Github user icexelloss commented on a diff in the pull request: https://github.com/apache/spark/pull/19575#discussion_r163986071 --- Diff: docs/sql-programming-guide.md --- @@ -1640,6 +1640,250 @@ Configuration of Hive is done by placing your `hive-site.xml`, `core-site.xml` a You may run `./bin/spark-sql --help` for a complete list of all available options. +# PySpark Usage Guide for Pandas with Arrow + +## Arrow in Spark + +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 + +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. The current supported version is 0.8.0. +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 Pandas using the call +`toPandas()` and when creating a Spark DataFrame from Pandas with `createDataFrame(pandas_df)`. +To use Arrow when executing these calls, it first must be enabled by setting the Spark configuration +'spark.sql.execution.arrow.enabled' to 'true', this is disabled by default. + +<div class="codetabs"> +<div data-lang="python" markdown="1"> +{% highlight python %} + +import numpy as np +import pandas as pd + +# Enable Arrow, 'spark' is an existing SparkSession +spark.conf.set("spark.sql.execution.arrow.enabled", "true") + +# Generate sample data +pdf = pd.DataFrame(np.random.rand(100, 3)) + +# Create a Spark DataFrame from Pandas data using Arrow +df = spark.createDataFrame(pdf) + +# Convert the Spark DataFrame to a local Pandas DataFrame +selpdf = df.select("*").toPandas() + +{% endhighlight %} +</div> +</div> + +Using the above optimizations with Arrow will produce the same results as when Arrow is not +enabled. Not all Spark data types are currently supported and an error will be raised if a column +has an unsupported type, see [Supported Types](#supported-types). + +## Pandas UDFs (a.k.a Vectorized UDFs) + +With Arrow, we introduce a new type of UDF - pandas UDF. Pandas UDF is defined with a new function +`pyspark.sql.functions.pandas_udf` and allows user to use functions that operate on `pandas.Series` +and `pandas.DataFrame` with Spark. Currently, there are two types of pandas UDF: Scalar and Group Map. + +### Scalar + +Scalar pandas UDFs are used for vectorizing scalar operations. They can used with functions such as `select` --- End diff -- I think pandas is lower case because the references I found are lower case: https://pandas.pydata.org/
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