+1 Thank you Wenchen!
On Mon, May 12, 2025 at 10:03 AM Yuming Wang <yumw...@apache.org> wrote: > +1 > > On Tue, May 13, 2025 at 12:07 AM Gengliang Wang <ltn...@gmail.com> wrote: > >> +1 >> >> On Mon, May 12, 2025 at 6:52 AM Wenchen Fan <cloud0...@gmail.com> wrote: >> >>> I'll start with my own +1. >>> >>> All the known blockers are fixed, and I verified that the new Spark >>> Connect distribution works as expected. >>> >>> On Fri, May 9, 2025 at 8:16 PM 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 15 (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-rc5 (commit >>>> f35a2ee6dc7833ea0cff757147132c9fdc26c113) >>>> https://github.com/apache/spark/tree/v4.0.0-rc5 >>>> >>>> The release files, including signatures, digests, etc. can be found at: >>>> https://dist.apache.org/repos/dist/dev/spark/v4.0.0-rc5-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-1483/ >>>> >>>> The documentation corresponding to this release can be found at: >>>> https://dist.apache.org/repos/dist/dev/spark/v4.0.0-rc5-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-rc5. >>>> >>>> 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). >>>> >>>
