机器人的“神操作”，令人对智能制造新水准印象深刻。 在广东，依托持续的研发投入、技术改造和体制机制创新，制造业正乘势转型升级，走向数字化、自动化、智能化，这一过程犹如烹制一桌高质量发展粤式大餐，“色香味”俱全。 “智”造增“色”：机器换人迎来热潮 原本人头攒动、环境嘈杂的生产车间，逐渐由精密的高度自动化生产线代替。在广东，“智造力”正成为制造业企业标配。 单条生产线的工人数量从160人降到51人、生产换型时间从45分钟降到9分钟、品质控制点从6个增加到108个……美的南沙工厂通过使用全智能生产线，实现效率和品质的双重提升。 “工人少了，但产量在递增，成本效率品质方面改善明显。”美的集团副总裁兼CIO张小懿说，美的通过自动化、数字化的支撑，正在编织一张工业互联网。 美的发展的背后，是广东制造业不断修炼“内功”的火热实践。 走进拓斯达位于东莞的厂区，在工业机器人装配测试车间，近百台机器人排成5列，有节奏地进行出厂前的自动测试。不久后，它们将进入更多工厂的车间，成为生产的主力军。 “智能制造引发机器换人热潮，国内工业机器人市场需求快速增长。”拓斯达产品研发中心总经理徐必业说，未来将持续增加研发投入，重点布局工业传感器、标准工艺设备等领域。 2018年起，广东大力发展智能制造：投入130亿元实施两轮工业技改三年行动计划，惠及3万多家企业，推动3000家工业企业“上云上平台”，工业机器人产量占全国1/5，先进制造业和高技术制造业增加值占规模以上工业增加值比重分别达56.4%和31.5%，制造业提质增效进展明显。 创新添“香”：从要素驱动转向创新驱动 国内研发成功“3D结构光人脸识别技术”、识别速度从0.1秒降至0.001秒、商用跨镜追踪技术……过去4年，广州云从信息科技公司主攻技术研发，成长为国内人工智能行业的佼佼者。 耀眼的成绩离不开研发支撑。云从科技总经理林明都介绍，公司研发团队已达700余人，其中80%以上拥有硕士及以上学历。该团队瞄准“卡脖子”的技术难点潜心钻研，持续突破核心技术。 云从科技的发展，是广东制造业把技术创新摆在核心位置，实现从要素驱动向创新驱动转变的缩影。 近年，广东采取多项措施增加技术有效供给，先后出台“科创12条”等政策，把企业作为科技创新的主体，并着力加强基础研究和应用基础研究，为高质量发展赋能。 数据显示，2018年，广东研发经费支出超过2500亿元，PCT国际专利申请量约占全国一半，有效发明专利量达24.9万件，稳居全国前列。区域创新综合能力排名连续两年居全国第一。 创新“引力”效果渐显，投资加速向高端产业聚集已成趋势。目前，广东规模以上工业企业总量超5万家，在全国名列前茅。国家级高新技术企业达4.5万家，近两年翻了一倍多。巴斯夫、埃克森美孚、富士康、LG等一批投资过百亿美元的项目逐一落地。 政策调“味”：加强制度供给 优化营商环境 2009年，刚从清华大学博士毕业的佘振定，带着一份“人工皮肤”项目的商业计划书南下深圳创业。 接触多家风投机构无果后，是深圳清华大学研究院相中了这个项目，决定进行孵化，并许诺研发团队可以持有项目70%的股份。 这坚定了佘振定在深圳创业的决心，他创办了深圳兰度生物科技有限公司。 后来，该团队从牛跟腱里提取高纯度胶原，制成了可媲美真实皮肤的双层“人工皮肤”，一经上市便填补了国内空白。多轮融资后，公司市值也翻了几番，达到15亿元。 “只有真正把市场作为配置创新资源的关键要素，才更有利于激发成果转化的积极性。”深圳清华大学研究院常务副院长刘伟强说。截至2018年末，该研究院已累计孵化企业2500多家，培养上市公司21家。 近年来，广东大力支持实体经济发展，以制度创新“扫清”制造业“绊脚石”，力争政府“不缺席”“不包揽”，市场“挑大梁”“说了算”。 实行个税补贴等优惠政策吸引海内外高端人才，推行“零跑动”、“秒批”、注册易等改革举措方便企业办事……一系列涉企惠企政策不断出台，持续释放各类市场主体活力。 截至2018年末，广东个体工商户和企业数量达1146万户，其中企业492万户，均稳居全国第一。其中，主营业务收入超百亿、千亿元的企业分别达276家、29家，进入世界500强的企业13家。这是2018年8月1日无人机拍摄的位于山西省朔州市的安太堡露天煤矿复垦区。新华社记者 曹阳 摄 新华社太原7月22日电 题：三晋大地起苍黄——新中国成立70年山西发展巡礼 新华社记者于振海、梁晓飞、王井怀 太行之西，黄河之东，表里山河，资源富集。 新中国成立70年来，勤劳智慧的三晋儿女在党的领导下，发扬太行精神、吕梁精神、右玉精神，在15万平方公里的土地上，建设起共和国能源重化工基地。如今，又担当起新时代能源革命排头兵重任，展开决战贫困攻坚战，朝着高质量发展之路、向着全面建成小康社会目标阔步前进。 山西，正以全新姿态挺进新时代。 当好“能源革命排头兵” 山西潞安集团石圪节煤矿曾以“艰苦奋斗、勤俭办矿”精神闻名全国。3年前，关闭了矿井，转型发展油用牡丹。前不久，其生产的牡丹籽油出口到了韩国。 石圪节“退煤种花”的同时，千里之外的千万吨级矿井同忻煤矿建成了山西首个智能化工作面，只用在地面控制室里，轻轻一点鼠标，地层深处的“乌金”就会滚滚不息地涌上地面。 新中国成立70年来，山西累计产煤190多亿吨，运往全国的煤炭130多亿吨。 山西因煤而名，也因煤而困。粗放的采矿业，经年累月，导致生态破坏、环境污染、矿难频发，经济发展患上了“煤炭依赖症”；多年来，始终走不出“煤炭市场好时，无暇转型；市场不好，无力转型”的怪圈。 关键时刻，新一届党中央为山西把脉，开出两剂良方：资源型经济转型综合配套改革和供给侧结构性改革。山西省委省政府深化“供改”与“综改”，明确了“能源革命排头兵”“资源型经济转型示范区”“内陆地区对外开放新高地”的目标定位。山西，开始走上高质量发展新征程。 尽管煤价持续高位运行，山西仍坚决淘汰落后产能，3年来关闭88座煤矿，退出落后产能8841万吨，煤炭去产能规模全国第一。煤矿数量由新中国成立后最多的4万多座，降到了1000座以内，先进产能占比提升到57%。 如今，山西1天开采的煤，相当于1949年全年的煤炭产量。通过科技创新，煤炭从燃料变原料，变身为高端润滑油、高端蜡、化妆品。 新型综合能源异军突起。风电、光伏装机总容量突破2000万千瓦，新能源装机容量进入全国前5。煤层气抽采利用量占全国的90%以上。晋电外送能力超过3800万千瓦，越来越多的清洁电力输往全国。 大同建设“氢都”，吕梁培育“数谷”，晋城发展“光谷”，阳泉打造“纳谷”……一批老煤城贴上新名片。晋能集团转型清洁能源，晋煤集团主攻燃气产业，潞安集团整合现代煤化工，阳煤集团发力煤机制造……多家老煤企密集调整主业。 全省上下推动国企改革、开发区改革、电力体制改革、医疗体制改革、营商环境改革等，一些关键领域改革迈向全国第一方阵，发展态势焕然一新。 山西率先启动企业投资项目承诺制改革，用一纸承诺代替“一箩筐材料”，企业自行办理的审批事项压减80%，项目从立项到开工时间平均缩短一半以上。 开发区成为山西转型发展的主战场。2017年山西批准新设开发区15家，2018年新设24家，今年已新设立11家。各开发区围绕先进装备制造、新能源汽车、节能环保、大数据、新材料、现代煤化工等精准招商。 今年5月，山西锦波生物医药股份有限公司与中国科学院、复旦大学共同发布人源Ⅲ型胶原蛋白，这是山西转型综改示范区催生的最新科技成果。去年一年，这里新增高新技术企业110家，初步形成电子信息装备、装备制造及汽车、生物医药及食品、新材料及加工等4个千亿级产业集群。 甩掉贫苦穷困的帽子 “人说山西好风光，地肥水美五谷香……”一曲《人说山西好风光》，唱出了山西人的家乡情。 山西，也是英雄、光荣的土地。在中国革命斗争中，留下了数不尽的光荣故事。新中国成立以来，这里孕育出了西沟精神、大寨精神、锡崖沟精神、大泉山精神…… 然而，巍巍太行，遍布干石山；绵绵吕梁，满是残垣沟壑。因土地瘠薄、干旱缺水，全国14个集中连片特困地区，太行、吕梁就占了两个，山西58个贫困县分布其间。 党的十八大以来，山西打响声势浩大、前所未有的脱贫攻坚战。 2017年9月，家住吕梁山深处、年过七旬的刘福有、杨娥子老两口，卖掉了家里的两头大犍牛，搬进了岢岚县广惠园移民新村。只有6户人家共13人的赵家洼村在民政部门销了号，拉开了山西决战深度贫困的序幕。 刘福有没花一分钱，就搬进了新楼房，后续扶持政策随之跟进。 很快，山西从7993个贫困村中，确定了3350个需要整村搬迁的深度贫困村，以“打不赢脱贫攻坚战，就对不起这块红色土地”的态度和决心，采取超常举措，啃深度贫困“硬骨头”。 到今年5月底，3350个深度贫困村已有3084个完成整体搬迁，1502个集中安置点已竣工1493个。 数万名扶贫干部奔走在太行吕梁山上。长治市扶贫办主任段志岗上任3年，跑了全市933个贫困村的一半以上。“时间紧任务重，群众眼里看着，心里盼着，不使劲不行啊。”他说。 扶贫干部们从太行精神、吕梁精神中汲取力量。在革命老区武乡县，楼则峪村第一书记闫小波进村后，挨家挨户走，一家一家问，连老太太爱看什么电视节目都记在心里。他带领村民发展电商，把当地小米卖出了好价钱。离任时，村民给他塞上自己做的鞋垫，表达对他的感激和不舍。 今年，山西将确保最后17个贫困县全部“摘帽”，22万贫困人口脱贫，确保2020年同全国一道进入全面小康社会。 绘就新的“山西好风光” 汾河，是山西最著名的河流。 党的十八大以来，山西把生态文明建设摆在更加突出地位，以环境保护倒逼经济高质量转型，积极回应人民群众的生态关切，让人们能望见星空、看见绿山、闻到花香。 山西实施大水网建设，启动占全省总面积72%的七河流域 {} {"audioInfo":{"langDec":"chin","decSc":1.0,"nlpSc":0.0,"gender":"m","audioFiles":[{"decSc":1.0,"side":"c","gender":"m","bits":16,"langSc":0.0,"url":"http://10.5.3.85/heluAB.wav","vtl":135560,"codec":6,"channels":1,"rate":8000,"spkn":0,"name":"heluAB.wav","tl":211962}],"langSc":0.64250153,"lang":"chin"}} +a451790d-42f8-48e5-88f4-ba21118e63e6 13326037312 81 0031 8 (0551)17198025 852 2 0 1568294179731 2116 asp fk 1127000 1 0 0 0 0 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chin chin 0.64250153 ff 0.0 1126000 1124000 1568249840130 1568249839006 卷 新华社记者王正忠、杨玉华、徐海涛、姜刚 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（1）安徽70年：改革创新书写时代答卷 这是安徽凤阳小岗村（2018年9月27日无人机拍摄）。 新华社记者 张端 摄 这是一方敢为人先的热土：开启农村改革巨幕的“大包干”、闯出个体经济之路的“傻子瓜子”、冲出重围高擎自主品牌大旗的奇瑞汽车……一次次书写时代传奇。 这是一片勇于革新的天地：税费改革、医药卫生体制改革、文化体制改革、全面创新改革试验……一次次引领风气之先。 这是一处创新喷涌的泉源：全球首颗量子通信卫星上天、智能语音技术站上全球制高点、世界最薄的玻璃问世、“人造太阳”正求解人类终极能源难题……一次次刷新进步的前沿。 从新中国成立时一个落后的农业省，到今天实现农业大省、工业大省、科技大省的“三级跳”，安徽以70年的精彩蝶变书写时代答卷，以改革创新的昂扬姿态挺进新时代。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（3）安徽70年：改革创新书写时代答卷 在安徽凤阳小岗村，农民驾驶收割机收割水稻（2018年9月27日摄）。 新华社记者 刘军喜 摄 昔日农业省今成“科创极” 八百里巢湖之滨，坐落着全国首处以创新为主题的场馆。其中“人造太阳”、强磁场装置、量子通信等高精尖的科技创新成果令八方来宾感到震撼。 安徽，曾长期是经济发展的洼地，不沿边、不靠海、工业基础薄弱。新中国成立初期，一产占比高达75%以上。70年沧桑巨变，到2018年，安徽的三产结构已变为8.8：46.1：45.1，农业占比退居十分之一以下，昔日的农业大省成为以家电、汽车、建材等为主的工业大省和以电子信息、平板显示、智能语音等领衔的科技产业大省。 巨变的背后，是安徽把科技创新作为转型升级的突破口。早在1995年安徽就提出实施“科教兴皖”战略，2008年启动合芜蚌自主创新综合试验区建设，实施创新驱动发展之路。2013年，安徽成为全国第二个开展创新型省份建设试点工作的地区，随后获批建设合芜蚌国家自主创新示范区和系统推进全面创新改革试验区。2017年初，合肥综合性国家科学中心获批。2018年，合肥滨湖科学城挂牌成立……安徽的创新名片越擦越亮。 多年的探索实践，安徽已形成从原始创新到技术开发、成果转化、企业孵化，再到新兴产业的“全创新链”，走出一条具有安徽特色的中西部创新发展之路。 在位于合肥北郊的合肥综合性国家科学中心聚变堆园区，聚变堆主机关键系统综合研究设施工程正在紧张建设。这个由安徽省政府主要领导率队答辩争取来的国家大科学装置，与之前建成的合肥“人造太阳”装置相呼应，使安徽成为世界一流的核聚变能源创新研究基地。瞄准前沿、前瞻布局、潜心投入，安徽已建成“同步辐射光源”“全超导托卡马克”“稳态强磁场”三大国际科技前沿科研平台，并在量子通信、智能语音等科研领域达到国际领跑或并跑水平。安徽的区域创新能力已连续7年跻身全国第一方阵。 贴近产业抓创新，集聚资源搞科研，不断破除体制机制障碍，让安徽驶入创新发展快车道。安徽省统计局局长吴劲松说，党的十八大以来，安徽的新兴产业增加值不断提升。 目前，安徽的战略性新兴产业对全省工业增长的贡献率已超三分之一。在合肥，形成了国内唯一拥有6代线、8.5代线和10.5代线的新型平板显示产业基地。在蚌埠，以蚌埠玻璃工业设计研究院为核心的新型玻璃产业，诞生了我国首条具有自主知识产权的8.5代TFT-LCD玻璃基板生产线，成功研发世界最薄的0.12毫米超薄触控玻璃；在芜湖，形成了以中电科钻石飞机为代表的通用航空产业集群，飞机交付量处于国内领先行列。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（6）安徽70年：改革创新书写时代答卷 观众在2018世界制造业大会上观看蚌埠玻璃工业设计研究院展出的柔性触控玻璃（2018年5月25日摄）。 新华社记者 张端 摄 改革不停步发展活力涌 经济因改革而活，发展因创新而兴。今年上半年，安徽的经济呈现出“加速跑、趋向优”的良好态势。生产总值增长8%，居全国第6位，规模以上工业增加值增速居第6位，城镇、农村常住居民人均可支配收入增速分别居第3位和第4位。 “速度位次靠前，发展质效提升，内生动力增强。”安徽省统计局副局长赵金宝将全省上半年经济运行的亮点概括为三句话。 今年7月12日，三只松鼠股份有限公司在深交所上市。创立7年来的这家企业，不断颠覆传统销售模式，年销售额从零增加到2018年的82亿元。“创新发展是企业前进的根本。”公司党委书记潘道伟说，上市能加速公司数字化供应链战略的推进，为消费者提供更好的产品和服务。 作为民营经济的试验田，如今的江淮大地仍是一片创新创业的热土，敢闯敢试的改革火种继续在这里播撒。现在的安徽，平均每天新登记注册企业925家，每天获授44件发明专利。 经济“逆势上扬”，靠的是改革，是服务，是创新。十八大以来，安徽着力推动“放管服”改革，首创权力、责任清单的“安徽模式”，让公共服务做“加法”、中介服务做“减法”。而群众办事“最多跑一次”“互联网+政务服务”等创举，正推动安徽迈向审批事项少、办事效率高、投资环境优、市场主体和人民群众获得感强的省份之一。 唯改革才有出路，改革要常讲常新。目前，安徽正持续将一系列新的改革推向深入： ——政务服务改革。努力将省级行政许可事项减到全国最少，企业开办时间压缩至全国最短……到今年底，目标实现80%个人事项全程网办，部分事项“秒批秒办”。 ——全面创新改革。加快建设合肥综合性国家科学中心，加紧布局一批世界前沿的大科学装置，下放科技成果处置权、收益权、使用权，探索实行首席科学家全权负责制……目标建成具有国际影响力的科技创新中心和产业创新中心。 ——农业农村改革。持续深化农村承包地“三权分置”改革，稳步推进农村集体产权制度改革……围绕处理好农民与土地关系主线，努力在农村改革上继续走在前列。 增进民生福祉，乃经济发展之根本目的。居住在集中连片贫困山区金寨县大湾村村民陈泽申前年主动要求摘掉“贫困帽”。得益于党和政府的扶贫政策，他家盖了新房，流转了山地，有了公益性岗位，每年收入三四万元。此前，他将扭转命运的期望全部寄托在“改变家居风水”上面。 为了兑现“小康路上一个都不能掉队”的庄严承诺，安徽把以人民为中心的理念贯穿到经济社会发展各个领域。在实施脱贫攻坚“十大工程”等举措作用下，全省贫困人口由2012年底的679.1万人下降到2018年底的49.9万人，年均减贫100万人以上；贫困发生率由12.6%下降到0.93%；22个贫困县先后“摘帽”。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（7）安徽70年：改革创新书写时代答卷 2017年9月29日，世界首条量子 {} 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chin chin 0.64250153 mm 0.0 2042000 2040000 1568249840153 1568249839068 、芜湖、马鞍山等8市纳入长三角规划，安徽正式从地理上的长江下游进入长三角世界级城市群。去年11月，长三角一体化发展上升为国家战略。 坐拥皖江水，面向太平洋。安徽迎来历史性的重大发展机遇。中国区域经济学会顾问程必定认为，长三角上升为国家战略对安徽是百年未有的机遇，这意味着，作为长三角一员，安徽将跻身配置全球资源的开放门户、迎来更高质量一体化发展的崭新阶段。 “以前想到外地注册一个公司，交材料、核准名称、领证件，一切顺利也得跑三趟，现在真是节省了很多成本。”去年11月，合肥与星途科技服务有限公司负责人池天水，在合肥高新区的办事窗口领到了由浙江杭州工商部门颁发的营业执照。这也是该区通过长三角“一网通办”业务平台，发出的首张跨省异地登记的营业执照。 致力于要素破壁、深度融入，安徽各地也在抢抓长三角一体化机遇，争当落实国家战略的排头兵。池州、铜陵等地立足区位优势，承接长三角的产业分工，迈向价值链更高端；芜湖市提出“等高对接”，在长三角一体化中找准自身优势、谋求互补发展。 取消长三角区域高速公路省界收费站，推进长三角地区异地就医直接结算，打造长三角“安全消费大市场”……今年5月22日在芜湖举行的2019年长三角地区三省一市主要领导座谈会上，沪苏浙皖相关部门签署了10个打破分割、加速一体化的协议。 “扬皖所长、主动作为。”安徽省委主要负责人表示，安徽是长三角的重要方面军，在区位、科技、产业、生态、能源等方面具有自身的独特优势。安徽未来将把准自身定位，扬创新之长，努力打造长三角科技创新的重要策源地；扬产业之长，努力打造长三角高质量发展的重要增长极；扬区位之长，努力打造长三角世界级城市群的重要副中心；扬生态之长，努力打造长三角生态文明建设的重要样板区，在国家战略下自觉行动当好尖兵！ 新华社广州7月23日电 题：高质量发展的粤式“色香味”——广东制造业一线见闻 新华社记者毛鑫、邓瑞璇、魏玉坤 手机扫描二维码下单，经过10道全自动工序，不到2分钟就可拿到专属机器人模型。在广东拓斯达科技股份有限公司展厅，扫码定制机器人的“神操作”，令人对智能制造新水准印象深刻。 在广东，依托持续的研发投入、技术改造和体制机制创新，制造业正乘势转型升级，走向数字化、自动化、智能化，这一过程犹如烹制一桌高质量发展粤式大餐，“色香味”俱全。 “智”造增“色”：机器换人迎来热潮 原本人头攒动、环境嘈杂的生产车间，逐渐由精密的高度自动化生产线代替。在广东，“智造力”正成为制造业企业标配。 单条生产线的工人数量从160人降到51人、生产换型时间从45分钟降到9分钟、品质控制点从6个增加到108个……美的南沙工厂通过使用全智能生产线，实现效率和品质的双重提升。 “工人少了，但产量在递增，成本效率品质方面改善明显。”美的集团副总裁兼CIO张小懿说，美的通过自动化、数字化的支撑，正在编织一张工业互联网。 美的发展的背后，是广东制造业不断修炼“内功”的火热实践。 走进拓斯达位于东莞的厂区，在工业机器人装配测试车间，近百台机器人排成5列，有节奏地进行出厂前的自动测试。不久后，它们将进入更多工厂的车间，成为生产的主力军。 “智能制造引发机器换人热潮，国内工业机器人市场需求快速增长。”拓斯达产品研发中心总经理徐必业说，未来将持续增加研发投入，重点布局工业传感器、标准工艺设备等领域。 2018年起，广东大力发展智能制造：投入130亿元实施两轮工业技改三年行动计划，惠及3万多家企业，推动3000家工业企业“上云上平台”，工业机器人产量占全国1/5，先进制造业和高技术制造业增加值占规模以上工业增加值比重分别达56.4%和31.5%，制造业提质增效进展明显。 创新添“香”：从要素驱动转向创新驱动 国内研发成功“3D结构光人脸识别技术”、识别速度从0.1秒降至0.001秒、商用跨镜追踪技术……过去4年，广州云从信息科技公司主攻技术研发，成长为国内人工智能行业的佼佼者。 耀眼的成绩离不开研发支撑。云从科技总经理林明都介绍，公司研发团队已达700余人，其中80%以上拥有硕士及以上学历。该团队瞄准“卡脖子”的技术难点潜心钻研，持续突破核心技术。 云从科技的发展，是广东制造业把技术创新摆在核心位置，实现从要素驱动向创新驱动转变的缩影。 近年，广东采取多项措施增加技术有效供给，先后出台“科创12条”等政策，把企业作为科技创新的主体，并着力加强基础研究和应用基础研究，为高质量发展赋能。 数据显示，2018年，广东研发经费支出超过2500亿元，PCT国际专利申请量约占全国一半，有效发明专利量达24.9万件，稳居全国前列。区域创新综合能力排名连续两年居全国第一。 创新“引力”效果渐显，投资加速向高端产业聚集已成趋势。目前，广东规模以上工业企业总量超5万家，在全国名列前茅。国家级高新技术企业达4.5万家，近两年翻了一倍多。巴斯夫、埃克森美孚、富士康、LG等一批投资过百亿美元的项目逐一落地。 政策调“味”：加强制度供给 优化营商环境 2009年，刚从清华大学博士毕业的佘振定，带着一份“人工皮肤”项目的商业计划书南下深圳创业。 接触多家风投机构无果后，是深圳清华大学研究院相中了这个项目，决定进行孵化，并许诺研发团队可以持有项目70%的股份。 这坚定了佘振定在深圳创业的决心，他创办了深圳兰度生物科技有限公司。 后来，该团队从牛跟腱里提取高纯度胶原，制成了可媲美真实皮肤的双层“人工皮肤”，一经上市便填补了国内空白。多轮融资后，公司市值也翻了几番，达到15亿元。 “只有真正把市场作为配置创新资源的关键要素，才更有利于激发成果转化的积极性。”深圳清华大学研究院常务副院长刘伟强说。截至2018年末，该研究院已累计孵化企业2500多家，培养上市公司21家。 近年来，广东大力支持实体经济发展，以制度创新“扫清”制造业“绊脚石”，力争政府“不缺席”“不包揽”，市场“挑大梁”“说了算”。 实行个税补贴等优惠政策吸引海内外高端人才，推行“零跑动”、“秒批”、注册易等改革举措方便企业办事……一系列涉企惠企政策不断出台，持续释放各类市场主体活力。 截至2018年末，广东个体工商户和企业数量达1146万户，其中企业492万户，均稳居全国第一。其中，主营业务收入超百亿、千亿元的企业分别达276家、29家，进入世界500强的企业13家。这是2018年8月1日无人机拍摄的位于山西省朔州市的安太堡露天煤矿复垦区。新华社记者 曹阳 摄 新华社太原7月22日电 题：三晋大地起苍黄——新中国成立70年山西发展巡礼 新华社记者于振海、梁晓飞、王井怀 太行之西，黄河之东，表里山河，资源富集。 新中国成立70年来，勤劳智慧的三晋儿女在党的领导下，发扬太行精神、吕梁精神、右玉精神，在15万平方公里的土地上，建设起共和国能源重化工基地。如今，又担当起新时代能源革命排头兵重任，展开决战贫困攻坚战，朝着高质量发展之路、向着全面建成小康社会目标阔步前进。 山西，正以全新姿态挺进新时代。 当好“能源革命排头兵” 山西潞安集团石圪节煤矿曾以“艰苦奋斗、勤俭办矿”精神闻名全国。3年前，关闭了矿井，转型发展油用牡丹。前不久，其生产的牡丹籽油出口到了韩国。 石圪节“退煤种花”的同时，千里之外的千万吨级矿井同忻煤矿建成了山西首个智能化工作面，只用在地面控制室里，轻轻一点鼠标，地层深处的“乌金”就会滚滚不息地涌上地面。 新中国成立70年来，山西累计产煤190多亿吨，运往全国的煤炭130多亿吨。 山西因煤而名，也因煤而困。粗放的采矿业，经年累月，导致生态破坏、环境污染、矿难频发，经济发展患上了“煤炭依赖症”；多年来，始终走不出“煤炭市场好时，无暇转型；市场不好，无力转型”的怪圈。 关键时刻，新一届党中央为山西把脉，开出两剂良方：资源型经济转型综合配套改革和供给侧结构性改革。山西省委省政府深化“供改”与“综改”，明确了“能源革命排头兵”“资源型经济转型示范区”“内陆地区对外开放新高地”的目标定位。山西，开始走上高质量发展新征程。 尽管煤价持续高位运行，山西仍坚决淘汰落后产能，3年来关闭88座煤矿，退出落后产能8841万吨，煤炭去产能规模全国第一。煤矿数量由新中国成立后最多的4万多座，降到了1000座以内，先进产能占比提升到57%。 如今，山西1天开采的煤，相当于1949年全年的煤炭产量。通过科技创新，煤炭从燃料变原料，变身为高端润滑油、高端蜡、化妆品。 新型综合能源异军突起。风电、光伏装机总容量突破2000万千瓦，新能源装机容量进入全国前5。煤层气抽采利用量占全国的90%以上。晋电外送能力超过3800万千瓦，越来越多的清洁电力输往全国。 大同建设“氢都”，吕梁培育“数谷”，晋城发展“光谷”，阳泉打造“纳谷”……一批老煤城贴上新名片。晋能集团转型清洁能源，晋煤集团主攻燃气产业，潞安集团整合现代煤化工，阳煤集团发力煤机制造……多家老煤企密集调整主业。 全省上下推动国企改革、开发区改革、电力体制改革、医疗体制改革、营商环境改革等，一些关键领域改革迈向全国第一方阵，发展态势焕然一新。 山西率先启动企业投资项目承诺制改革，用一纸承诺代替“一箩筐材料”，企业自行办理的审批事项压减80%，项目从立项到开工时间平均缩短一半以上。 开发区成为山西转型发展的主战场。2017年山西批准新设开发区15家，2018年新设24家，今年已新设立11家。各开发区围绕先进装备制造、新能源汽车、节能环保、大数据、新材料、现代煤化工等精准招商。 今年5月，山西锦波生物医药股份有限公司与中国科学院、复旦大学共同发布人源Ⅲ型胶原蛋白，这是山西转型综改示范区催生的最新科技成果。去年一年，这里新增高新技术企业110家，初步形成电子信息装备、装备制造及汽车、生物医药及食品、新材料及加工等4个千亿级产业集群。 甩掉贫苦穷困的帽子 “人说山西好风光，地肥水美五谷香……”一曲《人说山西好风光》，唱出了山西人的家乡情。 山西，也是英雄、光荣的土地。在中国革命斗争中，留下了数不尽的光荣故事。新中国成立以来，这里孕育出了西沟精神、大寨精神、锡崖沟精神、大泉山精神…… 然而，巍巍太行，遍布干石山；绵绵吕梁，满是残垣沟壑。因土地瘠薄、干旱缺水，全国14个集中连片特困地区，太行、吕梁就占了两个，山西58个贫困县分布其间。 党的十八大以来，山西打响声势浩大、前所未有的脱贫攻坚战。 2017年9月，家住吕梁山深处、年过七旬的刘福有、杨娥子老两口，卖掉了家里的两头大犍牛，搬进了岢岚县广惠园移民新村。只有6户人家共13人的赵家洼村在民政部门销了号，拉开了山西决战深度贫困的序幕。 刘福有没花一分钱，就搬进了新楼房，后续扶持政策随之跟进。 很快，山西从7993个贫困村中，确定了3350个需要整村搬迁的深度贫困村，以“打不赢脱贫攻坚战，就对不起这块红色土地”的态度和决心，采取超常举措，啃深度贫困“硬骨头”。 到今年5月底，3350个深度贫困村已有3084个完成整体搬迁，1502个集中安置点已竣工1493个。 数万名扶贫干部奔走在太行吕梁山上。长治市扶贫办主任段志岗上任3年，跑了全市933个贫困村的一半以上。“时间紧任务重，群众眼里看着，心里盼着，不使劲不行啊。”他说。 扶贫干部们从太行精神、吕梁精神中汲取力量。在革命老区武乡县，楼则峪村第一书记闫小波进村后，挨家挨户走，一家一家问，连老太太爱看什么电视节目都记在心里。他带领村民发展电商，把当地小米卖出了好价钱。离任时，村民给他塞上自己做的鞋垫，表达对他的感激和不舍。 今年，山西将确保最后17个贫困县全部“摘帽”，22万贫困人口脱贫，确保2020年同全国一道进入全面小康社会。 绘就新的“山西好风光” 汾河，是山西最著名的河流。 党的十八大以来，山西把生态文明建设摆在更加突出地位，以环境保护倒逼经济高质量转型，积极回应人民群众的生态关切，让人们能望见星空、看见绿山、闻到花香。 山西实施大水网建设，启动占全省总面积72%的七河流域治理工程，省政府每周公布汾河流域治污情况，确保到明年全面消除劣Ⅴ类断面。太原、临汾等汾河河段再现“一川清流，两岸锦绣”。 水的命脉在山，山的灵魂在树。在支离破碎的黄土高原，有的地方“种树比养孩子还难”，生存条件恶劣。 在吕梁山区的永和县，永和关、乾 {} {"audioInfo":{"langDec":"chin","decSc":1.0,"nlpSc":0.0,"gender":"m","audioFiles":[{"decSc":1.0,"side":"c","gender":"m","bits":16,"langSc":0.0,"url":"http://10.5.3.85/heluAB.wav","vtl":135560,"codec":6,"channels":1,"rate":8000,"spkn":0,"name":"heluAB.wav","tl":211962}],"langSc":0.64250153,"lang":"chin"}} +c2eabec9-b8a7-405b-80d9-e73692d586f4 0086(021)77426829 81 0031 8 13326037312 44 0 0 1568252700180 945 asp fk 943000 1 0 0 0 0 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chin chin 0.64250153 ff 0.0 942000 940000 1568249840177 1568249839114 好生活、实现共同富裕而不懈奋斗。毛泽东同志在新中国成立之初就提出了我国发展富强的目标，指出“这个富，是共同的富，这个强，是共同的强，大家都有份”。邓小平同志多次强调共同富裕，指出“社会主义不是少数人富起来、大多数人穷，不是那个样子。社会主义最大的优越性就是共同富裕，这是体现社会主义本质的一个东西”。江泽民同志强调：“实现共同富裕是社会主义的根本原则和本质特征，绝不能动摇。”胡锦涛同志也要求“使全体人民共享改革发展成果，使全体人民朝着共同富裕的方向稳步前进”。 进入新时代，我们走上了创造美好生活、逐步实现全体人民共同富裕的新征程。习近平总书记指出，共同富裕是中国特色社会主义的根本原则，实现共同富裕是我们党的重要使命。他还强调，“我们追求的发展是造福人民的发展，我们追求的富裕是全体人民共同富裕”，要“让发展成果更多更公平惠及全体人民，不断促进人的全面发展，朝着实现全体人民共同富裕不断迈进”。 经过长期艰苦奋斗，我们党在实践中形成了先富带动后富、逐步实现共同富裕的规律性认识，推动人民生活质量和社会共享水平显著提升。粮票、布票、肉票、鱼票、油票、豆腐票、副食本、工业券等百姓生活曾经离不开的票证，已经进入了历史博物馆。忍饥挨饿、缺吃少穿、生活困顿这些几千年来困扰我国人民的问题总体上一去不复返了，中国人民迎来了从温饱不足到小康富裕的伟大飞跃。 （39）实现全体人民共同富裕的宏伟目标，最终靠的是发展。发展是基础，唯有发展才能满足人民对美好生活的热切向往。没有发展，没有扎扎实实的发展成果，共同富裕就无从谈起。 要毫不动摇坚持发展是硬道理、发展应该是科学发展和高质量发展的战略思想，举全民之力推进中国特色社会主义事业，不断把“蛋糕”做大。同时，还要在不断发展的基础上把促进社会公平正义的事情做好，把不断做大的“蛋糕”分好，让社会主义制度的优越性更加充分体现出来，让实现全体人民共同富裕在广大人民现实生活中更加充分展示出来。绝不能出现“富者累巨万，而贫者食糟糠”的现象。 实现全体人民共同富裕的目标需要一个很长的历史过程，是由一个一个阶段性目标逐步达成的。我国正处于并将长期处于社会主义初级阶段，我们不能做超越阶段的事情，但也不是说在逐步实现共同富裕方面就无所作为，而是要根据现有条件把能做的事情尽量做起来，积小胜为大胜，不断朝着全体人民共同富裕的目标前进。 4.群众路线是党的生命线和根本工作路线 （40）习近平总书记指出：“群众路线是我们党的生命线和根本工作路线，是我们党永葆青春活力和战斗力的重要传家宝。”不论过去、现在和将来，我们都要坚持一切为了群众，一切依靠群众，从群众中来，到群众中去，把党的正确主张变为群众的自觉行动，把群众路线贯彻到治国理政全部活动之中。 坚持群众路线，核心的问题是党要始终保持同人民群众的血肉联系，一刻也不脱离群众。密切联系群众，是党的性质和宗旨的体现。我们党是在同人民群众的密切联系中成长、发展、壮大起来的，是靠宣传群众、组织群众、依靠群众起家，从胜利走向胜利的。党的最大政治优势是密切联系群众，党执政后的最大危险是脱离群众。能否保持党同人民群众的血肉联系，决定着党的事业的成败。要从政治的高度深刻认识密切联系群众的重要性，任何时候任何情况下，与人民群众同呼吸共命运的立场不能变，全心全意为人民服务的宗旨不能忘，坚信群众是真正英雄的历史唯物主义观点不能丢。 （41）群众路线是我们党始终坚持的根本工作方法。党的领导工作的正确方法，就是将群众意见集中起来形成正确的决策，又到群众中宣传解释，将决策化为群众的行动，并在群众实践中检验这些决策是否正确。 坚持走群众路线，绝不是喊喊口号走走过场，而是要诚心诚意、实打实做。要善于通过提出并贯彻正确的理论和路线方针政策带领人民前进，善于从人民的实践创造和发展要求中完善政策主张，善于从群众中寻找解决问题的方案和办法，使作出的决策和决策的执行充分体现民心民意。深入研究新形势下群众工作的规律和特点，把党的优良传统和新技术新手段结合起来，学会通过网络走群众路线，提高做好群众工作的本领。 贯彻党的群众路线，“知”是基础、是前提，“行”是重点、是关键。必须以知促行、以行促知，做到知行合一，既解决认识提高问题，又解决行动自觉问题，使群众路线落地稳、扎根深，融入经济社会发展全过程，贯穿到党的全部工作中。 新华社合肥7月23日电 题：安徽70年：改革创新书写时代答卷 新华社记者王正忠、杨玉华、徐海涛、姜刚 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（1）安徽70年：改革创新书写时代答卷 这是安徽凤阳小岗村（2018年9月27日无人机拍摄）。 新华社记者 张端 摄 这是一方敢为人先的热土：开启农村改革巨幕的“大包干”、闯出个体经济之路的“傻子瓜子”、冲出重围高擎自主品牌大旗的奇瑞汽车……一次次书写时代传奇。 这是一片勇于革新的天地：税费改革、医药卫生体制改革、文化体制改革、全面创新改革试验……一次次引领风气之先。 这是一处创新喷涌的泉源：全球首颗量子通信卫星上天、智能语音技术站上全球制高点、世界最薄的玻璃问世、“人造太阳”正求解人类终极能源难题……一次次刷新进步的前沿。 从新中国成立时一个落后的农业省，到今天实现农业大省、工业大省、科技大省的“三级跳”，安徽以70年的精彩蝶变书写时代答卷，以改革创新的昂扬姿态挺进新时代。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（3）安徽70年：改革创新书写时代答卷 在安徽凤阳小岗村，农民驾驶收割机收割水稻（2018年9月27日摄）。 新华社记者 刘军喜 摄 昔日农业省今成“科创极” 八百里巢湖之滨，坐 {} {"audioInfo":{"langDec":"chin","decSc":1.0,"nlpSc":0.0,"gender":"m","audioFiles":[{"decSc":1.0,"side":"c","gender":"m","bits":16,"langSc":0.0,"url":"http://10.5.3.85/heluAB.wav","vtl":135560,"codec":6,"channels":1,"rate":8000,"spkn":0,"name":"heluAB.wav","tl":211962}],"langSc":0.64250153,"lang":"chin"}} +04a548aa-a103-4ffd-b72c-81b6cb2ea420 0086(021)77426829 82 0031 2 13924865616 33 0 0 1568249850352 857 asp fk 2450000 1 0 0 0 0 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cant cant 0.64250153 mm 0.0 2449000 2447000 1568249840221 1568249839126 兴、答应不答应作为衡量一切工作得失的根本标准，努力向历史、向人民交出新的更加优异的答卷。 3.朝着实现全体人民共同富裕不断迈进 （38）共同富裕，是马克思主义的一个基本目标。按照马克思、恩格斯的构想，共产主义社会将彻底消除阶级之间、城乡之间、脑力劳动和体力劳动之间的对立和差别，实行各尽所能、按需分配，真正实现社会共享、实现每个人自由而全面的发展。到那时，“生产将以所有的人富裕为目的”，“所有人共同享受大家创造出来的福利”。实现共同富裕，反映了社会主义的本质要求，体现了以人民为中心的根本立场。 我们党始终带领人民为创造美好生活、实现共同富裕而不懈奋斗。毛泽东同志在新中国成立之初就提出了我国发展富强的目标，指出“这个富，是共同的富，这个强，是共同的强，大家都有份”。邓小平同志多次强调共同富裕，指出“社会主义不是少数人富起来、大多数人穷，不是那个样子。社会主义最大的优越性就是共同富裕，这是体现社会主义本质的一个东西”。江泽民同志强调：“实现共同富裕是社会主义的根本原则和本质特征，绝不能动摇。”胡锦涛同志也要求“使全体人民共享改革发展成果，使全体人民朝着共同富裕的方向稳步前进”。 进入新时代，我们走上了创造美好生活、逐步实现全体人民共同富裕的新征程。习近平总书记指出，共同富裕是中国特色社会主义的根本原则，实现共同富裕是我们党的重要使命。他还强调，“我们追求的发展是造福人民的发展，我们追求的富裕是全体人民共同富裕”，要“让发展成果更多更公平惠及全体人民，不断促进人的全面发展，朝着实现全体人民共同富裕不断迈进”。 经过长期艰苦奋斗，我们党在实践中形成了先富带动后富、逐步实现共同富裕的规律性认识，推动人民生活质量和社会共享水平显著提升。粮票、布票、肉票、鱼票、油票、豆腐票、副食本、工业券等百姓生活曾经离不开的票证，已经进入了历史博物馆。忍饥挨饿、缺吃少穿、生活困顿这些几千年来困扰我国人民的问题总体上一去不复返了，中国人民迎来了从温饱不足到小康富裕的伟大飞跃。 （39）实现全体人民共同富裕的宏伟目标，最终靠的是发展。发展是基础，唯有发展才能满足人民对美好生活的热切向往。没有发展，没有扎扎实实的发展成果，共同富裕就无从谈起。 要毫不动摇坚持发展是硬道理、发展应该是科学发展和高质量发展的战略思想，举全民之力推进中国特色社会主义事业，不断把“蛋糕”做大。同时，还要在不断发展的基础上把促进社会公平正义的事情做好，把不断做大的“蛋糕”分好，让社会主义制度的优越性更加充分体现出来，让实现全体人民共同富裕在广大人民现实生活中更加充分展示出来。绝不能出现“富者累巨万，而贫者食糟糠”的现象。 实现全体人民共同富裕的目标需要一个很长的历史过程，是由一个一个阶段性目标逐步达成的。我国正处于并将长期处于社会主义初级阶段，我们不能做超越阶段的事情，但也不是说在逐步实现共同富裕方面就无所作为，而是要根据现有条件把能做的事情尽量做起来，积小胜为大胜，不断朝着全体人民共同富裕的目标前进。 4.群众路线是党的生命线和根本工作路线 （40）习近平总书记指出：“群众路线是我们党的生命线和根本工作路线，是我们党永葆青春活力和战斗力的重要传家宝。”不论过去、现在和将来，我们都要坚持一切为了群众，一切依靠群众，从群众中来，到群众中去，把党的正确主张变为群众的自觉行动，把群众路线贯彻到治国理政全部活动之中。 坚持群众路线，核心的问题是党要始终保持同人民群众的血肉联系，一刻也不脱离群众。密切联系群众，是党的性质和宗旨的体现。我们党是在同人民群众的密切联系中成长、发展、壮大起来的，是靠宣传群众、组织群众、依靠群众起家，从胜利走向胜利的。党的最大政治优势是密切联系群众，党执政后的最大危险是脱离群众。能否保持党同人民群众的血肉联系，决定着党的事业的成败。要从政治的高度深刻认识密切联系群众的重要性，任何时候任何情况下，与人民群众同呼吸共命运的立场不能变，全心全意为人民服务的宗旨不能忘，坚信群众是真正英雄的历史唯物主义观点不能丢。 （41）群众路线是我们党始终坚持的根本工作方法。党的领导工作的正确方法，就是将群众意见集中起来形成正确的决策，又到群众中宣传解释，将决策化为群众的行动，并在群众实践中检验这些决策是否正确。 坚持走群众路线，绝不是喊喊口号走走过场，而是要诚心诚意、实打实做。要善于通过提出并贯彻正确的理论和路线方针政策带领人民前进，善于从人民的实践创造和发展要求中完善政策主张，善于从群众中寻找解决问题的方案和办法，使作出的决策和决策的执行充分体现民心民意。深入研究新形势下群众工作的规律和特点，把党的优良传统和新技术新手段结合起来，学会通过网络走群众路线，提高做好群众工作的本领。 贯彻党的群众路线，“知”是基础、是前提，“行”是重点、是关键。必须以知促行、以行促知，做到知行合一，既解决认识提高问题，又解决行动自觉问题，使群众路线落地稳、扎根深，融入经济社会发展全过程，贯穿到党的全部工作中。 新华社合肥7月23日电 题：安徽70年：改革创新书写时代答卷 新华社记者王正忠、杨玉华、徐海涛、姜刚 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（1）安徽70年：改革创新书写时代答卷 这是安徽凤阳小岗村（2018年9月27日无人机拍摄）。 新华社记者 张端 摄 这是一方敢为人先的热土：开启农村改革巨幕的“大包干”、闯出个体经济之路的“傻子瓜子”、冲出重围高擎自主品牌大旗的奇瑞汽车……一次次书写时代传奇。 这是一片勇于革新的天地：税费改革、医药卫生体制改革、文化体制改革、全面创新改革试验……一次次引领风气之先。 这是一处创新喷涌的泉源：全球首颗量子通信卫星上天、智能语音技术站上全球制高点、世界最薄的玻璃问世、“人造太阳”正求解人类终极能源难题……一次次刷新进步的前沿。 从新中国成立时一个落后的农业省，到今天实现农业大省、工业大省、科技大省的“三级跳”，安徽以70年的精彩蝶变书写时代答卷，以改革创新的昂扬姿态挺进新时代。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（3）安徽70年：改革创新书写时代答卷 在安徽凤阳小岗村，农民驾驶收割机收割水稻（2018年9月27日摄）。 新华社记者 刘军喜 摄 昔日农业省今成“科创极” 八百里巢湖之滨，坐落着全国首处以创新为主题的场馆。其中“人造太阳”、强磁场装置、量子通信等高精尖的科技创新成果令八方来宾感到震撼。 安徽，曾长期是经济发展的洼地，不沿边、不靠海、工业基础薄弱。新中国成立初期，一产占比高达75%以上。70年沧桑巨变，到2018年，安徽的三产结构已变为8.8：46.1：45.1，农业占比退居十分之一以下，昔日的农业大省成为以家电、汽车、建材等为主的工业大省和以电子信息、平板显示、智能语音等领衔的科技产业大省。 巨变的背后，是安徽把科技创新作为转型升级的突破口。早在1995年安徽就提出实施“科教兴皖”战略，2008年启动合芜蚌自主创新综合试验区建设，实施创新驱动发展之路。2013年，安徽成为全国第二个开展创新型省份建设试点工作的地区，随后获批建设合芜蚌国家自主创新示范区和系统推进全面创新改革试验区。2017年初，合肥综合性国家科学中心获批。2018年，合肥滨湖科学城挂牌成立……安徽的创新名片越擦越亮。 多年的探索实践，安徽已形成从原始创新到技术开发、成果转化、企业孵化，再到新兴产业的“全创新链”，走出一条具有安徽特色的中西部创新发展之路。 在位于合肥北郊的合肥综合性国家科学中心聚变堆园区，聚变堆主机关键系统综合研究设施工程正在紧张建设。这个由安徽省政府主要领导率队答辩争取来的国家大科学装置，与之前建成的合肥“人造太阳”装置相呼应，使安徽成为世界一流的核聚变能源创新研究基地。瞄准前沿、前瞻布局、潜心投入，安徽已建成“同步辐射光源”“全超导托卡马克”“稳态强磁场”三大国际科技前沿科研平台，并在量子通信、智能语音等科研领域达到国际领跑或并跑水平。安徽的区域创新能力已连续7年跻身全国第一方阵。 贴近产业抓创新，集聚资源搞科研，不断破除体制机制障碍，让安徽驶入创新发展快车道。安徽省统计局局长吴劲松说，党的十八大以来，安徽的新兴产业增加值不断提升。 目前，安徽的战略性新兴产业对全省工业增长的贡献率已超三分之一。在合肥，形成了国内唯一拥有6代线、8.5代线和10.5代线的新型平板显示产业基地。在蚌埠，以蚌埠玻璃工业设计研究院为核心的新型玻璃产业，诞生了我国首条具有自主知识产权的8.5代TFT-LCD玻璃基板生产线，成功研发世界最薄的0.12毫米超薄触控玻璃；在芜湖，形成了以中电科钻石飞机为代表的通用航空产业集群，飞机交付量处于国内领先行列。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（6）安徽70年：改革创新书写时代答卷 观众在2018世界制造业大会上观看蚌埠玻璃工业设计研究院展出的柔性触控玻璃（2018年5月25日摄）。 新华社记者 张端 摄 改革不停步发展活力涌 经济因改革而活，发展因创新而兴。今年上半年，安徽的经济呈现出“加速跑、趋向优”的良好态势。生产总值增长8%，居全国第6位，规模以上工业增加值增速居第6位，城镇、农村常住居民人均可支配收入增速分别居第3位和第4位。 “速度位次靠前，发展质效提升，内生动力增强。”安徽省统计局副局长赵金宝将全省上半年经济运行的亮点概括为三句话。 今年7月12日，三只松鼠股份有限公司在深交所上市。创立7年来的这家企业，不断颠覆传统销售模式，年销售额从零增加到2018年的82亿元。“创新发展是企业前进的根本。”公司党委书记潘道伟说，上市能加速公司数字化供应链战略的推进，为消费者提供更好的产品和服务。 作为民营经济的试验田，如今的江淮大地仍是一片创新创业的热土，敢闯敢试的改革火种继续在这里播撒。现在的安徽，平均每天新登记注册企业925家，每天获授44件发明专利。 经济“逆势上扬”，靠的是改革，是服务，是创新。十八大以来，安徽着力推动“放管服”改革，首创权力、责任清单的“安徽模式”，让公共服务做“加法”、中介服务做“减法”。而群众办事“最多跑一次”“互联网+政务服务”等创举，正推动安徽迈向审批事项少、办事效率高、投资环境优、市场主体和人民群众获得感强的省份之一。 唯改革才有出路，改革要常讲常新。目前，安徽正持续将一系列新的改革推向深入： ——政务服务改革。努力将省级行政许可事项减到全国最少，企业开办时间压缩至全国最短……到今年底，目标实现80%个人事项全程网办，部分事项“秒批秒办”。 ——全面创新改革。加快建设合肥综合性国家科学中心，加紧布局一批世界前沿的大科学装置，下放科技成果处置权、收益权、使用权，探索实行首席科学家全权负责制……目标建成具有国际影响力的科技创新中心和产业创新中心。 ——农业农村改革。持续深化农村承包地“三权分置”改革，稳步推进农村集体产权制度改革……围绕处理好农民与土地关系主线，努力在农村改革上继续走在前列。 增进民生福祉，乃经济发展之根本目的。居住在集中连片贫困山区金寨县大湾村村民陈泽申前年主动要求摘掉“贫困帽”。得益于党和政府的扶贫政策，他家盖了新房，流转了山地，有了公益性岗位，每年收入三四万元。此前，他将扭转命运的期望全部寄托在“改变家居风水”上面。 为了兑现“小康路上一个都不能掉队”的庄严承诺，安徽把以人民为中心的理念贯穿到经济社会发展各个领域。在实施脱贫攻坚“十大工程”等举措作用下，全省贫困人口由2012年底的679.1万人下降到2018年底的49.9万人，年均减贫100万人以上；贫困发生率由12.6%下降到0.93%；22个贫困县先后“摘帽”。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（7）安徽70年：改革创新书写时代答卷 2017年9月29日，世界首条量子保密通信干线——“京沪干线”正式开通。科研人员在中国科学技术大学先进技术研究院量子通信“京沪干线”总控中心工作。 新华社记者 刘军喜 摄 融入长三角勇于当“尖兵” 早在7年前，位于安徽滁州市的中新苏滁高新技术产业开发区便开工建设，拉开了苏皖两省共建园区的大幕。此后，园区内产业实现深度对接、集群发展，这里成为安徽融入长三角开放合作的典范。 记者近日走进苏滁高新区看到，一排排标准化厂房内，一片热火朝天的生产场面；以新加坡理念建设的邻里中心内，百姓购物、办事功能一应俱全。“截至6月底，从长三角引进的项目数占74%。”该区管委会主任杨广兰说，该区去年经营收入突破100亿元，同比增长逾30%。 2016年5月，中央将安徽合肥、芜湖、马鞍山等8市纳入长三角规划，安徽正式从地理上的长江下游进入长三角世界级城市群。去年11月，长三角一体化发展上升为国家战略。 坐拥皖江水，面向太平洋。安徽迎来历史性的重大发展机遇。中国区域经济学会顾问程必定认为，长三角上升为国家战略对安徽是百年未有的机遇，这意味着，作为长三角一员，安徽将跻身配置全球资源的开放门户、迎来更高质量一体化发展的崭新阶段。 “以前想到外地注册一个公司，交材料、核准名称、领证件，一切顺利也得跑三趟，现在真是节省了很多成本。”去年11月，合肥与星途科技服务有限公司负责人池天水，在合肥高新区的办事窗口领到了由浙江杭州工商部门颁发的营业执照。这也是该区通过长三角“一网通办”业务平台，发出的首张跨省异地登记的营业执照。 致力于要素破壁、深度融入，安徽各地也在抢抓长三角一体化机遇，争当落实国家战略的排头兵。池州、铜陵等地立足区位优势，承接长三角的产业分工，迈向价值链更高端；芜湖市提出“等高对接”，在长三角一体化中找准自身优势、谋求互补发展。 取消长三角区域高速公路省界收费站，推进长三角地区异地就医直接结算，打造长三角“安全消费大市场”……今年5月22日在芜湖举行的2019年长三角地区三省一市主要领导座谈会上，沪苏浙皖相关部门签署了10个打破分割、加速一体化的协议。 “扬皖所长、主动作为。”安徽省委主要负责人表示，安徽是长三角的重要方面军，在区位、科技、产业、生态、能源等方面具有自身的独特优势。安徽未来将把准自身定位，扬创新之长，努力打造长三角科技创新的重要策源地；扬产业之长，努力打造长三角高质量发展的重要增长极；扬区位之长，努力打造长三角世界级城市群的重要副中心；扬生态之长，努力打造长三角生态文明建设的重要样板区，在国家战略下自觉行动当好尖兵！ 新华社广州7月23日电 题：高质量发展的粤式“色香味”——广东制造业一线见闻 新华社记者毛鑫、邓瑞璇、魏玉坤 手机扫描二维码下单，经过10道全自动工序，不到2分钟就可拿到专属机器人模型。在广东拓斯达科技股份有限公司展厅，扫码定制机器人的“神操作”，令人对智能制造新水准印象深刻。 在广东， {} {"audioInfo":{"langDec":"chin","decSc":1.0,"nlpSc":0.0,"gender":"m","audioFiles":[{"decSc":1.0,"side":"c","gender":"m","bits":16,"langSc":0.0,"url":"http://10.5.3.85/heluAB.wav","vtl":135560,"codec":6,"channels":1,"rate":8000,"spkn":0,"name":"heluAB.wav","tl":211962}],"langSc":0.64250153,"lang":"chin"}} +45c0ded1-c608-4a49-981d-faf720442a59 13378149447 49 0031 8 13376907227 1 5 0 1568289879606 2004 asp fk 3686000 1 0 0 0 0 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chin chin 0.64250153 fm 0.0 3685000 3683000 1568249840233 1568249839132 中心工作。 新华社记者 刘军喜 摄 融入长三角勇于当“尖兵” 早在7年前，位于安徽滁州市的中新苏滁高新技术产业开发区便开工建设，拉开了苏皖两省共建园区的大幕。此后，园区内产业实现深度对接、集群发展，这里成为安徽融入长三角开放合作的典范。 记者近日走进苏滁高新区看到，一排排标准化厂房内，一片热火朝天的生产场面；以新加坡理念建设的邻里中心内，百姓购物、办事功能一应俱全。“截至6月底，从长三角引进的项目数占74%。”该区管委会主任杨广兰说，该区去年经营收入突破100亿元，同比增长逾30%。 2016年5月，中央将安徽合肥、芜湖、马鞍山等8市纳入长三角规划，安徽正式从地理上的长江下游进入长三角世界级城市群。去年11月，长三角一体化发展上升为国家战略。 坐拥皖江水，面向太平洋。安徽迎来历史性的重大发展机遇。中国区域经济学会顾问程必定认为，长三角上升为国家战略对安徽是百年未有的机遇，这意味着，作为长三角一员，安徽将跻身配置全球资源的开放门户、迎来更高质量一体化发展的崭新阶段。 “以前想到外地注册一个公司，交材料、核准名称、领证件，一切顺利也得跑三趟，现在真是节省了很多成本。”去年11月，合肥与星途科技服务有限公司负责人池天水，在合肥高新区的办事窗口领到了由浙江杭州工商部门颁发的营业执照。这也是该区通过长三角“一网通办”业务平台，发出的首张跨省异地登记的营业执照。 致力于要素破壁、深度融入，安徽各地也在抢抓长三角一体化机遇，争当落实国家战略的排头兵。池州、铜陵等地立足区位优势，承接长三角的产业分工，迈向价值链更高端；芜湖市提出“等高对接”，在长三角一体化中找准自身优势、谋求互补发展。 取消长三角区域高速公路省界收费站，推进长三角地区异地就医直接结算，打造长三角“安全消费大市场”……今年5月22日在芜湖举行的2019年长三角地区三省一市主要领导座谈会上，沪苏浙皖相关部门签署了10个打破分割、加速一体化的协议。 “扬皖所长、主动作为。”安徽省委主要负责人表示，安徽是长三角的重要方面军，在区位、科技、产业、生态、能源等方面具有自身的独特优势。安徽未来将把准自身定位，扬创新之长，努力打造长三角科技创新的重要策源地；扬产业之长，努力打造长三角高质量发展的重要增长极；扬区位之长，努力打造长三角世界级城市群的重要副中心；扬生态之长，努力打造长三角生态文明建设的重要样板区，在国家战略下自觉行动当好尖兵！ 新华社广州7月23日电 题：高质量发展的粤式“色香味”——广东制造业一线见闻 新华社记者毛鑫、邓瑞璇、魏玉坤 手机扫描二维码下单，经过10道全自动工序，不到2分钟就可拿到专属机器人模型。在广东拓斯达科技股份有限公司展厅，扫码定制机器人的“神操作”，令人对智能制造新水准印象深刻。 在广东，依托持续的研发投入、技术改造和体制机制创新，制造业正乘势转型升级，走向数字化、自动化、智能化，这一过程犹如烹制一桌高质量发展粤式大餐，“色香味”俱全。 “智”造增“色”：机器换人迎来热潮 原本人头攒动、环境嘈杂的生产车间，逐渐由精密的高度自动化生产线代替。在广东，“智造力”正成为制造业企业标配。 单条生产线的工人数量从160人降到51人、生产换型时间从45分钟降到9分钟、品质控制点从6个增加到108个……美的南沙工厂通过使用全智能生产线，实现效率和品质的双重提升。 “工人少了，但产量在递增，成本效率品质方面改善明显。”美的集团副总裁兼CIO张小懿说，美的通过自动化、数字化的支撑，正在编织一张工业互联网。 美的发展的背后，是广东制造业不断修炼“内功”的火热实践。 走进拓斯达位于东莞的厂区，在工业机器人装配测试车间，近百台机器人排成5列，有节奏地进行出厂前的自动测试。不久后，它们将进入更多工厂的车间，成为生产的主力军。 “智能制造引发机器换人热潮，国内工业机器人市场需求快速增长。”拓斯达产品研发中心总经理徐必业说，未来将持续增加研发投入，重点布局工业传感器、标准工艺设备等领域。 2018年起，广东大力发展智能制造：投入130亿元实施两轮工业技改三年行动计划，惠及3万多家企业，推动3000家工业企业“上云上平台”，工业机器人产量占全国1/5，先进制造业和高技术制造业增加值占规模以上工业增加值比重分别达56.4%和31.5%，制造业提质增效进展明显。 创新添“香”：从要素驱动转向创新驱动 国内研发成功“3D结构光人脸识别技术”、识别速度从0.1秒降至0.001秒、商用跨镜追踪技术……过去4年，广州云从信息科技公司主攻技术研发，成长为国内人工智能行业的佼佼者。 耀眼的成绩离不开研发支撑。云从科技总经理林明都介绍，公司研发团队已达700余人，其中80%以上拥有硕士及以上学历。该团队瞄准“卡脖子”的技术难点潜心钻研，持续突破核心技术。 云从科技的发展，是广东制造业把技术创新摆在核心位置，实现从要素驱动向创新驱动转变的缩影。 近年，广东采取多项措施增加技术有效供给，先后出台“科创12条”等政策，把企业作为科技创新的主体，并着力加强基础研究和应用基础研究，为高质量发展赋能。 数据显示，2018年，广东研发经费支出超过2500亿元，PCT国际专利申请量约占全国一半，有效发明专利量达24.9万件，稳居全国前列。区域创新综合能力排名连续两年居全国第一。 创新“引力”效果渐显，投资加速向高端产业聚集已成趋势。目前，广东规模以上工业企业总量超5万家，在全国名列前茅。国家级高新技术企业达4.5万家，近两年翻了一倍多。巴斯夫、埃克森美孚、富士康、LG等一批投资过百亿美元的项目逐一落地。 政策调“味”：加强制度供给 优化营商环境 2009年，刚从清华大学博士毕业的佘振定，带着一份“人工皮肤”项目的商业计划书南下深圳创业。 接触多家风投机构无果后，是深圳清华大学研究院相中了这个项目，决定进行孵化，并许诺研发团队可以持有项目70%的股份。 这坚定了佘振定在深圳创业的决心，他创办了深圳兰度生物科技有限公司。 后来，该团队从牛跟腱里提取高纯度胶原，制成了可媲美真实皮肤的双层“人工皮肤”，一经上市便填补了国内空白。多轮融资后，公司市值也翻了几番，达到15亿元。 “只有真正把市场作为配置创新资源的关键要素，才更有利于激发成果转化的积极性。”深圳清华大学研究院常务副院长刘伟强说。截至2018年末，该研究院已累计孵化企业2500多家，培养上市公司21家。 近年来，广东大力支持实体经济发展，以制度创新“扫清”制造业“绊脚石”，力争政府“不缺席”“不包揽”，市场“挑大梁”“说了算”。 实行个税补贴等优惠政策吸引海内外高端人才，推行“零跑动”、“秒批”、注册易等改革举措方便企业办事……一系列涉企惠企政策不断出台，持续释放各类市场主体活力。 截至2018年末，广东个体工商户和企业数量达1146万户，其中企业492万户，均稳居全国第一。其中，主营业务收入超百亿、千亿元的企业分别达276家、29家，进入世界500强的企业13家。这是2018年8月1日无人机拍摄的位于山西省朔州市的安太堡露天煤矿复垦区。新华社记者 曹阳 摄 新华社太原7月22日电 题：三晋大地起苍黄——新中国成立70年山西发展巡礼 新华社记者于振海、梁晓飞、王井怀 太行之西，黄河之东，表里山河，资源富集。 新中国成立70年来，勤劳智慧的三晋儿女在党的领导下，发扬太行精神、吕梁精神、右玉精神，在15万平方公里的土地上，建设起共和国能源重化工基地。如今，又担当起新时代能源革命排头兵重任，展开决战贫困攻坚战，朝着高质量发展之路、向着全面建成小康社会目标阔步前进。 山西，正以全新姿态挺进新时代。 当好“能源革命排头兵” 山西潞安集团石圪节煤矿曾以“艰苦奋斗、勤俭办矿”精神闻名全国。3年前，关闭了矿井，转型发展油用牡丹。前不久，其生产的牡丹籽油出口到了韩国。 石圪节“退煤种花”的同时，千里之外的千万吨级矿井同忻煤矿建成了山西首个智能化工作面，只用在地面控制室里，轻轻一点鼠标，地层深处的“乌金”就会滚滚不息地涌上地面。 新中国成立70年来，山西累计产煤190多亿吨，运往全国的煤炭130多亿吨。 山西因煤而名，也因煤而困。粗放的采矿业，经年累月，导致生态破坏、环境污染、矿难频发，经济发展患上了“煤炭依赖症”；多年来，始终走不出“煤炭市场好时，无暇转型；市场不好，无力转型”的怪圈。 关键时刻，新一届党中央为山西把脉，开出两剂良方：资源型经济转型综合配套改革和供给侧结构性改革。山西省委省政府深化“供改”与“综改”，明确了“能源革命排头兵”“资源型经济转型示范区”“内陆地区对外开放新高地”的目标定位。山西，开始走上高质量发展新征程。 尽管煤价持续高位运行，山西仍坚决淘汰落后产能，3年来关闭88座煤矿，退出落后产能8841万吨，煤炭去产能规模全国第一。煤矿数量由新中国成立后最多的4万多座，降到了1000座以内，先进产能占比提升到57%。 如今，山西1天开采的煤，相当于1949年全年的煤炭产量。通过科技创新，煤炭从燃料变原料，变身为高端润滑油、高端蜡、化妆品。 新型综合能源异军突起。风电、光伏装机总容量突破2000万千瓦，新能源装机容量进入全国前5。煤层气抽采利用量占全国的90%以上。晋电外送能力超过3800万千瓦，越来越多的清洁电力输往全国。 大同建设“氢都”，吕梁培育“数谷”，晋城发展“光谷”，阳泉打造“纳谷”……一批老煤城贴上新名片。晋能集团转型清洁能源，晋煤集团主攻燃气产业，潞安集团整合现代煤化工，阳煤集团发力煤机制造……多家老煤企密集调整主业。 全省上下推动国企改革、开发区改革、电力体制改革、医疗体制改革、营商环境改革等，一些关键领域改革迈向全国第一方阵，发展态势焕然一新。 山西率先启动企业投资项目承诺制改革，用一纸承诺代替“一箩筐材料”，企业自行办理的审批事项压减80%，项目从立项到开工时间平均缩短一半以上。 开发区成为山西转型发展的主战场。2017年山西批准新设开发区15家，2018年新设24家，今年已新设立11家。各开发区围绕先进装备制造、新能源汽车、节能环保、大数据、新材料、现代煤化工等精准招商。 今年5月，山西锦波生物医药股份有限公司与中国科学院、复旦大学共同发布人源Ⅲ型胶原蛋白，这是山西转型综改示范区催生的最新科技成果。去年一年，这里新增高新技术企业110家，初步形成电子信息装备、装备制造及汽车、生物医药及食品、新材料及加工等4个千亿级产业集群。 甩掉贫苦穷困的帽子 “人说山西好风光，地肥水美五谷香……”一曲《人说山西好风光》，唱出了山西人的家乡情。 山西，也是英雄、光荣的土地。在中国革命斗争中，留下了数不尽的光荣故事。新中国成立以来，这里孕育出了西沟精神、大寨精神、锡崖沟精神、大泉山精神…… 然而，巍巍太行，遍布干石山；绵绵吕梁，满是残垣沟壑。因土地瘠薄、干旱缺水，全国14个集中连片特困地区，太行、吕梁就占了两个，山西58个贫困县分布其间。 党的十八大以来，山西打响声势浩大、前所未有的脱贫攻坚战。 2017年9月，家住吕梁山深处、年过七旬的刘福有、杨娥子老两口，卖掉了家里的两头大犍牛，搬进了岢岚县广惠园移民新村。只有6户人家共13人的赵家洼村在民政部门销了号，拉开了山西决战深度贫困的序幕。 刘福有没花一分钱，就搬进了新楼房，后续扶持政策随之跟进。 很快，山西从7993个贫困村中，确定了3350个需要整村搬迁的深度贫困村，以“打不赢脱贫攻坚战，就对不起这块红色土地”的态度和决心，采取超常举措，啃深度贫困“硬骨头”。 到今年5月底，3350个深度贫困村已有3084个完成整体搬迁，1502个集中安置点已竣工1493个。 数万名扶贫干部奔走在太行吕梁山上。长治市扶贫办主任段志岗上任3年，跑了全市933个贫困村的一半以上。“时间紧任务重，群众眼里看着，心里盼着，不使劲不行啊。”他说。 扶贫干部们从太行精神、吕梁精神中汲取力量。在革命老区武乡县，楼则峪村第一书记闫小波进村后，挨家挨户走，一家一家问，连老太太爱看什么电视节目都记在心里。他带领村民发展电商，把当地小米卖出了好价钱。离任时，村民给他塞上自己做的鞋垫，表达对他的感激和不舍。 今年，山西将确保最后17个贫困县全部“摘帽”，22万贫困人口脱贫，确保2020年同全国一道进入全面小康社会。 绘就新的“山西好风光” 汾河，是山西最著名的河流。 党的十八大以来，山西把生态文明建设摆在更加突出地位，以环境保护倒逼经济高质量转型，积极回应人民群众的生态关切，让人们能望见星空、看见绿山、闻到花香。 山西实施大水网建设，启动占全省总面积72%的七河流域治理工程，省政府每周公布汾河流域治污情况，确保到明年全面消除劣Ⅴ类断面。太原、临汾等汾河河段再现“一川清流，两岸锦绣”。 水的命脉在山，山的灵魂在树。在支离破碎的黄土高原，有的地方“种树比养孩子还难”，生存条件恶劣。 在吕梁山区的永和县，永和关、乾坤湾等临近黄河的陡峭石质山上，现在已是绿油油一片。当地生态防护林从新中国成立初的不足5000亩，增加到现在的34万亩。 在太行山深处的壶关县，一道长达5000公里、全部由石头垒成的森林防护墙蔚为壮观，当地森林覆盖率从1978年的5%增至50%以上。 在毛乌素沙漠边缘的右玉县，新中国成立以来，历任县委书记一任接着一任干，林木绿化率达到55%，创造出让荒漠变绿洲的“右玉精神”。 几代人披荆斩棘，换来今日绿水青山。山西省森林覆盖率从新中国成立初期的2.4%提升至20.5%。全国生态气象公报显示，近20年来，山西植被生态质量改善位居全国前列。 山西曾是“千泉之省”，但由于地下水超采严重，尤其是采煤采矿破坏地下水系，造成19处岩溶大泉大部分衰减，6处断流干涸或流量锐减。去年以来，50座涉及泉域重点保护范围的煤矿，6座被列为去产能关闭矿井，2座被注销采矿证，42座办理了采矿证变更手续。 山西地下水位逐年回升。“晋祠三绝”之一的难老泉已断流25年，地下水水位距泉水出水口最高时有9层楼高，如今只差1.61米，难老泉复流在望。 山西也是蓝天保卫战的主战场。“宁可牺牲点GDP，也要把环保指标提上去。”山西以壮士断腕的勇气，向污染开刀。山西省生态环境厅数据显示，2018年山西二氧化硫、PM2.5、一氧化碳等主要污染物浓度较2013年分别下降49.23%、28.57%、36.17%。 如今，山青、水绿、空气好的山西，吸引了八方游客，旅游产业正成为山西新的经济增长极。黄河、长城、太行3条旅游公路开工建设，100个3A级乡村旅游示范村挂牌。去年，山西接待旅游人数达到5.6亿人次，旅游总收入进入全国前十。 华夏古文明，山西好风光——新时代3700万三晋儿女，为故乡骄傲！参考消息网7月25日报道外媒称，美国商务部长罗斯7月23日对彭博电视表示，特朗普政府计划在未来几周内处理美国科技企业提出的向被列入“黑名单”的华为技术有限公司出售产品的豁免申请。 路透社7月23日报道，“我们会迅速处理这些申请，”罗斯接受采访时表示，“目前已经收到35家公司提出的50多份申请，我们预计在未来几周内会有结果。” 当被追问及是否会在两周内有结果时，罗斯说，“我说的是在未来几周内。” 罗斯发表上述言论的前一天，美国总统特朗普在白宫举行的会议上向几家大型科技公司高管表示，对于美国企业提出的向华为销售产品的请求，政府会“及时”做出决定。 罗斯说，决定是否给予公司豁免是一个跨部门的程序，涉及美国商务部、国务院、国防部和能源部。 “这不是件简单的事情，但总的来说，我们遵循的原则是……从国家安全的角度来看，那些不敏感的东西，我们会以非常支持的态度来对待，”罗斯说，“那些敏感的东西则另当别论，我们必须非常、非常谨慎地处理。”据外媒报道称，印度尼西亚气象和地球物理局（BMKG）近日的监测报告指出，印度尼西亚目前很多地方正在遭遇干旱的影响，比如印度尼西亚的爪哇、峇厘和努沙登加拉大部分的地区已经正在干旱的肆虐之下，而面临长期严重旱灾的威胁，不少地区已经出现了比较严重的水资源匮乏状况。 而来自印度尼西亚气象和地球物理局的气象专家指出，除了这几个地区，印尼还有更多地区例如北亚齐、廖省、占碑，南苏门答腊、朋姑鲁，南苏拉威西南部、西/东加里曼丹和巴布亚南部等地也都面临着长期干旱的可能性。对于印度尼西亚这个热带雨林气候的热带国家来说，这样的状况对于该国的农业和居民生活可能带来明显影响。 事实上，东南亚地区的干旱并非只限于印度尼西亚，从今年春季开始，东南亚地区的泰国、菲律宾等国也先后遭遇了持续干旱的影响。这一系列的异常情况值得我们警惕——印度尼西亚等地的干旱天气或许意味着，厄尔尼诺的威力仍然停留在地球大气层中，还并未出现明显减弱！ 此前，一系列分析数据显示，从2018年秋冬季发展而来的厄尔尼诺已经出现了减弱态势，比如在6月下旬中下旬以来，热带中东太平洋上的关键区水温出现了明显降温，并且持续低于厄尔尼诺阈值（大于等于0.5摄氏度），这种情况被认为是厄尔尼诺即将减弱的征兆。 而来自美国海洋大气管理局（NOAA）的监测数据则指出，太平洋深层的海水偏暖已经减弱，这将不能再给热带中东太平洋地区的表层海水提供暖水，因此太平洋厄尔尼诺的减弱已是大势所趋。其预测认为，厄尔尼诺将在北半球的夏季（6-8月）间逐渐减弱，并且将可能在今年秋冬季转为中性状态。 尽管厄尔尼诺的减弱已是大势所趋，但此前厄尔尼诺对于大气层的影响似乎还并未减弱，比如印度尼西亚等地的干旱正是厄尔尼诺的证明——在厄尔尼诺期间，因为热带中东太平洋水温偏暖，将使得热带中东太平洋上有更多上升气流成云致雨，而西太平洋印度尼西亚等地则转为下沉气流，变为干旱少雨的状态。因此印度尼西亚等地的干旱提醒我们，太平洋地区的厄尔尼诺仍在持续捣乱，我国接下来还得小心它带来的极端天气！近日，据媒体报道，日本和韩国之间的贸易冲突已经彻底失控，最近美国的这两大盟友终于大打出手，先是日本在半导体材料上对韩国进行限制出口，随后两国摩擦越来越严重，两国甚至已放话要搞死对方，大火已烧到国防领域，有消息称两国将重新评估《军事情报保护协定》，而这将影响美军的东北亚军事联盟，就算特朗普劝阻也没用了，外界对此表示：两大盟友在敏感时刻突然摊牌，表明美国的影响力和领导力大幅下降，美国真的要头疼了。 图为在独岛对峙的日韩军舰 这一轮的日韩冲突起源于日本对于某些历史问题的处理态度刺激了敏感而自卑的韩国，韩国对日本的某些激烈举动则引发了日本对韩国高新技术企业的全面制裁。现在两国之间的冲突已经趋于白热化，韩国民间的反日呼声很高，抗议活动甚至波及了在韩的日本外交官。 在民间的各种小打小闹弄得一塌糊涂的时候，韩国高层也准备给日本以颜色。据韩国军方高层人士透露，韩方很可能会谋求“修改”日韩之间的《军事情报保护协定》。这份旨在共享日韩之间机密情报的协定是在美国框架下签署的，象征日韩“和解”并一起被纳入美国的亚太战略之中。如果韩国改动该协定成功，将动摇美国在亚太地区的立足基础。 图为首尔的抗议游行现场 尽管日本与韩国摩擦不断，但是在靠拢美国方面，双方的立场出奇的一致。由于国土狭小，国力贫弱，只有依赖域外大国的力量才能获得生存与发展的机会。由于二战结束之后的国际格局，韩国与日本便乘上了美国的顺风车，得以发展起来。通过在美国的亚太战略当中扮演前进基地与桥头堡的角色，日韩都收到了相当不错的报酬。由于体量不大，通过美国订单与承接产业转移，两国的经济成功起飞，成为了有声有色的发达国家。 韩国打破与日本的默契，寻求日韩决裂的道路，实际上是试图倒逼美国进场仲裁，利用目前被日本制裁打到无力还手的局面营造受害者形象，争取美国做出有利于自己的判决。不过，这一动摇美国在亚太地区实力根基的行为能否得到美国的谅解，目前还是个未知数。为了达成自己微不足道的一个目的而去砸掉老大家里的饭锅，怎么看都不会是个明智的决定，虽然美国的实力已经不如从前，整治一个不听话的小弟想来也不太困难。 图为韩国抗议者与防暴警察冲突 长期以来，韩国一直表现出一种极端自负又极端自卑的复杂。一直在试图摆脱宗主国可怕的文化影响力的同时，韩国却根本无法摆脱自己深受影响的既成事实，形成了极端敏感乃至有些神经质的集体性格。而过敏的后果，一般情况下都是成为笑柄。根据俄罗斯塔斯社7月24日最新报道称，就在近日，中方新一轮的国防白皮书已经发表。在这其中我们将对俄罗斯的军事关系，列在同等重要的位置，对美国的军事关系已经列居第二位，此举等于把最后一层窗户纸捅破。要知道在过去很长一段时间之内，中方跟美方虽说在很多时候观点都处于对立面，但两国在军事领域的合作，还是要远远大于对抗的。在此前我们发表的所有国防白皮书中，都把对美国的军事关系列在头等重要的位置。而近10年以来，随着中方在海空军事力量等领域全面崛起，美国人也开始将中方看成他们的头等威胁，在各个领域疯狂对我们进行打压。 面对美国人咄咄逼人的霸权主义行径，中俄这两个爱好和平的国家，唯有联起手来才能对付得了五角大楼的四处扩张。我们可以看出，如今中方在与俄罗斯的军事合作领域，已经上升到了一个很深的层次。举个例子来说，2018年俄罗斯在远东地区举行了代号为“东方-2018”的大规模战略性演习。在过去，这种演习只有俄罗斯和白俄罗斯可以派遣兵力参加，属于绝密等级。然而在“东方-2018”演习之中，我们整整动用了3000人，以及数百辆坦克装甲车辆的庞大兵力深入俄罗斯内陆，足见两国两军的战略互信等级之高。 就在近日，中俄两国的5架大型军用飞机，飞抵了韩国独岛周边的防空识别区。其中俄罗斯的一架A-50预警机，甚至不慎闯入了韩国领空，引发对方战斗机开火。仅仅从国际关系角度来看，俄罗斯此举是侵犯韩国领空的行为。然而透过现象看本质，此次中俄两国预警机和轰炸机一起集结在日本海海域，是过去从未发生过的。对此外界普遍猜测，中俄两国是在演练在冲突爆发之时，如何封锁日本海。在过去半年多的时间之内，美国一边联合多家网络巨头公司，对我方进行猛烈打压；另一方面在俄罗斯的西部边境以及叙利亚，疯狂压缩莫斯科的生存空间。一系列举动都将中俄关系推得越来越近。 如今我国在国防白皮书中，已经不继续遮掩了。众所周知的世界上三个军事力量最强的国家，一旦其中两个结合起来对 {} {"audioInfo":{"langDec":"chin","decSc":1.0,"nlpSc":0.0,"gender":"m","audioFiles":[{"decSc":1.0,"side":"c","gender":"m","bits":16,"langSc":0.0,"url":"http://10.5.3.85/heluAB.wav","vtl":135560,"codec":6,"channels":1,"rate":8000,"spkn":0,"name":"heluAB.wav","tl":211962}],"langSc":0.64250153,"lang":"chin"}} +cff43f86-ae81-4bbc-90dd-b7de39bdda1b 0086(021)77426829 82 0031 2 0086(021)60080162 886 6 0 1568230183633 329 asp fk 1615000 1 0 0 0 0 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chin chin 0.64250153 fm 0.0 1614000 1612000 1568249840256 1568249839152 展质效提升，内生动力增强。”安徽省统计局副局长赵金宝将全省上半年经济运行的亮点概括为三句话。 今年7月12日，三只松鼠股份有限公司在深交所上市。创立7年来的这家企业，不断颠覆传统销售模式，年销售额从零增加到2018年的82亿元。“创新发展是企业前进的根本。”公司党委书记潘道伟说，上市能加速公司数字化供应链战略的推进，为消费者提供更好的产品和服务。 作为民营经济的试验田，如今的江淮大地仍是一片创新创业的热土，敢闯敢试的改革火种继续在这里播撒。现在的安徽，平均每天新登记注册企业925家，每天获授44件发明专利。 经济“逆势上扬”，靠的是改革，是服务，是创新。十八大以来，安徽着力推动“放管服”改革，首创权力、责任清单的“安徽模式”，让公共服务做“加法”、中介服务做“减法”。而群众办事“最多跑一次”“互联网+政务服务”等创举，正推动安徽迈向审批事项少、办事效率高、投资环境优、市场主体和人民群众获得感强的省份之一。 唯改革才有出路，改革要常讲常新。目前，安徽正持续将一系列新的改革推向深入： ——政务服务改革。努力将省级行政许可事项减到全国最少，企业开办时间压缩至全国最短……到今年底，目标实现80%个人事项全程网办，部分事项“秒批秒办”。 ——全面创新改革。加快建设合肥综合性国家科学中心，加紧布局一批世界前沿的大科学装置，下放科技成果处置权、收益权、使用权，探索实行首席科学家全权负责制……目标建成具有国际影响力的科技创新中心和产业创新中心。 ——农业农村改革。持续深化农村承包地“三权分置”改革，稳步推进农村集体产权制度改革……围绕处理好农民与土地关系主线，努力在农村改革上继续走在前列。 增进民生福祉，乃经济发展之根本目的。居住在集中连片贫困山区金寨县大湾村村民陈泽申前年主动要求摘掉“贫困帽”。得益于党和政府的扶贫政策，他家盖了新房，流转了山地，有了公益性岗位，每年收入三四万元。此前，他将扭转命运的期望全部寄托在“改变家居风水”上面。 为了兑现“小康路上一个都不能掉队”的庄严承诺，安徽把以人民为中心的理念贯穿到经济社会发展各个领域。在实施脱贫攻坚“十大工程”等举措作用下，全省贫困人口由2012年底的679.1万人下降到2018年底的49.9万人，年均减贫100万人以上；贫困发生率由12.6%下降到0.93%；22个贫困县先后“摘帽”。 （壮丽70年·奋斗新时代——共和国发展成就巡礼·图文互动）（7）安徽70年：改革创新书写时代答卷 2017年9月29日，世界首条量子保密通信干线——“京沪干线”正式开通。科研人员在中国科学技术大学先进技术研究院量子通信“京沪干线”总控中心工作。 新华社记者 刘军喜 摄 融入长三角勇于当“尖兵” 早在7年前，位于安徽滁州市的中新苏滁高新技术产业开发区便开工建设，拉开了苏皖两省共建园区的大幕。此后，园区内产业实现深度对接、集群发展，这里成为安徽融入长三角开放合作的典范。 记者近日走进苏滁高新区看到，一排排标准化厂房内，一片热火朝天的生产场面；以新加坡理念建设的邻里中心内，百姓购物、办事功能一应俱全。“截至6月底，从长三角引进的项目数占74%。”该区管委会主任杨广兰说，该区去年经营收入突破100亿元，同比增长逾30%。 2016年5月，中央将安徽合肥、芜湖、马鞍山等8市纳入长三角规划，安徽正式从地理上的长江下游进入长三角世界级城市群。去年11月，长三角一体化发展上升为国家战略。 坐拥皖江水，面向太平洋。安徽迎来历史性的重大发展机遇。中国区域经济学会顾问程必定认为，长三角上升为国家战略对安徽是百年未有的机遇，这意味着，作为长三角一员，安徽将跻身配置全球资源的开放门户、迎来更高质量一体化发展的崭新阶段。 “以前想到外地注册一个公司，交材料、核准名称、领证件，一切顺利也得跑三趟，现在真是节省了很多成本。”去年11月，合肥与星途科技服务有限公司负责人池天水，在合肥高新区的办事窗口领到了由浙江杭州工商部门颁发的营业执照。这也是该区通过长三角“一网通办”业务平台，发出的首张跨省异地登记的营业执照。 致力于要素破壁、深度融入，安徽各地也在抢抓长三角一体化机遇，争当落实国家战略的排头兵。池州、铜陵等地立足区位优势，承接长三角的产业分工，迈向价值链更高端；芜湖市提出“等高对接”，在长三角一体化中找准自身优势、谋求互补发展。 取消长三角区域高速公路省界收费站，推进长三角地区异地就医直接结算，打造长三角“安全消费大市场”……今年5月22日在芜湖举行的2019年长三角地区三省一市主要领导座谈会上，沪苏浙皖相关部门签署了10个打破分割、加速一体化的协议。 “扬皖所长、主动作为。”安徽省委主要负责人表示，安徽是长三角的重要方面军，在区位、科技、产业、生态、能源等方面具有自身的独特优势。安徽未来将把准自身定位，扬创新之长，努力打造长三角科技创新的重要策源地；扬产业之长，努力打造长三角高质量发展的重要增长极；扬区位之长，努力打造长三角世界级城市群的重要副中心；扬生态之长，努力打造长三角生态文明建设的重要样板区，在国家战略下自觉行动当好尖兵！ 新华社广州7月23日电 题：高质量发展的粤式“色香味”——广东制造业一线见闻 新华社记者毛鑫、邓瑞璇、魏玉坤 手机扫描二维码下单，经过10道全自动工序，不到2分钟就可拿到专属机器人模型。在广东拓斯达科技股份有限公司展厅，扫码定制机器人的“神操作”，令人对智能制造新水准印象深刻。 在广东，依托持续的研发投入、技术改造和体制机制创新，制造业正乘势转型升级，走向数字化、自动化、智能化，这一过程犹如烹制一桌高质量发展粤式大餐，“色香味”俱全。 “智”造增“色”：机器换人迎来热潮 原本人头攒动、环境嘈杂的生产车间，逐渐由精密的高度自动化生产线代替。在广东，“智造力”正成为制造业企业标配。 单条生产线的工人数量从160人降到51人、生产换型时间从45分钟降到9分钟、品质控制点从6个增加到108个……美的南沙工厂通过使用全智能生产线，实现效率和品质的双重提升。 “工人少了，但产量在递增，成本效率品质方面改善明显。”美的集团副总裁兼CIO张小懿说，美的通过自动化、数字化的支撑，正在编织一张工业互联网。 美的发展的背后，是广东制造业不断修炼“内功”的火热实践。 走进拓斯达位于东莞的厂区，在工业机器人装配测试车间，近百台机器人排成5列，有节奏地进行出厂前的自动测试。不久后，它们将进入更多工厂的车间，成为生产的主力军。 “智能制造引发机器换人热潮，国内工业机器人市场需求快速增长。”拓斯达产品研发中心总经理徐必业说，未来将持续增加研发投入，重点布局工业传感器、标准工艺设备等领域。 2018年起，广东大力发展智能制造：投入130亿元实施两轮工业技改三年行动计划，惠及3万多家企业，推动3000家工业企业“上云上平台”，工业机器人产量占全国1/5，先进制造业和高技术制造业增加值占规模以上工业增加值比重分别达56.4%和31.5%，制造业提质增效进展明显。 创新添“香”：从要素驱动转向创新驱动 国内研发成功“3D结构光人脸识别技术”、识别速度从0.1秒降至0.001秒、商用跨镜追踪技术……过去4年，广州云从信息科技公司主攻技术研发，成长为国内人工智能行业的佼佼者。 耀眼的成绩离不开研发支撑。云从科技总经理林明都介绍，公司研发团队已达700余人，其中80%以上拥有硕士及以上学历。该团队瞄准“卡脖子”的技术难点潜心钻研，持续突破核心技术。 云从科技的发展，是广东制造业把技术创新摆在核心位置，实现从要素驱动向创新驱动转变的缩影。 近年，广东采取多项措施增加技术有效供给，先后出台“科创12条”等政策，把企业作为科技创新的主体，并着力加强基础研究和应用基础研究，为高质量发展赋能。 数据显示，2018年，广东研发经费支出超过2500亿元，PCT国际专利申请量约占全国一半，有效发明专利量达24.9万件，稳居全国前列。区域创新综合能力排名连续两年居全国第一。 创新“引力”效果渐显，投资加速向高端产业聚集已成趋势。目前，广东规模以上工业企业总量超5万家，在全国名列前茅。国家级高新技术企业达4.5万家，近两年翻了一倍多。巴斯夫、埃克森美孚、富士康、LG等一批投资过百亿美元的项目逐一落地。 政策调“味”：加强制度供给 优化营商环境 2009年，刚从清华大学博士毕业的佘振定，带着一份“人工皮肤”项目的商业计划书南下深圳创业。 接触多家风投机构无果后，是深圳清华大学研究院相中了这个项目，决定进行孵化，并许诺研发团队可以持有项目70%的股份。 这坚定了佘振定在深圳创业的决心，他创办了深圳兰度生物科技有限公司。 后来，该团队从牛跟腱里提取高纯度胶原，制成了可媲美真实皮肤的双层“人工皮肤”，一经上市便填补了国内空白。多轮融资后，公司市值也翻了几番，达到15亿元。 “只有真正把市场作为配置创新资源的关键要素，才更有利于激发成果转化的积极性。”深圳清华大学研究院常务副院长刘伟强说。截至2018年末，该研究院已累计孵化企业2500多家，培养上市公司21家。 近年来，广东大力支持实体经济发展，以制度创新“扫清”制造业“绊脚石”，力争政府“不缺席”“不包揽”，市场“挑大梁”“说了算”。 实行个税补贴等优惠政策吸引海内外高端人才，推行“零跑动”、“秒批”、注册易等改革举措方便企业办事……一系列涉企惠企政策不断出台，持续释放各类市场主体活力。 截至2018年末，广东个体工商户和企业数量达1146万户，其中企业492万户，均稳居全国第一。其中，主营业务收入超百亿、千亿元的企业分别达276家、29家，进入世界500强的企业13家。这是2018年8月1日无人机拍摄的位于山西省朔州 {} {"audioInfo":{"langDec":"chin","decSc":1.0,"nlpSc":0.0,"gender":"m","audioFiles":[{"decSc":1.0,"side":"c","gender":"m","bits":16,"langSc":0.0,"url":"http://10.5.3.85/heluAB.wav","vtl":135560,"codec":6,"channels":1,"rate":8000,"spkn":0,"name":"heluAB.wav","tl":211962}],"langSc":0.64250153,"lang":"chin"}} diff --git a/integration/spark/src/test/resources/jsonFiles/data/allPrimitiveType.json b/integration/spark/src/test/resources/jsonFiles/data/allPrimitiveType.json index d61ff14993a..2ed656d8034 100644 --- a/integration/spark/src/test/resources/jsonFiles/data/allPrimitiveType.json +++ b/integration/spark/src/test/resources/jsonFiles/data/allPrimitiveType.json @@ -8,5 +8,6 @@ "dateField": "2019-03-02", "timeField": "2019-02-12 03:03:34", "decimalField" : 55.35, - "binaryField" : "abc" + "binaryField" : "abc", + "floatField": 23.33 } diff --git a/integration/spark/src/test/resources/jsonFiles/data/allPrimitiveTypeBadRecord.json b/integration/spark/src/test/resources/jsonFiles/data/allPrimitiveTypeBadRecord.json index ae150471149..b3678f2ac2f 100644 --- a/integration/spark/src/test/resources/jsonFiles/data/allPrimitiveTypeBadRecord.json +++ b/integration/spark/src/test/resources/jsonFiles/data/allPrimitiveTypeBadRecord.json @@ -7,5 +7,6 @@ "boolField": 10, "dateField": 12345, "timeField": 12345, - "decimalField" : "String" + "decimalField" : "String", + "floatField": "string" } diff --git a/integration/spark/src/test/scala/org/apache/carbondata/integration/spark/testsuite/complexType/TestComplexDataType.scala b/integration/spark/src/test/scala/org/apache/carbondata/integration/spark/testsuite/complexType/TestComplexDataType.scala index bf3cf93bd5e..0e40de36a3a 100644 --- a/integration/spark/src/test/scala/org/apache/carbondata/integration/spark/testsuite/complexType/TestComplexDataType.scala +++ b/integration/spark/src/test/scala/org/apache/carbondata/integration/spark/testsuite/complexType/TestComplexDataType.scala @@ -1263,7 +1263,7 @@ class TestComplexDataType extends QueryTest with BeforeAndAfterAll { checkAnswer(sql(s"select count(1) from ${tableName}"), Seq(Row(10))) checkAnswer(sql(s"select modela[0][0], modela_ms[0][1] from ${tableName} " + s"where id = 'e01a1773-bd37-40be-a1de-d7e74837a281'"), - Seq(Row(0.0, 0.10781755))) + Seq(Row(0.0, 0.107818f))) sql(s"drop table if exists ${tableName}") } // scalastyle:on lineLength diff --git a/integration/spark/src/test/scala/org/apache/carbondata/integration/spark/testsuite/complexType/TestComplexTypeQuery.scala b/integration/spark/src/test/scala/org/apache/carbondata/integration/spark/testsuite/complexType/TestComplexTypeQuery.scala index a2e9be36026..596cc3a2f6e 100644 --- a/integration/spark/src/test/scala/org/apache/carbondata/integration/spark/testsuite/complexType/TestComplexTypeQuery.scala +++ b/integration/spark/src/test/scala/org/apache/carbondata/integration/spark/testsuite/complexType/TestComplexTypeQuery.scala @@ -55,7 +55,7 @@ class TestComplexTypeQuery extends QueryTest with BeforeAndAfterAll { "array, locationinfo array>, " + "proddate struct>, gamePointId " + - "double,contractNumber double) STORED AS carbondata") + "float,contractNumber double) STORED AS carbondata") sql("LOAD DATA local inpath '" + resourcesPath + "/complextypesample.csv' INTO table complexcarbontable OPTIONS('DELIMITER'=',', " + "'QUOTECHAR'='\"', 'FILEHEADER'='deviceInformationId,channelsId,ROMSize,ROMName," + @@ -67,7 +67,7 @@ class TestComplexTypeQuery extends QueryTest with BeforeAndAfterAll { "array, locationinfo array>, " + "proddate struct>, gamePointId " + - "double,contractNumber double)row format delimited fields terminated by ',' collection " + + "float,contractNumber double)row format delimited fields terminated by ',' collection " + "items terminated by '$' map keys terminated by ':'") sql(s"LOAD DATA local inpath '$resourcesPath/complextypesample.csv' INTO table " + s"complexhivetable") @@ -132,7 +132,7 @@ class TestComplexTypeQuery extends QueryTest with BeforeAndAfterAll { "imsi:string>, MAC array, locationinfo array>, proddate struct>, gamePointId double,contractNumber double) " + + "activeDeactivedate:array>, gamePointId float,contractNumber double) " + "STORED AS carbondata") sql("LOAD DATA local inpath '" + resourcesPath + "/complextypespecialchardelimiter.csv' INTO table complexcarbonwithspecialchardelimiter " + @@ -145,7 +145,7 @@ class TestComplexTypeQuery extends QueryTest with BeforeAndAfterAll { "imsi:string>, MAC array, locationinfo array>, proddate struct>, gamePointId double,contractNumber double)row format " + + "activeDeactivedate:array>, gamePointId float,contractNumber double)row format " + "delimited fields terminated by ',' collection items terminated by '^' map keys terminated " + "by '*'") sql("LOAD DATA local inpath '" + resourcesPath + diff --git a/integration/spark/src/test/scala/org/apache/carbondata/integration/spark/testsuite/complexType/TestCreateTableWithFloat.scala b/integration/spark/src/test/scala/org/apache/carbondata/integration/spark/testsuite/complexType/TestCreateTableWithFloat.scala new file mode 100644 index 00000000000..12df7eea8b3 --- /dev/null +++ b/integration/spark/src/test/scala/org/apache/carbondata/integration/spark/testsuite/complexType/TestCreateTableWithFloat.scala @@ -0,0 +1,68 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.carbondata.integration.spark.testsuite.complexType + +import org.apache.spark.sql.Row +import org.apache.spark.sql.test.util.QueryTest +import org.scalatest.BeforeAndAfterAll + +/** + * Test class of creating and loading for carbon table with float + * + */ +class TestCreateTableWithFloat extends QueryTest with BeforeAndAfterAll { + + var dataPath: String = _ + var countNum: Array[Row] = _ + var floatFiled: Array[Row] = _ + var arrayField: Array[Row] = _ + var structField: Array[Row] = _ + + override def beforeAll: Unit = { + dataPath = s"$resourcesPath/sampleComplex.csv" + sql("drop table if exists floatComplex") + } + + test("test creating carbon table with float in complex type") { + try { + sql("CREATE TABLE floatComplex (Id int, number float, name string, " + + "gamePoint array, mac struct) " + + "STORED AS carbondata") + val desc = sql("desc formatted floatComplex").collect() + assert(desc.apply(1).get(1).equals("float")) + assert(desc.apply(3).get(1).equals("array")) + assert(desc.apply(4).get(1).equals("struct")) + sql(s"LOAD DATA LOCAL INPATH '$dataPath' INTO TABLE floatComplex") + countNum = sql(s"SELECT COUNT(*) FROM floatComplex").collect + floatFiled = sql("SELECT number FROM floatComplex SORT BY Id").collect + arrayField = sql("SELECT gamePoint FROM floatComplex SORT BY Id").collect + structField = sql("SELECT mac.num FROM floatComplex SORT BY Id").collect + } catch { + case e : Throwable => fail(e) + } + // assert that load and query is successful + assertResult(Array(Row(3)))(countNum) + assertResult(Array(Row(1.5), Row(2.0), Row(3.0)))(floatFiled) + assertResult(Array(Row(null), Row(null), Row(null)))(arrayField) + assertResult(Array(Row(3.0), Row(1.5), Row(2.0)))(structField) + } + + override def afterAll: Unit = { + sql("drop table if exists floatComplex") + } +} diff --git a/integration/spark/src/test/scala/org/apache/carbondata/integration/spark/testsuite/dataload/TestLoadDataGeneral.scala b/integration/spark/src/test/scala/org/apache/carbondata/integration/spark/testsuite/dataload/TestLoadDataGeneral.scala index 2456a23b6f3..5e0db1dd6e1 100644 --- a/integration/spark/src/test/scala/org/apache/carbondata/integration/spark/testsuite/dataload/TestLoadDataGeneral.scala +++ b/integration/spark/src/test/scala/org/apache/carbondata/integration/spark/testsuite/dataload/TestLoadDataGeneral.scala @@ -400,7 +400,7 @@ class TestLoadDataGeneral extends QueryTest with BeforeAndAfterEach { " stored as carbondata" + " tblproperties('INVERTED_INDEX'='`a`bc`!!d`', 'SORT_COLUMNS'='`a`bc`!!d`')") sql("insert into special_char values('1','joey','hud', 2, 2.2, 2.3456, 5)") - checkAnswer(sql("select * from special_char"), Seq(Row("1", "joey", "hud", 2, 2.2, 2.3456, 5))) + checkAnswer(sql("select * from special_char"), Seq(Row("1", "joey", "hud", 2, 2.2f, 2.3456, 5))) val df = sql("describe formatted special_char").collect() assert(df.exists(_.get(0).toString.contains("i#d"))) assert(df.exists(_.get(0).toString.contains("nam(e"))) diff --git a/integration/spark/src/test/scala/org/apache/carbondata/integration/spark/testsuite/primitiveTypes/FloatDataTypeTestCase.scala b/integration/spark/src/test/scala/org/apache/carbondata/integration/spark/testsuite/primitiveTypes/FloatDataTypeTestCase.scala index 704412f76d8..b521b662ef4 100644 --- a/integration/spark/src/test/scala/org/apache/carbondata/integration/spark/testsuite/primitiveTypes/FloatDataTypeTestCase.scala +++ b/integration/spark/src/test/scala/org/apache/carbondata/integration/spark/testsuite/primitiveTypes/FloatDataTypeTestCase.scala @@ -38,7 +38,6 @@ class FloatDataTypeTestCase extends QueryTest with BeforeAndAfterAll { sql(s""" LOAD DATA LOCAL INPATH '$resourcesPath/floatSample.csv' into table tfloat """) - } test("select row whose rating is more than 2.8 from tfloat") { @@ -47,12 +46,72 @@ class FloatDataTypeTestCase extends QueryTest with BeforeAndAfterAll { Seq(Row(6))) } + test("test col greater than equal literal") { + checkAnswer( + sql("SELECT ID FROM tfloat where rating >= 2.8"), + Seq(Row(6))) + } + + test("test col less than literal") { + checkAnswer( + sql("SELECT ID FROM tfloat where rating < 2.8"), + Seq(Row(1), Row(2), Row(3), Row(4), Row(5), Row(7), Row(8), Row(9), Row(10))) + } + + test("test col less than equal literal") { + checkAnswer( + sql("SELECT ID FROM tfloat where rating <= 2.8"), + Seq(Row(1), Row(2), Row(3), Row(4), Row(5), Row(7), Row(8), Row(9), Row(10))) + } + test("select row whose rating is 3.5 from tfloat") { checkAnswer( sql("SELECT ID FROM tfloat where rating=3.5"), Seq(Row(6))) } + test("test col not equal literal") { + checkAnswer( + sql("SELECT ID FROM tfloat where rating != 3.5"), + Seq(Row(1), Row(2), Row(3), Row(4), Row(5), Row(7), Row(8), Row(9), Row(10))) + } + + test("test col is null") { + checkAnswer( + sql("SELECT ID FROM tfloat where rating is null"), + Seq()) + } + + test("test col is not null") { + checkAnswer( + sql("SELECT ID FROM tfloat where rating is not null"), + Seq(Row(1), Row(2), Row(3), Row(4), Row(5), Row(6), Row(7), Row(8), Row(9), Row(10))) + } + + test("test col in literal list") { + checkAnswer( + sql("SELECT ID FROM tfloat where rating in (3.5, 2.5)"), + Seq(Row(6))) + } + + test("test col not in literal list") { + checkAnswer( + sql("SELECT ID FROM tfloat where rating not in (3.5, 2.5)"), + Seq(Row(1), Row(2), Row(3), Row(4), Row(5), Row(7), Row(8), Row(9), Row(10))) + } + + test("test and") { + checkAnswer( + sql("SELECT ID FROM tfloat where rating > 2.8 and id = 6"), + Seq(Row(6))) + } + + test("test or") { + checkAnswer( + sql("SELECT ID FROM tfloat where rating < 2.8 or id = 6"), + Seq(Row(1), Row(2), Row(3), Row(4), Row(5), Row(6), Row(7), Row(8), Row(9), Row(10))) + } + test("select sum of rating column from tfloat") { checkAnswer( sql("SELECT sum(rating) FROM tfloat"), diff --git a/integration/spark/src/test/scala/org/apache/carbondata/integration/spark/testsuite/primitiveTypes/TestAdaptiveEncodingForPrimitiveTypes.scala b/integration/spark/src/test/scala/org/apache/carbondata/integration/spark/testsuite/primitiveTypes/TestAdaptiveEncodingForPrimitiveTypes.scala index c5e45b3a3f5..cbda953e972 100644 --- a/integration/spark/src/test/scala/org/apache/carbondata/integration/spark/testsuite/primitiveTypes/TestAdaptiveEncodingForPrimitiveTypes.scala +++ b/integration/spark/src/test/scala/org/apache/carbondata/integration/spark/testsuite/primitiveTypes/TestAdaptiveEncodingForPrimitiveTypes.scala @@ -368,11 +368,11 @@ class TestAdaptiveEncodingForPrimitiveTypes extends QueryTest with BeforeAndAfte sql(s"LOAD DATA INPATH '$rootPath/examples/spark/src/main/resources/streamSample.csv' " + s"INTO TABLE complexTable OPTIONS('HEADER'='TRUE')") checkAnswer(sql("select id,name, salary from complexTable"), - Seq(Row(100000001, "batch_1", 0.1), - Row(100000002, "batch_2", 0.2), - Row(100000003, "batch_3", 0.3), - Row(100000004, "batch_4", 0.4), - Row(100000005, "batch_5", 0.5))) + Seq(Row(100000001, "batch_1", 0.1f), + Row(100000002, "batch_2", 0.2f), + Row(100000003, "batch_3", 0.3f), + Row(100000004, "batch_4", 0.4f), + Row(100000005, "batch_5", 0.5f))) sql("drop table if exists complexTable") } diff --git a/integration/spark/src/test/scala/org/apache/carbondata/spark/testsuite/alterTable/TestAlterTableAddColumns.scala b/integration/spark/src/test/scala/org/apache/carbondata/spark/testsuite/alterTable/TestAlterTableAddColumns.scala index c74dd137998..efbfb6a6101 100644 --- a/integration/spark/src/test/scala/org/apache/carbondata/spark/testsuite/alterTable/TestAlterTableAddColumns.scala +++ b/integration/spark/src/test/scala/org/apache/carbondata/spark/testsuite/alterTable/TestAlterTableAddColumns.scala @@ -136,7 +136,7 @@ class TestAlterTableAddColumns extends QueryTest with BeforeAndAfterAll { s"""select id, name, file, add_column_ls, map_column, struct_column | from ${ tableName } where id = 10""".stripMargin), Seq(Row(10, "name10", mutable.WrappedArray.make(Array(mutable.WrappedArray.make( - Array(1.4, 2.4)), mutable.WrappedArray.make(Array(1.4, 2.4)))), + Array(1.4f, 2.4f)), mutable.WrappedArray.make(Array(1.4f, 2.4f)))), ("999" * 12000), Map(4 -> 4), Row(4)))) sql(s"""DROP TABLE IF EXISTS ${ tableName }""") diff --git a/integration/spark/src/test/scala/org/apache/carbondata/spark/testsuite/createTable/TestCreateTableAsSelect.scala b/integration/spark/src/test/scala/org/apache/carbondata/spark/testsuite/createTable/TestCreateTableAsSelect.scala index 0bd8f73b7cd..7ff14c38c6b 100644 --- a/integration/spark/src/test/scala/org/apache/carbondata/spark/testsuite/createTable/TestCreateTableAsSelect.scala +++ b/integration/spark/src/test/scala/org/apache/carbondata/spark/testsuite/createTable/TestCreateTableAsSelect.scala @@ -469,6 +469,18 @@ class TestCreateTableAsSelect extends QueryTest with BeforeAndAfterAll { checkAnswer(sql("select * from target_table"), Seq(Row("shenzhen", "shenzhen"))) } + test("test float column") { + sql("create table if not exists float_table(f float)") + sql("insert into table float_table select 12.36") + sql("create table carbon_table stored as carbondata as select * from float_table") + checkAnswer(sql("select * from carbon_table"), + sql("select * from float_table")) + sql("create table carbon_table1(f float) stored as carbondata") + sql("insert into table carbon_table1 select 12.36") + checkAnswer(sql("select * from carbon_table1"), + sql("select * from float_table")) + } + override def afterAll { sql("DROP TABLE IF EXISTS carbon_ctas_test") sql("DROP TABLE IF EXISTS parquet_ctas_test") @@ -485,5 +497,8 @@ class TestCreateTableAsSelect extends QueryTest with BeforeAndAfterAll { sql("DROP TABLE IF EXISTS ctas_if_table_name") sql("DROP TABLE IF EXISTS source_table") sql("DROP TABLE IF EXISTS target_table") + sql("DROP TABLE IF EXISTS float_table") + sql("DROP TABLE IF EXISTS carbon_table") + sql("DROP TABLE IF EXISTS carbon_table1") } } diff --git a/integration/spark/src/test/scala/org/apache/carbondata/spark/testsuite/createTable/TestNonTransactionalCarbonTableJsonWriter.scala b/integration/spark/src/test/scala/org/apache/carbondata/spark/testsuite/createTable/TestNonTransactionalCarbonTableJsonWriter.scala index f71b7ee44d8..d836e89bec9 100644 --- a/integration/spark/src/test/scala/org/apache/carbondata/spark/testsuite/createTable/TestNonTransactionalCarbonTableJsonWriter.scala +++ b/integration/spark/src/test/scala/org/apache/carbondata/spark/testsuite/createTable/TestNonTransactionalCarbonTableJsonWriter.scala @@ -120,7 +120,7 @@ class TestNonTransactionalCarbonTableJsonWriter extends QueryTest with BeforeAnd var dataPath: String = null dataPath = resourcesPath + "/jsonFiles/data/allPrimitiveType.json" - val fields = new Array[Field](9) + val fields = new Array[Field](10) fields(0) = new Field("stringField", DataTypes.STRING) fields(1) = new Field("intField", DataTypes.INT) fields(2) = new Field("shortField", DataTypes.SHORT) @@ -130,6 +130,7 @@ class TestNonTransactionalCarbonTableJsonWriter extends QueryTest with BeforeAnd fields(6) = new Field("dateField", DataTypes.DATE) fields(7) = new Field("timeField", DataTypes.TIMESTAMP) fields(8) = new Field("decimalField", DataTypes.createDecimalType(8, 2)) + fields(9) = new Field("floatField", DataTypes.FLOAT) val jsonRow = readFromFile(dataPath) writeCarbonFileFromJsonRowInput(jsonRow, new Schema(fields)) @@ -148,7 +149,8 @@ class TestNonTransactionalCarbonTableJsonWriter extends QueryTest with BeforeAnd false, java.sql.Date.valueOf("2019-03-02"), Timestamp.valueOf("2019-02-12 03:03:34"), - 55.35))) + 55.35, + 23.33f))) sql("DROP TABLE sdkOutputTable") // drop table should not delete the files @@ -162,7 +164,7 @@ class TestNonTransactionalCarbonTableJsonWriter extends QueryTest with BeforeAnd var dataPath: String = null dataPath = resourcesPath + "/jsonFiles/data/allPrimitiveTypeBadRecord.json" - val fields = new Array[Field](9) + val fields = new Array[Field](10) fields(0) = new Field("stringField", DataTypes.STRING) fields(1) = new Field("intField", DataTypes.INT) fields(2) = new Field("shortField", DataTypes.SHORT) @@ -172,6 +174,7 @@ class TestNonTransactionalCarbonTableJsonWriter extends QueryTest with BeforeAnd fields(6) = new Field("dateField", DataTypes.DATE) fields(7) = new Field("timeField", DataTypes.TIMESTAMP) fields(8) = new Field("decimalField", DataTypes.createDecimalType(8, 2)) + fields(9) = new Field("floatField", DataTypes.FLOAT) val jsonRow = readFromFile(dataPath) var exception = intercept[java.lang.AssertionError] { @@ -307,12 +310,13 @@ class TestNonTransactionalCarbonTableJsonWriter extends QueryTest with BeforeAnd FileUtils.deleteDirectory(new File(writerPath)) var dataPath: String = null dataPath = resourcesPath + "/jsonFiles/data/PrimitiveTypeWithNull.json" - val fields = new Array[Field](5) + val fields = new Array[Field](6) fields(0) = new Field("stringField", DataTypes.STRING) fields(1) = new Field("intField", DataTypes.INT) fields(2) = new Field("shortField", DataTypes.SHORT) fields(3) = new Field("longField", DataTypes.LONG) fields(4) = new Field("doubleField", DataTypes.DOUBLE) + fields(5) = new Field("floatField", DataTypes.FLOAT) val jsonRow = readFromFile(dataPath) writeCarbonFileFromJsonRowInput(jsonRow, new Schema(fields)) assert(new File(writerPath).exists()) @@ -321,7 +325,7 @@ class TestNonTransactionalCarbonTableJsonWriter extends QueryTest with BeforeAnd s"""CREATE EXTERNAL TABLE sdkOutputTable STORED AS carbondata LOCATION |'$writerPath' """.stripMargin) checkAnswer(sql("select * from sdkOutputTable"), - Seq(Row(null, 26, null, null, null))) + Seq(Row(null, 26, null, null, null, null))) sql("DROP TABLE sdkOutputTable") // drop table should not delete the files assert(new File(writerPath).listFiles().length > 0) diff --git a/integration/spark/src/test/scala/org/apache/carbondata/spark/testsuite/dataload/TestRangeColumnDataLoad.scala b/integration/spark/src/test/scala/org/apache/carbondata/spark/testsuite/dataload/TestRangeColumnDataLoad.scala index f426afb01da..be01374fffa 100644 --- a/integration/spark/src/test/scala/org/apache/carbondata/spark/testsuite/dataload/TestRangeColumnDataLoad.scala +++ b/integration/spark/src/test/scala/org/apache/carbondata/spark/testsuite/dataload/TestRangeColumnDataLoad.scala @@ -908,8 +908,9 @@ class TestRangeColumnDataLoad extends QueryTest with BeforeAndAfterEach with Bef } else if (2 == lastCol) { // Float data generation for (i <- start until (start + line)) { + val f = ((1990 + i) + (i % 3.14)).toFloat write - .println(i + "," + "n" + i + "," + "c" + (i % 10000) + "," + (1990 + i) + (i % 3.14)) + .println(i + "," + "n" + i + "," + "c" + (i % 10000) + "," + f) } } else if (3 == lastCol) { // Null data generation diff --git a/integration/spark/src/test/scala/org/apache/spark/carbondata/TestStreamingTableOpName.scala b/integration/spark/src/test/scala/org/apache/spark/carbondata/TestStreamingTableOpName.scala index 256317ed7f9..59511f81d4e 100644 --- a/integration/spark/src/test/scala/org/apache/spark/carbondata/TestStreamingTableOpName.scala +++ b/integration/spark/src/test/scala/org/apache/spark/carbondata/TestStreamingTableOpName.scala @@ -375,29 +375,29 @@ class TestStreamingTableOpName extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter where id > 49 and id < 100000002"), - Seq(Row(50, "name_50", "city_50", 500000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(50, "name_50", "city_50", 500000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) assert(totalParts >= partitionNums("select * from stream_table_filter where id > 49 and id < 100000002")) checkAnswer( sql("select * from stream_table_filter where id between 50 and 100000001"), - Seq(Row(50, "name_50", "city_50", 500000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(50, "name_50", "city_50", 500000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) assert(totalParts >= partitionNums("select * from stream_table_filter where id between 50 and 100000001")) checkAnswer( sql("select * from stream_table_filter where name in ('name_9','name_10', 'name_11', 'name_12') and id <> 10 and id not in (11, 12)"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) assert(streamParts >= partitionNums("select * from stream_table_filter where name in ('name_9','name_10', 'name_11', 'name_12') and id <> 10 and id not in (11, 12)")) checkAnswer( sql("select * from stream_table_filter where name = 'name_3'"), - Seq(Row(3, "name_3", "city_3", 30000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(3, "name_3", "city_3", 30000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) assert(streamParts >= partitionNums("select * from stream_table_filter where name = 'name_3'")) checkAnswer( sql("select * from stream_table_filter where name like '%me_3%' and id < 30"), - Seq(Row(3, "name_3", "city_3", 30000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(3, "name_3", "city_3", 30000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) assert(streamParts >= partitionNums("select * from stream_table_filter where name like '%me_3%' and id < 30")) checkAnswer(sql("select count(*) from stream_table_filter where name like '%ame%'"), @@ -410,24 +410,24 @@ class TestStreamingTableOpName extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter where name >= 'name_3' and id < 4"), - Seq(Row(3, "name_3", "city_3", 30000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(3, "name_3", "city_3", 30000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) assert(streamParts >= partitionNums("select * from stream_table_filter where name >= 'name_3' and id < 4")) checkAnswer( sql("select * from stream_table_filter where id in (9, 10, 11, 12) and name <> 'name_10' and name not in ('name_11', 'name_12')"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) assert(streamParts >= partitionNums("select * from stream_table_filter where id in (9, 10, 11, 12) and name <> 'name_10' and name not in ('name_11', 'name_12')")) checkAnswer( sql("select * from stream_table_filter where city = 'city_1'"), - Seq(Row(1, "name_1", "city_1", 10000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(1, "name_1", "city_1", 10000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) assert(totalParts >= partitionNums("select * from stream_table_filter where city = 'city_1'")) checkAnswer( sql("select * from stream_table_filter where city like '%ty_1%' and ( id < 10 or id >= 100000001)"), - Seq(Row(1, "name_1", "city_1", 10000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(1, "name_1", "city_1", 10000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) assert(totalParts >= partitionNums("select * from stream_table_filter where city like '%ty_1%' and ( id < 10 or id >= 100000001)")) checkAnswer(sql("select count(*) from stream_table_filter where city like '%city%'"), @@ -436,174 +436,174 @@ class TestStreamingTableOpName extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter where city > 'city_09' and city < 'city_10'"), - Seq(Row(1, "name_1", "city_1", 10000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(1, "name_1", "city_1", 10000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) assert(totalParts >= partitionNums("select * from stream_table_filter where city > 'city_09' and city < 'city_10'")) checkAnswer( sql("select * from stream_table_filter where city between 'city_09' and 'city_1'"), - Seq(Row(1, "name_1", "city_1", 10000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(1, "name_1", "city_1", 10000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where id in (9, 10, 11, 12) and city <> 'city_10' and city not in ('city_11', 'city_12')"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) assert(streamParts >= partitionNums("select * from stream_table_filter where id in (9, 10, 11, 12) and city <> 'city_10' and city not in ('city_11', 'city_12')")) checkAnswer( sql("select * from stream_table_filter where salary = 90000"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) assert(streamParts >= partitionNums("select * from stream_table_filter where salary = 90000")) checkAnswer( sql("select * from stream_table_filter where salary > 80000 and salary <= 100000"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), - Row(10, "name_10", "city_10", 100000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), + Row(10, "name_10", "city_10", 100000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) assert(streamParts >= partitionNums("select * from stream_table_filter where salary > 80000 and salary <= 100000")) checkAnswer( sql("select * from stream_table_filter where salary between 80001 and 90000"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) assert(streamParts >= partitionNums("select * from stream_table_filter where salary between 80001 and 90000")) checkAnswer( sql("select * from stream_table_filter where id in (9, 10, 11, 12) and salary <> 100000.0 and salary not in (110000.0, 120000.0)"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) assert(streamParts >= partitionNums("select * from stream_table_filter where id in (9, 10, 11, 12) and salary <> 100000.0 and salary not in (110000.0, 120000.0)")) checkAnswer( sql("select * from stream_table_filter where tax = 0.04 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) assert(streamParts >= partitionNums("select * from stream_table_filter where tax = 0.04 and id < 100")) checkAnswer( sql("select * from stream_table_filter where tax >= 0.04 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) assert(streamParts >= partitionNums("select * from stream_table_filter where tax >= 0.04 and id < 100")) checkAnswer( sql("select * from stream_table_filter where tax < 0.05 and tax > 0.02 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) assert(streamParts >= partitionNums("select * from stream_table_filter where tax < 0.05 and tax > 0.02 and id < 100")) checkAnswer( sql("select * from stream_table_filter where tax between 0.02 and 0.04 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) assert(streamParts >= partitionNums("select * from stream_table_filter where tax between 0.02 and 0.04 and id < 100")) checkAnswer( sql("select * from stream_table_filter where id in (9, 10) and tax <> 0.01"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) assert(streamParts >= partitionNums("select * from stream_table_filter where id in (9, 10) and tax <> 0.01")) checkAnswer( sql("select * from stream_table_filter where percent = 80.04 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) assert(streamParts >= partitionNums("select * from stream_table_filter where percent = 80.04 and id < 100")) checkAnswer( sql("select * from stream_table_filter where percent >= 80.04 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) assert(streamParts >= partitionNums("select * from stream_table_filter where percent >= 80.04 and id < 100")) checkAnswer( sql("select * from stream_table_filter where percent < 80.05 and percent > 80.02 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) assert(streamParts >= partitionNums("select * from stream_table_filter where percent < 80.05 and percent > 80.02 and id < 100")) checkAnswer( sql("select * from stream_table_filter where percent between 80.02 and 80.05 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) assert(streamParts >= partitionNums("select * from stream_table_filter where percent between 80.02 and 80.05 and id < 100")) checkAnswer( sql("select * from stream_table_filter where id in (9, 10) and percent <> 80.01"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) assert(streamParts >= partitionNums("select * from stream_table_filter where id in (9, 10) and percent <> 80.01")) checkAnswer( sql("select * from stream_table_filter where birthday between '1990-01-04' and '1990-01-05'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), - Row(100000005, "batch_5", "city_5", 0.5, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), + Row(100000005, "batch_5", "city_5", 0.5f, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0")))) assert(totalParts >= partitionNums("select * from stream_table_filter where birthday between '1990-01-04' and '1990-01-05'")) checkAnswer( sql("select * from stream_table_filter where birthday = '1990-01-04'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) assert(totalParts >= partitionNums("select * from stream_table_filter where birthday = '1990-01-04'")) checkAnswer( sql("select * from stream_table_filter where birthday > '1990-01-03' and birthday <= '1990-01-04'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) assert(totalParts >= partitionNums("select * from stream_table_filter where birthday > '1990-01-03' and birthday <= '1990-01-04'")) checkAnswer( sql("select * from stream_table_filter where birthday between '1990-01-04' and '1990-01-05'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), - Row(100000005, "batch_5", "city_5", 0.5, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), + Row(100000005, "batch_5", "city_5", 0.5f, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0")))) assert(totalParts >= partitionNums("select * from stream_table_filter where birthday between '1990-01-04' and '1990-01-05'")) checkAnswer( sql("select * from stream_table_filter where id in (9, 10) and birthday <> '1990-01-01'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) assert(streamParts >= partitionNums("select * from stream_table_filter where id in (9, 10) and birthday <> '1990-01-01'")) checkAnswer( sql("select * from stream_table_filter where register = '2010-01-04 10:01:01'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) assert(totalParts >= partitionNums("select * from stream_table_filter where register = '2010-01-04 10:01:01'")) checkAnswer( sql("select * from stream_table_filter where register > '2010-01-03 10:01:01' and register <= '2010-01-04 10:01:01'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) assert(totalParts >= partitionNums("select * from stream_table_filter where register > '2010-01-03 10:01:01' and register <= '2010-01-04 10:01:01'")) checkAnswer( sql("select * from stream_table_filter where register between '2010-01-04 10:01:01' and '2010-01-05 10:01:01'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), - Row(100000005, "batch_5", "city_5", 0.5, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), + Row(100000005, "batch_5", "city_5", 0.5f, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0")))) assert(totalParts >= partitionNums("select * from stream_table_filter where register between '2010-01-04 10:01:01' and '2010-01-05 10:01:01'")) checkAnswer( sql("select * from stream_table_filter where id in (9, 10) and register <> '2010-01-01 10:01:01'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) assert(totalParts >= partitionNums("select * from stream_table_filter where id in (9, 10) and register <> '2010-01-01 10:01:01'")) checkAnswer( sql("select * from stream_table_filter where updated = '2010-01-04 10:01:01'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) assert(totalParts >= partitionNums("select * from stream_table_filter where updated = '2010-01-04 10:01:01'")) checkAnswer( sql("select * from stream_table_filter where updated > '2010-01-03 10:01:01' and register <= '2010-01-04 10:01:01'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) assert(totalParts >= partitionNums("select * from stream_table_filter where updated > '2010-01-03 10:01:01' and register <= '2010-01-04 10:01:01'")) checkAnswer( sql("select * from stream_table_filter where updated between '2010-01-04 10:01:01' and '2010-01-05 10:01:01'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), - Row(100000005, "batch_5", "city_5", 0.5, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), + Row(100000005, "batch_5", "city_5", 0.5f, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0")))) assert(totalParts >= partitionNums("select * from stream_table_filter where updated between '2010-01-04 10:01:01' and '2010-01-05 10:01:01'")) checkAnswer( sql("select * from stream_table_filter where id in (9, 10) and updated <> '2010-01-01 10:01:01'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) assert(streamParts >= partitionNums("select * from stream_table_filter where id in (9, 10) and updated <> '2010-01-01 10:01:01'")) checkAnswer( sql("select * from stream_table_filter where id is null order by name"), Seq(Row(null, "", "", null, null, null, null, null, null), - Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) assert(totalParts >= partitionNums("select * from stream_table_filter where id is null order by name")) checkAnswer( @@ -613,7 +613,7 @@ class TestStreamingTableOpName extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter where id is null and name <> ''"), - Seq(Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) assert(totalParts == partitionNums("select * from stream_table_filter where id is null and name <> ''")) checkAnswer( @@ -623,7 +623,7 @@ class TestStreamingTableOpName extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter where id is null and city <> ''"), - Seq(Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) assert(totalParts == partitionNums("select * from stream_table_filter where id is null and city <> ''")) checkAnswer( @@ -633,7 +633,7 @@ class TestStreamingTableOpName extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter where id is null and salary is not null"), - Seq(Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) assert(totalParts == partitionNums("select * from stream_table_filter where id is null and salary is not null")) checkAnswer( @@ -643,7 +643,7 @@ class TestStreamingTableOpName extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter where id is null and tax is not null"), - Seq(Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) assert(totalParts == partitionNums("select * from stream_table_filter where id is null and tax is not null")) checkAnswer( @@ -653,7 +653,7 @@ class TestStreamingTableOpName extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter where id is null and percent is not null"), - Seq(Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) assert(totalParts == partitionNums("select * from stream_table_filter where id is null and percent is not null")) checkAnswer( @@ -663,7 +663,7 @@ class TestStreamingTableOpName extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter where id is null and birthday is not null"), - Seq(Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) assert(totalParts == partitionNums("select * from stream_table_filter where id is null and birthday is not null")) checkAnswer( @@ -673,7 +673,7 @@ class TestStreamingTableOpName extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter where id is null and register is not null"), - Seq(Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) assert(totalParts == partitionNums("select * from stream_table_filter where id is null and register is not null")) checkAnswer( @@ -683,7 +683,7 @@ class TestStreamingTableOpName extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter where id is null and updated is not null"), - Seq(Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) assert(totalParts == partitionNums("select * from stream_table_filter where id is null and updated is not null")) // agg @@ -703,9 +703,9 @@ class TestStreamingTableOpName extends QueryTest with BeforeAndAfterAll { "and city <> '' " + "group by city " + "order by city"), - Seq(Row("city_1", 2, 100000002, 50000001, 10000.0, 0.1), - Row("city_2", 1, 100000002, 100000002, 0.2, 0.2), - Row("city_3", 2, 100000006, 50000003, 30000.0, 0.3))) + Seq(Row("city_1", 2, 100000002, 50000001, 10000.0f, 0.1f), + Row("city_2", 1, 100000002, 100000002, 0.2f, 0.2f), + Row("city_3", 2, 100000006, 50000003, 30000.0f, 0.3f))) assert(totalParts >= partitionNums( "select city, count(id), sum(id), cast(avg(id) as integer), " + "max(salary), min(salary) " + @@ -770,25 +770,25 @@ class TestStreamingTableOpName extends QueryTest with BeforeAndAfterAll { // filter checkAnswer( sql("select * from stream_table_filter_complex where id = 1"), - Seq(Row(1, "name_1", "city_1", 10000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 1)))) + Seq(Row(1, "name_1", "city_1", 10000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 1)))) checkAnswer( sql("select * from stream_table_filter_complex where id > 49 and id < 100000002"), - Seq(Row(50, "name_50", "city_50", 500000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_50", "school_5050")), 50)), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 20)))) + Seq(Row(50, "name_50", "city_50", 500000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_50", "school_5050")), 50)), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 20)))) checkAnswer( sql("select * from stream_table_filter_complex where id between 50 and 100000001"), - Seq(Row(50, "name_50", "city_50", 500000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_50", "school_5050")), 50)), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 20)))) + Seq(Row(50, "name_50", "city_50", 500000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_50", "school_5050")), 50)), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 20)))) checkAnswer( sql("select * from stream_table_filter_complex where name = 'name_3'"), - Seq(Row(3, "name_3", "city_3", 30000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_3", "school_33")), 3)))) + Seq(Row(3, "name_3", "city_3", 30000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_3", "school_33")), 3)))) checkAnswer( sql("select * from stream_table_filter_complex where name like '%me_3%' and id < 30"), - Seq(Row(3, "name_3", "city_3", 30000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_3", "school_33")), 3)))) + Seq(Row(3, "name_3", "city_3", 30000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_3", "school_33")), 3)))) checkAnswer(sql("select count(*) from stream_table_filter_complex where name like '%ame%'"), Seq(Row(49))) @@ -798,134 +798,134 @@ class TestStreamingTableOpName extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter_complex where name >= 'name_3' and id < 4"), - Seq(Row(3, "name_3", "city_3", 30000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_3", "school_33")), 3)))) + Seq(Row(3, "name_3", "city_3", 30000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_3", "school_33")), 3)))) checkAnswer( sql("select * from stream_table_filter_complex where city = 'city_1'"), - Seq(Row(1, "name_1", "city_1", 10000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 1)), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 20)))) + Seq(Row(1, "name_1", "city_1", 10000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 1)), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 20)))) checkAnswer( sql("select * from stream_table_filter_complex where city like '%ty_1%' and ( id < 10 or id >= 100000001)"), - Seq(Row(1, "name_1", "city_1", 10000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 1)), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 20)))) + Seq(Row(1, "name_1", "city_1", 10000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 1)), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 20)))) checkAnswer(sql("select count(*) from stream_table_filter_complex where city like '%city%'"), Seq(Row(54))) checkAnswer( sql("select * from stream_table_filter_complex where city > 'city_09' and city < 'city_10'"), - Seq(Row(1, "name_1", "city_1", 10000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 1)), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 20)))) + Seq(Row(1, "name_1", "city_1", 10000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 1)), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 20)))) checkAnswer( sql("select * from stream_table_filter_complex where city between 'city_09' and 'city_1'"), - Seq(Row(1, "name_1", "city_1", 10000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 1)), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 20)))) + Seq(Row(1, "name_1", "city_1", 10000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 1)), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 20)))) checkAnswer( sql("select * from stream_table_filter_complex where salary = 90000"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) checkAnswer( sql("select * from stream_table_filter_complex where salary > 80000 and salary <= 100000"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), - Row(10, "name_10", "city_10", 100000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_10", "school_1010")), 10)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), + Row(10, "name_10", "city_10", 100000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_10", "school_1010")), 10)))) checkAnswer( sql("select * from stream_table_filter_complex where salary between 80001 and 90000"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) checkAnswer( sql("select * from stream_table_filter_complex where tax = 0.04 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) checkAnswer( sql("select * from stream_table_filter_complex where tax >= 0.04 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) checkAnswer( sql("select * from stream_table_filter_complex where tax < 0.05 and tax > 0.02 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) checkAnswer( sql("select * from stream_table_filter_complex where tax between 0.02 and 0.04 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) checkAnswer( sql("select * from stream_table_filter_complex where percent = 80.04 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) checkAnswer( sql("select * from stream_table_filter_complex where percent >= 80.04 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) checkAnswer( sql("select * from stream_table_filter_complex where percent < 80.05 and percent > 80.02 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) checkAnswer( sql("select * from stream_table_filter_complex where percent between 80.02 and 80.05 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) checkAnswer( sql("select * from stream_table_filter_complex where birthday between '1990-01-04' and '1990-01-05'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)), - Row(100000005, "batch_5", "city_5", 0.5, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Row(wrap(Array("school_5", "school_55")), 60)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)), + Row(100000005, "batch_5", "city_5", 0.5f, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Row(wrap(Array("school_5", "school_55")), 60)))) checkAnswer( sql("select * from stream_table_filter_complex where birthday = '1990-01-04'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)))) checkAnswer( sql("select * from stream_table_filter_complex where birthday > '1990-01-03' and birthday <= '1990-01-04'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)))) checkAnswer( sql("select * from stream_table_filter_complex where birthday between '1990-01-04' and '1990-01-05'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)), - Row(100000005, "batch_5", "city_5", 0.5, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Row(wrap(Array("school_5", "school_55")), 60)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)), + Row(100000005, "batch_5", "city_5", 0.5f, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Row(wrap(Array("school_5", "school_55")), 60)))) checkAnswer( sql("select * from stream_table_filter_complex where register = '2010-01-04 10:01:01'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)))) checkAnswer( sql("select * from stream_table_filter_complex where register > '2010-01-03 10:01:01' and register <= '2010-01-04 10:01:01'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)))) checkAnswer( sql("select * from stream_table_filter_complex where register between '2010-01-04 10:01:01' and '2010-01-05 10:01:01'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)), - Row(100000005, "batch_5", "city_5", 0.5, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Row(wrap(Array("school_5", "school_55")), 60)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)), + Row(100000005, "batch_5", "city_5", 0.5f, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Row(wrap(Array("school_5", "school_55")), 60)))) checkAnswer( sql("select * from stream_table_filter_complex where updated = '2010-01-04 10:01:01'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)))) checkAnswer( sql("select * from stream_table_filter_complex where updated > '2010-01-03 10:01:01' and register <= '2010-01-04 10:01:01'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)))) checkAnswer( sql("select * from stream_table_filter_complex where updated between '2010-01-04 10:01:01' and '2010-01-05 10:01:01'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)), - Row(100000005, "batch_5", "city_5", 0.5, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Row(wrap(Array("school_5", "school_55")), 60)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)), + Row(100000005, "batch_5", "city_5", 0.5f, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Row(wrap(Array("school_5", "school_55")), 60)))) checkAnswer( sql("select * from stream_table_filter_complex where id is null order by name"), Seq(Row(null, "", "", null, null, null, null, null, null, Row(mutable.WrappedArray.make(Array()), null)), - Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) + Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) checkAnswer( sql("select * from stream_table_filter_complex where name = ''"), @@ -933,7 +933,7 @@ class TestStreamingTableOpName extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter_complex where id is null and name <> ''"), - Seq(Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) + Seq(Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) checkAnswer( sql("select * from stream_table_filter_complex where city = ''"), @@ -941,7 +941,7 @@ class TestStreamingTableOpName extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter_complex where id is null and city <> ''"), - Seq(Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) + Seq(Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) checkAnswer( sql("select * from stream_table_filter_complex where salary is null"), @@ -949,7 +949,7 @@ class TestStreamingTableOpName extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter_complex where id is null and salary is not null"), - Seq(Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) + Seq(Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) checkAnswer( sql("select * from stream_table_filter_complex where tax is null"), @@ -957,7 +957,7 @@ class TestStreamingTableOpName extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter_complex where id is null and tax is not null"), - Seq(Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) + Seq(Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) checkAnswer( sql("select * from stream_table_filter_complex where percent is null"), @@ -965,7 +965,7 @@ class TestStreamingTableOpName extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter_complex where id is null and salary is not null"), - Seq(Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) + Seq(Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) checkAnswer( sql("select * from stream_table_filter_complex where birthday is null"), @@ -973,7 +973,7 @@ class TestStreamingTableOpName extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter_complex where id is null and birthday is not null"), - Seq(Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) + Seq(Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) checkAnswer( sql("select * from stream_table_filter_complex where register is null"), @@ -981,7 +981,7 @@ class TestStreamingTableOpName extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter_complex where id is null and register is not null"), - Seq(Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) + Seq(Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) checkAnswer( sql("select * from stream_table_filter_complex where updated is null"), @@ -989,7 +989,7 @@ class TestStreamingTableOpName extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter_complex where id is null and updated is not null"), - Seq(Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) + Seq(Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) // agg checkAnswer( @@ -1005,9 +1005,9 @@ class TestStreamingTableOpName extends QueryTest with BeforeAndAfterAll { "and city <> '' " + "group by city " + "order by city"), - Seq(Row("city_1", 2, 100000002, 10, 10000.0, 0.1), - Row("city_2", 1, 100000002, 30, 0.2, 0.2), - Row("city_3", 2, 100000006, 21, 30000.0, 0.3))) + Seq(Row("city_1", 2, 100000002, 10, 10000.0f, 0.1f), + Row("city_2", 1, 100000002, 30, 0.2f, 0.2f), + Row("city_3", 2, 100000006, 21, 30000.0f, 0.3f))) } test("test deleting streaming segment by ID while ingesting") { diff --git a/integration/spark/src/test/scala/org/apache/spark/carbondata/TestStreamingTableQueryFilter.scala b/integration/spark/src/test/scala/org/apache/spark/carbondata/TestStreamingTableQueryFilter.scala index 4232b085690..8aaa539d87c 100644 --- a/integration/spark/src/test/scala/org/apache/spark/carbondata/TestStreamingTableQueryFilter.scala +++ b/integration/spark/src/test/scala/org/apache/spark/carbondata/TestStreamingTableQueryFilter.scala @@ -98,25 +98,25 @@ class TestStreamingTableQueryFilter extends QueryTest with BeforeAndAfterAll { // filter checkAnswer( sql("select * from streaming_table_filter.stream_filter where id = 1"), - Seq(Row(1, "name_1", "city_1", 10000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("1" + longStrValue), Row(wrap(Array("school_1", "school_11")), 1)))) + Seq(Row(1, "name_1", "city_1", 10000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("1" + longStrValue), Row(wrap(Array("school_1", "school_11")), 1)))) checkAnswer( sql("select * from streaming_table_filter.stream_filter where id > 49 and id < 100000002"), - Seq(Row(50, "name_50", "city_50", 500000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("50" + longStrValue), Row(wrap(Array("school_50", "school_5050")), 50)), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("1" + longStrValue), Row(wrap(Array("school_1", "school_11")), 20)))) + Seq(Row(50, "name_50", "city_50", 500000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("50" + longStrValue), Row(wrap(Array("school_50", "school_5050")), 50)), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("1" + longStrValue), Row(wrap(Array("school_1", "school_11")), 20)))) checkAnswer( sql("select * from streaming_table_filter.stream_filter where id between 50 and 100000001"), - Seq(Row(50, "name_50", "city_50", 500000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("50" + longStrValue), Row(wrap(Array("school_50", "school_5050")), 50)), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("1" + longStrValue), Row(wrap(Array("school_1", "school_11")), 20)))) + Seq(Row(50, "name_50", "city_50", 500000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("50" + longStrValue), Row(wrap(Array("school_50", "school_5050")), 50)), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("1" + longStrValue), Row(wrap(Array("school_1", "school_11")), 20)))) checkAnswer( sql("select * from streaming_table_filter.stream_filter where salary = 490000.0 and percent = 80.01"), - Seq(Row(49, "name_49", "city_49", 490000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("49" + longStrValue), Row(wrap(Array("school_49", "school_4949")), 49)))) + Seq(Row(49, "name_49", "city_49", 490000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("49" + longStrValue), Row(wrap(Array("school_49", "school_4949")), 49)))) checkAnswer( sql("select * from streaming_table_filter.stream_filter where id > 20 and salary = 300000.0 and file.age > 25"), - Seq(Row(30, "name_30", "city_30", 300000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("30" + longStrValue), Row(wrap(Array("school_30", "school_3030")), 30)))) + Seq(Row(30, "name_30", "city_30", 300000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("30" + longStrValue), Row(wrap(Array("school_30", "school_3030")), 30)))) } def createWriteSocketThread( diff --git a/integration/spark/src/test/scala/org/apache/spark/carbondata/TestStreamingTableWithLongString.scala b/integration/spark/src/test/scala/org/apache/spark/carbondata/TestStreamingTableWithLongString.scala index ef6a459a91d..2ac4718f365 100644 --- a/integration/spark/src/test/scala/org/apache/spark/carbondata/TestStreamingTableWithLongString.scala +++ b/integration/spark/src/test/scala/org/apache/spark/carbondata/TestStreamingTableWithLongString.scala @@ -109,7 +109,7 @@ class TestStreamingTableWithLongString extends QueryTest with BeforeAndAfterAll ) val row = sql("select * from streaming_longstr.stream_table_longstr_file order by id").head() - val exceptedRow = Row(10, "name_10", "city_10", 100000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), "10" + longStrValue) + val exceptedRow = Row(10, "name_10", "city_10", 100000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), "10" + longStrValue) assertResult(exceptedRow)(row) new File(csvDataDir).delete() } @@ -140,17 +140,17 @@ class TestStreamingTableWithLongString extends QueryTest with BeforeAndAfterAll checkAnswer( sql("select * from streaming_longstr.stream_table_longstr where id = 1"), - Seq(Row(1, "name_1", "city_1", 10000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("1" + longStrValue)))) + Seq(Row(1, "name_1", "city_1", 10000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("1" + longStrValue)))) checkAnswer( sql("select * from streaming_longstr.stream_table_longstr where id > 49 and id < 100000002"), - Seq(Row(50, "name_50", "city_50", 500000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("50" + longStrValue)), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("1" + longStrValue)))) + Seq(Row(50, "name_50", "city_50", 500000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("50" + longStrValue)), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("1" + longStrValue)))) checkAnswer( sql("select * from streaming_longstr.stream_table_longstr where id between 50 and 100000001"), - Seq(Row(50, "name_50", "city_50", 500000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("50" + longStrValue)), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("1" + longStrValue)))) + Seq(Row(50, "name_50", "city_50", 500000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("50" + longStrValue)), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("1" + longStrValue)))) sql("show segments for table streaming_longstr.stream_table_longstr").collect() sql("alter table streaming_longstr.stream_table_longstr finish streaming") @@ -171,17 +171,17 @@ class TestStreamingTableWithLongString extends QueryTest with BeforeAndAfterAll checkAnswer( sql("select * from streaming_longstr.stream_table_longstr where id = 1"), - Seq(Row(1, "name_1", "city_1", 10000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("1" + longStrValue)))) + Seq(Row(1, "name_1", "city_1", 10000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("1" + longStrValue)))) checkAnswer( sql("select * from streaming_longstr.stream_table_longstr where id > 49 and id < 100000002"), - Seq(Row(50, "name_50", "city_50", 500000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("50" + longStrValue)), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("1" + longStrValue)))) + Seq(Row(50, "name_50", "city_50", 500000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("50" + longStrValue)), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("1" + longStrValue)))) checkAnswer( sql("select * from streaming_longstr.stream_table_longstr where id between 50 and 100000001"), - Seq(Row(50, "name_50", "city_50", 500000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("50" + longStrValue)), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("1" + longStrValue)))) + Seq(Row(50, "name_50", "city_50", 500000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("50" + longStrValue)), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("1" + longStrValue)))) sql("alter table streaming_longstr.stream_table_longstr compact 'major'") sql("show segments for table streaming_longstr.stream_table_longstr").collect() @@ -200,17 +200,17 @@ class TestStreamingTableWithLongString extends QueryTest with BeforeAndAfterAll checkAnswer( sql("select * from streaming_longstr.stream_table_longstr where id = 1"), - Seq(Row(1, "name_1", "city_1", 10000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("1" + longStrValue)))) + Seq(Row(1, "name_1", "city_1", 10000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("1" + longStrValue)))) checkAnswer( sql("select * from streaming_longstr.stream_table_longstr where id > 49 and id < 100000002"), - Seq(Row(50, "name_50", "city_50", 500000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("50" + longStrValue)), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("1" + longStrValue)))) + Seq(Row(50, "name_50", "city_50", 500000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("50" + longStrValue)), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("1" + longStrValue)))) checkAnswer( sql("select * from streaming_longstr.stream_table_longstr where id between 50 and 100000001"), - Seq(Row(50, "name_50", "city_50", 500000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("50" + longStrValue)), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("1" + longStrValue)))) + Seq(Row(50, "name_50", "city_50", 500000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("50" + longStrValue)), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("1" + longStrValue)))) } test("[CARBONDATA-3497] Support to write long string for streaming table: " + @@ -243,17 +243,17 @@ class TestStreamingTableWithLongString extends QueryTest with BeforeAndAfterAll // filter checkAnswer( sql("select * from streaming_longstr.stream_table_longstr_complex where id = 1"), - Seq(Row(1, "name_1", "city_1", 10000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("1" + longStrValue), Row(wrap(Array("school_1", "school_11")), 1)))) + Seq(Row(1, "name_1", "city_1", 10000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("1" + longStrValue), Row(wrap(Array("school_1", "school_11")), 1)))) checkAnswer( sql("select * from streaming_longstr.stream_table_longstr_complex where id > 49 and id < 100000002"), - Seq(Row(50, "name_50", "city_50", 500000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("50" + longStrValue), Row(wrap(Array("school_50", "school_5050")), 50)), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("1" + longStrValue), Row(wrap(Array("school_1", "school_11")), 20)))) + Seq(Row(50, "name_50", "city_50", 500000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("50" + longStrValue), Row(wrap(Array("school_50", "school_5050")), 50)), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("1" + longStrValue), Row(wrap(Array("school_1", "school_11")), 20)))) checkAnswer( sql("select * from streaming_longstr.stream_table_longstr_complex where id between 50 and 100000001"), - Seq(Row(50, "name_50", "city_50", 500000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("50" + longStrValue), Row(wrap(Array("school_50", "school_5050")), 50)), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("1" + longStrValue), Row(wrap(Array("school_1", "school_11")), 20)))) + Seq(Row(50, "name_50", "city_50", 500000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("50" + longStrValue), Row(wrap(Array("school_50", "school_5050")), 50)), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("1" + longStrValue), Row(wrap(Array("school_1", "school_11")), 20)))) } test("[CARBONDATA-3497] Support to write long string for streaming table: StreamSQL") { @@ -329,7 +329,7 @@ class TestStreamingTableWithLongString extends QueryTest with BeforeAndAfterAll checkAnswer(sql("select count(*) from sink"), Seq(Row(10))) val row = sql("select * from sink order by id").head() - val exceptedRow = Row(11, "name_11", "city_11", 110000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("11" + longStrValue)) + val exceptedRow = Row(11, "name_11", "city_11", 110000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), ("11" + longStrValue)) assertResult(exceptedRow)(row) sql("SHOW STREAMS").collect() diff --git a/integration/spark/src/test/scala/org/apache/spark/carbondata/TestStreamingTableWithRowParser.scala b/integration/spark/src/test/scala/org/apache/spark/carbondata/TestStreamingTableWithRowParser.scala index a36c6ee7c6e..90ab5a2651f 100644 --- a/integration/spark/src/test/scala/org/apache/spark/carbondata/TestStreamingTableWithRowParser.scala +++ b/integration/spark/src/test/scala/org/apache/spark/carbondata/TestStreamingTableWithRowParser.scala @@ -107,29 +107,29 @@ class TestStreamingTableWithRowParser extends QueryTest with BeforeAndAfterAll { // filter checkAnswer( sql("select * from stream_table_filter where id = 1"), - Seq(Row(1, "name_1", "city_1", 10000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(1, "name_1", "city_1", 10000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where id > 49 and id < 100000002"), - Seq(Row(50, "name_50", "city_50", 500000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(50, "name_50", "city_50", 500000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where id between 50 and 100000001"), - Seq(Row(50, "name_50", "city_50", 500000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(50, "name_50", "city_50", 500000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where name in ('name_9','name_10', 'name_11', 'name_12') and id <> 10 and id not in (11, 12)"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where name = 'name_3'"), - Seq(Row(3, "name_3", "city_3", 30000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(3, "name_3", "city_3", 30000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where name like '%me_3%' and id < 30"), - Seq(Row(3, "name_3", "city_3", 30000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(3, "name_3", "city_3", 30000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) checkAnswer(sql("select count(*) from stream_table_filter where name like '%ame%'"), Seq(Row(49))) @@ -139,166 +139,166 @@ class TestStreamingTableWithRowParser extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter where name >= 'name_3' and id < 4"), - Seq(Row(3, "name_3", "city_3", 30000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(3, "name_3", "city_3", 30000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where id in (9, 10, 11, 12) and name <> 'name_10' and name not in ('name_11', 'name_12')"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where city = 'city_1'"), - Seq(Row(1, "name_1", "city_1", 10000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(1, "name_1", "city_1", 10000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where city like '%ty_1%' and ( id < 10 or id >= 100000001)"), - Seq(Row(1, "name_1", "city_1", 10000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(1, "name_1", "city_1", 10000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) checkAnswer(sql("select count(*) from stream_table_filter where city like '%city%'"), Seq(Row(54))) checkAnswer( sql("select * from stream_table_filter where city > 'city_09' and city < 'city_10'"), - Seq(Row(1, "name_1", "city_1", 10000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(1, "name_1", "city_1", 10000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where city between 'city_09' and 'city_1'"), - Seq(Row(1, "name_1", "city_1", 10000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(1, "name_1", "city_1", 10000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where id in (9, 10, 11, 12) and city <> 'city_10' and city not in ('city_11', 'city_12')"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where salary = 90000"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where salary > 80000 and salary <= 100000"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), - Row(10, "name_10", "city_10", 100000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), + Row(10, "name_10", "city_10", 100000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where salary between 80001 and 90000"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where id in (9, 10, 11, 12) and salary <> 100000.0 and salary not in (110000.0, 120000.0)"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where tax = 0.04 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where tax >= 0.04 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where tax < 0.05 and tax > 0.02 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where tax between 0.02 and 0.04 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where id in (9, 10) and tax <> 0.01"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where percent = 80.04 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where percent >= 80.04 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where percent < 80.05 and percent > 80.02 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where percent between 80.02 and 80.05 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where id in (9, 10) and percent <> 80.01"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where birthday between '1990-01-04' and '1990-01-05'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), - Row(100000005, "batch_5", "city_5", 0.5, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), + Row(100000005, "batch_5", "city_5", 0.5f, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where birthday = '1990-01-04'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where birthday > '1990-01-03' and birthday <= '1990-01-04'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where birthday between '1990-01-04' and '1990-01-05'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), - Row(100000005, "batch_5", "city_5", 0.5, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), + Row(100000005, "batch_5", "city_5", 0.5f, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where id in (9, 10) and birthday <> '1990-01-01'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where register = '2010-01-04 10:01:01'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where register > '2010-01-03 10:01:01' and register <= '2010-01-04 10:01:01'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where register between '2010-01-04 10:01:01' and '2010-01-05 10:01:01'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), - Row(100000005, "batch_5", "city_5", 0.5, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), + Row(100000005, "batch_5", "city_5", 0.5f, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where id in (9, 10) and register <> '2010-01-01 10:01:01'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where updated = '2010-01-04 10:01:01'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where updated > '2010-01-03 10:01:01' and register <= '2010-01-04 10:01:01'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where updated between '2010-01-04 10:01:01' and '2010-01-05 10:01:01'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), - Row(100000005, "batch_5", "city_5", 0.5, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")), + Row(100000005, "batch_5", "city_5", 0.5f, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where id in (9, 10) and updated <> '2010-01-01 10:01:01'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where id is null order by name"), Seq(Row(null, "", "", null, null, null, null, null, null), - Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where name = ''"), @@ -306,7 +306,7 @@ class TestStreamingTableWithRowParser extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter where id is null and name <> ''"), - Seq(Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where city = ''"), @@ -314,7 +314,7 @@ class TestStreamingTableWithRowParser extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter where id is null and city <> ''"), - Seq(Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where salary is null"), @@ -322,7 +322,7 @@ class TestStreamingTableWithRowParser extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter where id is null and salary is not null"), - Seq(Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where tax is null"), @@ -330,7 +330,7 @@ class TestStreamingTableWithRowParser extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter where id is null and tax is not null"), - Seq(Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where percent is null"), @@ -338,7 +338,7 @@ class TestStreamingTableWithRowParser extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter where id is null and percent is not null"), - Seq(Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where birthday is null"), @@ -346,12 +346,12 @@ class TestStreamingTableWithRowParser extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter where id is null and birthday is not null"), - Seq(Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where register is null"), Seq(Row(null, "", "", null, null, null, null, null, null), - Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0")))) checkAnswer( sql("select * from stream_table_filter where id is null and register is not null"), @@ -363,7 +363,7 @@ class TestStreamingTableWithRowParser extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter where id is null and updated is not null"), - Seq(Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0")))) + Seq(Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0")))) // agg checkAnswer( @@ -379,9 +379,9 @@ class TestStreamingTableWithRowParser extends QueryTest with BeforeAndAfterAll { "and city <> '' " + "group by city " + "order by city"), - Seq(Row("city_1", 2, 100000002, 50000001, 10000.0, 0.1), - Row("city_2", 1, 100000002, 100000002, 0.2, 0.2), - Row("city_3", 2, 100000006, 50000003, 30000.0, 0.3))) + Seq(Row("city_1", 2, 100000002, 50000001, 10000.0f, 0.1f), + Row("city_2", 1, 100000002, 100000002, 0.2f, 0.2f), + Row("city_3", 2, 100000006, 50000003, 30000.0f, 0.3f))) // batch loading for(_ <- 0 to 2) { @@ -438,25 +438,25 @@ class TestStreamingTableWithRowParser extends QueryTest with BeforeAndAfterAll { // filter checkAnswer( sql("select * from stream_table_filter_complex where id = 1"), - Seq(Row(1, "name_1", "city_1", 10000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 1)))) + Seq(Row(1, "name_1", "city_1", 10000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 1)))) checkAnswer( sql("select * from stream_table_filter_complex where id > 49 and id < 100000002"), - Seq(Row(50, "name_50", "city_50", 500000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_50", "school_5050")), 50)), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 20)))) + Seq(Row(50, "name_50", "city_50", 500000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_50", "school_5050")), 50)), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 20)))) checkAnswer( sql("select * from stream_table_filter_complex where id between 50 and 100000001"), - Seq(Row(50, "name_50", "city_50", 500000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_50", "school_5050")), 50)), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 20)))) + Seq(Row(50, "name_50", "city_50", 500000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_50", "school_5050")), 50)), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 20)))) checkAnswer( sql("select * from stream_table_filter_complex where name = 'name_3'"), - Seq(Row(3, "name_3", "city_3", 30000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_3", "school_33")), 3)))) + Seq(Row(3, "name_3", "city_3", 30000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_3", "school_33")), 3)))) checkAnswer( sql("select * from stream_table_filter_complex where name like '%me_3%' and id < 30"), - Seq(Row(3, "name_3", "city_3", 30000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_3", "school_33")), 3)))) + Seq(Row(3, "name_3", "city_3", 30000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_3", "school_33")), 3)))) checkAnswer(sql("select count(*) from stream_table_filter_complex where name like '%ame%'"), Seq(Row(49))) @@ -466,134 +466,134 @@ class TestStreamingTableWithRowParser extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter_complex where name >= 'name_3' and id < 4"), - Seq(Row(3, "name_3", "city_3", 30000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_3", "school_33")), 3)))) + Seq(Row(3, "name_3", "city_3", 30000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_3", "school_33")), 3)))) checkAnswer( sql("select * from stream_table_filter_complex where city = 'city_1'"), - Seq(Row(1, "name_1", "city_1", 10000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 1)), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 20)))) + Seq(Row(1, "name_1", "city_1", 10000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 1)), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 20)))) checkAnswer( sql("select * from stream_table_filter_complex where city like '%ty_1%' and ( id < 10 or id >= 100000001)"), - Seq(Row(1, "name_1", "city_1", 10000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 1)), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 20)))) + Seq(Row(1, "name_1", "city_1", 10000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 1)), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 20)))) checkAnswer(sql("select count(*) from stream_table_filter_complex where city like '%city%'"), Seq(Row(54))) checkAnswer( sql("select * from stream_table_filter_complex where city > 'city_09' and city < 'city_10'"), - Seq(Row(1, "name_1", "city_1", 10000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 1)), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 20)))) + Seq(Row(1, "name_1", "city_1", 10000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 1)), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 20)))) checkAnswer( sql("select * from stream_table_filter_complex where city between 'city_09' and 'city_1'"), - Seq(Row(1, "name_1", "city_1", 10000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 1)), - Row(100000001, "batch_1", "city_1", 0.1, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 20)))) + Seq(Row(1, "name_1", "city_1", 10000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 1)), + Row(100000001, "batch_1", "city_1", 0.1f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_1", "school_11")), 20)))) checkAnswer( sql("select * from stream_table_filter_complex where salary = 90000"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) checkAnswer( sql("select * from stream_table_filter_complex where salary > 80000 and salary <= 100000"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), - Row(10, "name_10", "city_10", 100000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_10", "school_1010")), 10)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), + Row(10, "name_10", "city_10", 100000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_10", "school_1010")), 10)))) checkAnswer( sql("select * from stream_table_filter_complex where salary between 80001 and 90000"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) checkAnswer( sql("select * from stream_table_filter_complex where tax = 0.04 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) checkAnswer( sql("select * from stream_table_filter_complex where tax >= 0.04 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) checkAnswer( sql("select * from stream_table_filter_complex where tax < 0.05 and tax > 0.02 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) checkAnswer( sql("select * from stream_table_filter_complex where tax between 0.02 and 0.04 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) checkAnswer( sql("select * from stream_table_filter_complex where percent = 80.04 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) checkAnswer( sql("select * from stream_table_filter_complex where percent >= 80.04 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) checkAnswer( sql("select * from stream_table_filter_complex where percent < 80.05 and percent > 80.02 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) checkAnswer( sql("select * from stream_table_filter_complex where percent between 80.02 and 80.05 and id < 100"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)))) checkAnswer( sql("select * from stream_table_filter_complex where birthday between '1990-01-04' and '1990-01-05'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)), - Row(100000005, "batch_5", "city_5", 0.5, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Row(wrap(Array("school_5", "school_55")), 60)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)), + Row(100000005, "batch_5", "city_5", 0.5f, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Row(wrap(Array("school_5", "school_55")), 60)))) checkAnswer( sql("select * from stream_table_filter_complex where birthday = '1990-01-04'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)))) checkAnswer( sql("select * from stream_table_filter_complex where birthday > '1990-01-03' and birthday <= '1990-01-04'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)))) checkAnswer( sql("select * from stream_table_filter_complex where birthday between '1990-01-04' and '1990-01-05'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)), - Row(100000005, "batch_5", "city_5", 0.5, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Row(wrap(Array("school_5", "school_55")), 60)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)), + Row(100000005, "batch_5", "city_5", 0.5f, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Row(wrap(Array("school_5", "school_55")), 60)))) checkAnswer( sql("select * from stream_table_filter_complex where register = '2010-01-04 10:01:01'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)))) checkAnswer( sql("select * from stream_table_filter_complex where register > '2010-01-03 10:01:01' and register <= '2010-01-04 10:01:01'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)))) checkAnswer( sql("select * from stream_table_filter_complex where register between '2010-01-04 10:01:01' and '2010-01-05 10:01:01'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)), - Row(100000005, "batch_5", "city_5", 0.5, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Row(wrap(Array("school_5", "school_55")), 60)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)), + Row(100000005, "batch_5", "city_5", 0.5f, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Row(wrap(Array("school_5", "school_55")), 60)))) checkAnswer( sql("select * from stream_table_filter_complex where updated = '2010-01-04 10:01:01'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)))) checkAnswer( sql("select * from stream_table_filter_complex where updated > '2010-01-03 10:01:01' and register <= '2010-01-04 10:01:01'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)))) checkAnswer( sql("select * from stream_table_filter_complex where updated between '2010-01-04 10:01:01' and '2010-01-05 10:01:01'"), - Seq(Row(9, "name_9", "city_9", 90000.0, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), - Row(100000004, "batch_4", "city_4", 0.4, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)), - Row(100000005, "batch_5", "city_5", 0.5, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Row(wrap(Array("school_5", "school_55")), 60)))) + Seq(Row(9, "name_9", "city_9", 90000.0f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_9", "school_99")), 9)), + Row(100000004, "batch_4", "city_4", 0.4f, BigDecimal.valueOf(0.04), 80.04, Date.valueOf("1990-01-04"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Timestamp.valueOf("2010-01-04 10:01:01.0"), Row(wrap(Array("school_4", "school_44")), 50)), + Row(100000005, "batch_5", "city_5", 0.5f, BigDecimal.valueOf(0.05), 80.05, Date.valueOf("1990-01-05"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Timestamp.valueOf("2010-01-05 10:01:01.0"), Row(wrap(Array("school_5", "school_55")), 60)))) checkAnswer( sql("select * from stream_table_filter_complex where id is null order by name"), Seq(Row(null, "", "", null, null, null, null, null, null, null), - Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) + Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) checkAnswer( sql("select * from stream_table_filter_complex where name = ''"), @@ -601,7 +601,7 @@ class TestStreamingTableWithRowParser extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter_complex where id is null and name <> ''"), - Seq(Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) + Seq(Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) checkAnswer( sql("select * from stream_table_filter_complex where city = ''"), @@ -609,7 +609,7 @@ class TestStreamingTableWithRowParser extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter_complex where id is null and city <> ''"), - Seq(Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) + Seq(Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) checkAnswer( sql("select * from stream_table_filter_complex where salary is null"), @@ -617,7 +617,7 @@ class TestStreamingTableWithRowParser extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter_complex where id is null and salary is not null"), - Seq(Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) + Seq(Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) checkAnswer( sql("select * from stream_table_filter_complex where tax is null"), @@ -625,7 +625,7 @@ class TestStreamingTableWithRowParser extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter_complex where id is null and tax is not null"), - Seq(Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) + Seq(Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) checkAnswer( sql("select * from stream_table_filter_complex where percent is null"), @@ -633,7 +633,7 @@ class TestStreamingTableWithRowParser extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter_complex where id is null and salary is not null"), - Seq(Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) + Seq(Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) checkAnswer( sql("select * from stream_table_filter_complex where birthday is null"), @@ -641,12 +641,12 @@ class TestStreamingTableWithRowParser extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter_complex where id is null and birthday is not null"), - Seq(Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) + Seq(Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) checkAnswer( sql("select * from stream_table_filter_complex where register is null"), Seq(Row(null, "", "", null, null, null, null, null, null, null), - Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) + Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) checkAnswer( sql("select * from stream_table_filter_complex where id is null and register is not null"), @@ -658,7 +658,7 @@ class TestStreamingTableWithRowParser extends QueryTest with BeforeAndAfterAll { checkAnswer( sql("select * from stream_table_filter_complex where id is null and updated is not null"), - Seq(Row(null, "name_6", "city_6", 60000.0, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) + Seq(Row(null, "name_6", "city_6", 60000.0f, BigDecimal.valueOf(0.01), 80.01, Date.valueOf("1990-01-01"), null, Timestamp.valueOf("2010-01-01 10:01:01.0"), Row(wrap(Array("school_6", "school_66")), 6)))) // agg checkAnswer( @@ -674,9 +674,9 @@ class TestStreamingTableWithRowParser extends QueryTest with BeforeAndAfterAll { "and city <> '' " + "group by city " + "order by city"), - Seq(Row("city_1", 2, 100000002, 10, 10000.0, 0.1), - Row("city_2", 1, 100000002, 30, 0.2, 0.2), - Row("city_3", 2, 100000006, 21, 30000.0, 0.3))) + Seq(Row("city_1", 2, 100000002, 10, 10000.0f, 0.1f), + Row("city_2", 1, 100000002, 30, 0.2f, 0.2f), + Row("city_3", 2, 100000006, 21, 30000.0f, 0.3f))) } test("alter on stream table with dictionary, sort_columns and complex column") { diff --git a/integration/spark/src/test/scala/org/apache/spark/carbondata/query/SubQueryTestSuite.scala b/integration/spark/src/test/scala/org/apache/spark/carbondata/query/SubQueryTestSuite.scala index b95c882bf31..2927c82da4a 100644 --- a/integration/spark/src/test/scala/org/apache/spark/carbondata/query/SubQueryTestSuite.scala +++ b/integration/spark/src/test/scala/org/apache/spark/carbondata/query/SubQueryTestSuite.scala @@ -36,7 +36,7 @@ class SubQueryTestSuite extends QueryTest with BeforeAndAfterAll { checkAnswer(sql( "select * from subquery where id in(select id from subquery where name in(select name from" + " subquery where rating=2.0))"), - Seq(Row(2, "ghj", 2.0), Row(3, "ghj", 3.0))) + Seq(Row(2, "ghj", 2.0f), Row(3, "ghj", 3.0f))) } test("test to check Broad cast filter works") { diff --git a/integration/spark/src/test/scala/org/apache/spark/sql/carbondata/datasource/TestCreateTableUsingSparkCarbonFileFormat.scala b/integration/spark/src/test/scala/org/apache/spark/sql/carbondata/datasource/TestCreateTableUsingSparkCarbonFileFormat.scala index 773798a1302..2f6a693b35f 100644 --- a/integration/spark/src/test/scala/org/apache/spark/sql/carbondata/datasource/TestCreateTableUsingSparkCarbonFileFormat.scala +++ b/integration/spark/src/test/scala/org/apache/spark/sql/carbondata/datasource/TestCreateTableUsingSparkCarbonFileFormat.scala @@ -367,8 +367,8 @@ class TestCreateTableUsingSparkCarbonFileFormat extends QueryTest with BeforeAnd Seq(Row(50000))) // expected answer for min max flag. FInally there should be 2 blocklets with one blocklet // having min max flag as false for email column and other as true - val blocklet1MinMaxFlag = Array(true, true, true, true, true, false, true, true, true) - val blocklet2MinMaxFlag = Array(true, true, true, true, true, true, true, true, true) + val blocklet1MinMaxFlag = Array(true, true, true, true, true, false, true, true, true, true) + val blocklet2MinMaxFlag = Array(true, true, true, true, true, true, true, true, true, true) val expectedMinMaxFlag = Array(blocklet1MinMaxFlag, blocklet2MinMaxFlag) validateMinMaxFlag(expectedMinMaxFlag, 2) @@ -381,7 +381,7 @@ class TestCreateTableUsingSparkCarbonFileFormat extends QueryTest with BeforeAnd def buildTestDataMuliBlockLet(recordsInBlocklet1: Int, recordsInBlocklet2: Int): Unit = { FileUtils.deleteDirectory(new File(writerPath)) - val fields = new Array[Field](8) + val fields = new Array[Field](9) fields(0) = new Field("myid", DataTypes.INT) fields(1) = new Field("event_id", DataTypes.STRING) fields(2) = new Field("eve_time", DataTypes.DATE) @@ -390,6 +390,7 @@ class TestCreateTableUsingSparkCarbonFileFormat extends QueryTest with BeforeAnd fields(5) = new Field("subject", DataTypes.STRING) fields(6) = new Field("from_email", DataTypes.STRING) fields(7) = new Field("sal", DataTypes.DOUBLE) + fields(8) = new Field("floatField", DataTypes.FLOAT) import scala.collection.JavaConverters._ val emailDataBlocklet1 = "FromEmail" val emailDataBlocklet2 = "Email for testing min max for allowed chars" @@ -411,7 +412,8 @@ class TestCreateTableUsingSparkCarbonFileFormat extends QueryTest with BeforeAnd "" + date_F.format(time) + "$" + date_F.format(time), "Subject_0", emailDataBlocklet1, - "" + new Random().nextDouble())) + "" + new Random().nextDouble(), + "" + new Random().nextFloat())) } for (i <- 1 to recordsInBlocklet2) { val time = new Date(System.currentTimeMillis()) diff --git a/sdk/sdk/src/main/java/org/apache/carbondata/sdk/file/TestUtil.java b/sdk/sdk/src/main/java/org/apache/carbondata/sdk/file/TestUtil.java index a4279b4706c..0bf517148b2 100644 --- a/sdk/sdk/src/main/java/org/apache/carbondata/sdk/file/TestUtil.java +++ b/sdk/sdk/src/main/java/org/apache/carbondata/sdk/file/TestUtil.java @@ -115,7 +115,8 @@ public static void writeFilesAndVerify(int rows, Schema schema, String path, Str for (int i = 0; i < rows; i++) { writer.write(new String[]{ - "robot" + (i % 10), String.valueOf(i % 3000000), String.valueOf((double) i / 2)}); + "robot" + (i % 10), String.valueOf(i % 3000000), String.valueOf((double) i / 2), + String.valueOf((float) i / 2)}); } writer.close(); } catch (Exception e) { diff --git a/sdk/sdk/src/test/java/org/apache/carbondata/sdk/file/CSVCarbonWriterTest.java b/sdk/sdk/src/test/java/org/apache/carbondata/sdk/file/CSVCarbonWriterTest.java index 883e6fc0eb6..c8ce2da4f6c 100644 --- a/sdk/sdk/src/test/java/org/apache/carbondata/sdk/file/CSVCarbonWriterTest.java +++ b/sdk/sdk/src/test/java/org/apache/carbondata/sdk/file/CSVCarbonWriterTest.java @@ -230,7 +230,7 @@ public void testAllPrimitiveDataType() throws IOException { String path = "./testWriteFiles"; FileUtils.deleteDirectory(new File(path)); - Field[] fields = new Field[9]; + Field[] fields = new Field[10]; fields[0] = new Field("stringField", DataTypes.STRING); fields[1] = new Field("intField", DataTypes.INT); fields[2] = new Field("shortField", DataTypes.SHORT); @@ -240,6 +240,7 @@ public void testAllPrimitiveDataType() throws IOException { fields[6] = new Field("dateField", DataTypes.DATE); fields[7] = new Field("timeField", DataTypes.TIMESTAMP); fields[8] = new Field("decimalField", DataTypes.createDecimalType(8, 2)); + fields[9] = new Field("floatField", DataTypes.FLOAT); try { CarbonWriterBuilder builder = CarbonWriter.builder().outputPath(path); @@ -255,7 +256,8 @@ public void testAllPrimitiveDataType() throws IOException { true, "2019-03-02", "2019-02-12 03:03:34", - "1.234567" + "1.234567", + ((float) i / 2) }; writer.write(row); } diff --git a/sdk/sdk/src/test/java/org/apache/carbondata/sdk/file/CarbonIUDTest.java b/sdk/sdk/src/test/java/org/apache/carbondata/sdk/file/CarbonIUDTest.java index c8cf8daff74..140ec87bdf7 100644 --- a/sdk/sdk/src/test/java/org/apache/carbondata/sdk/file/CarbonIUDTest.java +++ b/sdk/sdk/src/test/java/org/apache/carbondata/sdk/file/CarbonIUDTest.java @@ -43,10 +43,11 @@ public class CarbonIUDTest { public void testDelete() throws Exception { String path = "./testWriteFiles"; FileUtils.deleteDirectory(new File(path)); - Field[] fields = new Field[3]; + Field[] fields = new Field[4]; fields[0] = new Field("name", DataTypes.STRING); fields[1] = new Field("age", DataTypes.INT); fields[2] = new Field("doubleField", DataTypes.DOUBLE); + fields[3] = new Field("floatField", DataTypes.FLOAT); TestUtil.writeFilesAndVerify(10, new Schema(fields), path); CarbonIUD.getInstance().delete(path, "age", "0").commit(); CarbonIUD.getInstance().delete(path, "age", "1").delete(path, "name", "robot1").commit(); @@ -58,7 +59,7 @@ public void testDelete() throws Exception { .delete(path, "name", "robot8").delete(path, "age", "6").delete(path, "age", "7").commit(); CarbonReader reader = - CarbonReader.builder(path).projection(new String[] { "name", "age", "doubleField" }) + CarbonReader.builder(path).projection(new String[] { "name", "age", "doubleField", "floatField" }) .build(); int i = 0; @@ -67,6 +68,7 @@ public void testDelete() throws Exception { assert (((String) row[0]).contains("robot8") || ((String) row[0]).contains("robot9")); assert (((int) (row[1])) > 7); assert ((double) row[2] > 3.5); + assert ((float) row[3] > 3.5); i++; } Assert.assertEquals(i, 2); @@ -122,10 +124,11 @@ public void testUpdateOnDateType() throws Exception { public void testDeleteWithConditionalExpressions() throws Exception { String path = "./testWriteFiles"; FileUtils.deleteDirectory(new File(path)); - Field[] fields = new Field[3]; + Field[] fields = new Field[4]; fields[0] = new Field("name", DataTypes.STRING); fields[1] = new Field("age", DataTypes.INT); fields[2] = new Field("doubleField", DataTypes.DOUBLE); + fields[3] = new Field("floatField", DataTypes.FLOAT); TestUtil.writeFilesAndVerify(20, new Schema(fields), path); ColumnExpression columnExpression1 = new ColumnExpression("age", DataTypes.INT); LessThanExpression lessThanExpression = @@ -149,7 +152,7 @@ public void testDeleteWithConditionalExpressions() throws Exception { CarbonIUD.getInstance().delete(path, greaterThanEqualToExpression); CarbonReader reader = - CarbonReader.builder(path).projection(new String[] { "name", "age", "doubleField" }) + CarbonReader.builder(path).projection(new String[] { "name", "age", "doubleField", "floatField" }) .build(); int i = 0; @@ -167,25 +170,31 @@ public void testDeleteWithConditionalExpressions() throws Exception { public void testDeleteWithAndFilter() throws Exception { String path = "./testWriteFiles"; FileUtils.deleteDirectory(new File(path)); - Field[] fields = new Field[3]; + Field[] fields = new Field[4]; fields[0] = new Field("name", DataTypes.STRING); fields[1] = new Field("age", DataTypes.INT); fields[2] = new Field("doubleField", DataTypes.DOUBLE); + fields[3] = new Field("floatField", DataTypes.FLOAT); TestUtil.writeFilesAndVerify(20, new Schema(fields), path); ColumnExpression columnExpression = new ColumnExpression("doubleField", DataTypes.DOUBLE); - LessThanExpression lessThanExpression1 = + LessThanExpression lessThanExpression = new LessThanExpression(columnExpression, new LiteralExpression("3.5", DataTypes.DOUBLE)); + ColumnExpression columnExpression1 = new ColumnExpression("floatField", DataTypes.FLOAT); + LessThanExpression lessThanExpression1 = + new LessThanExpression(columnExpression1, new LiteralExpression("3.5", DataTypes.FLOAT)); + ColumnExpression columnExpression2 = new ColumnExpression("age", DataTypes.INT); LessThanExpression lessThanExpression2 = new LessThanExpression(columnExpression2, new LiteralExpression("4", DataTypes.INT)); - AndExpression andExpression = new AndExpression(lessThanExpression1, lessThanExpression2); + AndExpression andExpression = new AndExpression(lessThanExpression1, + new AndExpression(lessThanExpression1, lessThanExpression2)); CarbonIUD.getInstance().delete(path, andExpression); CarbonReader reader = - CarbonReader.builder(path).projection(new String[] { "name", "age", "doubleField" }) + CarbonReader.builder(path).projection(new String[] { "name", "age", "doubleField", "floatField" }) .build(); int i = 0; @@ -203,31 +212,37 @@ public void testDeleteWithAndFilter() throws Exception { public void testDeleteWithORFilter() throws Exception { String path = "./testWriteFiles"; FileUtils.deleteDirectory(new File(path)); - Field[] fields = new Field[3]; + Field[] fields = new Field[4]; fields[0] = new Field("name", DataTypes.STRING); fields[1] = new Field("age", DataTypes.INT); fields[2] = new Field("doubleField", DataTypes.DOUBLE); + fields[3] = new Field("floatField", DataTypes.FLOAT); TestUtil.writeFilesAndVerify(20, new Schema(fields), path); ColumnExpression columnExpression = new ColumnExpression("doubleField", DataTypes.DOUBLE); LessThanExpression lessThanExpression = new LessThanExpression(columnExpression, new LiteralExpression("3.5", DataTypes.DOUBLE)); + ColumnExpression columnExpression1 = new ColumnExpression("floatField", DataTypes.FLOAT); + LessThanExpression lessThanExpression1 = + new LessThanExpression(columnExpression1, new LiteralExpression("3.5", DataTypes.FLOAT)); + ColumnExpression columnExpression2 = new ColumnExpression("age", DataTypes.INT); GreaterThanExpression greaterThanExpression = new GreaterThanExpression(columnExpression2, new LiteralExpression("11", DataTypes.INT)); - OrExpression orExpression = new OrExpression(lessThanExpression, greaterThanExpression); + OrExpression orExpression = new OrExpression(lessThanExpression, + new OrExpression(lessThanExpression1, greaterThanExpression)); CarbonIUD.getInstance().delete(path, orExpression); CarbonReader reader = - CarbonReader.builder(path).projection(new String[] { "name", "age", "doubleField" }) + CarbonReader.builder(path).projection(new String[] { "name", "age", "doubleField", "floatField" }) .build(); int i = 0; while (reader.hasNext()) { Object[] row = (Object[]) reader.readNextRow(); - assert (!((int) row[1] > 11 || (double) row[2] < 3.5)); + assert (!((int) row[1] > 11 || (double) row[2] < 3.5) || (float) row[3] < 3.5); i++; } Assert.assertEquals(i, 5); @@ -241,17 +256,18 @@ public void testDeleteAtMultiplePaths() throws Exception { String path2 = "./testWriteFiles2"; FileUtils.deleteDirectory(new File(path1)); FileUtils.deleteDirectory(new File(path2)); - Field[] fields = new Field[3]; + Field[] fields = new Field[4]; fields[0] = new Field("name", DataTypes.STRING); fields[1] = new Field("age", DataTypes.INT); fields[2] = new Field("doubleField", DataTypes.DOUBLE); + fields[3] = new Field("floatField", DataTypes.FLOAT); TestUtil.writeFilesAndVerify(10, new Schema(fields), path1); TestUtil.writeFilesAndVerify(10, new Schema(fields), path2); CarbonIUD.getInstance().delete(path1, "age", "2").delete(path2, "age", "3") .delete(path1, "name", "robot2").delete(path2, "name", "robot3").commit(); CarbonReader reader1 = - CarbonReader.builder(path1).projection(new String[] { "name", "age", "doubleField" }) + CarbonReader.builder(path1).projection(new String[] { "name", "age", "doubleField", "floatField" }) .build(); int i = 0; @@ -260,6 +276,7 @@ public void testDeleteAtMultiplePaths() throws Exception { assert (!(((String) row[0]).contains("robot2"))); assert (((int) (row[1])) != 2); assert ((double) row[2] != 1.0); + assert ((float) row[3] != 1.0); i++; } Assert.assertEquals(i, 9); @@ -267,7 +284,7 @@ public void testDeleteAtMultiplePaths() throws Exception { FileUtils.deleteDirectory(new File(path1)); CarbonReader reader2 = - CarbonReader.builder(path2).projection(new String[] { "name", "age", "doubleField" }) + CarbonReader.builder(path2).projection(new String[] { "name", "age", "doubleField", "floatField" }) .build(); i = 0; @@ -276,6 +293,7 @@ public void testDeleteAtMultiplePaths() throws Exception { assert (!(((String) row[0]).contains("robot3"))); assert (((int) (row[1])) != 3); assert ((double) row[2] != 1.5); + assert ((float) row[3] != 1.5); i++; } Assert.assertEquals(i, 9); @@ -287,17 +305,18 @@ public void testDeleteAtMultiplePaths() throws Exception { public void testDeleteInMultipleSegments() throws Exception { String path = "./testWriteFiles"; FileUtils.deleteDirectory(new File(path)); - Field[] fields = new Field[3]; + Field[] fields = new Field[4]; fields[0] = new Field("name", DataTypes.STRING); fields[1] = new Field("age", DataTypes.INT); fields[2] = new Field("doubleField", DataTypes.DOUBLE); + fields[3] = new Field("floatField", DataTypes.FLOAT); TestUtil.writeFilesAndVerify(10, new Schema(fields), path); TestUtil.writeFilesAndVerify(10, new Schema(fields), path); TestUtil.writeFilesAndVerify(10, new Schema(fields), path); CarbonIUD.getInstance().delete(path, "age", "2").delete(path, "name", "robot2").commit(); CarbonReader reader = - CarbonReader.builder(path).projection(new String[] { "name", "age", "doubleField" }) + CarbonReader.builder(path).projection(new String[] { "name", "age", "doubleField", "floatField" }) .build(); int i = 0; @@ -306,6 +325,7 @@ public void testDeleteInMultipleSegments() throws Exception { assert (!(((String) row[0]).contains("robot2"))); assert (((int) (row[1])) != 2); assert ((double) row[2] != 1.0); + assert ((float) row[3] != 1.0); i++; } Assert.assertEquals(i, 27); @@ -317,10 +337,11 @@ public void testDeleteInMultipleSegments() throws Exception { public void testUpdate() throws Exception { String path = "./testWriteFiles"; FileUtils.deleteDirectory(new File(path)); - Field[] fields = new Field[3]; + Field[] fields = new Field[4]; fields[0] = new Field("name", DataTypes.STRING); fields[1] = new Field("age", DataTypes.INT); fields[2] = new Field("doubleField", DataTypes.DOUBLE); + fields[3] = new Field("floatField", DataTypes.FLOAT); TestUtil.writeFilesAndVerify(20, new Schema(fields), path); CarbonIUD.getInstance().update(path, "age", "2", "age", "3").commit(); CarbonIUD.getInstance().update(path, "name", "robot2", "age", "3") @@ -331,7 +352,7 @@ public void testUpdate() throws Exception { new EqualToExpression(columnExpression1, new LiteralExpression("3", DataTypes.INT)); CarbonReader reader1 = - CarbonReader.builder(path).projection(new String[] { "name", "age", "doubleField" }) + CarbonReader.builder(path).projection(new String[] { "name", "age", "doubleField", "floatField" }) .filter(equalToExpression1).build(); int i = 0; @@ -362,10 +383,11 @@ public void testUpdate() throws Exception { public void testDeleteOnUpdatedRows() throws Exception { String path = "./testWriteFiles"; FileUtils.deleteDirectory(new File(path)); - Field[] fields = new Field[3]; + Field[] fields = new Field[4]; fields[0] = new Field("name", DataTypes.STRING); fields[1] = new Field("age", DataTypes.INT); fields[2] = new Field("doubleField", DataTypes.DOUBLE); + fields[3] = new Field("floatField", DataTypes.FLOAT); TestUtil.writeFilesAndVerify(20, new Schema(fields), path); Map updateColumnToValue = new HashMap<>(); updateColumnToValue.put("name", "robot"); @@ -376,13 +398,14 @@ public void testDeleteOnUpdatedRows() throws Exception { new LessThanExpression(columnExpression, new LiteralExpression("10", DataTypes.INT)); CarbonIUD.getInstance().update(path, lessThanExpression, updateColumnToValue); CarbonIUD.getInstance().delete(path, "doubleField", "2.0").commit(); + CarbonIUD.getInstance().delete(path, "floatField", "1.5").commit(); ColumnExpression columnExpression1 = new ColumnExpression("age", DataTypes.INT); EqualToExpression equalToExpression = new EqualToExpression(columnExpression1, new LiteralExpression("24", DataTypes.INT)); CarbonReader reader = - CarbonReader.builder(path).projection(new String[] { "name", "age", "doubleField" }) + CarbonReader.builder(path).projection(new String[] { "name", "age", "doubleField", "floatField" }) .filter(equalToExpression).build(); int i = 0; @@ -392,7 +415,7 @@ public void testDeleteOnUpdatedRows() throws Exception { assert (((int) (row[1])) == 24); i++; } - Assert.assertEquals(i, 9); + Assert.assertEquals(i, 8); reader.close(); FileUtils.deleteDirectory(new File(path)); } @@ -401,10 +424,11 @@ public void testDeleteOnUpdatedRows() throws Exception { public void testDeleteAndUpdateTogether() throws Exception { String path = "./testWriteFiles"; FileUtils.deleteDirectory(new File(path)); - Field[] fields = new Field[3]; + Field[] fields = new Field[4]; fields[0] = new Field("name", DataTypes.STRING); fields[1] = new Field("age", DataTypes.INT); fields[2] = new Field("doubleField", DataTypes.DOUBLE); + fields[3] = new Field("floatField", DataTypes.FLOAT); TestUtil.writeFilesAndVerify(20, new Schema(fields), path); CarbonIUD.getInstance().delete(path, "name", "robot0").delete(path, "name", "robot1") .delete(path, "name", "robot2").delete(path, "age", "0").delete(path, "age", "1") @@ -412,7 +436,7 @@ public void testDeleteAndUpdateTogether() throws Exception { .commit(); CarbonReader reader = - CarbonReader.builder(path).projection(new String[] { "name", "age", "doubleField" }) + CarbonReader.builder(path).projection(new String[] { "name", "age", "doubleField", "floatField" }) .build(); int i = 0; @@ -431,7 +455,7 @@ public void testDeleteAndUpdateTogether() throws Exception { public void testIUDOnDifferentDataType() throws Exception { String path = "./testWriteFiles"; FileUtils.deleteDirectory(new File(path)); - Field[] fields = new Field[11]; + Field[] fields = new Field[12]; fields[0] = new Field("stringField", DataTypes.STRING); fields[1] = new Field("shortField", DataTypes.SHORT); fields[2] = new Field("intField", DataTypes.INT); @@ -443,6 +467,7 @@ public void testIUDOnDifferentDataType() throws Exception { fields[8] = new Field("decimalField", DataTypes.createDecimalType(8, 2)); fields[9] = new Field("varcharField", DataTypes.VARCHAR); fields[10] = new Field("arrayField", DataTypes.createArrayType(DataTypes.STRING)); + fields[11] = new Field("floatField", DataTypes.FLOAT); CarbonWriter writer = CarbonWriter.builder().outputPath(path).withLoadOption("complex_delimiter_level_1", "#") .withCsvInput(new Schema(fields)).writtenBy("IUDTest").build(); @@ -463,7 +488,7 @@ public void testIUDOnDifferentDataType() throws Exception { new String[] { "robot" + (i % 10), String.valueOf(i % 10000), String.valueOf(i), String.valueOf(Long.MAX_VALUE - i), String.valueOf((double) i / 2), String.valueOf(boolValue), dateValue, "2019-02-12 03:03:34", decimalValue, - "varchar" + (i % 10), "Hello#World#From#Carbon" }; + "varchar" + (i % 10), "Hello#World#From#Carbon", String.valueOf((float) i / 2)}; writer.write(row2); } writer.close(); @@ -477,6 +502,7 @@ public void testIUDOnDifferentDataType() throws Exception { CarbonIUD.getInstance().delete(path, "decimalField", "12.37").commit(); CarbonIUD.getInstance().delete(path, "dateField", "2019-03-03").commit(); CarbonIUD.getInstance().delete(path, "timeField", "2019-02-12 03:03:34").commit(); + CarbonIUD.getInstance().delete(path, "floatField", "2.0").commit(); File[] indexFiles = new File(path).listFiles(new FilenameFilter() { @Override diff --git a/sdk/sdk/src/test/java/org/apache/carbondata/sdk/file/CarbonReaderTest.java b/sdk/sdk/src/test/java/org/apache/carbondata/sdk/file/CarbonReaderTest.java index 1433bb5c93f..3d9386db743 100644 --- a/sdk/sdk/src/test/java/org/apache/carbondata/sdk/file/CarbonReaderTest.java +++ b/sdk/sdk/src/test/java/org/apache/carbondata/sdk/file/CarbonReaderTest.java @@ -299,10 +299,11 @@ public void testReadWithFilterOfNonTransactionalAnd() throws IOException, Interr FileUtils.deleteDirectory(new File(path)); IndexStoreManager.getInstance() .clearIndexCache(AbsoluteTableIdentifier.from(path), false); - Field[] fields = new Field[3]; + Field[] fields = new Field[4]; fields[0] = new Field("name", DataTypes.STRING); fields[1] = new Field("age", DataTypes.INT); fields[2] = new Field("doubleField", DataTypes.DOUBLE); + fields[3] = new Field("floatField", DataTypes.FLOAT); TestUtil.writeFilesAndVerify(200, new Schema(fields), path); @@ -310,14 +311,19 @@ public void testReadWithFilterOfNonTransactionalAnd() throws IOException, Interr EqualToExpression equalToExpression = new EqualToExpression(columnExpression, new LiteralExpression("3.5", DataTypes.DOUBLE)); + ColumnExpression columnExpression1 = new ColumnExpression("floatField", DataTypes.FLOAT); + EqualToExpression equalToExpression1 = new EqualToExpression(columnExpression1, + new LiteralExpression("3.5", DataTypes.FLOAT)); + ColumnExpression columnExpression2 = new ColumnExpression("name", DataTypes.STRING); EqualToExpression equalToExpression2 = new EqualToExpression(columnExpression2, new LiteralExpression("robot7", DataTypes.STRING)); - AndExpression andExpression = new AndExpression(equalToExpression, equalToExpression2); + AndExpression andExpression = new AndExpression(equalToExpression, + new AndExpression(equalToExpression1, equalToExpression2)); CarbonReader reader = CarbonReader .builder(path, "_temp") - .projection(new String[]{"name", "age", "doubleField"}) + .projection(new String[]{"name", "age", "doubleField", "floatField"}) .filter(andExpression) .build(); @@ -327,6 +333,7 @@ public void testReadWithFilterOfNonTransactionalAnd() throws IOException, Interr assert (((String) row[0]).contains("robot7")); assert (7 == (int) (row[1])); assert (3.5 == (double) (row[2])); + assert (3.5 == (float) (row[3])); i++; } Assert.assertEquals(i, 1); @@ -342,10 +349,11 @@ public void testReadWithFilterOfNonTransactionalOr() throws IOException, Interru FileUtils.deleteDirectory(new File(path)); IndexStoreManager.getInstance() .clearIndexCache(AbsoluteTableIdentifier.from(path), false); - Field[] fields = new Field[3]; + Field[] fields = new Field[4]; fields[0] = new Field("name", DataTypes.STRING); fields[1] = new Field("age", DataTypes.INT); fields[2] = new Field("doubleField", DataTypes.DOUBLE); + fields[3] = new Field("floatField", DataTypes.FLOAT); TestUtil.writeFilesAndVerify(200, new Schema(fields), path); @@ -353,14 +361,19 @@ public void testReadWithFilterOfNonTransactionalOr() throws IOException, Interru EqualToExpression equalToExpression = new EqualToExpression(columnExpression, new LiteralExpression("3.5", DataTypes.DOUBLE)); + ColumnExpression columnExpression1 = new ColumnExpression("floatField", DataTypes.FLOAT); + EqualToExpression equalToExpression1 = new EqualToExpression(columnExpression1, + new LiteralExpression("3.5", DataTypes.FLOAT)); + ColumnExpression columnExpression2 = new ColumnExpression("name", DataTypes.STRING); EqualToExpression equalToExpression2 = new EqualToExpression(columnExpression2, new LiteralExpression("robot7", DataTypes.STRING)); - OrExpression orExpression = new OrExpression(equalToExpression, equalToExpression2); + OrExpression orExpression = new OrExpression(equalToExpression, + new OrExpression(equalToExpression1, equalToExpression2)); CarbonReader reader = CarbonReader .builder(path, "_temp") - .projection(new String[]{"name", "age", "doubleField"}) + .projection(new String[]{"name", "age", "doubleField", "floatField"}) .filter(orExpression) .build(); @@ -370,6 +383,7 @@ public void testReadWithFilterOfNonTransactionalOr() throws IOException, Interru assert (((String) row[0]).contains("robot7")); assert (7 == ((int) (row[1]) % 10)); assert (0.5 == ((double) (row[2]) % 1)); + assert (0.5 == ((float) (row[3]) % 1)); i++; } Assert.assertEquals(i, 20); @@ -385,10 +399,11 @@ public void testReadWithFilterOfNonTransactionalGreaterThan() throws IOException FileUtils.deleteDirectory(new File(path)); IndexStoreManager.getInstance() .clearIndexCache(AbsoluteTableIdentifier.from(path), false); - Field[] fields = new Field[3]; + Field[] fields = new Field[4]; fields[0] = new Field("name", DataTypes.STRING); fields[1] = new Field("age", DataTypes.INT); fields[2] = new Field("doubleField", DataTypes.DOUBLE); + fields[3] = new Field("floatField", DataTypes.FLOAT); TestUtil.writeFilesAndVerify(200, new Schema(fields), path); @@ -396,14 +411,19 @@ public void testReadWithFilterOfNonTransactionalGreaterThan() throws IOException GreaterThanExpression greaterThanExpression = new GreaterThanExpression(columnExpression, new LiteralExpression("13.5", DataTypes.DOUBLE)); + ColumnExpression columnExpression1 = new ColumnExpression("floatField", DataTypes.FLOAT); + GreaterThanExpression greaterThanExpression1 = new GreaterThanExpression(columnExpression1, + new LiteralExpression("13.5", DataTypes.FLOAT)); + ColumnExpression columnExpression2 = new ColumnExpression("name", DataTypes.STRING); EqualToExpression equalToExpression2 = new EqualToExpression(columnExpression2, new LiteralExpression("robot7", DataTypes.STRING)); - AndExpression andExpression = new AndExpression(greaterThanExpression, equalToExpression2); + AndExpression andExpression = new AndExpression(greaterThanExpression, + new AndExpression(greaterThanExpression1, equalToExpression2)); CarbonReader reader = CarbonReader .builder(path, "_temp") - .projection(new String[]{"name", "age", "doubleField"}) + .projection(new String[]{"name", "age", "doubleField", "floatField"}) .filter(andExpression) .build(); @@ -413,6 +433,7 @@ public void testReadWithFilterOfNonTransactionalGreaterThan() throws IOException assert (((String) row[0]).contains("robot7")); assert (7 == ((int) (row[1]) % 10)); assert ((double) row[2] > 13.5); + assert ((float) row[3] > 13.5); i++; } Assert.assertEquals(i, 17); @@ -426,10 +447,11 @@ public void testReadWithFilterOfNonTransactionalGreaterThan() throws IOException public void testReadWithFilterEqualSet() throws IOException, InterruptedException { String path = "./testWriteFiles"; FileUtils.deleteDirectory(new File(path)); - Field[] fields = new Field[3]; + Field[] fields = new Field[4]; fields[0] = new Field("name", DataTypes.STRING); fields[1] = new Field("age", DataTypes.INT); fields[2] = new Field("doubleField", DataTypes.DOUBLE); + fields[3] = new Field("floatField", DataTypes.FLOAT); TestUtil.writeFilesAndVerify(200, new Schema(fields), path); @@ -439,7 +461,7 @@ public void testReadWithFilterEqualSet() throws IOException, InterruptedExceptio CarbonReader reader = CarbonReader .builder(path, "_temp") - .projection(new String[]{"name", "age", "doubleField"}) + .projection(new String[]{"name", "age", "doubleField", "floatField"}) .filter(prepareEqualToExpressionSet("name", "String", values)) .build(); @@ -449,9 +471,11 @@ public void testReadWithFilterEqualSet() throws IOException, InterruptedExceptio if (((String) row[0]).contains("robot7")) { assert (7 == ((int) (row[1]) % 10)); assert (0.5 == ((double) (row[2]) % 1)); + assert (0.5 == ((float) (row[3]) % 1)); } else if (((String) row[0]).contains("robot1")) { assert (1 == ((int) (row[1]) % 10)); assert (0.5 == ((double) (row[2]) % 1)); + assert (0.5 == ((float) (row[3]) % 1)); } else { Assert.assertTrue(false); } @@ -467,7 +491,7 @@ public void testReadWithFilterEqualSet() throws IOException, InterruptedExceptio CarbonReader reader2 = CarbonReader .builder(path, "_temp") - .projection(new String[]{"name", "age", "doubleField"}) + .projection(new String[]{"name", "age", "doubleField", "floatField"}) .filter(prepareEqualToExpressionSet("age", "int", values2)) .build(); @@ -477,9 +501,11 @@ public void testReadWithFilterEqualSet() throws IOException, InterruptedExceptio if (((String) row[0]).contains("robot7")) { assert (7 == ((int) (row[1]) % 10)); assert (0.5 == ((double) (row[2]) % 1)); + assert (0.5 == ((float) (row[3]) % 1)); } else if (((String) row[0]).contains("robot1")) { assert (1 == ((int) (row[1]) % 10)); assert (0.5 == ((double) (row[2]) % 1)); + assert (0.5 == ((float) (row[3]) % 1)); } else { Assert.assertTrue(false); } @@ -494,8 +520,9 @@ public void testReadWithFilterEqualSet() throws IOException, InterruptedExceptio values3.add(3.5); CarbonReader reader3 = CarbonReader .builder(path, "_temp") - .projection(new String[]{"name", "age", "doubleField"}) + .projection(new String[]{"name", "age", "doubleField", "floatField"}) .filter(prepareEqualToExpressionSet("doubleField", "double", values3)) + .filter(prepareEqualToExpressionSet("floatField", "float", values3)) .build(); i = 0; @@ -504,9 +531,11 @@ public void testReadWithFilterEqualSet() throws IOException, InterruptedExceptio if (((String) row[0]).contains("robot7")) { assert (7 == ((int) (row[1]) % 10)); assert (0.5 == ((double) (row[2]) % 1)); + assert (0.5 == ((float) (row[3]) % 1)); } else if (((String) row[0]).contains("robot1")) { assert (1 == ((int) (row[1]) % 10)); assert (0.5 == ((double) (row[2]) % 1)); + assert (0.5 == ((float) (row[3]) % 1)); } else { Assert.assertTrue(false); } @@ -517,7 +546,7 @@ public void testReadWithFilterEqualSet() throws IOException, InterruptedExceptio CarbonReader reader4 = CarbonReader .builder(path, "_temp") - .projection(new String[]{"name", "age", "doubleField"}) + .projection(new String[]{"name", "age", "doubleField", "floatField"}) .filter(prepareEqualToExpression("name", "string", "robot7")) .build(); @@ -527,6 +556,7 @@ public void testReadWithFilterEqualSet() throws IOException, InterruptedExceptio if (((String) row[0]).contains("robot7")) { assert (7 == ((int) (row[1]) % 10)); assert (0.5 == ((double) (row[2]) % 1)); + assert (0.5 == ((float) (row[3]) % 1)); } else { Assert.assertTrue(false); } @@ -542,7 +572,7 @@ public void testReadWithFilterEqualSet() throws IOException, InterruptedExceptio CarbonReader reader5 = CarbonReader .builder(path, "_temp") - .projection(new String[]{"name", "age", "doubleField"}) + .projection(new String[]{"name", "age", "doubleField", "floatField"}) .filter(prepareOrExpression(expressions)) .build(); @@ -552,12 +582,15 @@ public void testReadWithFilterEqualSet() throws IOException, InterruptedExceptio if (((String) row[0]).contains("robot7")) { assert (7 == ((int) (row[1]) % 10)); assert (0.5 == ((double) (row[2]) % 1)); + assert (0.5 == ((float) (row[3]) % 1)); } else if (((String) row[0]).contains("robot1")) { assert (1 == ((int) (row[1]) % 10)); assert (0.5 == ((double) (row[2]) % 1)); + assert (0.5 == ((float) (row[3]) % 1)); } else if (((String) row[0]).contains("robot2")) { assert (2 == ((int) (row[1]) % 10)); assert (0 == ((double) (row[2]) % 1)); + assert (0 == ((float) (row[3]) % 1)); } else { Assert.assertTrue(false); } @@ -577,10 +610,11 @@ public void testReadWithFilterOfNonTransactionalLessThan() throws IOException, I FileUtils.deleteDirectory(new File(path)); IndexStoreManager.getInstance() .clearIndexCache(AbsoluteTableIdentifier.from(path), false); - Field[] fields = new Field[3]; + Field[] fields = new Field[4]; fields[0] = new Field("name", DataTypes.STRING); fields[1] = new Field("age", DataTypes.INT); fields[2] = new Field("doubleField", DataTypes.DOUBLE); + fields[3] = new Field("floatField", DataTypes.FLOAT); TestUtil.writeFilesAndVerify(200, new Schema(fields), path); @@ -588,14 +622,19 @@ public void testReadWithFilterOfNonTransactionalLessThan() throws IOException, I LessThanExpression lessThanExpression = new LessThanExpression(columnExpression, new LiteralExpression("13.5", DataTypes.DOUBLE)); + ColumnExpression columnExpression1 = new ColumnExpression("floatField", DataTypes.FLOAT); + LessThanExpression lessThanExpression1 = new LessThanExpression(columnExpression1, + new LiteralExpression("13.5", DataTypes.FLOAT)); + ColumnExpression columnExpression2 = new ColumnExpression("name", DataTypes.STRING); EqualToExpression equalToExpression2 = new EqualToExpression(columnExpression2, new LiteralExpression("robot7", DataTypes.STRING)); - AndExpression andExpression = new AndExpression(lessThanExpression, equalToExpression2); + AndExpression andExpression = new AndExpression(lessThanExpression, + new AndExpression(lessThanExpression1, equalToExpression2)); CarbonReader reader = CarbonReader .builder(path, "_temp") - .projection(new String[]{"name", "age", "doubleField"}) + .projection(new String[]{"name", "age", "doubleField", "floatField"}) .filter(andExpression) .build(); @@ -605,6 +644,7 @@ public void testReadWithFilterOfNonTransactionalLessThan() throws IOException, I assert (((String) row[0]).contains("robot7")); assert (7 == ((int) (row[1]) % 10)); assert ((double) row[2] < 13.5); + assert ((float) row[3] < 13.5); i++; } Assert.assertEquals(i, 2); @@ -620,10 +660,11 @@ public void testReadWithFilterOfNonTransactionalNotEqual() throws IOException, I FileUtils.deleteDirectory(new File(path)); IndexStoreManager.getInstance() .clearIndexCache(AbsoluteTableIdentifier.from(path), false); - Field[] fields = new Field[3]; + Field[] fields = new Field[4]; fields[0] = new Field("name", DataTypes.STRING); fields[1] = new Field("age", DataTypes.INT); fields[2] = new Field("doubleField", DataTypes.DOUBLE); + fields[3] = new Field("floatField", DataTypes.FLOAT); TestUtil.writeFilesAndVerify(200, new Schema(fields), path); @@ -631,14 +672,19 @@ public void testReadWithFilterOfNonTransactionalNotEqual() throws IOException, I LessThanExpression lessThanExpression = new LessThanExpression(columnExpression, new LiteralExpression("13.5", DataTypes.DOUBLE)); + ColumnExpression columnExpression1 = new ColumnExpression("floatField", DataTypes.FLOAT); + LessThanExpression lessThanExpression1 = new LessThanExpression(columnExpression1, + new LiteralExpression("13.5", DataTypes.FLOAT)); + ColumnExpression columnExpression2 = new ColumnExpression("name", DataTypes.STRING); NotEqualsExpression notEqualsExpression = new NotEqualsExpression(columnExpression2, new LiteralExpression("robot7", DataTypes.STRING)); - AndExpression andExpression = new AndExpression(lessThanExpression, notEqualsExpression); + AndExpression andExpression = new AndExpression(lessThanExpression, + new AndExpression(lessThanExpression1, notEqualsExpression)); CarbonReader reader = CarbonReader .builder(path, "_temp") - .projection(new String[]{"name", "age", "doubleField"}) + .projection(new String[]{"name", "age", "doubleField", "floatField"}) .filter(andExpression) .build(); @@ -648,6 +694,7 @@ public void testReadWithFilterOfNonTransactionalNotEqual() throws IOException, I assert (!((String) row[0]).contains("robot7")); assert (7 != ((int) (row[1]) % 10)); assert ((double) row[2] < 13.5); + assert ((float) row[3] < 13.5); i++; } Assert.assertEquals(i, 25); @@ -662,10 +709,11 @@ public void testReadWithFilterOfNonTransactionalIn() throws IOException, Interru String path = "./testWriteFiles"; FileUtils.deleteDirectory(new File(path)); - Field[] fields = new Field[3]; + Field[] fields = new Field[4]; fields[0] = new Field("name", DataTypes.STRING); fields[1] = new Field("age", DataTypes.INT); fields[2] = new Field("doubleField", DataTypes.DOUBLE); + fields[3] = new Field("floatField", DataTypes.FLOAT); TestUtil.writeFilesAndVerify(200, new Schema(fields), path); IndexStoreManager.getInstance() @@ -674,14 +722,19 @@ public void testReadWithFilterOfNonTransactionalIn() throws IOException, Interru LessThanExpression lessThanExpression = new LessThanExpression(columnExpression, new LiteralExpression("13.5", DataTypes.DOUBLE)); + ColumnExpression columnExpression1 = new ColumnExpression("floatField", DataTypes.FLOAT); + LessThanExpression lessThanExpression1 = new LessThanExpression(columnExpression1, + new LiteralExpression("13.5", DataTypes.FLOAT)); + ColumnExpression columnExpression2 = new ColumnExpression("name", DataTypes.STRING); InExpression inExpression = new InExpression(columnExpression2, new LiteralExpression("robot7", DataTypes.STRING)); - AndExpression andExpression = new AndExpression(lessThanExpression, inExpression); + AndExpression andExpression = new AndExpression(lessThanExpression, + new AndExpression(lessThanExpression1, inExpression)); CarbonReader reader = CarbonReader .builder(path, "_temp") - .projection(new String[]{"name", "age", "doubleField"}) + .projection(new String[]{"name", "age", "doubleField", "floatField"}) .filter(andExpression) .build(); @@ -691,6 +744,7 @@ public void testReadWithFilterOfNonTransactionalIn() throws IOException, Interru assert (((String) row[0]).contains("robot7")); assert (7 == ((int) (row[1]) % 10)); assert ((double) row[2] < 13.5); + assert ((float) row[3] < 13.5); i++; } Assert.assertEquals(i, 2); @@ -706,10 +760,11 @@ public void testReadWithFilterOfNonTransactionalNotIn() throws IOException, Inte FileUtils.deleteDirectory(new File(path)); IndexStoreManager.getInstance() .clearIndexCache(AbsoluteTableIdentifier.from(path), false); - Field[] fields = new Field[3]; + Field[] fields = new Field[4]; fields[0] = new Field("name", DataTypes.STRING); fields[1] = new Field("age", DataTypes.INT); fields[2] = new Field("doubleField", DataTypes.DOUBLE); + fields[3] = new Field("floatField", DataTypes.FLOAT); TestUtil.writeFilesAndVerify(200, new Schema(fields), path); @@ -717,14 +772,19 @@ public void testReadWithFilterOfNonTransactionalNotIn() throws IOException, Inte LessThanExpression lessThanExpression = new LessThanExpression(columnExpression, new LiteralExpression("13.5", DataTypes.DOUBLE)); + ColumnExpression columnExpression1 = new ColumnExpression("floatField", DataTypes.FLOAT); + LessThanExpression lessThanExpression1 = new LessThanExpression(columnExpression1, + new LiteralExpression("13.5", DataTypes.FLOAT)); + ColumnExpression columnExpression2 = new ColumnExpression("name", DataTypes.STRING); NotInExpression notInExpression = new NotInExpression(columnExpression2, new LiteralExpression("robot7", DataTypes.STRING)); - AndExpression andExpression = new AndExpression(lessThanExpression, notInExpression); + AndExpression andExpression = new AndExpression(lessThanExpression, + new AndExpression(lessThanExpression1, notInExpression)); CarbonReader reader = CarbonReader .builder(path, "_temp") - .projection(new String[]{"name", "age", "doubleField"}) + .projection(new String[]{"name", "age", "doubleField", "floatField"}) .filter(andExpression) .build(); @@ -734,6 +794,7 @@ public void testReadWithFilterOfNonTransactionalNotIn() throws IOException, Inte assert (!((String) row[0]).contains("robot7")); assert (7 != ((int) (row[1]) % 10)); assert ((double) row[2] < 13.5); + assert ((float) row[3] < 13.5); i++; } Assert.assertEquals(i, 25); @@ -1065,7 +1126,7 @@ public void testTimeStampAndBadRecord() throws IOException, InterruptedException String path = "./testWriteFiles"; FileUtils.deleteDirectory(new File(path)); - Field[] fields = new Field[9]; + Field[] fields = new Field[10]; fields[0] = new Field("stringField", DataTypes.STRING); fields[1] = new Field("intField", DataTypes.INT); fields[2] = new Field("shortField", DataTypes.SHORT); @@ -1075,6 +1136,7 @@ public void testTimeStampAndBadRecord() throws IOException, InterruptedException fields[6] = new Field("dateField", DataTypes.DATE); fields[7] = new Field("timeField", DataTypes.TIMESTAMP); fields[8] = new Field("decimalField", DataTypes.createDecimalType(8, 2)); + fields[9] = new Field("floatField", DataTypes.FLOAT); try { CarbonWriterBuilder builder = CarbonWriter.builder().outputPath(path); @@ -1091,7 +1153,8 @@ public void testTimeStampAndBadRecord() throws IOException, InterruptedException String.valueOf(true), "2018-05-12", "2018-05-12", - "12.345" + "12.345", + String.valueOf((float) i / 2), }; writer.write(row); String[] row2 = new String[]{ @@ -1103,7 +1166,8 @@ public void testTimeStampAndBadRecord() throws IOException, InterruptedException String.valueOf(true), "2019-03-02", "2019-02-12 03:03:34", - "12.345" + "12.345", + String.valueOf((float) i / 2), }; writer.write(row2); } @@ -1135,7 +1199,8 @@ public boolean accept(File pathname) { , "boolField" , "dateField" , "timeField" - , "decimalField"}) + , "decimalField" + , "floatField"}) .build(); int i = 0; @@ -1149,6 +1214,7 @@ public boolean accept(File pathname) { Assert.assertEquals(true, (boolean) row[5]); Assert.assertEquals("2019-03-02", row[6]); Assert.assertEquals("2019-02-12 03:03:34", row[7]); + Assert.assertEquals((float) id / 2, row[9]); i++; } Assert.assertEquals(i, 100); @@ -1181,7 +1247,7 @@ public void testReadSchemaInDataFileAndSort() throws IOException, InterruptedExc String path = "./testWriteFiles"; FileUtils.deleteDirectory(new File(path)); - Field[] fields = new Field[9]; + Field[] fields = new Field[10]; fields[0] = new Field("stringField", DataTypes.STRING); fields[1] = new Field("shortField", DataTypes.SHORT); fields[2] = new Field("intField", DataTypes.INT); @@ -1191,6 +1257,7 @@ public void testReadSchemaInDataFileAndSort() throws IOException, InterruptedExc fields[6] = new Field("dateField", DataTypes.DATE); fields[7] = new Field("timeField", DataTypes.TIMESTAMP); fields[8] = new Field("decimalField", DataTypes.createDecimalType(8, 2)); + fields[9] = new Field("floatField", DataTypes.FLOAT); try { CarbonWriterBuilder builder = CarbonWriter.builder().outputPath(path); @@ -1207,7 +1274,8 @@ public void testReadSchemaInDataFileAndSort() throws IOException, InterruptedExc String.valueOf(true), "2019-03-02", "2019-02-12 03:03:34", - "12.345" + "12.345", + String.valueOf((float) i / 2), }; writer.write(row2); } @@ -1259,6 +1327,7 @@ public int compare(Field o1, Field o2) { Assert.assertEquals(true, (boolean) row[5]); Assert.assertEquals("2019-03-02", row[6]); Assert.assertEquals("2019-02-12 03:03:34", row[7]); + Assert.assertEquals((float) id / 2, row[9]); i++; } Assert.assertEquals(i, 100); @@ -1291,7 +1360,7 @@ public void testReadUserSchema() throws IOException, InterruptedException { String path = "./testWriteFiles"; FileUtils.deleteDirectory(new File(path)); - Field[] fields = new Field[9]; + Field[] fields = new Field[10]; fields[0] = new Field("stringField", DataTypes.STRING); fields[1] = new Field("shortField", DataTypes.SHORT); fields[2] = new Field("intField", DataTypes.INT); @@ -1301,6 +1370,7 @@ public void testReadUserSchema() throws IOException, InterruptedException { fields[6] = new Field("dateField", DataTypes.DATE); fields[7] = new Field("timeField", DataTypes.TIMESTAMP); fields[8] = new Field("decimalField", DataTypes.createDecimalType(8, 2)); + fields[9] = new Field("floatField", DataTypes.FLOAT); try { CarbonWriterBuilder builder = CarbonWriter.builder().outputPath(path) @@ -1318,7 +1388,8 @@ public void testReadUserSchema() throws IOException, InterruptedException { String.valueOf(true), "2019-03-02", "2019-02-12 03:03:34", - "12.345" + "12.345", + String.valueOf((float) i / 2), }; writer.write(row2); } @@ -1370,6 +1441,7 @@ public boolean accept(File dir, String name) { Assert.assertEquals(true, (boolean) row[5]); Assert.assertEquals("2019-03-02", row[6]); Assert.assertEquals("2019-02-12 03:03:34", row[7]); + Assert.assertEquals((float) id / 2, row[9]); i++; } Assert.assertEquals(i, 100); @@ -1657,7 +1729,7 @@ public void testReadSchemaFromDataFileArrayString() { try { FileUtils.deleteDirectory(new File(path)); - Field[] fields = new Field[11]; + Field[] fields = new Field[12]; fields[0] = new Field("stringField", DataTypes.STRING); fields[1] = new Field("shortField", DataTypes.SHORT); fields[2] = new Field("intField", DataTypes.INT); @@ -1669,6 +1741,7 @@ public void testReadSchemaFromDataFileArrayString() { fields[8] = new Field("decimalField", DataTypes.createDecimalType(8, 2)); fields[9] = new Field("varcharField", DataTypes.VARCHAR); fields[10] = new Field("arrayField", DataTypes.createArrayType(DataTypes.STRING)); + fields[11] = new Field("floatField", DataTypes.FLOAT); Map map = new HashMap<>(); map.put("complex_delimiter_level_1", "#"); CarbonWriter writer = CarbonWriter.builder() @@ -1690,7 +1763,8 @@ public void testReadSchemaFromDataFileArrayString() { "2019-02-12 03:03:34", "12.345", "varchar", - "Hello#World#From#Carbon" + "Hello#World#From#Carbon", + String.valueOf((float) i / 2), }; writer.write(row2); } diff --git a/sdk/sdk/src/test/java/org/apache/carbondata/sdk/file/PaginationCarbonReaderTest.java b/sdk/sdk/src/test/java/org/apache/carbondata/sdk/file/PaginationCarbonReaderTest.java index d0d60bd170f..4fc183ed48e 100644 --- a/sdk/sdk/src/test/java/org/apache/carbondata/sdk/file/PaginationCarbonReaderTest.java +++ b/sdk/sdk/src/test/java/org/apache/carbondata/sdk/file/PaginationCarbonReaderTest.java @@ -282,11 +282,11 @@ public void testSDKPaginationFilter() throws IOException, InterruptedException { @Test public void testSDKPaginationInsertData() throws IOException, InvalidLoadOptionException, InterruptedException { List data1 = new ArrayList(); - String[] row1 = {"1", "AAA", "3", "3444345.66", "true", "1979-12-09", "2011-2-10 1:00:20", "Pune", "IT"}; - String[] row2 = {"2", "BBB", "2", "543124.66", "false", "1987-2-19", "2017-1-1 12:00:20", "Bangalore", "DATA"}; - String[] row3 = {"3", "CCC", "1", "787878.888", "false", "1982-05-12", "2015-12-1 2:20:20", "Pune", "DATA"}; - String[] row4 = {"4", "DDD", "1", "99999.24", "true", "1981-04-09", "2000-1-15 7:00:20", "Delhi", "MAINS"}; - String[] row5 = {"5", "EEE", "3", "545656.99", "true", "1987-12-09", "2017-11-25 04:00:20", "Delhi", "IT"}; + String[] row1 = {"1", "AAA", "3", "3444345.66", "true", "1979-12-09", "2011-2-10 1:00:20", "Pune", "IT", "3444345.66"}; + String[] row2 = {"2", "BBB", "2", "543124.66", "false", "1987-2-19", "2017-1-1 12:00:20", "Bangalore", "DATA", "543124.66"}; + String[] row3 = {"3", "CCC", "1", "787878.888", "false", "1982-05-12", "2015-12-1 2:20:20", "Pune", "DATA", "787878.888"}; + String[] row4 = {"4", "DDD", "1", "99999.24", "true", "1981-04-09", "2000-1-15 7:00:20", "Delhi", "MAINS", "99999.24"}; + String[] row5 = {"5", "EEE", "3", "545656.99", "true", "1987-12-09", "2017-11-25 04:00:20", "Delhi", "IT", "545656.99"}; data1.add(row1); data1.add(row2); @@ -296,7 +296,7 @@ public void testSDKPaginationInsertData() throws IOException, InvalidLoadOptionE String path = "./testWriteFiles"; FileUtils.deleteDirectory(new File(path)); - Field[] fields = new Field[9]; + Field[] fields = new Field[10]; fields[0] = new Field("id", DataTypes.INT); fields[1] = new Field("name", DataTypes.STRING); fields[2] = new Field("rank", DataTypes.SHORT); @@ -306,6 +306,7 @@ public void testSDKPaginationInsertData() throws IOException, InvalidLoadOptionE fields[6] = new Field("doj", DataTypes.TIMESTAMP); fields[7] = new Field("city", DataTypes.STRING); fields[8] = new Field("dept", DataTypes.STRING); + fields[9] = new Field("floatField", DataTypes.FLOAT); CarbonWriterBuilder builder = CarbonWriter.builder() .outputPath(path).withBlockletSize(1).withBlockSize(2); @@ -315,7 +316,7 @@ public void testSDKPaginationInsertData() throws IOException, InvalidLoadOptionE } writer.close(); - String[] row = {"222", "Daisy", "3", "334.456", "true", "1956-11-08", "2013-12-10 12:00:20", "Pune", "IT"}; + String[] row = {"222", "Daisy", "3", "334.456", "true", "1956-11-08", "2013-12-10 12:00:20", "Pune", "IT", "334.456"}; writer = CarbonWriter.builder() .outputPath(path).withBlockletSize(1).withBlockSize(2) .withCsvInput(new Schema(fields)).writtenBy("TestUtil").build(); @@ -329,7 +330,7 @@ public void testSDKPaginationInsertData() throws IOException, InvalidLoadOptionE PaginationCarbonReader paginationCarbonReader = (PaginationCarbonReader) CarbonReader.builder(path, "_temp") .withPaginationSupport().projection(new String[] - {"id", "name", "rank", "salary", "active", "dob", "doj", "city", "dept"}).build(); + {"id", "name", "rank", "salary", "active", "dob", "doj", "city", "dept", "floatField"}).build(); assert (paginationCarbonReader.getTotalRows() == 6); Object[] rows = paginationCarbonReader.read(1, 6); assert (rows.length == 6); @@ -337,7 +338,7 @@ public void testSDKPaginationInsertData() throws IOException, InvalidLoadOptionE CarbonIUD.getInstance().delete(path, "name", "AAA").commit(); CarbonReaderBuilder carbonReaderBuilder = CarbonReader.builder(path, "_temp") - .withPaginationSupport().projection(new String[]{"id", "name", "rank", "salary", "active", "dob", "doj", "city", "dept"}); + .withPaginationSupport().projection(new String[]{"id", "name", "rank", "salary", "active", "dob", "doj", "city", "dept", "floatField"}); paginationCarbonReader = (PaginationCarbonReader) carbonReaderBuilder.build(); assert (paginationCarbonReader.getTotalRows() == 5); @@ -349,7 +350,7 @@ public void testSDKPaginationInsertData() throws IOException, InvalidLoadOptionE paginationCarbonReader = (PaginationCarbonReader) CarbonReader.builder(path, "_temp") .withPaginationSupport().projection(new String[] - {"id", "name", "rank", "salary", "active", "dob", "doj", "city", "dept"}).build(); + {"id", "name", "rank", "salary", "active", "dob", "doj", "city", "dept", "floatField"}).build(); assert (paginationCarbonReader.getTotalRows() == 5); rows = paginationCarbonReader.read(1, 5); assert (rows.length == 5); diff --git a/streaming/src/main/java/org/apache/carbondata/streaming/CarbonStreamRecordWriter.java b/streaming/src/main/java/org/apache/carbondata/streaming/CarbonStreamRecordWriter.java index 8db1bd7bee9..7dac2b340ad 100644 --- a/streaming/src/main/java/org/apache/carbondata/streaming/CarbonStreamRecordWriter.java +++ b/streaming/src/main/java/org/apache/carbondata/streaming/CarbonStreamRecordWriter.java @@ -281,6 +281,9 @@ public void write(Void key, Object value) throws IOException { } else if (dataType == DataTypes.LONG) { output.writeLong((long) columnValue); output.msrStatsCollectors[msrCount].update((long) columnValue); + } else if (dataType == DataTypes.FLOAT) { + output.writeFloat((float) columnValue); + output.msrStatsCollectors[msrCount].update((float) columnValue); } else if (dataType == DataTypes.DOUBLE) { output.writeDouble((double) columnValue); output.msrStatsCollectors[msrCount].update((double) columnValue); diff --git a/streaming/src/main/java/org/apache/carbondata/streaming/StreamBlockletWriter.java b/streaming/src/main/java/org/apache/carbondata/streaming/StreamBlockletWriter.java index 0391525aa10..75743062450 100644 --- a/streaming/src/main/java/org/apache/carbondata/streaming/StreamBlockletWriter.java +++ b/streaming/src/main/java/org/apache/carbondata/streaming/StreamBlockletWriter.java @@ -168,6 +168,10 @@ void writeLong(long val) { count += 8; } + void writeFloat(float val) { + writeInt(Float.floatToIntBits(val)); + } + void writeDouble(double val) { writeLong(Double.doubleToLongBits(val)); } diff --git a/tools/cli/src/main/java/org/apache/carbondata/tool/DataFile.java b/tools/cli/src/main/java/org/apache/carbondata/tool/DataFile.java index cb9f1d6bd14..c8a49f5c5ce 100644 --- a/tools/cli/src/main/java/org/apache/carbondata/tool/DataFile.java +++ b/tools/cli/src/main/java/org/apache/carbondata/tool/DataFile.java @@ -496,6 +496,11 @@ private double computePercentage(byte[] data, byte[] min, byte[] max, ColumnSche dataValue = ByteUtil.toLong(data, 0, data.length) - minValue; factorValue = ByteUtil.toLong(max, 0, max.length) - ByteUtil.toLong(min, 0, min.length); + } else if (column.getDataType() == DataTypes.FLOAT) { + minValue = ByteUtil.toFloat(min, 0, min.length); + dataValue = ByteUtil.toFloat(data, 0, data.length) - minValue; + factorValue = + ByteUtil.toFloat(max, 0, max.length) - ByteUtil.toFloat(min, 0, min.length); } else if (column.getDataType() == DataTypes.DOUBLE) { minValue = ByteUtil.toDouble(min, 0, min.length); dataValue = ByteUtil.toDouble(data, 0, data.length) - minValue;