A 5x regression on the history server loading seems like it _might_ be
worth an RC3? I know a lot of large deployments have performance difficulty
with the history server already.


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On Tue, Oct 13, 2026 at 6:35 AM L. C. Hsieh <[email protected]> wrote:

> FYI, this RC includes SPARK-59407/SPARK-59804, which made line splitting
> in the event log replay reader about 5x slower than in 4.2.0 (History
> Server UI loading and event log compaction). SPARK-60158 fixes it and is
> already on branch-4.3, but it landed just after RC2 was cut. It is a
> performance regression, not a correctness issue, so I don’t consider it a
> blocker, but it would be good to pick it up if there is another RC.
>
> On Mon, Oct 12, 2026 at 5:30 PM <[email protected]> wrote:
>
>> Please vote on releasing the following candidate as Apache Spark version
>> 4.3.0.
>>
>> The vote is open until Thu, 15 Oct 2026 18:30:01 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-rc2 (commit c0fd65d3f4f):
>> https://github.com/apache/spark/tree/v4.3.0-rc2
>>
>> The release files, including signatures, digests, etc. can be found at:
>> https://dist.apache.org/repos/dist/dev/spark/v4.3.0-rc2-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-1538/
>>
>> The documentation corresponding to this release can be found at:
>> https://dist.apache.org/repos/dist/dev/spark/v4.3.0-rc2-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-rc2-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).
>>
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