+1, I think having preview release would be great. Tom
On Fri, Sep 13, 2019 at 4:55 AM Stavros Kontopoulos < stavros.kontopou...@lightbend.com> wrote: > +1 as a contributor and as a user. Given the amount of testing required > for all the new cool stuff like java 11 support, major > refactorings/deprecations etc, a preview version would help a lot the > community making adoption smoother long term. I would also add to the list > of issues, Scala 2.13 support ( > https://issues.apache.org/jira/browse/SPARK-25075) assuming things will > move forward faster the next few months. > > On Fri, Sep 13, 2019 at 11:08 AM Driesprong, Fokko <fo...@driesprong.frl> > wrote: > >> Michael Heuer, that's an interesting issue. >> >> 1.8.2 to 1.9.0 is almost binary compatible (94%): >> http://people.apache.org/~busbey/avro/1.9.0-RC4/1.8.2_to_1.9.0RC4_compat_report.html. >> Most of the stuff is removing the Jackson and Netty API from Avro's public >> API and deprecating the Joda library. I would strongly advise moving to >> 1.9.1 since there are some regression issues, for Java most important: >> https://jira.apache.org/jira/browse/AVRO-2400 >> >> I'd love to dive into the issue that you describe and I'm curious if the >> issue is still there with Avro 1.9.1. I'm a bit busy at the moment but >> might have some time this weekend to dive into it. >> >> Cheers, Fokko Driesprong >> >> >> Op vr 13 sep. 2019 om 02:32 schreef Reynold Xin <r...@databricks.com>: >> >>> +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 >>>> <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 <https://amzn.to/2MaRAG9> >>>> YouTube Live Streams: https://www.youtube.com/user/holdenkarau >>>> >>> >>> > >
