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
