Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/19575#discussion_r164002789
--- Diff: docs/sql-programming-guide.md ---
@@ -1640,6 +1640,204 @@ 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`
--- End diff --
I think it's fine.
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