+1 Dongjoon
On Mon, Sep 16, 2024 at 10:57 AM Holden Karau <holden.ka...@gmail.com> wrote: > +1 > > Twitter: https://twitter.com/holdenkarau > Books (Learning Spark, High Performance Spark, etc.): > https://amzn.to/2MaRAG9 <https://amzn.to/2MaRAG9> > YouTube Live Streams: https://www.youtube.com/user/holdenkarau > Pronouns: she/her > > > On Mon, Sep 16, 2024 at 10:55 AM Zhou Jiang <zhou.c.ji...@gmail.com> > wrote: > >> + 1 >> Sent from my iPhone >> >> On Sep 16, 2024, at 01:04, Dongjoon Hyun <dongjoon.h...@gmail.com> wrote: >> >> >> >> Please vote on releasing the following candidate as Apache Spark version >> 4.0.0-preview2. >> >> The vote is open until September 20th 1AM (PDT) and passes if a majority >> +1 PMC votes are cast, with a minimum of 3 +1 votes. >> >> [ ] +1 Release this package as Apache Spark 4.0.0-preview2 >> [ ] -1 Do not release this package because ... >> >> To learn more about Apache Spark, please see https://spark.apache.org/ >> >> The tag to be voted on is v4.0.0-preview2-rc1 (commit >> f0d465e09b8d89d5e56ec21f4bd7e3ecbeeb318a) >> https://github.com/apache/spark/tree/v4.0.0-preview2-rc1 >> >> The release files, including signatures, digests, etc. can be found at: >> https://dist.apache.org/repos/dist/dev/spark/v4.0.0-preview2-rc1-bin/ >> >> Signatures used for Spark RCs can be found in this file: >> https://dist.apache.org/repos/dist/dev/spark/KEYS >> >> The staging repository for this release can be found at: >> https://repository.apache.org/content/repositories/orgapachespark-1468/ >> >> The documentation corresponding to this release can be found at: >> https://dist.apache.org/repos/dist/dev/spark/v4.0.0-preview2-rc1-docs/ >> >> The list of bug fixes going into 4.0.0-preview2 can be found at the >> following URL: >> https://issues.apache.org/jira/projects/SPARK/versions/12353359 >> >> This release is using the release script of the tag v4.0.0-preview2-rc1. >> >> FAQ >> >> ========================= >> How can I help test this release? >> ========================= >> >> If you are a Spark user, you can help us test this release by taking >> an existing Spark workload and running on this release candidate, then >> reporting any regressions. >> >> If you're working in PySpark you can set up a virtual env and install >> the current RC and see if anything important breaks, in the Java/Scala >> you can add the staging repository to your projects resolvers and test >> with the RC (make sure to clean up the artifact cache before/after so >> you don't end up building with a out of date RC going forward). >> >>
