+1, thanks. On Tue, 15 Jun 2021, 16:17 Gengliang Wang, <ltn...@gmail.com> wrote:
> Hi, > > As the expected release date is close, I would like to volunteer as the > release manager for Apache Spark 3.2.0. > > Thanks, > Gengliang > > On Mon, Apr 12, 2021 at 1:59 PM Wenchen Fan <cloud0...@gmail.com> wrote: > >> An update: we found a mistake that we picked the Spark 3.2 release date >> based on the scheduled release date of 3.1. However, 3.1 was delayed and >> released on March 2. In order to have a full 6 months development for 3.2, >> the target release date for 3.2 should be September 2. >> >> I'm updating the release dates in >> https://github.com/apache/spark-website/pull/331 >> >> Thanks, >> Wenchen >> >> On Thu, Mar 11, 2021 at 11:17 PM Dongjoon Hyun <dongjoon.h...@gmail.com> >> wrote: >> >>> Thank you, Xiao, Wenchen and Hyukjin. >>> >>> Bests, >>> Dongjoon. >>> >>> >>> On Thu, Mar 11, 2021 at 2:15 AM Hyukjin Kwon <gurwls...@gmail.com> >>> wrote: >>> >>>> Just for an update, I will send a discussion email about my idea late >>>> this week or early next week. >>>> >>>> 2021년 3월 11일 (목) 오후 7:00, Wenchen Fan <cloud0...@gmail.com>님이 작성: >>>> >>>>> There are many projects going on right now, such as new DS v2 APIs, >>>>> ANSI interval types, join improvement, disaggregated shuffle, etc. I don't >>>>> think it's realistic to do the branch cut in April. >>>>> >>>>> I'm +1 to release 3.2 around July, but it doesn't mean we have to cut >>>>> the branch 3 months earlier. We should make the release process faster and >>>>> cut the branch around June probably. >>>>> >>>>> >>>>> >>>>> On Thu, Mar 11, 2021 at 4:41 AM Xiao Li <gatorsm...@gmail.com> wrote: >>>>> >>>>>> Below are some nice-to-have features we can work on in Spark 3.2: Lateral >>>>>> Join support <https://issues.apache.org/jira/browse/SPARK-28379>, >>>>>> interval data type, timestamp without time zone, un-nesting arbitrary >>>>>> queries, the returned metrics of DSV2, and error message standardization. >>>>>> Spark 3.2 will be another exciting release I believe! >>>>>> >>>>>> Go Spark! >>>>>> >>>>>> Xiao >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> Dongjoon Hyun <dongjoon.h...@gmail.com> 于2021年3月10日周三 下午12:25写道： >>>>>> >>>>>>> Hi, Xiao. >>>>>>> >>>>>>> This thread started 13 days ago. Since you asked the community about >>>>>>> major features or timelines at that time, could you share your roadmap >>>>>>> or >>>>>>> expectations if you have something in your mind? >>>>>>> >>>>>>> > 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. >>>>>>> > 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? >>>>>>> >>>>>>> Bests, >>>>>>> Dongjoon. >>>>>>> >>>>>>> >>>>>>> >>>>>>> On Wed, Mar 3, 2021 at 2:58 PM Dongjoon Hyun < >>>>>>> dongjoon.h...@gmail.com> wrote: >>>>>>> >>>>>>>> Hi, John. >>>>>>>> >>>>>>>> This thread aims to share your expectations and goals (and maybe >>>>>>>> work progress) to Apache Spark 3.2 because we are making this >>>>>>>> together. :) >>>>>>>> >>>>>>>> Bests, >>>>>>>> Dongjoon. >>>>>>>> >>>>>>>> >>>>>>>> On Wed, Mar 3, 2021 at 1:59 PM John Zhuge <jzh...@apache.org> >>>>>>>> wrote: >>>>>>>> >>>>>>>>> Hi Dongjoon, >>>>>>>>> >>>>>>>>> Is it possible to get ViewCatalog in? The community already had >>>>>>>>> fairly detailed discussions. >>>>>>>>> >>>>>>>>> Thanks, >>>>>>>>> John >>>>>>>>> >>>>>>>>> On Thu, Feb 25, 2021 at 8:57 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. >>>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> -- >>>>>>>>> John Zhuge >>>>>>>>> >>>>>>>>
