+1 (non-binding) verified RC4 (tag v4.2.0-rc4, commit f92a807c06b) on macOS/arm64.
- Good GPG signatures and SHA512 sums on all artifacts (source, three binaries, the PySpark/SparkR tarballs) against KEYS. - Built the full source tree from the tag with -Phive -Phive-thriftserver, got a clean BUILD SUCCESS across all 39 modules. - Ran quick smoke tests across every API surface: Java, Scala, SQL, and PySpark job from the bundled distro; all look good. - Sanity-checked the binary dist (RELEASE, LICENSE/NOTICE/licenses all present) and ran dev/check-license; RAT passes. - Diffed the docs against 4.1.0 and analyzed the changes (new pages, migration guides and version refs); all look good. Thank you Huaxin Gao! On 2026/06/27 00:21:32 [email protected] wrote: > Please vote on releasing the following candidate as Apache Spark version > 4.2.0. > > The vote is open until Mon, 29 Jun 2026 18:21:31 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 4.2.0 > [ ] -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 v4.2.0-rc4 (commit f92a807c06b): > https://github.com/apache/spark/tree/v4.2.0-rc4 > > The release files, including signatures, digests, etc. can be found at: > https://dist.apache.org/repos/dist/dev/spark/v4.2.0-rc4-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-1524/ > > The documentation corresponding to this release can be found at: > https://dist.apache.org/repos/dist/dev/spark/v4.2.0-rc4-docs/ > > The list of bug fixes going into 4.2.0 can be found at the following URL: > https://issues.apache.org/jira/projects/SPARK/versions/12356380 > > 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/v4.2.0-rc4-bin/pyspark-4.2.0.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]
