ASF board report for November

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ASF board report for November

Matei Zaharia
It’s time to submit Spark's quarterly ASF board report on November 14th, so I wanted to run the text by everyone to make sure we’re not missing something. Let me know whether I missed anything:


Apache Spark is a fast and general engine for large-scale data processing. It offers high-level APIs in Java, Scala, Python and R as well as a rich set of libraries including stream processing, machine learning, and graph analytics.

Project status:

- We released Apache Spark 2.4.0 on Nov 2nd, 2018 as our newest feature release. Spark 2.4’s features include a barrier execution mode for machine learning computations, higher-order functions in Spark SQL, pivot syntax in SQL, a built-in Apache Avro data source, Kubernetes improvements, and experimental support for Scala 2.12, as well as multiple smaller features and fixes. The release notes are available at

- We released Apache Spark 2.3.2 on Sept 24th, 2018 as a bug fix release for the 2.3 branch.

- Multiple dev discussions are under way about the next feature release, which is likely to be Spark 3.0, on our dev and user mailing lists. Some of the key questions are which JDK, Scala, Python, R, Hadoop and Hive versions to support, as well as whether to remove certain deprecated APIs. We encourage everyone in the community to give feedback on these discussions through the mailing lists and JIRA.


- We are continuing engagement with various organizations.

Latest releases:

- Nov 2nd, 2018: Spark 2.4.0
- Sept 24th, 2018: Spark 2.3.2
- July 2nd, 2018: Spark 2.2.2

Committers and PMC:

- We added six new committers since the last report: Shane Knapp, Dongjoon Hyun, Kazuaki Ishizaki, Xingbo Jiang, Yinan Li, and Takeshi Yamamuro.
- The latest committer was added on September 18th, 2018 (Kazuaki Ishizaki).
- The latest PMC member was added on Jan 12th, 2018 (Xiao Li).

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