Thank you, Sean. Ya, exactly, we can release 2.4.8 as a normal release first and use 2.4.9 as the EOL release.
Since 2.4.7 was released almost 6 months ago, 2.4.8 is a little late in terms of the cadence. Bests, Dongjoon. On Wed, Mar 3, 2021 at 10:55 AM Sean Owen <sro...@gmail.com> wrote: > For reference, 2.3.x was maintained from February 2018 (2.3.0) to Sep 2019 > (2.3.4), or about 19 months. The 2.4 branch should probably be maintained > longer than that, as the final 2.x branch. 2.4.0 was released in Nov 2018. > A final release in, say, April 2021 would be about 30 months. That feels > about right timing-wise. > > We should in any event release 2.4.8, yes. We can of course choose to > release a 2.4.9 if some critical issue is found, later. > > But yeah based on the velocity of back-ports to 2.4.x, it seems about time > to call it EOL. > > Sean > > > On Wed, Mar 3, 2021 at 12:05 PM Dongjoon Hyun <dongjoon.h...@gmail.com> > wrote: > >> Hi, All. >> >> We successfully completed Apache Spark 3.1.1 and 3.0.2 releases and >> started 3.2.0 discussion already. >> >> Let's talk about branch-2.4 because there exists some discussions on JIRA >> and GitHub about skipping backporting to 2.4. >> >> Since `branch-2.4` has been maintained well as LTS, I'd like to suggest >> having Apache Spark 2.4.8 release as the official EOL release of 2.4 line >> in order to focus on 3.x more from now. Please note that `branch-2.4` will >> be frozen officially like `branch-2.3` after EOL release. >> >> - Apache Spark 2.4.0 was released on November 2, 2018. >> - Apache Spark 2.4.7 was released on September 12, 2020. >> - Since v2.4.7 tag, `branch-2.4` has 134 commits including the following >> 12 correctness issues. >> >> ## CORRECTNESS ISSUE >> SPARK-30201 HiveOutputWriter standardOI should use >> ObjectInspectorCopyOption.DEFAULT >> SPARK-30228 Update zstd-jni to 1.4.4-3 >> SPARK-30894 The nullability of Size function should not depend on >> SQLConf.get >> SPARK-32635 When pyspark.sql.functions.lit() function is used with >> dataframe cache, it returns wrong result >> SPARK-32908 percentile_approx() returns incorrect results >> SPARK-33183 Bug in optimizer rule EliminateSorts >> SPARK-33290 REFRESH TABLE should invalidate cache even though the table >> itself may not be cached >> SPARK-33593 Vector reader got incorrect data with binary partition value >> SPARK-33726 Duplicate field names causes wrong answers during aggregation >> SPARK-34187 Use available offset range obtained during polling when >> checking offset validation >> SPARK-34212 For parquet table, after changing the precision and scale of >> decimal type in hive, spark reads incorrect value >> SPARK-34229 Avro should read decimal values with the file schema >> >> ## SECURITY ISSUE >> SPARK-33333 Upgrade Jetty to 9.4.28.v20200408 >> SPARK-33831 Update to jetty 9.4.34 >> SPARK-34449 Upgrade Jetty to fix CVE-2020-27218 >> >> What do you think about this? >> >> Bests, >> Dongjoon. >> >
