+1 - Tested RC3 with Delta Lake. All our Scala and Python tests pass. On Fri, May 31, 2024 at 3:24 PM Xiao Li <gatorsm...@gmail.com> wrote:
> +1 > > Cheng Pan <pan3...@gmail.com> 于2024年5月30日周四 09:48写道： > >> +1 (non-binding) >> >> - All links are valid >> - Run some basic quires using YARN client mode with Apache Hadoop v3.3.6, >> HMS 2.3.9 >> - Pass integration tests with Apache Kyuubi v1.9.1 RC0 >> >> Thanks, >> Cheng Pan >> >> >> On May 29, 2024, at 02:48, Wenchen Fan <cloud0...@gmail.com> wrote: >> >> Please vote on releasing the following candidate as Apache Spark version >> 4.0.0-preview1. >> >> The vote is open until May 31 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-preview1 >> [ ] -1 Do not release this package because ... >> >> To learn more about Apache Spark, please see http://spark.apache.org/ >> >> The tag to be voted on is v4.0.0-preview1-rc2 (commit >> 7cfe5a6e44e8d7079ae29ad3e2cee7231cd3dc66): >> https://github.com/apache/spark/tree/v4.0.0-preview1-rc3 >> >> The release files, including signatures, digests, etc. can be found at: >> https://dist.apache.org/repos/dist/dev/spark/v4.0.0-preview1-rc3-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-1456/ >> >> The documentation corresponding to this release can be found at: >> https://dist.apache.org/repos/dist/dev/spark/v4.0.0-preview1-rc3-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 >> >> 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 you don't end up building with an out of date RC going forward). >> >> >>
