+1 for Data Source V2 Aggregate push down huaxin gao <huaxin.ga...@gmail.com> 于2021年2月27日周六 上午4:20写道：
> Thanks Dongjoon and Xiao for the discussion. I would like to add Data > Source V2 Aggregate push down to the list. I am currently working on > JDBC Data Source V2 Aggregate push down, but the common code can be used > for the file based V2 Data Source as well. For example, MAX and MIN can be > pushed down to Parquet and Orc, since they can use statistics information > to perform these operations efficiently. Quite a few users are > interested in this Aggregate push down feature and the preliminary > performance test for JDBC Aggregate push down is positive. So I think it is > a valuable feature to add for Spark 3.2. > > Thanks, > Huaxin > > On Fri, Feb 26, 2021 at 11:13 AM Xiao Li <gatorsm...@gmail.com> wrote: > >> Thank you, Dongjoon, for initiating this discussion. Let us keep it open. >> It might take 1-2 weeks to collect from the community all the features >> we plan to build and ship in 3.2 since we just finished the 3.1 voting. >> >> >>> 3. +100 for Apache Spark 3.2.0 in July 2021. Maybe, we need `branch-cut` >>> in April because we took 3 month for Spark 3.1 release. >> >> >> TBH, cutting the branch this April does not look good to me. That means, >> we only have one month left for feature development of Spark 3.2. Do we >> have enough features in the current master branch? If not, are we able to >> finish major features we collected here? Do they have a timeline or project >> plan? >> >> Xiao >> >> Dongjoon Hyun <dongjoon.h...@gmail.com> 于2021年2月26日周五 上午10:07写道： >> >>> Thank you, Mridul and Sean. >>> >>> 1. Yes, `2017` was a typo. Java 17 is scheduled September 2021. And, of >>> course, it's a nice-to-have status. :) >>> >>> 2. `Push based shuffle and disaggregated shuffle`. Definitely. Thanks >>> for sharing, >>> >>> 3. +100 for Apache Spark 3.2.0 in July 2021. Maybe, we need `branch-cut` >>> in April because we took 3 month for Spark 3.1 release. >>> Let's update our release roadmap of the Apache Spark website. >>> >>> > I'd roughly expect 3.2 in, say, July of this year, given the usual >>> cadence. No reason it couldn't be a little sooner or later. There is >>> already some good stuff in 3.2 and will be a good minor release in 5-6 >>> months. >>> >>> Bests, >>> Dongjoon. >>> >>> >>> >>> On Thu, Feb 25, 2021 at 9:33 AM Sean Owen <sro...@gmail.com> wrote: >>> >>>> I'd roughly expect 3.2 in, say, July of this year, given the usual >>>> cadence. No reason it couldn't be a little sooner or later. There is >>>> already some good stuff in 3.2 and will be a good minor release in 5-6 >>>> months. >>>> >>>> On Thu, Feb 25, 2021 at 10:57 AM Dongjoon Hyun <dongjoon.h...@gmail.com> >>>> wrote: >>>> >>>>> Hi, All. >>>>> >>>>> Since we have been preparing Apache Spark 3.2.0 in master branch since >>>>> December 2020, March seems to be a good time to share our thoughts and >>>>> aspirations on Apache Spark 3.2. >>>>> >>>>> According to the progress on Apache Spark 3.1 release, Apache Spark >>>>> 3.2 seems to be the last minor release of this year. Given the timeframe, >>>>> we might consider the following. (This is a small set. Please add your >>>>> thoughts to this limited list.) >>>>> >>>>> # Languages >>>>> >>>>> - Scala 2.13 Support: This was expected on 3.1 via SPARK-25075 but >>>>> slipped out. Currently, we are trying to use Scala 2.13.5 via SPARK-34505 >>>>> and investigating the publishing issue. Thank you for your contributions >>>>> and feedback on this. >>>>> >>>>> - Java 17 LTS Support: Java 17 LTS will arrive in September 2017. Like >>>>> Java 11, we need lots of support from our dependencies. Let's see. >>>>> >>>>> - Python 3.6 Deprecation(?): Python 3.6 community support ends at >>>>> 2021-12-23. So, the deprecation is not required yet, but we had better >>>>> prepare it because we don't have an ETA of Apache Spark 3.3 in 2022. >>>>> >>>>> - SparkR CRAN publishing: As we know, it's discontinued so far. >>>>> Resuming it depends on the success of Apache SparkR 3.1.1 CRAN publishing. >>>>> If it succeeds to revive it, we can keep publishing. Otherwise, I believe >>>>> we had better drop it from the releasing work item list officially. >>>>> >>>>> # Dependencies >>>>> >>>>> - Apache Hadoop 3.3.2: Hadoop 3.2.0 becomes the default Hadoop profile >>>>> in Apache Spark 3.1. Currently, Spark master branch lives on Hadoop >>>>> 3.2.2's >>>>> shaded clients via SPARK-33212. So far, there is one on-going report at >>>>> YARN environment. We hope it will be fixed soon at Spark 3.2 timeframe and >>>>> we can move toward Hadoop 3.3.2. >>>>> >>>>> - Apache Hive 2.3.9: Spark 3.0 starts to use Hive 2.3.7 by default >>>>> instead of old Hive 1.2 fork. Spark 3.1 removed hive-1.2 profile >>>>> completely >>>>> via SPARK-32981 and replaced the generated hive-service-rpc code with the >>>>> official dependency via SPARK-32981. We are steadily improving this area >>>>> and will consume Hive 2.3.9 if available. >>>>> >>>>> - K8s Client 4.13.2: During K8s GA activity, Spark 3.1 upgrades K8s >>>>> client dependency to 4.12.0. Spark 3.2 upgrades it to 4.13.2 in order to >>>>> support K8s model 1.19. >>>>> >>>>> - Kafka Client 2.8: To bring the client fixes, Spark 3.1 is using >>>>> Kafka Client 2.6. For Spark 3.2, SPARK-33913 upgraded to Kafka 2.7 with >>>>> Scala 2.12.13, but it was reverted later due to Scala 2.12.13 issue. Since >>>>> KAFKA-12357 fixed the Scala requirement two days ago, Spark 3.2 will go >>>>> with Kafka Client 2.8 hopefully. >>>>> >>>>> # Some Features >>>>> >>>>> - Data Source v2: Spark 3.2 will deliver much richer DSv2 with Apache >>>>> Iceberg integration. Especially, we hope the on-going function catalog >>>>> SPIP >>>>> and up-coming storage partitioned join SPIP can be delivered as a part of >>>>> Spark 3.2 and become an additional foundation. >>>>> >>>>> - Columnar Encryption: As of today, Apache Spark master branch >>>>> supports columnar encryption via Apache ORC 1.6 and it's documented via >>>>> SPARK-34036. Also, upcoming Apache Parquet 1.12 has a similar capability. >>>>> Hopefully, Apache Spark 3.2 is going to be the first release to have this >>>>> feature officially. Any feedback is welcome. >>>>> >>>>> - Improved ZStandard Support: Spark 3.2 will bring more benefits for >>>>> ZStandard users: 1) SPARK-34340 added native ZSTD JNI buffer pool support >>>>> for all IO operations, 2) SPARK-33978 makes ORC datasource support ZSTD >>>>> compression, 3) SPARK-34503 sets ZSTD as the default codec for event log >>>>> compression, 4) SPARK-34479 aims to support ZSTD at Avro data source. >>>>> Also, >>>>> the upcoming Parquet 1.12 supports ZSTD (and supports JNI buffer pool), >>>>> too. I'm expecting more benefits. >>>>> >>>>> - Structure Streaming with RocksDB backend: According to the latest >>>>> update, it looks active enough for merging to master branch in Spark 3.2. >>>>> >>>>> Please share your thoughts and let's build better Apache Spark 3.2 >>>>> together. >>>>> >>>>> Bests, >>>>> Dongjoon. >>>>> >>>>
