Hi all, I would like to volunteer to be the release manager of Spark 3 preview, thanks!
Sean Owen <sro...@gmail.com> 于2019年9月13日周五 上午11:21写道： > Well, great to hear the unanimous support for a Spark 3 preview > release. Now, I don't know how to make releases myself :) I would > first open it up to our revered release managers: would anyone be > interested in trying to make one? sounds like it's not too soon to get > what's in master out for evaluation, as there aren't any major > deficiencies left, although a number of items to consider for the > final release. > > I think we just need one release, targeting Hadoop 3.x / Hive 2.x in > order to make it possible to test with JDK 11. (We're only on Scala > 2.12 at this point.) > > On Thu, Sep 12, 2019 at 7:32 PM Reynold Xin <r...@databricks.com> wrote: > > > > +1! Long due for a preview release. > > > > > > On Thu, Sep 12, 2019 at 5:26 PM, Holden Karau <hol...@pigscanfly.ca> > wrote: > >> > >> I like the idea from the PoV of giving folks something to start testing > against and exploring so they can raise issues with us earlier in the > process and we have more time to make calls around this. > >> > >> On Thu, Sep 12, 2019 at 4:15 PM John Zhuge <jzh...@apache.org> wrote: > >>> > >>> +1 Like the idea as a user and a DSv2 contributor. > >>> > >>> On Thu, Sep 12, 2019 at 4:10 PM Jungtaek Lim <kabh...@gmail.com> > wrote: > >>>> > >>>> +1 (as a contributor) from me to have preview release on Spark 3 as > it would help to test the feature. When to cut preview release is > questionable, as major works are ideally to be done before that - if we are > intended to introduce new features before official release, that should > work regardless of this, but if we are intended to have opportunity to test > earlier, ideally it should. > >>>> > >>>> As a one of contributors in structured streaming area, I'd like to > add some items for Spark 3.0, both "must be done" and "better to have". For > "better to have", I pick some items for new features which committers > reviewed couple of rounds and dropped off without soft-reject (No valid > reason to stop). For Spark 2.4 users, only added feature for structured > streaming is Kafka delegation token. (given we assume revising Kafka > consumer pool as improvement) I hope we provide some gifts for structured > streaming users in Spark 3.0 envelope. > >>>> > >>>> > must be done > >>>> * SPARK-26154 Stream-stream joins - left outer join gives > inconsistent output > >>>> It's a correctness issue with multiple users reported, being reported > at Nov. 2018. There's a way to reproduce it consistently, and we have a > patch submitted at Jan. 2019 to fix it. > >>>> > >>>> > better to have > >>>> * SPARK-23539 Add support for Kafka headers in Structured Streaming > >>>> * SPARK-26848 Introduce new option to Kafka source - specify > timestamp to start and end offset > >>>> * SPARK-20568 Delete files after processing in structured streaming > >>>> > >>>> There're some more new features/improvements items in SS, but given > we're talking about ramping-down, above list might be realistic one. > >>>> > >>>> > >>>> > >>>> On Thu, Sep 12, 2019 at 9:53 AM Jean Georges Perrin <j...@jgp.net> > wrote: > >>>>> > >>>>> As a user/non committer, +1 > >>>>> > >>>>> I love the idea of an early 3.0.0 so we can test current dev against > it, I know the final 3.x will probably need another round of testing when > it gets out, but less for sure... I know I could checkout and compile, but > having a “packaged” preversion is great if it does not take too much time > to the team... > >>>>> > >>>>> jg > >>>>> > >>>>> > >>>>> On Sep 11, 2019, at 20:40, Hyukjin Kwon <gurwls...@gmail.com> wrote: > >>>>> > >>>>> +1 from me too but I would like to know what other people think too. > >>>>> > >>>>> 2019년 9월 12일 (목) 오전 9:07, Dongjoon Hyun <dongjoon.h...@gmail.com>님이 > 작성: > >>>>>> > >>>>>> Thank you, Sean. > >>>>>> > >>>>>> I'm also +1 for the following three. > >>>>>> > >>>>>> 1. Start to ramp down (by the official branch-3.0 cut) > >>>>>> 2. Apache Spark 3.0.0-preview in 2019 > >>>>>> 3. Apache Spark 3.0.0 in early 2020 > >>>>>> > >>>>>> For JDK11 clean-up, it will meet the timeline and `3.0.0-preview` > helps it a lot. > >>>>>> > >>>>>> After this discussion, can we have some timeline for `Spark 3.0 > Release Window` in our versioning-policy page? > >>>>>> > >>>>>> - https://spark.apache.org/versioning-policy.html > >>>>>> > >>>>>> Bests, > >>>>>> Dongjoon. > >>>>>> > >>>>>> > >>>>>> On Wed, Sep 11, 2019 at 11:54 AM Michael Heuer <heue...@gmail.com> > wrote: > >>>>>>> > >>>>>>> I would love to see Spark + Hadoop + Parquet + Avro compatibility > problems resolved, e.g. > >>>>>>> > >>>>>>> https://issues.apache.org/jira/browse/SPARK-25588 > >>>>>>> https://issues.apache.org/jira/browse/SPARK-27781 > >>>>>>> > >>>>>>> Note that Avro is now at 1.9.1, binary-incompatible with 1.8.x. > As far as I know, Parquet has not cut a release based on this new version. > >>>>>>> > >>>>>>> Then out of curiosity, are the new Spark Graph APIs targeting 3.0? > >>>>>>> > >>>>>>> https://github.com/apache/spark/pull/24851 > >>>>>>> https://github.com/apache/spark/pull/24297 > >>>>>>> > >>>>>>> michael > >>>>>>> > >>>>>>> > >>>>>>> On Sep 11, 2019, at 1:37 PM, Sean Owen <sro...@apache.org> wrote: > >>>>>>> > >>>>>>> I'm curious what current feelings are about ramping down towards a > >>>>>>> Spark 3 release. It feels close to ready. There is no fixed date, > >>>>>>> though in the past we had informally tossed around "back end of > 2019". > >>>>>>> For reference, Spark 1 was May 2014, Spark 2 was July 2016. I'd > expect > >>>>>>> Spark 2 to last longer, so to speak, but feels like Spark 3 is > coming > >>>>>>> due. > >>>>>>> > >>>>>>> What are the few major items that must get done for Spark 3, in > your > >>>>>>> opinion? Below are all of the open JIRAs for 3.0 (which everyone > >>>>>>> should feel free to update with things that aren't really needed > for > >>>>>>> Spark 3; I already triaged some). > >>>>>>> > >>>>>>> For me, it's: > >>>>>>> - DSv2? > >>>>>>> - Finishing touches on the Hive, JDK 11 update > >>>>>>> > >>>>>>> What about considering a preview release earlier, as happened for > >>>>>>> Spark 2, to get feedback much earlier than the RC cycle? Could that > >>>>>>> even happen ... about now? > >>>>>>> > >>>>>>> I'm also wondering what a realistic estimate of Spark 3 release > is. My > >>>>>>> guess is quite early 2020, from here. > >>>>>>> > >>>>>>> > >>>>>>> > >>>>>>> SPARK-29014 DataSourceV2: Clean up current, default, and session > catalog uses > >>>>>>> SPARK-28900 Test Pyspark, SparkR on JDK 11 with run-tests > >>>>>>> SPARK-28883 Fix a flaky test: ThriftServerQueryTestSuite > >>>>>>> SPARK-28717 Update SQL ALTER TABLE RENAME to use TableCatalog API > >>>>>>> SPARK-28588 Build a SQL reference doc > >>>>>>> SPARK-28629 Capture the missing rules in HiveSessionStateBuilder > >>>>>>> SPARK-28684 Hive module support JDK 11 > >>>>>>> SPARK-28548 explain() shows wrong result for persisted DataFrames > >>>>>>> after some operations > >>>>>>> SPARK-28372 Document Spark WEB UI > >>>>>>> SPARK-28476 Support ALTER DATABASE SET LOCATION > >>>>>>> SPARK-28264 Revisiting Python / pandas UDF > >>>>>>> SPARK-28301 fix the behavior of table name resolution with > multi-catalog > >>>>>>> SPARK-28155 do not leak SaveMode to file source v2 > >>>>>>> SPARK-28103 Cannot infer filters from union table with empty local > >>>>>>> relation table properly > >>>>>>> SPARK-28024 Incorrect numeric values when out of range > >>>>>>> SPARK-27936 Support local dependency uploading from --py-files > >>>>>>> SPARK-27884 Deprecate Python 2 support in Spark 3.0 > >>>>>>> SPARK-27763 Port test cases from PostgreSQL to Spark SQL > >>>>>>> SPARK-27780 Shuffle server & client should be versioned to enable > >>>>>>> smoother upgrade > >>>>>>> SPARK-27714 Support Join Reorder based on Genetic Algorithm when > the # > >>>>>>> of joined tables > 12 > >>>>>>> SPARK-27471 Reorganize public v2 catalog API > >>>>>>> SPARK-27520 Introduce a global config system to replace > hadoopConfiguration > >>>>>>> SPARK-24625 put all the backward compatible behavior change configs > >>>>>>> under spark.sql.legacy.* > >>>>>>> SPARK-24640 size(null) returns null > >>>>>>> SPARK-24702 Unable to cast to calendar interval in spark sql. > >>>>>>> SPARK-24838 Support uncorrelated IN/EXISTS subqueries for more > operators > >>>>>>> SPARK-24941 Add RDDBarrier.coalesce() function > >>>>>>> SPARK-25017 Add test suite for ContextBarrierState > >>>>>>> SPARK-25083 remove the type erasure hack in data source scan > >>>>>>> SPARK-25383 Image data source supports sample pushdown > >>>>>>> SPARK-27272 Enable blacklisting of node/executor on fetch failures > by default > >>>>>>> SPARK-27296 User Defined Aggregating Functions (UDAFs) have a major > >>>>>>> efficiency problem > >>>>>>> SPARK-25128 multiple simultaneous job submissions against k8s > backend > >>>>>>> cause driver pods to hang > >>>>>>> SPARK-26731 remove EOLed spark jobs from jenkins > >>>>>>> SPARK-26664 Make DecimalType's minimum adjusted scale configurable > >>>>>>> SPARK-21559 Remove Mesos fine-grained mode > >>>>>>> SPARK-24942 Improve cluster resource management with jobs > containing > >>>>>>> barrier stage > >>>>>>> SPARK-25914 Separate projection from grouping and aggregate in > logical Aggregate > >>>>>>> SPARK-26022 PySpark Comparison with Pandas > >>>>>>> SPARK-20964 Make some keywords reserved along with the ANSI/SQL > standard > >>>>>>> SPARK-26221 Improve Spark SQL instrumentation and metrics > >>>>>>> SPARK-26425 Add more constraint checks in file streaming source to > >>>>>>> avoid checkpoint corruption > >>>>>>> SPARK-25843 Redesign rangeBetween API > >>>>>>> SPARK-25841 Redesign window function rangeBetween API > >>>>>>> SPARK-25752 Add trait to easily whitelist logical operators that > >>>>>>> produce named output from CleanupAliases > >>>>>>> SPARK-23210 Introduce the concept of default value to schema > >>>>>>> SPARK-25640 Clarify/Improve EvalType for grouped aggregate and > window aggregate > >>>>>>> SPARK-25531 new write APIs for data source v2 > >>>>>>> SPARK-25547 Pluggable jdbc connection factory > >>>>>>> SPARK-20845 Support specification of column names in INSERT INTO > >>>>>>> SPARK-24417 Build and Run Spark on JDK11 > >>>>>>> SPARK-24724 Discuss necessary info and access in barrier mode + > Kubernetes > >>>>>>> SPARK-24725 Discuss necessary info and access in barrier mode + > Mesos > >>>>>>> SPARK-25074 Implement maxNumConcurrentTasks() in > >>>>>>> MesosFineGrainedSchedulerBackend > >>>>>>> SPARK-23710 Upgrade the built-in Hive to 2.3.5 for hadoop-3.2 > >>>>>>> SPARK-25186 Stabilize Data Source V2 API > >>>>>>> SPARK-25376 Scenarios we should handle but missed in 2.4 for > barrier > >>>>>>> execution mode > >>>>>>> SPARK-25390 data source V2 API refactoring > >>>>>>> SPARK-7768 Make user-defined type (UDT) API public > >>>>>>> SPARK-14922 Alter Table Drop Partition Using Predicate-based > Partition Spec > >>>>>>> SPARK-15691 Refactor and improve Hive support > >>>>>>> SPARK-15694 Implement ScriptTransformation in sql/core > >>>>>>> SPARK-16217 Support SELECT INTO statement > >>>>>>> SPARK-16452 basic INFORMATION_SCHEMA support > >>>>>>> SPARK-18134 SQL: MapType in Group BY and Joins not working > >>>>>>> SPARK-18245 Improving support for bucketed table > >>>>>>> SPARK-19842 Informational Referential Integrity Constraints > Support in Spark > >>>>>>> SPARK-22231 Support of map, filter, withColumn, dropColumn in > nested > >>>>>>> list of structures > >>>>>>> SPARK-22632 Fix the behavior of timestamp values for R's DataFrame > to > >>>>>>> respect session timezone > >>>>>>> SPARK-22386 Data Source V2 improvements > >>>>>>> SPARK-24723 Discuss necessary info and access in barrier mode + > YARN > >>>>>>> > >>>>>>> > --------------------------------------------------------------------- > >>>>>>> To unsubscribe e-mail: dev-unsubscr...@spark.apache.org > >>>>>>> > >>>>>>> > >>>> > >>>> > >>>> -- > >>>> Name : Jungtaek Lim > >>>> Blog : http://medium.com/@heartsavior > >>>> Twitter : http://twitter.com/heartsavior > >>>> LinkedIn : http://www.linkedin.com/in/heartsavior > >>> > >>> > >>> > >>> -- > >>> John Zhuge > >> > >> > >> > >> -- > >> Twitter: https://twitter.com/holdenkarau > >> Books (Learning Spark, High Performance Spark, etc.): > https://amzn.to/2MaRAG9 > >> YouTube Live Streams: https://www.youtube.com/user/holdenkarau > > > > > > --------------------------------------------------------------------- > To unsubscribe e-mail: dev-unsubscr...@spark.apache.org > >
