+1 On 2025/05/14 00:21:11 Ruifeng Zheng wrote: > +1 > > On Wed, May 14, 2025 at 7:01 AM Gengliang Wang <ltn...@gmail.com> wrote: > > > +1 > > > > On Tue, May 13, 2025 at 3:57 PM Hyukjin Kwon <gurwls...@apache.org> wrote: > > > >> +1 > >> > >> On Wed, 14 May 2025 at 07:29, Wenchen Fan <cloud0...@gmail.com> wrote: > >> > >>> Same as before, I'll start with my own +1. > >>> > >>> On Wed, May 14, 2025 at 12:28 AM Wenchen Fan <cloud0...@gmail.com> > >>> wrote: > >>> > >>>> Please vote on releasing the following candidate as Apache Spark > >>>> version 4.0.0. > >>>> > >>>> The vote is open until May 16 (PST) 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 > >>>> [ ] -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-rc6 (commit > >>>> 9a99ecb03a2d35f5f38decd686b55511a5c7c535) > >>>> https://github.com/apache/spark/tree/v4.0.0-rc6 > >>>> > >>>> The release files, including signatures, digests, etc. can be found at: > >>>> https://dist.apache.org/repos/dist/dev/spark/v4.0.0-rc6-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-1484/ > >>>> > >>>> The documentation corresponding to this release can be found at: > >>>> https://dist.apache.org/repos/dist/dev/spark/v4.0.0-rc6-docs/ > >>>> > >>>> The list of bug fixes going into 4.0.0 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-rc6. > >>>> > >>>> 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). > >>>> > >>> >
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