Thanks Maxim for volunteering to drive the release! I support the plan (March 15th) to perform a release branch cut.
Btw, would we be open for modification of critical/blocker issues after the release branch cut? I have a blocker JIRA ticket and the PR is open for reviewing, but need some time to gain traction as well as going through actual reviews. My guess is yes but to confirm again. On Fri, Mar 4, 2022 at 4:20 AM Dongjoon Hyun <dongjoon.h...@gmail.com> wrote: > Thank you, Max, for volunteering for Apache Spark 3.3 release manager. > > Ya, I'm also +1 for the original plan. > > Dongjoon > > On Thu, Mar 3, 2022 at 10:52 AM Mridul Muralidharan <mri...@gmail.com> > wrote: > >> >> Agree with Sean, code freeze by mid March sounds good. >> >> Regards, >> Mridul >> >> On Thu, Mar 3, 2022 at 12:47 PM Sean Owen <sro...@gmail.com> wrote: >> >>> I think it's fine to pursue the existing plan - code freeze in two weeks >>> and try to close off key remaining issues. Final release pending on how >>> those go, and testing, but fine to get the ball rolling. >>> >>> On Thu, Mar 3, 2022 at 12:45 PM Maxim Gekk >>> <maxim.g...@databricks.com.invalid> wrote: >>> >>>> Hello All, >>>> >>>> I would like to bring on the table the theme about the new Spark >>>> release 3.3. According to the public schedule at >>>> https://spark.apache.org/versioning-policy.html, we planned to start >>>> the code freeze and release branch cut on March 15th, 2022. Since this date >>>> is coming soon, I would like to take your attention on the topic and gather >>>> objections that you might have. >>>> >>>> Bellow is the list of ongoing and active SPIPs: >>>> >>>> Spark SQL: >>>> - [SPARK-31357] DataSourceV2: Catalog API for view metadata >>>> - [SPARK-35801] Row-level operations in Data Source V2 >>>> - [SPARK-37166] Storage Partitioned Join >>>> >>>> Spark Core: >>>> - [SPARK-20624] Add better handling for node shutdown >>>> - [SPARK-25299] Use remote storage for persisting shuffle data >>>> >>>> PySpark: >>>> - [SPARK-26413] RDD Arrow Support in Spark Core and PySpark >>>> >>>> Kubernetes: >>>> - [SPARK-36057] Support Customized Kubernetes Schedulers >>>> >>>> Probably, we should finish if there are any remaining works for Spark >>>> 3.3, and switch to QA mode, cut a branch and keep everything on track. I >>>> would like to volunteer to help drive this process. >>>> >>>> Best regards, >>>> Max Gekk >>>> >>>