+1 for better support for disaggregated shuffle (push-based shuffle is a great example, also there are Facebook shuffle service <https://databricks.com/session/cosco-an-efficient-facebook-scale-shuffle-service> and Uber remote shuffle service <https://github.com/uber/RemoteShuffleService>). There were previously some community sync up meetings on this, but kind of discontinued. Are people interested to continue the sync up meeting on this?
On Fri, Feb 26, 2021 at 6:41 PM Yi Wu <yi...@databricks.com> wrote: > +1 to continue the incompleted push-based shuffle. > > -- > Yi > > On Fri, Feb 26, 2021 at 1:26 AM Mridul Muralidharan <mri...@gmail.com> > wrote: > >> >> >> Nit: Java 17 -> should be available by Sept 2021 :-) >> Adoption would also depend on some of our nontrivial dependencies >> supporting it - it might be a stretch to get it in for Apache Spark 3.2 ? >> >> Features: >> Push based shuffle and disaggregated shuffle should also be in 3.2 >> >> >> Regards, >> Mridul >> >> >> >> >> >> >> 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. >>> >>
