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藏经阁-Improving Python and Spark Performance and Interoperability.

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藏经阁-Improving Python and Spark Performance and Interoperability

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www.twosigma.com

Improving Python and Spark

Performance and Interoperability

Wes McKinney @wesmckinn

Spark Summit East 2017

February 9, 2017

•  Currently: Software Architect at Two Sigma Investments

•  Creator of Python pandas project

•  PMC member for Apache Arrow and Apache Parquet

•  Other Python projects: Ibis, Feather, statsmodels

•  Formerly: Cloudera, DataPad, AQR

•  Author of Python for Data Analysis

Important Legal Information

The information presented here is offered for informational purposes only and should not be

used for any other purpose (including, without limitation, the making of investment decisions).

Examples provided herein are for illustrative purposes only and are not necessarily based on

actual data. Nothing herein constitutes: an offer to sell or the solicitation of any offer to buy any

security or other interest; tax advice; or investment advice. This presentation shall remain the

property of Two Sigma Investments, LP (“Two Sigma”) and Two Sigma reserves the right to

require the return of this presentation at any time.

Some of the images, logos or other material used herein may be protected by copyright and/or

trademark. If so, such copyrights and/or trademarks are most likely owned by the entity that

created the material and are used purely for identification and comment as fair use under

international copyright and/or trademark laws. Use of such image, copyright or trademark does

not imply any association with such organization (or endorsement of such organization) by Two

Sigma, nor vice versa.

This talk

4 February 9, 2017

•  Why some parts of PySpark are “slow”

•  Technology that can help make things faster

•  Work we have done to make improvements

•  Future roadmap

Python and Spark

February 9, 2017

•  Spark is implemented in Scala, runs on the Java virtual machine (JVM)

•  Spark has Python and R APIs with partial or full coverage for many parts of

the Scala Spark API

•  In some Spark tasks, Python is only a scripting front-end.

•  This means no interpreted Python code is executed once the Spark

job starts

•  Other PySpark jobs suffer performance and interoperability issues that we’re

going to analyze in this talk

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