+1 (non-binding)
On 18/10/2025 03:08, Jules Damji wrote:
+ (non-binding)
—
Sent from my iPhone
Pardon the dumb thumb typos :)
On Sep 26, 2025, at 2:22 AM, Cheng Pan<[email protected]> wrote:
+1 (non-binding)
I tested it with Hadoop 3.4.2, in both SIMPLE and KERBEROS mode -
SparkPi, Connect Server, and History Server work as expected.
Thanks,
Cheng Pan
On Fri, Sep 26, 2025 at 4:56 PM Max Gekk<[email protected]> wrote:
+1
On Wed, Sep 24, 2025 at 3:49 PM Wenchen Fan<[email protected]> wrote:
+1
On Wed, Sep 24, 2025 at 7:29 PM<[email protected]> wrote:
Please vote on releasing the following candidate as Apache Spark version
4.1.0-preview2.
The vote is open until Sat, 27 Sep 2025 05:26:22 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 4.1.0-preview2
[ ] -1 Do not release this package because ...
To learn more about Apache Spark, please seehttps://spark.apache.org/
The tag to be voted on is v4.1.0-preview2-rc1 (commit c5ff48cc2b2):
https://github.com/apache/spark/tree/v4.1.0-preview2-rc1
The release files, including signatures, digests, etc. can be found at:
https://dist.apache.org/repos/dist/dev/spark/v4.1.0-preview2-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-1503/
The documentation corresponding to this release can be found at:
https://dist.apache.org/repos/dist/dev/spark/v4.1.0-preview2-rc1-docs/
The list of bug fixes going into 4.1.0-preview2 can be found at the following
URL:
https://issues.apache.org/jira/projects/SPARK/versions/12355581
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
installhttps://dist.apache.org/repos/dist/dev/spark/v4.1.0-preview2-rc1-bin/pyspark-4.1.0.dev2.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you don't end up building with an out of date RC going forward).
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