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.)
- 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.
- 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.