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. >
