Hi Flink Community!
The release of Apache Flink 1.5 has happened (yay!) - so it is a good time
to start talking about what to do for release 1.6.
*== Suggested release timeline ==*
I would propose to release around *end of July* (that is 8-9 weeks from
The rational behind that: There was a lot of effort in release testing
automation (end-to-end tests, scripted stress tests) as part of release
1.5. You may have noticed the big set of new modules under
"flink-end-to-end-tests" in the Flink repository. It delayed the 1.5
release a bit, and needs to continue as part of the coming release cycle,
but should help make releasing more lightweight from now on.
(Side note: There are also some nightly stress tests that we created and
run at data Artisans, and where we are looking whether and in which way it
would make sense to contribute them to Flink.)
*== Features and focus areas ==*
We had a lot of big and heavy features in Flink 1.5, with FLIP-6, the new
network stack, recovery, SQL joins and client, ... Following something like
a "tick-tock-model", I would suggest to focus the next release more on
integrations, tooling, and reducing user friction.
Of course, this does not mean that no other pull request gets reviewed, an
no other topic will be examined - it is simply meant as a help to
understand where to expect more activity during the next release cycle.
Note that these are really the coarse focus areas - don't read this as a
This list is my first suggestion, based on discussions with committers,
users, and mailing list questions.
- Support Java 9 and Scala 2.12
- Smoothen the integration in Container environment, like "Flink as a
Library", and easier integration with Kubernetes services and other proxies.
- Polish the remaing parts of the FLIP-6 rewrite
- Improve state backends with asynchronous timer snapshots, efficient
timer deletes, state TTL, and broadcast state support in RocksDB.
- Extends Streaming Sinks:
- Bucketing Sink should support S3 properly (compensate for eventual
consistency), work with Flink's shaded S3 file systems, and efficiently
support formats that compress/index arcoss individual rows (Parquet, ORC,
- Support ElasticSearch's new REST API
- Smoothen State Evolution to support type conversion on snapshot restore
- Enhance Stream SQL and CEP
- Add support for "update by key" Table Sources
- Add more table sources and sinks (Kafka, Kinesis, Files, K/V stores)
- Expand SQL client
- Integrate CEP and SQL, through MATCH_RECOGNIZE clause
- Improve CEP Performance of SharedBuffer on RocksDB