[GitHub] [spark] HyukjinKwon commented on a change in pull request #29806: [SPARK-32187][PYTHON][DOCS] Doc on Python packaging

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HyukjinKwon commented on a change in pull request #29806:
URL: https://github.com/apache/spark/pull/29806#discussion_r494009107



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File path: python/docs/source/user_guide/python_packaging.rst
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+..  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.
+
+
+################
+Python packaging
+################
+
+When you want to run your PySpark application on a cluster (like YARN, Kubernetes, Mesos, ..) you need to make sure that the your code
+and all used libraries are available on the executors.
+
+As an example let's say you may want to run the `Pandas UDF's examples <arrow_pandas.rst#series-to-scalar>`_.
+As it uses pyarrow as an underlying implementation we need to make sure to have pyarrow installed on each executor on the cluster. Otherwise you may get errors such as 
+``ModuleNotFoundError: No module named 'pyarrow'``.
+
+Here is the script ``main.py`` from the previous example that will be executed on the cluster:
+
+.. code-block:: python
+
+  import pandas as pd
+  from pyspark.sql.functions import pandas_udf, PandasUDFType
+  from pyspark.sql import SparkSession
+
+  def main(spark):
+    df = spark.createDataFrame(
+      [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)],
+      ("id", "v"))
+
+    @pandas_udf("double", PandasUDFType.GROUPED_AGG)
+    def mean_udf(v: pd.Series):
+      return v.mean()
+
+    print(df.groupby("id").agg(mean_udf(df['v'])).collect())
+
+
+  if __name__ == "__main__":
+    spark = SparkSession.builder.getOrCreate()
+    main(spark)
+
+
+There are multiple ways to ship the dependencies to the cluster:
+
+- Using py-files
+- Using a zipped virtual environment
+- Using PEX
+- Using Docker
+
+
+**************
+Using py-files
+**************
+
+PySpark allows to upload python files to the executors by setting the configuration setting ``spark.submit.pyFiles`` or by directly calling `addPyFile
+<../reference/api/pyspark.SparkContext.addPyFile.rst>`_ on the SparkContext.
+
+This is an easy way to ship additional custom Python code to the cluster. You can just add individual files or zip whole packages and upload them. 
+Using `addPyFile <../reference/api/pyspark.SparkContext.addPyFile.rst>`_ allows to upload code even after having started your job.
+
+It doesn't allow to add packages built as `Wheels <https://www.python.org/dev/peps/pep-0427/>`_ and therefore doesn't allowing to include dependencies with native code.
+
+
+**********************************
+Using a zipped virtual environment

Review comment:
       Yeah, it does.




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