It seems this patch(https://github.com/apache/parquet-java/pull/3196) can avoid deadlock issue if using Parquet 1.15.1.
On Wed, Apr 16, 2025 at 5:39 PM Niranjan Jayakar <n...@databricks.com.invalid> wrote: > I found another bug introduced in 4.0 that breaks Spark connect client x > server compatibility: https://github.com/apache/spark/pull/50604. > > Once merged, this should be included in the next RC. > > On Thu, Apr 10, 2025 at 5:21 PM Wenchen Fan <cloud0...@gmail.com> wrote: > >> Please vote on releasing the following candidate as Apache Spark version >> 4.0.0. >> >> The vote is open until April 15 (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 4.0.0 >> [ ] -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 v4.0.0-rc4 (commit >> e0801d9d8e33cd8835f3e3beed99a3588c16b776) >> https://github.com/apache/spark/tree/v4.0.0-rc4 >> >> The release files, including signatures, digests, etc. can be found at: >> https://dist.apache.org/repos/dist/dev/spark/v4.0.0-rc4-bin/ >> >> Signatures used for Spark RCs can be found in this file: >> https://dist.apache.org/repos/dist/dev/spark/KEYS >> >> The staging repository for this release can be found at: >> https://repository.apache.org/content/repositories/orgapachespark-1480/ >> >> The documentation corresponding to this release can be found at: >> https://dist.apache.org/repos/dist/dev/spark/v4.0.0-rc4-docs/ >> >> The list of bug fixes going into 4.0.0 can be found at the following URL: >> https://issues.apache.org/jira/projects/SPARK/versions/12353359 >> >> This release is using the release script of the tag v4.0.0-rc4. >> >> 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 and see if anything important breaks, in the Java/Scala >> you can add the staging repository to your projects resolvers and test >> with the RC (make sure to clean up the artifact cache before/after so >> you don't end up building with a out of date RC going forward). >> >
