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dataframe.go
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dataframe.go
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// Copyright 2019 Nick Poorman
//
// Licensed 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 dataframe
import (
"bytes"
"encoding/json"
"errors"
"fmt"
"io"
"sort"
"strings"
"sync/atomic"
// import "github.com/apache/arrow/go/v10/arrow"
"github.com/mattetti/filebuffer"
"github.com/apache/arrow/go/v10/arrow"
"github.com/apache/arrow/go/v10/arrow/array"
"github.com/apache/arrow/go/v10/arrow/ipc"
"github.com/apache/arrow/go/v10/arrow/memory"
"github.com/apache/arrow/go/v10/parquet"
"github.com/apache/arrow/go/v10/parquet/pqarrow"
"github.com/gomem/gomem/internal/constructors"
"github.com/gomem/gomem/internal/debug"
"github.com/gomem/gomem/pkg/iterator"
"github.com/gomem/gomem/pkg/smartbuilder"
)
// Dict is a map of string to array of data.
type Dict map[string]interface{}
// Option is an option that may be passed to a function.
type Option func(interface{}) error
// NewDataFrame creates a new data frame from the provided schema and arrays.
func NewDataFrame(mem memory.Allocator, schema *arrow.Schema, arrs []arrow.Array) (*DataFrame, error) {
df := &DataFrame{
refs: 1,
mem: mem,
schema: schema,
rows: -1,
mutator: NewMutator(mem),
}
if df.rows < 0 {
switch len(arrs) {
case 0:
df.rows = 0
default:
df.rows = int64(arrs[0].Len())
}
}
if df.schema == nil {
return nil, fmt.Errorf("dataframe: nil schema")
}
if len(df.schema.Fields()) != len(arrs) {
return nil, fmt.Errorf("dataframe: inconsistent schema/arrays")
}
for i, arr := range arrs {
ft := df.schema.Field(i)
if fmt.Sprintf("%s", arr.DataType()) != fmt.Sprintf("%s", ft.Type) {
return nil, fmt.Errorf("dataframe: column %q is inconsitent with schema (%s != %s)", ft.Name, arr.DataType(), ft.Type)
}
if int64(arr.Len()) < df.rows {
return nil, fmt.Errorf("dataframe: column %q expected length >= %d but got length %d", ft.Name, df.rows, arr.Len())
}
}
df.cols = make([]arrow.Column, len(arrs))
for i := range arrs {
func(i int) {
chunk := arrow.NewChunked(arrs[i].DataType(), []arrow.Array{arrs[i]})
defer chunk.Release()
col := arrow.NewColumn(df.schema.Field(i), chunk)
df.cols[i] = *col
}(i)
}
return df, nil
}
// NewDataFrameFromColumns returns a DataFrame interface.
func NewDataFrameFromColumns(mem memory.Allocator, cols []arrow.Column) (*DataFrame, error) {
var rows int64
if len(cols) > 0 {
rows = columnLen(cols[0])
}
return NewDataFrameFromShape(mem, cols, rows)
}
// NewDataFrameFromMem creates a new data frame from the provided in-memory data.
func NewDataFrameFromMem(mem memory.Allocator, dict Dict) (*DataFrame, error) {
var (
err error
arrs = make([]arrow.Array, 0, len(dict))
fields = make([]arrow.Field, 0, len(dict))
)
keys := make([]string, 0, len(dict))
for k := range dict {
keys = append(keys, k)
}
sort.Strings(keys)
for _, k := range keys {
v := dict[k]
arr, field, newInterfaceErr := constructors.NewInterfaceFromMem(mem, k, v, nil)
if newInterfaceErr != nil {
err = newInterfaceErr
break
}
arrs = append(arrs, arr)
fields = append(fields, *field)
}
defer func() {
for i := range arrs {
arrs[i].Release()
}
}()
if err != nil {
return nil, err
}
schema := arrow.NewSchema(fields, nil)
return NewDataFrame(mem, schema, arrs)
}
// NewDataFrameFromShape is the same as NewDataFrameFromColumns only it allows you to specify the number
// of rows in the DataFrame.
func NewDataFrameFromShape(mem memory.Allocator, cols []arrow.Column, rows int64) (*DataFrame, error) {
df := &DataFrame{
refs: 1,
mem: mem,
schema: buildSchema(cols),
cols: cols,
rows: rows,
mutator: NewMutator(mem),
}
// validate the data frame and its constituents.
// note we retain the columns after having validated the data frame
// in case the validation fails and panics (and would otherwise leak
// a ref-count on the columns.)
if err := df.validate(); err != nil {
return nil, err
}
for i := range df.cols {
df.cols[i].Retain()
}
return df, nil
}
func NewDataFrameFromTable(mem memory.Allocator, table arrow.Table) (*DataFrame, error) {
cols := make([]arrow.Column, table.NumCols())
for i := range cols {
col := table.Column(i)
cols[i] = *col
}
return NewDataFrameFromShape(mem, cols, table.NumRows())
}
func NewDataFrameFromRecord(mem memory.Allocator, record arrow.Record) (*DataFrame, error) {
return NewDataFrame(mem, record.Schema(), record.Columns())
}
// DataFrame is an immutable DataFrame that uses Arrow
// to store it's data in a standard columnar format.
type DataFrame struct {
refs int64 // reference count
mem memory.Allocator
schema *arrow.Schema
cols []arrow.Column
rows int64
// Mutations that can be performed on this DataFrame
// require a the Mutator to be set up.
mutator *Mutator
}
// Allocator returns the memory allocator for this DataFrame
func (df *DataFrame) Allocator() memory.Allocator {
return df.mem
}
// Column returns the column matching the given name.
func (df *DataFrame) Column(name string) *arrow.Column {
for i, col := range df.cols {
if col.Name() == name {
return &df.cols[i]
}
}
return nil
}
// ColumnAt returns the i-th column of this Frame.
func (df *DataFrame) ColumnAt(i int) *arrow.Column {
return &df.cols[i]
}
// Columns is the slice of Columns that make up this DataFrame.
func (df *DataFrame) Columns() []arrow.Column {
return df.cols
}
// ColumnNames is the slice of column names that make up this DataFrame.
func (df *DataFrame) ColumnNames() []string {
fields := df.schema.Fields()
names := make([]string, len(fields))
for i, field := range fields {
names[i] = field.Name
}
return names
}
// ColumnTypes is the slice of column types that make up this DataFrame.
func (df *DataFrame) ColumnTypes() []arrow.Field {
return df.schema.Fields()
}
// Equals checks for equality between this DataFrame and DataFrame d.
// nil elements at the same location are considered equal.
func (df *DataFrame) Equals(d *DataFrame) bool {
if !df.schema.Equal(d.schema) {
return false
}
// compare the columns
leftCols := df.Columns()
rightCols := d.Columns()
if len(leftCols) != len(rightCols) {
return false
}
for i := range leftCols {
leftCol := leftCols[i]
rightCol := rightCols[i]
// Could do this with a column iterator?
same := compareColumns(&leftCol, &rightCol)
if !same {
return false
}
}
return true
}
// NumCols returns the number of columns of this DataFrame using Go's len().
func (df *DataFrame) NumCols() int {
return len(df.cols)
}
// NumRows returns the number of rows of this DataFrame.
func (df *DataFrame) NumRows() int64 {
return df.rows
}
// Name returns the name of the i-th column of this DataFrame.
func (df *DataFrame) Name(i int) string {
return df.schema.Field(i).Name
}
// Dims retrieves the dimensions of a DataFrame.
func (df *DataFrame) Dims() (int, int64) {
return len(df.cols), df.rows
}
// Display builds out a string representation of the DataFrame that is useful for debugging.
// if chunkSize is <= 0, the biggest possible chunk will be selected.
func (df *DataFrame) Display(chunkSize int64) string {
tr := array.NewTableReader(NewTableFacade(df), chunkSize)
defer tr.Release()
n := 0
var output strings.Builder
for tr.Next() {
rec := tr.Record()
for i, col := range rec.Columns() {
fmt.Fprintf(&output, "rec[%d][%q]: %v\n", n, rec.ColumnName(i), col)
}
n++
}
return output.String()
}
/**
* These are column specific helpers
*/
// SelectColumns returns only columns matching names.
func (df *DataFrame) SelectColumns(names ...string) []arrow.Column {
if len(names) == 0 {
return []arrow.Column{}
}
set := make(map[string]struct{}, len(names))
for _, name := range names {
set[name] = struct{}{}
}
cols := make([]arrow.Column, 0, len(names))
dfColumns := df.Columns()
for i := range dfColumns {
if _, ok := set[dfColumns[i].Name()]; !ok {
continue
}
cols = append(cols, dfColumns[i])
}
return cols[:len(cols):len(cols)]
}
// RejectColumns returns only columns not matching names.
func (df *DataFrame) RejectColumns(names ...string) []arrow.Column {
if len(names) == 0 {
return df.Columns()
}
set := make(map[string]struct{}, len(names))
for _, name := range names {
set[name] = struct{}{}
}
cols := make([]arrow.Column, 0, df.NumCols()-len(names))
dfColumns := df.Columns()
for i := range dfColumns {
if _, drop := set[dfColumns[i].Name()]; drop {
continue
}
cols = append(cols, dfColumns[i])
}
return cols[:len(cols):len(cols)]
}
// Apply takes a series of MutationFunc and calls them with the existing DataFrame on the left.
func (df *DataFrame) Apply(fns ...MutationFunc) (*DataFrame, error) {
left, err := df.Copy()
if err != nil {
return nil, err
}
if len(fns) == 0 {
return left, err
}
for i := range fns {
left, err = func() (*DataFrame, error) {
defer left.Release()
return fns[i](left)
}()
if err != nil {
return nil, err
}
}
return left, err
}
// ApplyToColumnFunc is a type alias for a function that will be called for each element
// that is iterated over in a column. The return value will
type ApplyToColumnFunc func(v interface{}) (interface{}, error)
// ApplyToColumn creates a new DataFrame with the new column appended. The new column is built
// with the response values obtained from ApplyToColumnFunc. An error response value from
// ApplyToColumnFunc will cause ApplyToColumn to return immediately.
func (df *DataFrame) ApplyToColumn(columnName, newColumnName string, fn ApplyToColumnFunc) (*DataFrame, error) {
return df.Apply(func(df *DataFrame) (*DataFrame, error) {
// TODO(nickpoorman): refactor this
col := df.Column(columnName)
field := col.Field()
field.Name = newColumnName
schema := arrow.NewSchema([]arrow.Field{field}, nil)
builder := array.NewRecordBuilder(df.Allocator(), schema)
defer builder.Release()
smartBuilder := smartbuilder.NewSmartBuilder(builder)
valueIterator := iterator.NewValueIterator(col)
defer valueIterator.Release()
for valueIterator.Next() {
value := valueIterator.ValueInterface()
res, err := fn(value)
if err != nil {
return nil, err
}
smartBuilder.Append(0, res)
}
rec := builder.NewRecord()
defer rec.Release()
chunk := arrow.NewChunked(col.DataType(), rec.Columns())
defer chunk.Release()
newCol := arrow.NewColumn(field, chunk)
defer newCol.Release()
return df.AppendColumn(newCol)
})
}
/**
* The following functions will always return a new DataFrame.
*/
// AppendColumn builds a new DataFrame with the provided Column included.
func (df *DataFrame) AppendColumn(c *arrow.Column) (*DataFrame, error) {
nCols := len(df.cols)
cols := make([]arrow.Column, nCols+1)
copy(cols, df.cols)
cols[nCols] = *c
return NewDataFrameFromShape(df.mem, cols, df.rows)
}
// Copy returns a copy of this dataframe. The underlying byte buffers will not be copied.
func (df *DataFrame) Copy() (*DataFrame, error) {
nCols := len(df.cols)
cols := make([]arrow.Column, nCols)
copy(cols, df.cols)
return NewDataFrameFromShape(df.mem, cols, df.rows)
}
// CrossJoin returns a DataFrame containing the cross join of two DataFrames.
func (df *DataFrame) CrossJoin(right *DataFrame, opts ...Option) (*DataFrame, error) {
fn := df.mutator.CrossJoin(right, opts...)
return fn(df)
}
// Select the given DataFrame columns by name.
func (df *DataFrame) Select(names ...string) (*DataFrame, error) {
fn := df.mutator.Select(names...)
return fn(df)
}
// Drop the given DataFrame columns by name.
func (df *DataFrame) Drop(names ...string) (*DataFrame, error) {
fn := df.mutator.Drop(names...)
return fn(df)
}
// InnerJoin returns a DataFrame containing the inner join of two DataFrames.
func (df *DataFrame) InnerJoin(right *DataFrame, columns []string, opts ...Option) (*DataFrame, error) {
fn := df.mutator.InnerJoin(right, columns, opts...)
return fn(df)
}
// LeftJoin returns a DataFrame containing the left join of two DataFrames.
func (df *DataFrame) LeftJoin(right *DataFrame, columns []string, opts ...Option) (*DataFrame, error) {
fn := df.mutator.LeftJoin(right, columns, opts...)
return fn(df)
}
// OuterJoin returns a DataFrame containing the outer join of two DataFrames.
// Use union of keys from both frames, similar to a SQL full outer join.
func (df *DataFrame) OuterJoin(right *DataFrame, columns []string, opts ...Option) (*DataFrame, error) {
fn := df.mutator.OuterJoin(right, columns, opts...)
return fn(df)
}
// RightJoin returns a DataFrame containing the right join of two DataFrames.
func (df *DataFrame) RightJoin(right *DataFrame, columns []string, opts ...Option) (*DataFrame, error) {
fn := df.mutator.RightJoin(right, columns, opts...)
return fn(df)
}
// Slice creates a new DataFrame consisting of rows[beg:end].
func (df *DataFrame) Slice(beg, end int64) (*DataFrame, error) {
return df.mutator.Slice(beg, end)(df)
}
// Schema returns the schema of this Frame.
func (df *DataFrame) Schema() *arrow.Schema {
return df.schema
}
// Retain increases the reference count by 1.
// Retain may be called simultaneously from multiple goroutines.
func (df *DataFrame) Retain() {
atomic.AddInt64(&df.refs, 1)
}
// Release decreases the reference count by 1.
// When the reference count goes to zero, the memory is freed.
// Release may be called simultaneously from multiple goroutines.
func (df *DataFrame) Release() {
refs := atomic.AddInt64(&df.refs, -1)
debug.Assert(refs >= 0, "too many releases")
if refs == 0 {
for i := range df.cols {
df.cols[i].Release()
}
df.cols = nil
}
}
func (df *DataFrame) validate() error {
if len(df.Columns()) != len(df.schema.Fields()) {
return errors.New("dataframe validate(): table schema mismatch")
}
for i, col := range df.cols {
if !col.Field().Equal(df.schema.Field(i)) {
return fmt.Errorf("dataframe validate(): column field %q is inconsistent with schema", col.Name())
}
colLen := columnLen(col)
if colLen < df.rows {
return fmt.Errorf("dataframe validate(): column %q expected length >= %d but got length %d", col.Name(), df.rows, colLen)
}
}
return nil
}
func NewFrameFromArrowBytes(buf []byte, mem memory.Allocator) (*DataFrame, error) {
r := bytes.NewReader(buf)
rr, err := ipc.NewFileReader(r, ipc.WithAllocator(mem)) // TODO(twg) need to confirm allocator is required in this instance
if err != nil {
return nil, err
}
defer rr.Close()
records := make([]arrow.Record, rr.NumRecords(), rr.NumRecords())
i := 0
for {
rec, err := rr.Read()
if err == io.EOF {
break
} else if err != nil {
return nil, err
}
records[i] = rec
rec.Retain()
i++
}
records = records[:i]
table := array.NewTableFromRecords(rr.Schema(), records)
return NewDataFrameFromTable(mem, table)
}
func (df *DataFrame) ToBytes() ([]byte, error) {
buf := filebuffer.New(nil)
writer, err := ipc.NewFileWriter(buf, ipc.WithAllocator(df.mem), ipc.WithSchema(df.schema))
if err != nil {
return nil, err
}
chunkSize := int64(0)
table := NewTableFacade(df)
tr := array.NewTableReader(table, chunkSize)
defer tr.Release()
for tr.Next() {
arec := tr.Record()
err = writer.Write(arec)
if err != nil {
return nil, err
}
}
err = writer.Close()
buf.Seek(0, io.SeekStart)
return buf.Bytes(), err
}
func (df *DataFrame) ToParquet(w io.Writer, chunkSize int64) error {
props := parquet.NewWriterProperties(parquet.WithDictionaryDefault(false))
arrProps := pqarrow.DefaultWriterProps()
err := pqarrow.WriteTable(NewTableFacade(df), w, chunkSize, props, arrProps)
if err != nil {
return err
}
return nil
}
func compareColumns(left, right *arrow.Column) bool {
// We have to use value iterators and the only way to do that is to switch on the type
leftDtype := left.DataType()
rightDtype := right.DataType()
if leftDtype.ID() != rightDtype.ID() {
debug.Warnf("warning: comparing different types of columns: %v | %v", leftDtype.Name(), rightDtype.Name())
return false
}
// Let's use the stuff we already have to do all columns
it := iterator.NewStepIteratorForColumns([]arrow.Column{*left, *right})
defer it.Release()
for it.Next() {
stepValue := it.Values()
var elTPrev Element
for i := range stepValue.Values {
elT := StepValueElementAt(stepValue, i)
if elTPrev == nil {
elTPrev = elT
continue
}
eq, err := elT.EqStrict(elTPrev)
if err != nil {
debug.Warnf("warning: bullseye/dataframe#compareColumns: %v\n", err)
// types must not be equal
return false
}
if !eq {
return false
}
}
}
return true
}
func buildSchema(cols []arrow.Column) *arrow.Schema {
fields := make([]arrow.Field, 0, len(cols))
for i := range cols {
fields = append(fields, cols[i].Field())
}
return arrow.NewSchema(fields, nil)
}
// columnLen returns the number of rows in the Column.
// Because Arrow chunks arrays, you may encounter an overflow if
// there are MaxInt64 rows, i.e. 9223372036854775807.
func columnLen(col arrow.Column) int64 {
var length int64
for _, chunk := range col.Data().Chunks() {
// Keep our own counters instead of Chunked's
length += int64(chunk.Len())
}
return length
}
// added for json marshalling anotation
type Computation struct {
Int []int64 `json:"int,omitempty"`
Float []float64 `json:"float,omitempty"`
}
func (df *DataFrame) MarshalJSON() ([]byte, error) {
column := df.ColumnAt(0)
resolver := NewChunkResolver(column)
results := make(map[string]*Computation)
for n := resolver.NumRows() - 1; n >= 0; n-- {
c, i := resolver.Resolve(n)
for _, col := range df.Columns() {
resCol, ok := results[col.Name()]
if !ok {
// resCol = &Computation{Int: make([]int64, 0), Float: make([]float64, 0)}
resCol = &Computation{}
}
switch col.DataType().(type) {
case *arrow.Int32Type:
v := col.Data().Chunk(c).(*array.Int32).Int32Values()
resCol.Int = append(resCol.Int, int64(v[i]))
case *arrow.Int64Type:
v := col.Data().Chunk(c).(*array.Int64).Int64Values()
resCol.Int = append(resCol.Int, v[i])
case *arrow.Float64Type:
v := col.Data().Chunk(c).(*array.Float64).Float64Values()
resCol.Float = append(resCol.Float, v[i])
}
results[col.Name()] = resCol
}
}
return json.Marshal(results)
}
/*
rows contain the rowids of the items coming in
must be sorted ascending and already filtered out
for a given shard
*/
/*
type Changeset struct {
Rows []int64
table [][]interface{}
}
func (df *DataFrame) Import(changeSet ChangeSet) error {
column := df.ColumnAt(0)
resolver := NewChunkResolver(column)
start:=0
for i,row := range changeset.Rows{
start:=i
if row < resolver.NumRows{
c, i := resolver.Resolve(row)
df.SetRow(row,changeSet.table[i])
// we are setting
}else {
break; //the rest of the rows need to be appendened
}
}
// just need to create an arrow.Array set to concatenaate with
for i,row:= changSet.Table[start:]{
for _, col := range df.Columns() {
resCol, ok := results[col.Name()]
if !ok {
// resCol = &Computation{Int: make([]int64, 0), Float: make([]float64, 0)}
resCol = &Computation{}
}
switch col.DataType().(type) {
case *arrow.Int32Type:
v := col.Data().Chunk(c).(*array.Int32).Int32Values()
resCol.Int = append(resCol.Int, int64(v[i]))
case *arrow.Int64Type:
v := col.Data().Chunk(c).(*array.Int64).Int64Values()
resCol.Int = append(resCol.Int, v[i])
case *arrow.Float64Type:
v := col.Data().Chunk(c).(*array.Float64).Float64Values()
resCol.Float = append(resCol.Float, v[i])
}
results[col.Name()] = resCol
}
}
}
return json.Marshal(results)
}
*/
func (df *DataFrame) Dump() {
column := df.ColumnAt(0)
resolver := NewChunkResolver(column)
for n := resolver.NumRows() - 1; n >= 0; n-- {
c, i := resolver.Resolve(n)
fmt.Printf("%d ", n)
for _, col := range df.Columns() {
switch col.DataType().(type) {
case *arrow.Int32Type:
v := col.Data().Chunk(c).(*array.Int32).Int32Values()
fmt.Printf(" int:%d", v[i])
case *arrow.Int64Type:
v := col.Data().Chunk(c).(*array.Int64).Int64Values()
fmt.Printf(" int64:%d", v[i])
case *arrow.Float64Type:
v := col.Data().Chunk(c).(*array.Float64).Float64Values()
fmt.Printf(" float:%f", v[i])
}
}
fmt.Println()
}
}