Thank you for reporting, Cheng. Yes, it's not a block for this preview release.
Dongjoon. On Sat, Sep 27, 2025 at 12:42 AM Cheng Pan <[email protected]> wrote: > Found an issue[1] related to LOG4J, but it may not be serious enough to > interrupt this preview release. > > [1] https://github.com/apache/spark/pull/52475 > > Thanks, > Cheng Pan > > > > On Sep 26, 2025, at 16:54, Max Gekk <[email protected]> wrote: > > +1 > > On Wed, Sep 24, 2025 at 3:49 PM Wenchen Fan <[email protected]> wrote: > >> +1 >> >> On Wed, Sep 24, 2025 at 7:29 PM <[email protected]> wrote: >> >>> Please vote on releasing the following candidate as Apache Spark version >>> 4.1.0-preview2. >>> >>> The vote is open until Sat, 27 Sep 2025 05:26:22 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 4.1.0-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 v4.1.0-preview2-rc1 (commit c5ff48cc2b2): >>> https://github.com/apache/spark/tree/v4.1.0-preview2-rc1 >>> >>> The release files, including signatures, digests, etc. can be found at: >>> https://dist.apache.org/repos/dist/dev/spark/v4.1.0-preview2-rc1-bin/ >>> >>> Signatures used for Spark RCs can be found in this file: >>> https://downloads.apache.org/spark/KEYS >>> >>> The staging repository for this release can be found at: >>> https://repository.apache.org/content/repositories/orgapachespark-1503/ >>> >>> The documentation corresponding to this release can be found at: >>> https://dist.apache.org/repos/dist/dev/spark/v4.1.0-preview2-rc1-docs/ >>> >>> The list of bug fixes going into 4.1.0-preview2 can be found at the >>> following URL: >>> https://issues.apache.org/jira/projects/SPARK/versions/12355581 >>> >>> 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 via "pip install >>> https://dist.apache.org/repos/dist/dev/spark/v4.1.0-preview2-rc1-bin/pyspark-4.1.0.dev2.tar.gz >>> " >>> and see if anything important breaks. >>> In the Java/Scala, you can add the staging repository to your project's >>> 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). >>> >>> --------------------------------------------------------------------- >>> To unsubscribe e-mail: [email protected] >>> >>> >
