Thank you all.

I tested the following additionally with OpenJDK 11.0.8.

    - PySpark UT on Python 3.7.7 with Pandas 0.23.2 / PyArrow 0.15.1.
    - JDBC integration suite
    - K8s integration suite (except SparkR test)
      (Minikube: K8s Client v1.18.8, K8s Server v1.17.11)

For SparkR, it would be great if someone can check the current status
because SparkR was removed from CRAN.

    - I used `R 3.5.2 (2018-12-20)` like 3.0.0 vote, but `dev/make-distribution.sh`
      failed for me when I used the `--r` option. However, since the release artifact has SparkR,
      I believe it's a problem on my env.

    - Jenkins is using `R 3.6.3 (2020-02-29)` and it seems to succeed,
      but K8s IT seems to fail consistently due to some unknown reasons.
      "Run SparkR on simple dataframe.R example *** FAILED ***"

    - For Apache Spark 3.1, we are testing R 4.0 on `master` branch,
      but we don't have test coverage on `branch-3.0`.
      So, I'm wondering if Spark 3.0.1 supports R 4.0 without any issue.

In addition to the one which Xiao mentioned, we also had a late arrival on the correctness area,
but it wasn't a registered blocker before the 3.0.1 RC3 vote. Given that, it can be a part of 3.0.2.

    - [SPARK-31511][SQL] Make BytesToBytesMap iterators thread-safe


On Tue, Sep 1, 2020 at 7:54 AM Xiao Li <lixiao@databricks.com> wrote:
Want to change my vote to 0, because we are unable to produce an end-user query to hit this bug.  


On Mon, Aug 31, 2020 at 12:41 PM Xiao Li <lixiao@databricks.com> wrote:
-1 due to a regression introduced by a fix in 3.0.1. 


On Mon, Aug 31, 2020 at 9:26 AM Tom Graves <tgraves_cs@yahoo.com.invalid> wrote:


On Friday, August 28, 2020, 09:02:31 AM CDT, 郑瑞峰 <ruifengz@foxmail.com> wrote:

Please vote on releasing the following candidate as Apache Spark version 3.0.1.

The vote is open until Sep 2nd at 9AM 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 3.0.1
[ ] -1 Do not release this package because ...

To learn more about Apache Spark, please see http://spark.apache.org/

There are currently no issues targeting 3.0.1 (try project = SPARK AND "Target Version/s" = "3.0.1" AND status in (Open, Reopened, "In Progress"))

The tag to be voted on is v3.0.1-rc3 (commit dc04bf53fe821b7a07f817966c6c173f3b3788c6):

The release files, including signatures, digests, etc. can be found at:

Signatures used for Spark RCs can be found in this file:

The staging repository for this release can be found at:

The documentation corresponding to this release can be found at:

The list of bug fixes going into 3.0.1 can be found at the following URL:

This release is using the release script of the tag v3.0.1-rc3.


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 an out of date RC going forward).

What should happen to JIRA tickets still targeting 3.0.1?

The current list of open tickets targeted at 3.0.1 can be found at:
https://issues.apache.org/jira/projects/SPARK and search for "Target Version/s" = 3.0.1

Committers should look at those and triage. Extremely important bug
fixes, documentation, and API tweaks that impact compatibility should
be worked on immediately. Everything else please retarget to an
appropriate release.

But my bug isn't fixed?

In order to make timely releases, we will typically not hold the
release unless the bug in question is a regression from the previous
release. That being said, if there is something which is a regression
that has not been correctly targeted please ping me or a committer to
help target the issue.