I think it might be good to hold the point releases until the 4.3 RC passes
just incase we’ve got any blockers we discover and want to backport? I
don’t think it’s a “must” hold just it might make life easier.


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On Mon, Sep 28, 2026 at 6:20 PM Uroš Bojanić <[email protected]> wrote:

> Hi folks, thank you for working on this! Please also let me know if any of
> the issues above are blockers for other releases (e.g. 4.1.4, 4.0.5) or
> not; there are separate threads for those releases as well.
>
> On 2026/09/28 09:11:21 [email protected] wrote:
> > Please vote on releasing the following candidate as Apache Spark version
> 4.2.1.
> >
> > The vote is open until Thu, 01 Oct 2026 03:11:20 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 4.2.1
> > [ ] -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 v4.2.1-rc1 (commit 72c18760854):
> > https://github.com/apache/spark/tree/v4.2.1-rc1
> >
> > The release files, including signatures, digests, etc. can be found at:
> > https://dist.apache.org/repos/dist/dev/spark/v4.2.1-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-1531/
> >
> > The documentation corresponding to this release can be found at:
> > https://dist.apache.org/repos/dist/dev/spark/v4.2.1-rc1-docs/
> >
> > The list of bug fixes going into 4.2.1 can be found at the following URL:
> > https://issues.apache.org/jira/projects/SPARK/versions/12357191
> >
> > 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/v4.2.1-rc1-bin/pyspark-4.2.1.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).
> >
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> >
> >
>
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