+1 (non-binding), thanks Dongjoon. On Mon, Nov 17, 2025 at 10:54 AM Zhou Jiang <[email protected]> wrote:
> +1 (non-binding) - validated basic test suite for Spark Kubernetes > Operator. > > > On Nov 17, 2025, at 16:28, Cheng Pan <[email protected]> wrote: > > > > +1 (non-binding) > > > > Verified basic functionalities of Connect JDBC Driver via BeeLine with > Connect Server, by running some TPC-H queries. > > > > Thanks, > > Cheng Pan > > > > > > > >> On Nov 17, 2025, at 05:02, [email protected] wrote: > >> > >> Please vote on releasing the following candidate as Apache Spark > version 4.1.0-preview4. > >> > >> The vote is open until Wed, 19 Nov 2025 14:02:40 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 4.1.0-preview4 > >> [ ] -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-preview4-rc1 (commit c125aea395b): > >> https://github.com/apache/spark/tree/v4.1.0-preview4-rc1 > >> > >> The release files, including signatures, digests, etc. can be found at: > >> https://dist.apache.org/repos/dist/dev/spark/v4.1.0-preview4-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-1505/ > >> > >> The documentation corresponding to this release can be found at: > >> https://dist.apache.org/repos/dist/dev/spark/v4.1.0-preview4-rc1-docs/ > >> > >> The list of bug fixes going into 4.1.0-preview4 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-preview4-rc1-bin/pyspark-4.1.0.dev4.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] > >> > > > > --------------------------------------------------------------------- > To unsubscribe e-mail: [email protected] > >
