Support: Add dedicated documentation page about polyfills and utilities

docs/index-all.rst

@@ -18,3 +18,4 @@ CrateDB SQLAlchemy dialect -- all pages
     advanced-querying
     inspection-reflection
     dataframe
+    support

docs/index.rst

@@ -135,7 +135,7 @@ Load results into `pandas`_ DataFrame.
         print(df)
 
 
-Data types
+Data Types
 ==========
 
 The :ref:`DB API driver <crate-python:index>` and the SQLAlchemy dialect
@@ -150,6 +150,19 @@ extension types <using-extension-types>` documentation pages.
 
     data-types
 
+Support Utilities
+=================
+
+The package bundles a few support and utility functions that try to fill a few
+gaps you will observe when working with CrateDB, when compared with other
+databases. As a distributed OLAP database, it naturally does not include certain
+features usually found, for example, in OLTP databases.
+
+.. toctree::
+    :maxdepth: 2
+
+    support
+
 
 .. _examples:
 .. _by-example:

docs/overview.rst

@@ -1,9 +1,9 @@
 .. _overview:
 .. _using-sqlalchemy:
 
-========
-Overview
-========
+================
+Feature Overview
+================
 
 .. rubric:: Table of contents
 
@@ -300,15 +300,28 @@ would translate into the following declarative model:
     >>> log.id
     ...
 
+.. _auto-generated-identifiers:
 
-Auto-generated primary key
+Auto-generated identifiers
 ..........................
 
+CrateDB does not provide traditional sequences or ``SERIAL`` data type support,
+which enable automatically assigning incremental values when inserting records.
+However, it offers server-side support by providing an SQL function to generate
+random identifiers of ``STRING`` type, and client-side support for generating
+``INTEGER``-based identifiers on behalf of the SQLAlchemy dialect.
+
+.. _gen_random_text_uuid:
+
+``gen_random_text_uuid``
+~~~~~~~~~~~~~~~~~~~~~~~~
+
 CrateDB 4.5.0 added the :ref:`gen_random_text_uuid() <crate-reference:scalar-gen_random_text_uuid>`
 scalar function, which can also be used within an SQL DDL statement, in order to automatically
 assign random identifiers to newly inserted records on the server side.
 
 In this spirit, it is suitable to be used as a ``PRIMARY KEY`` constraint for SQLAlchemy.
+It works on SQLAlchemy-defined columns of type ``sa.String``.
 
 A table schema like this
 
@@ -334,6 +347,32 @@ would translate into the following declarative model:
     >>> item.id
     ...
 
+.. _timestamp-autoincrement:
+
+Timestamp-based Autoincrement
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+By using SQLAlchemy's ``sa.func.now()``, you can assign automatically generated
+identifiers to SQLAlchemy columns of types ``sa.BigInteger``, ``sa.DateTime``,
+and ``sa.String``.
+
+This emulates autoincrement / sequential ID behavior for designated columns, based
+on assigning timestamps on record insertion.
+
+    >>> class Item(Base):
+    ...     id = sa.Column("id", sa.BigInteger, default=func.now(), primary_key=True)
+    ...     name = sa.Column("name", sa.String)
+
+    >>> item = Item(name="Foobar")
+    >>> session.add(item)
+    >>> session.commit()
+    >>> item.id
+    ...
+
+There is a support utility which emulates autoincrement / sequential ID
+behavior for designated columns, based on assigning timestamps on record
+insertion. See :ref:`support-autoincrement`.
+
 
 .. _using-extension-types:
 

docs/support.md

@@ -0,0 +1,215 @@
+(support-features)=
+(support-utilities)=
+# Support Features
+
+The package bundles a few support and utility functions that try to fill a few
+gaps you will observe when working with CrateDB, a distributed OLAP database,
+since it lacks certain features, usually found in traditional OLTP databases.
+
+A few of the features outlined below are referred to as [polyfills], and
+emulate a few functionalities, for example, to satisfy compatibility issues on
+downstream frameworks or test suites. You can use them at your disposal, but
+you should know what you are doing, as some of them can seriously impact the
+performance.
+
+Other features include efficiency support utilities for 3rd-party frameworks,
+which can be used to increase performance, mostly on INSERT operations.
+
+
+(support-automatic-refresh)=
+## Automatic Table REFRESH after DML
+
+:::{rubric} Background
+:::
+CrateDB is [eventually consistent]. Data written with a former statement is
+not guaranteed to be fetched with the next following select statement for the
+affected rows.
+
+Data written to CrateDB is flushed periodically, the refresh interval is
+1000 milliseconds by default, and can be changed. More details can be found in
+the reference documentation about [table refreshing](inv:crate-reference#refresh_data).
+
+There are situations where stronger consistency is required, for example when
+needing to satisfy test suites of 3rd party frameworks, which usually do not
+take such special behavior of CrateDB into consideration. 
+
+:::{rubric} Utility
+:::
+- The `refresh_after_dml` utility will configure an SQLAlchemy engine or session
+  to automatically invoke `REFRESH TABLE` statements after each DML
+  operation (INSERT, UPDATE, DELETE).
+- Only relevant (dirty) entities / tables will be considered to be refreshed.
+
+:::{rubric} Synopsis
+:::
+```python
+import sqlalchemy as sa
+from sqlalchemy_cratedb.support import refresh_after_dml
+
+engine = sa.create_engine("crate://")
+refresh_after_dml(engine)
+```
+
+```python
+import sqlalchemy as sa
+from sqlalchemy.orm import sessionmaker
+from sqlalchemy_cratedb.support import refresh_after_dml
+
+engine = sa.create_engine("crate://")
+session = sessionmaker(bind=engine)()
+refresh_after_dml(session)
+```
+
+:::{warning}
+Refreshing the table after each DML operation can cause serious performance
+degradations, and should only be used on low-volume, low-traffic data,
+when applicable, and if you know what you are doing.
+:::
+
+
+(support-insert-bulk)=
+(support-pandas)=
+(support-dask)=
+## Support for pandas and Dask
+
+:::{rubric} Background
+:::
+CrateDB's [](inv:crate-reference#http-bulk-ops) interface enables efficient
+INSERT, UPDATE, and DELETE operations for batches of data. It allows to issue
+bulk operations, which are executed as single calls on the database server.
+
+:::{rubric} Utility
+:::
+The `insert_bulk` utility provides efficient bulk data transfers when using
+dataframe libraries like pandas and Dask. {ref}`dataframe` dedicates a whole
+page to corresponding topics, about choosing the right chunk sizes, concurrency
+settings, and beyond.
+
+:::{rubric} Synopsis
+:::
+Use `method=insert_bulk` on pandas' or Dask's `to_sql()` method.
+```python
+import sqlalchemy as sa
+from sqlalchemy_cratedb.support import insert_bulk
+from pueblo.testing.pandas import makeTimeDataFrame
+
+# Create a pandas DataFrame, and connect to CrateDB.
+df = makeTimeDataFrame(nper=42, freq="S")
+engine = sa.create_engine("crate://")
+
+# Insert content of DataFrame using batches of records.
+df.to_sql(
+    name="testdrive",
+    con=engine,
+    if_exists="replace",
+    index=False,
+    method=insert_bulk,
+)
+```
+
+(support-autoincrement)=
+## Synthetic Autoincrement using Timestamps
+
+:::{rubric} Background
+:::
+CrateDB does not provide traditional sequences or `SERIAL` data type support,
+which enable automatically assigning incremental values when inserting records.
+
+
+:::{rubric} Utility
+:::
+- The `patch_autoincrement_timestamp` utility emulates autoincrement /
+  sequential ID behavior for designated columns, based on assigning timestamps
+  on record insertion.
+- It will simply assign `sa.func.now()` as a column `default` on the ORM model
+  column. 
+- It works on the SQLAlchemy column types `sa.BigInteger`, `sa.DateTime`,
+  and `sa.String`.
+- You can use it if adjusting ORM models for your database adapter is not
+  an option.
+
+:::{rubric} Synopsis
+:::
+After activating the patch, you can use `autoincrement=True` on column definitions.
+```python
+import sqlalchemy as sa
+from sqlalchemy.orm import declarative_base
+from sqlalchemy_cratedb.support import patch_autoincrement_timestamp
+
+# Enable patch.
+patch_autoincrement_timestamp()
+
+# Define database schema.
+Base = declarative_base()
+
+class FooBar(Base):
+    id = sa.Column(sa.DateTime, primary_key=True, autoincrement=True)
+```
+
+:::{warning}
+CrateDB's [`TIMESTAMP`](inv:crate-reference#type-timestamp) data type provides
+milliseconds granularity. This has to be considered when evaluating collision
+safety in high-traffic environments.
+:::
+
+
+(support-unique)=
+## Synthetic UNIQUE Constraints
+
+:::{rubric} Background
+:::
+CrateDB does not provide `UNIQUE` constraints in DDL statements. Because of its
+distributed nature, supporting such a feature natively would cause expensive
+database cluster operations, deterring many benefits of using database clusters
+firsthand.
+
+:::{rubric} Utility
+:::
+- The `check_uniqueness_factory` utility emulates "unique constraints"
+  functionality by querying the table for unique values before invoking
+  SQL `INSERT` operations.
+- It uses SQLALchemy [](inv:sa#orm_event_toplevel), more specifically
+  the [before_insert] mapper event.
+- When the uniqueness constraint is violated, the adapter will raise a
+  corresponding exception.
+  ```python
+  IntegrityError: DuplicateKeyException in table 'foobar' on constraint 'name'
+  ```
+
+:::{rubric} Synopsis
+:::
+```python
+import sqlalchemy as sa
+from sqlalchemy.orm import declarative_base
+from sqlalchemy.event import listen
+from sqlalchemy_cratedb.support import check_uniqueness_factory
+
+# Define database schema.
+Base = declarative_base()
+
+class FooBar(Base):
+    id = sa.Column(sa.String, primary_key=True)
+    name = sa.Column(sa.String)
+
+# Add synthetic UNIQUE constraint on `name` column.
+listen(FooBar, "before_insert", check_uniqueness_factory(FooBar, "name"))
+```
+
+[before_insert]: https://docs.sqlalchemy.org/en/20/orm/events.html#sqlalchemy.orm.MapperEvents.before_insert
+
+:::{note}
+This feature will only work well if table data is consistent, which can be
+ensured by invoking a `REFRESH TABLE` statement after any DML operation.
+For conveniently enabling "always refresh", please refer to the documentation
+section about [](#support-automatic-refresh). 
+:::
+
+:::{warning}
+Querying the table before each INSERT operation can cause serious performance
+degradations, and should only be used on low-volume, low-traffic data,
+when applicable, and if you know what you are doing.
+:::
+
+
+[eventually consistent]: https://en.wikipedia.org/wiki/Eventual_consistency
+[polyfills]: https://en.wikipedia.org/wiki/Polyfill_(programming)