viirya commented on a change in pull request #29548: URL: https://github.com/apache/spark/pull/29548#discussion_r478769691
########## File path: python/docs/source/user_guide/arrow_pandas.rst ########## @@ -0,0 +1,411 @@ +.. 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. + +======================= +Apache Arrow in PySpark +======================= + +.. currentmodule:: pyspark.sql + +Apache Arrow is an in-memory columnar data format that is used in Spark to efficiently transfer +data between JVM and Python processes. This currently is most beneficial to Python users that +work with Pandas/NumPy data. Its usage is not automatic and might require some minor +changes to configuration or code to take full advantage and ensure compatibility. This guide will +give a high-level description of how to use Arrow in Spark and highlight any differences when +working with Arrow-enabled data. + +Ensure PyArrow Installed +------------------------ + +To use Apache Arrow in PySpark, `the recommended version of PyArrow <arrow_pandas.rst#recommended-pandas-and-pyarrow-versions>`_ +should be installed. +If you install PySpark using pip, then PyArrow can be brought in as an extra dependency of the +SQL module with the command ``pip install pyspark[sql]``. Otherwise, you must ensure that PyArrow +is installed and available on all cluster nodes. +You can install using pip or conda from the conda-forge channel. See PyArrow +`installation <https://arrow.apache.org/docs/python/install.html>`_ for details. + +Enabling for Conversion to/from Pandas +-------------------------------------- + +Arrow is available as an optimization when converting a Spark DataFrame to a Pandas DataFrame +using the call :meth:`DataFrame.toPandas` and when creating a Spark DataFrame from a Pandas DataFrame with +:meth:`SparkSession.createDataFrame`. To use Arrow when executing these calls, users need to first set +the Spark configuration ``spark.sql.execution.arrow.pyspark.enabled`` to ``true``. This is disabled by default. + +In addition, optimizations enabled by ``spark.sql.execution.arrow.pyspark.enabled`` could fallback automatically +to non-Arrow optimization implementation if an error occurs before the actual computation within Spark. +This can be controlled by ``spark.sql.execution.arrow.pyspark.fallback.enabled``. + +.. literalinclude:: ../../../../examples/src/main/python/sql/arrow.py + :language: python + :lines: 35-48 + :dedent: 4 + +Using the above optimizations with Arrow will produce the same results as when Arrow is not +enabled. + +Note that even with Arrow, :meth:`DataFrame.toPandas` results in the collection of all records in the +DataFrame to the driver program and should be done on a small subset of the data. Not all Spark +data types are currently supported and an error can be raised if a column has an unsupported type. +If an error occurs during :meth:`SparkSession.createDataFrame`, Spark will fall back to create the +DataFrame without Arrow. + +Pandas UDFs (a.k.a. Vectorized UDFs) +------------------------------------ + +.. currentmodule:: pyspark.sql.functions + +Pandas UDFs are user defined functions that are executed by Spark using +Arrow to transfer data and Pandas to work with the data, which allows vectorized operations. A Pandas +UDF is defined using the :meth:`pandas_udf` as a decorator or to wrap the function, and no additional +configuration is required. A Pandas UDF behaves as a regular PySpark function API in general. + +Before Spark 3.0, Pandas UDFs used to be defined with ``pyspark.sql.functions.PandasUDFType``. From Spark 3.0 +with Python 3.6+, you can also use `Python type hints <https://www.python.org/dev/peps/pep-0484>`_. +Using Python type hints are preferred and using ``pyspark.sql.functions.PandasUDFType`` will be deprecated in +the future release. + +.. currentmodule:: pyspark.sql.types + +Note that the type hint should use ``pandas.Series`` in all cases but there is one variant +that ``pandas.DataFrame`` should be used for its input or output type hint instead when the input +or output column is of :class:`StructType`. The following example shows a Pandas UDF which takes long +column, string column and struct column, and outputs a struct column. It requires the function to +specify the type hints of ``pandas.Series`` and ``pandas.DataFrame`` as below: + +.. literalinclude:: ../../../../examples/src/main/python/sql/arrow.py + :language: python + :lines: 54-78 + :dedent: 4 + +In the following sections, it describes the combinations of the supported type hints. For simplicity, +``pandas.DataFrame`` variant is omitted. + +Series to Series +~~~~~~~~~~~~~~~~ + +.. currentmodule:: pyspark.sql.functions + +The type hint can be expressed as ``pandas.Series``, ... -> ``pandas.Series``. + +By using :func:`pandas_udf` with the function having such type hints above, it creates a Pandas UDF where the given +function takes one or more ``pandas.Series`` and outputs one ``pandas.Series``. The output of the function should +always be of the same length as the input. Internally, PySpark will execute a Pandas UDF by splitting +columns into batches and calling the function for each batch as a subset of the data, then concatenating +the results together. + +The following example shows how to create this Pandas UDF that computes the product of 2 columns. + +.. literalinclude:: ../../../../examples/src/main/python/sql/arrow.py + :language: python + :lines: 82-112 + :dedent: 4 + +For detailed usage, please see :func:`pandas_udf`. + +Iterator of Series to Iterator of Series +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. currentmodule:: pyspark.sql.functions + +The type hint can be expressed as ``Iterator[pandas.Series]`` -> ``Iterator[pandas.Series]``. + +By using :func:`pandas_udf` with the function having such type hints above, it creates a Pandas UDF where the given +function takes an iterator of ``pandas.Series`` and outputs an iterator of ``pandas.Series``. The +length of the entire output from the function should be the same length of the entire input; therefore, it can +prefetch the data from the input iterator as long as the lengths are the same. +In this case, the created Pandas UDF requires one input column when the Pandas UDF is called. To use +multiple input columns, a different type hint is required. See Iterator of Multiple Series to Iterator +of Series. + +It is also useful when the UDF execution requires initializing some states although internally it works +identically as Series to Series case. The pseudocode below illustrates the example. + +.. code-block:: python + + @pandas_udf("long") + def calculate(iterator: Iterator[pd.Series]) -> Iterator[pd.Series]: + # Do some expensive initialization with a state + state = very_expensive_initialization() + for x in iterator: + # Use that state for whole iterator. + yield calculate_with_state(x, state) + + df.select(calculate("value")).show() + +The following example shows how to create this Pandas UDF: + +.. literalinclude:: ../../../../examples/src/main/python/sql/arrow.py + :language: python + :lines: 116-138 + :dedent: 4 + +For detailed usage, please see :func:`pandas_udf`. + +Iterator of Multiple Series to Iterator of Series +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. currentmodule:: pyspark.sql.functions + +The type hint can be expressed as ``Iterator[Tuple[pandas.Series, ...]]`` -> ``Iterator[pandas.Series]``. + +By using :func:`pandas_udf` with the function having such type hints above, it creates a Pandas UDF where the +given function takes an iterator of a tuple of multiple ``pandas.Series`` and outputs an iterator of ``pandas.Series``. +In this case, the created pandas UDF requires multiple input columns as many as the series in the tuple +when the Pandas UDF is called. Otherwise, it has the same characteristics and restrictions as Iterator of Series +to Iterator of Series case. + +The following example shows how to create this Pandas UDF: + +.. literalinclude:: ../../../../examples/src/main/python/sql/arrow.py + :language: python + :lines: 142-165 + :dedent: 4 + +For detailed usage, please see :func:`pandas_udf`. + +Series to Scalar +~~~~~~~~~~~~~~~~ + +.. currentmodule:: pyspark.sql.functions + +The type hint can be expressed as ``pandas.Series``, ... -> ``Any``. + +By using :func:`pandas_udf` with the function having such type hints above, it creates a Pandas UDF similar +to PySpark's aggregate functions. The given function takes `pandas.Series` and returns a scalar value. +The return type should be a primitive data type, and the returned scalar can be either a python +primitive type, e.g., ``int`` or ``float`` or a numpy data type, e.g., ``numpy.int64`` or ``numpy.float64``. +``Any`` should ideally be a specific scalar type accordingly. + +.. currentmodule:: pyspark.sql + +This UDF can be also used with :meth:`GroupedData.agg` and `Window`. +It defines an aggregation from one or more ``pandas.Series`` to a scalar value, where each ``pandas.Series`` +represents a column within the group or window. + +Note that this type of UDF does not support partial aggregation and all data for a group or window +will be loaded into memory. Also, only unbounded window is supported with Grouped aggregate Pandas +UDFs currently. The following example shows how to use this type of UDF to compute mean with a group-by +and window operations: + +.. literalinclude:: ../../../../examples/src/main/python/sql/arrow.py + :language: python + :lines: 169-210 + :dedent: 4 + +.. currentmodule:: pyspark.sql.functions + +For detailed usage, please see :func:`pandas_udf`. + +Pandas Function APIs +-------------------- + +.. currentmodule:: pyspark.sql + +Pandas Function APIs can directly apply a Python native function against the whole :class:`DataFrame` by +using Pandas instances. Internally it works similarly with Pandas UDFs by using Arrow to transfer +data and Pandas to work with the data, which allows vectorized operations. However, A Pandas Function +API behaves as a regular API under PySpark :class:`DataFrame` instead of :class:`Column`, and Python type hints in Pandas +Functions APIs are optional and do not affect how it works internally at this moment although they +might be required in the future. + +.. currentmodule:: pyspark.sql.functions + +From Spark 3.0, grouped map pandas UDF is now categorized as a separate Pandas Function API, +``DataFrame.groupby().applyInPandas()``. It is still possible to use it with ``pyspark.sql.functions.PandasUDFType`` +and ``DataFrame.groupby().apply()`` as it was; however, it is preferred to use +``DataFrame.groupby().applyInPandas()`` directly. Using ``pyspark.sql.functions.PandasUDFType`` will be deprecated +in the future. + +Grouped Map +~~~~~~~~~~~ + +.. currentmodule:: pyspark.sql + +Grouped map operations with Pandas instances are supported by ``DataFrame.groupby().applyInPandas()`` +which requires a Python function that takes a ``pandas.DataFrame`` and return another ``pandas.DataFrame``. +It maps each group to each ``pandas.DataFrame`` in the Python function. + +This API implements the "split-apply-combine" pattern which consists of three steps: + +* Split the data into groups by using :meth:`DataFrame.groupBy`. + +* Apply a function on each group. The input and output of the function are both ``pandas.DataFrame``. The input data contains all the rows and columns for each group. + +* Combine the results into a new PySpark :class:`DataFrame`. + +To use ``DataFrame.groupBy().applyInPandas()``, the user needs to define the following: + +* A Python function that defines the computation for each group. + +* A ``StructType`` object or a string that defines the schema of the output PySpark :class:`DataFrame`. + +The column labels of the returned ``pandas.DataFrame`` must either match the field names in the +defined output schema if specified as strings, or match the field data types by position if not +strings, e.g. integer indices. See `pandas.DataFrame <https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html#pandas.DataFrame>`_. +on how to label columns when constructing a ``pandas.DataFrame``. + +Note that all data for a group will be loaded into memory before the function is applied. This can +lead to out of memory exceptions, especially if the group sizes are skewed. The configuration for +`maxRecordsPerBatch <arrow_pandas.rst#setting-arrow-batch-size>`_ is not applied on groups and it is up to the user +to ensure that the grouped data will fit into the available memory. + +The following example shows how to use ``DataFrame.groupby().applyInPandas()`` to subtract the mean from each value +in the group. + +.. literalinclude:: ../../../../examples/src/main/python/sql/arrow.py + :language: python + :lines: 214-232 + :dedent: 4 + +For detailed usage, please see please see :meth:`GroupedData.applyInPandas` + +Map +~~~ + +Map operations with Pandas instances are supported by :meth:`DataFrame.mapInPandas` which maps an iterator +of ``pandas.DataFrame``\s to another iterator of ``pandas.DataFrame``\s that represents the current +PySpark :class:`DataFrame` and returns the result as a PySpark :class:`DataFrame`. The functions takes and outputs +an iterator of ``pandas.DataFrame``. It can return the output of arbitrary length in contrast to some +Pandas UDFs although internally it works similarly with Series to Series Pandas UDF. + +The following example shows how to use :meth:`DataFrame.mapInPandas`: + +.. literalinclude:: ../../../../examples/src/main/python/sql/arrow.py + :language: python + :lines: 236-247 + :dedent: 4 + +For detailed usage, please see :meth:`DataFrame.mapInPandas`. + +Co-grouped Map +~~~~~~~~~~~~~~ + +.. currentmodule:: pyspark.sql + +Co-grouped map operations with Pandas instances are supported by ``DataFrame.groupby().cogroup().applyInPandas()`` which +allows two PySpark :class:`DataFrame`\s to be cogrouped by a common key and then a Python function applied to each +cogroup. It consists of the following steps: + +* Shuffle the data such that the groups of each dataframe which share a key are cogrouped together. + +* Apply a function to each cogroup. The input of the function is two ``pandas.DataFrame`` (with an optional tuple representing the key). The output of the function is a ``pandas.DataFrame``. + +* Combine the ``pandas.DataFrame``\s from all groups into a new PySpark :class:`DataFrame`. + +To use ``groupBy().cogroup().applyInPandas()``, the user needs to define the following: + +* A Python function that defines the computation for each cogroup. + +* A ``StructType`` object or a string that defines the schema of the output PySpark :class:`DataFrame`. + +The column labels of the returned ``pandas.DataFrame`` must either match the field names in the +defined output schema if specified as strings, or match the field data types by position if not +strings, e.g. integer indices. See `pandas.DataFrame <https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html#pandas.DataFrame>`_. +on how to label columns when constructing a ``pandas.DataFrame``. + +Note that all data for a cogroup will be loaded into memory before the function is applied. This can lead to out of +memory exceptions, especially if the group sizes are skewed. The configuration for `maxRecordsPerBatch <arrow_pandas.rst#setting-arrow-batch-size>`_ +is not applied and it is up to the user to ensure that the cogrouped data will fit into the available memory. + +The following example shows how to use ``DataFrame.groupby().cogroup().applyInPandas()`` to perform an asof join between two datasets. + +.. literalinclude:: ../../../../examples/src/main/python/sql/arrow.py + :language: python + :lines: 251-273 + :dedent: 4 + + +For detailed usage, please see :meth:`PandasCogroupedOps.applyInPandas` + +Usage Notes +----------- + +Supported SQL Types +~~~~~~~~~~~~~~~~~~~ + +.. currentmodule:: pyspark.sql.types + +Currently, all Spark SQL data types are supported by Arrow-based conversion except :class:`MapType`, +:class:`ArrayType` of :class:`TimestampType`, and nested :class:`StructType`. + +Setting Arrow Batch Size +~~~~~~~~~~~~~~~~~~~~~~~~ + +Data partitions in Spark are converted into Arrow record batches, which can temporarily lead to +high memory usage in the JVM. To avoid possible out of memory exceptions, the size of the Arrow +record batches can be adjusted by setting the conf ``spark.sql.execution.arrow.maxRecordsPerBatch`` +to an integer that will determine the maximum number of rows for each batch. The default value is +10,000 records per batch. If the number of columns is large, the value should be adjusted +accordingly. Using this limit, each data partition will be made into 1 or more record batches for +processing. + +Timestamp with Time Zone Semantics +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. currentmodule:: pyspark.sql + +Spark internally stores timestamps as UTC values, and timestamp data that is brought in without +a specified time zone is converted as local time to UTC with microsecond resolution. When timestamp +data is exported or displayed in Spark, the session time zone is used to localize the timestamp +values. 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