The document discusses Apache Arrow and Pandas UDF on Apache Spark. It provides an overview of PySpark and Pandas, describes Python UDF and the new Pandas UDF feature, and explains how Pandas UDFs use Apache Arrow for efficient serialization and communication between the JVM and Python workers. The document outlines the physical operators implemented for different Pandas UDF types and the ongoing work to improve performance and functionality.

1. Apache Arrow and
Pandas UDF on Apache Spark
Takuya UESHIN
2018-12-08, Apache Arrow Tokyo Meetup 2018

2. 2
About Me
- Software Engineer @databricks
- Apache Spark Committer
- Twitter: @ueshin
- GitHub: github.com/ueshin

3. 3
Agenda
• Apache Spark and PySpark
• PySpark and Pandas
• Python UDF and Pandas UDF
• Pandas UDF and Apache Arrow
• Arrow IPC format and Converters
• Handling Communication
• Physical Operators
• Python worker
• Work In Progress
• Follow-up Events

4. 4
Agenda
• Apache Spark and PySpark
• PySpark and Pandas
• Python UDF and Pandas UDF
• Pandas UDF and Apache Arrow
• Arrow IPC format and Converters
• Handling Communication
• Physical Operators
• Physical Operators
• Work In Progress
• Follow-up Events

5. 5
Apache Spark and PySpark
“Apache Spark™ is a unified analytics engine for large-scale data
processing.”
https://spark.apache.org/
• The latest release:
2.4.0 (2018/11/02)
• PySpark is a Python API
• SparkR is an R API

6. 6
PySpark and Pandas
“pandas is an open source, BSD-licensed library providing
high-performance, easy-to-use data structures and data analysis
tools for the Python programming language.”
• https://pandas.pydata.org/
• The latest release: v0.23.4 Final (2018/08/03)
• PySpark supports Pandas >= "0.19.2"

7. 7
PySpark and Pandas
PySpark can convert data between PySpark DataFrame and
Pandas DataFrame.
• pdf = df.toPandas()
• df = spark.createDataFrame(pdf)
We can use Arrow as an intermediate format by setting config:
“spark.sql.execution.arrow.enabled” to “true” (“false” by default).

8. 8
Python UDF and Pandas UDF
• UDF: User Defined Function
• Python UDF
• Serialize/Deserialize data with Pickle
• Fetch data block, but invoke UDF row by row
• Pandas UDF
• Serialize/Deserialize data with Arrow
• Fetch data block, and invoke UDF block by block
• PandasUDFType: SCALAR, GROUPED_MAP, GROUPED_AGG
We don’t need any config, but the declaration is different.

9. 9
Python UDF and Pandas UDF
@udf(’double’)
def plus_one(v):
return v + 1
@pandas_udf(’double’, PandasUDFType.SCALAR)
def pandas_plus_one(v):
return v + 1

10. 10
Python UDF and Pandas UDF
• SCALAR
• A transformation: One or more Pandas Series -> One Pandas Series
• The length of the returned Pandas Series must be of the same as the
input Pandas Series
• GROUPED_MAP
• A transformation: One Pandas DataFrame -> One Pandas DataFrame
• The length of the returned Pandas DataFrame can be arbitrary
• GROUPED_AGG
• A transformation: One or more Pandas Series -> One scalar
• The returned value type should be a primitive data type

11. 11
Performance: Python UDF vs Pandas UDF
From a blog post: Introducing Pandas UDF for PySpark
• Plus One
• Cumulative Probability
• Subtract Mean
“Pandas UDFs perform much
better than Python UDFs,
ranging from 3x to over 100x.”

12. 12
Agenda
• Apache Spark and PySpark
• PySpark and Pandas
• Python UDF and Pandas UDF
• Pandas UDF and Apache Arrow
• Arrow IPC format and Converters
• Handling Communication
• Physical Operators
• Python worker
• Work In Progress
• Follow-up Events

13. 13
Apache Arrow
“A cross-language development platform for in-memory data”
https://arrow.apache.org/
• The latest release
- 0.11.0 (2018/10/08)
• Columnar In-Memory
• docs/memory_layout.html
PySpark supports Arrow >= "0.8.0"
• "0.10.0" is recommended

14. 14
Apache Arrow and Pandas UDF
• Use Arrow to Serialize/Deserialize data
• Streaming format for Interprocess messaging / communication (IPC)
• ArrowWriter and ArrowColumnVector
• Communicate JVM and Python worker via Socket
• ArrowPythonRunner
• worker.py
• Physical Operators for each PythonUDFType
• ArrowEvalPythonExec
• FlatMapGroupsInPandasExec
• AggregateInPandasExec

15. 15
Overview of Pandas UDF execution
Invoke UDF
Pandas
Pandas
RecordBatches
RecordBatches
Arrow
ArrowPythonRunner
PhysicalOperator
groups of rows
ColumnarBatches
ArrowColumnVectors
ArrowWriter ArrowStreamPandasSerializer
ArrowStreamPandasSerializer

16. 16
Arrow IPC format and Converters
Invoke UDF
Pandas
Pandas
RecordBatches
RecordBatches
Arrow
ArrowPythonRunner
PhysicalOperator
groups of rows
ColumnarBatches
ArrowColumnVectors
ArrowWriter ArrowStreamPandasSerializer
ArrowStreamPandasSerializer

17. 17
Encapsulated message format
• https://arrow.apache.org/docs/ipc.html
• Messages
• Schema, RecordBatch, DictionaryBatch, Tensor
• Formats
• Streaming format
– Schema + (DictionaryBatch + RecordBatch)+
• File format
– header + (Streaming format) + footer
Pandas UDFs use Streaming format.

18. 18
Arrow Converters in Spark
in Java/Scala
• ArrowWriter [src]
• A wrapper for writing VectorSchemaRoot and ValueVectors
• ArrowColumnVector [src]
• A wrapper for reading ValueVectors, works with ColumnarBatch
in Python
• ArrowStreamPandasSerializer [src]
• A wrapper for RecordBatchReader and RecordBatchWriter

19. 19
Handling Communication
Invoke UDF
Pandas
Pandas
RecordBatches
RecordBatches
Arrow
ArrowPythonRunner
PhysicalOperator
groups of rows
ColumnarBatches
ArrowColumnVectors
ArrowWriter ArrowStreamPandasSerializer
ArrowStreamPandasSerializer

20. 20
Handling Communication
ArrowPythonRunner [src]
• Handle the communication between JVM and the Python
worker
• Create or reuse a Python worker
• Open a Socket to communicate
• Write data to the socket with ArrowWriter in a separate thread
• Read data from the socket
• Return an iterator of ColumnarBatch of ArrowColumnVectors

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Physical Operators
Invoke UDF
Pandas
Pandas
RecordBatches
RecordBatches
Arrow
ArrowPythonRunner
PhysicalOperator
ArrowColumnVectors
ArrowWriter
groups of rows
ColumnarBatches
ArrowStreamPandasSerializer
ArrowStreamPandasSerializer

22. 22
Physical Operators
Create a RDD to execute the UDF.
• There are several operators for each PythonUDFType
• Group input data and pass to ArrowPythonRunner
• SCALAR: every configured number of rows
– “spark.sql.execution.arrow.maxRecordsPerBatch” (10,000 by default)
• GROUP_XXX: every group
• Read the result iterator of ColumnarBatch
• Return the iterator of rows over ColumnarBatches

23. 23
Python worker
Invoke UDF
Pandas
Pandas
RecordBatches
RecordBatches
Arrow
ArrowPythonRunner
PhysicalOperator
groups of rows
ColumnarBatches
ArrowColumnVectors
ArrowWriter ArrowStreamPandasSerializer
ArrowStreamPandasSerializer

24. 24
Python worker
worker.py [src]
• Open a Socket to communicate
• Set up a UDF execution for each PythonUDFType
• Create a map function
– prepare the arguments
– invoke the UDF
– check and return the result
• Execute the map function over the input iterator of Pandas
DataFrame
• Write back the results

25. 25
Agenda
• Apache Spark and PySpark
• PySpark and Pandas
• Python UDF and Pandas UDF
• Pandas UDF and Apache Arrow
• Arrow IPC format and Converters
• Handling Communication
• Physical Operators
• Python worker
• Work In Progress
• Follow-up Events

26. 26
Work In Progress
We can track issues related to Pandas UDF.
• [SPARK-22216] Improving PySpark/Pandas interoperability
• 37 subtasks in total
• 3 subtasks are in progress
• 4 subtasks are open

27. 27
Work In Progress
• Window Pandas UDF
• [SPARK-24561] User-defined window functions with pandas udf
(bounded window)
• Performance Improvement of toPandas -> merged!
• [SPARK-25274] Improve toPandas with Arrow by sending out-of-order
record batches
• SparkR
• [SPARK-25981] Arrow optimization for conversion from R DataFrame
to Spark DataFrame

28. 28
Agenda
• Apache Spark and PySpark
• PySpark and Pandas
• Python UDF and Pandas UDF
• Pandas UDF and Apache Arrow
• Arrow IPC format and Converters
• Handling Communication
• Physical Operators
• Python worker
• Work In Progress
• Follow-up Events

29. 29
Follow-up Events
Spark Developers Meetup
• 2018/12/15 (Sat) 10:00-18:00
• @ Yahoo! LODGE
• https://passmarket.yahoo.co.jp/event/show/detail/01a98dzxf
auj.html

30. 30
Follow-up Events
Hadoop/Spark Conference Japan 2019
• 2019/03/14 (Thu)
• @ Oi-machi
• http://hadoop.apache.jp/

31. 31
Follow-up Events
Spark+AI Summit 2019
• 2019/04/23 (Tue) - 04/25 (Thu)
• @ Moscone West Convention Center, San Francisco
• https://databricks.com/sparkaisummit/north-america

32. Thank you!

33. 33
Appendix
How to contribute?
• See: Contributing to Spark
• Open an issue on JIRA
• Send a pull-request at GitHub
• Communicate with committers and reviewers
• Congratulations!
Thanks for your contributions!

34. 34
Appendix
• PySpark Usage Guide for Pandas with Apache Arrow
• https://spark.apache.org/docs/latest/sql-pyspark-pandas-with-arrow.
html
• Vectorized UDF: Scalable Analysis with Python and PySpark
• https://databricks.com/session/vectorized-udf-scalable-analysis-with-
python-and-pyspark
• Demo for Apache Arrow Tokyo Meetup 2018
• https://databricks-prod-cloudfront.cloud.databricks.com/public/4027
ec902e239c93eaaa8714f173bcfc/142158605138935/354623205913920
1/7497868276316206/latest.html