+1
2026年4月29日(水) 23:08 Ruifeng Zheng <[email protected]>: > +1 > > On Wed, Apr 29, 2026 at 7:43 PM Wenchen Fan <[email protected]> wrote: > >> +1 >> >> On Tue, Apr 28, 2026 at 5:08 PM Hyukjin Kwon <[email protected]> >> wrote: >> >>> +1 >>> >>> PS: we will release 4.2.0 soon so this will be the last preview. >>> >>> On Tue, 28 Apr 2026 at 16:58, <[email protected]> wrote: >>> >>>> Please vote on releasing the following candidate as Apache Spark >>>> version 4.2.0-preview5. >>>> >>>> The vote is open until Fri, 01 May 2026 01:58:18 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-preview5 >>>> [ ] -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-preview5-rc1 (commit ba9d5b371c2): >>>> https://github.com/apache/spark/tree/v4.2.0-preview5-rc1 >>>> >>>> The release files, including signatures, digests, etc. can be found at: >>>> https://dist.apache.org/repos/dist/dev/spark/v4.2.0-preview5-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-1518/ >>>> >>>> The documentation corresponding to this release can be found at: >>>> https://dist.apache.org/repos/dist/dev/spark/v4.2.0-preview5-rc1-docs/ >>>> >>>> The list of bug fixes going into 4.2.0-preview5 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-preview5-rc1-bin/pyspark-4.2.0.dev5.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 you don't end up building with an out of date RC going forward).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] >>>> >>>>
