+1 (binding) Verified signatures/checksums, built from source, and tested Scala, SQL, and PySpark workloads. No issues found.
Jungtaek Lim <[email protected]> 于2026年10月26日周一 21:40写道： > starting with +1 (non-binding) > > * Source SHA-512 checksum and GPG signature pass with both Apache KEYS > files. > * All 27,135 packaged entries match tag commit > 98bc0212c94705434471627068f41e374ccab143, allowing for documented packaging > exclusions. > * Source versions and published Overview, SQL, PySpark, and Scala > documentation consistently report 4.3.0. > * Source LICENSE, NOTICE, and archive structure checks pass. > * This candidate’s signing key is present in the production KEYS file. > > > On Tue, Oct 27, 2026 at 12:35 PM <[email protected]> wrote: > >> Please vote on releasing the following candidate as Apache Spark version >> 4.3.0. >> >> The vote is open until Thu, 29 Oct 2026 21:35:11 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 4.3.0 >> [ ] -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 v4.3.0-rc3 (commit 98bc0212c94): >> https://github.com/apache/spark/tree/v4.3.0-rc3 >> >> The release files, including signatures, digests, etc. can be found at: >> https://dist.apache.org/repos/dist/dev/spark/v4.3.0-rc3-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-1542/ >> >> The documentation corresponding to this release can be found at: >> https://dist.apache.org/repos/dist/dev/spark/v4.3.0-rc3-docs/ >> >> The list of bug fixes going into 4.3.0 can be found at the following URL: >> https://issues.apache.org/jira/projects/SPARK/versions/12356944 >> >> 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/v4.3.0-rc3-bin/pyspark-4.3.0.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] >> >>
