Hi all! Recently several contributors, committers, and users asked about making it more visible in which way the project is currently going.
Users and developers can track the direction by following the discussion threads and JIRA, but due to the mass of discussions and open issues, it is very hard to get a good overall picture. Especially for new users and contributors, is is very hard to get a quick overview of the project direction. To fix this, I suggest to add a brief roadmap summary to the homepage. It is a bit of a commitment to keep that roadmap up to date, but I think the benefit for users justifies that. The Apache Beam project has added such a roadmap [1] <https://beam.apache.org/roadmap/>, which was received very well by the community, I would suggest to follow a similar structure here. If the community is in favor of this, I would volunteer to write a first version of such a roadmap. The points I would include are below. Best, Stephan [1] https://beam.apache.org/roadmap/ ======================================================== Disclaimer: Apache Flink is not governed or steered by any one single entity, but by its community and Project Management Committee (PMC). This is not a authoritative roadmap in the sense of a plan with a specific timeline. Instead, we share our vision for the future and major initiatives that are receiving attention and give users and contributors an understanding what they can look forward to. *Future Role of Table API and DataStream API* - Table API becomes first class citizen - Table API becomes primary API for analytics use cases * Declarative, automatic optimizations * No manual control over state and timers - DataStream API becomes primary API for applications and data pipeline use cases * Physical, user controls data types, no magic or optimizer * Explicit control over state and time *Batch Streaming Unification* - Table API unification (environments) (FLIP-32) - New unified source interface (FLIP-27) - Runtime operator unification & code reuse between DataStream / Table - Extending Table API to make it convenient API for all analytical use cases (easier mix in of UDFs) - Same join operators on bounded/unbounded Table API and DataStream API *Faster Batch (Bounded Streams)* - Much of this comes via Blink contribution/merging - Fine-grained Fault Tolerance on bounded data (Table API) - Batch Scheduling on bounded data (Table API) - External Shuffle Services Support on bounded streams - Caching of intermediate results on bounded data (Table API) - Extending DataStream API to explicitly model bounded streams (API breaking) - Add fine fault tolerance, scheduling, caching also to DataStream API *Streaming State Evolution* - Let all built-in serializers support stable evolution - First class support for other evolvable formats (Protobuf, Thrift) - Savepoint input/output format to modify / adjust savepoints *Simpler Event Time Handling* - Event Time Alignment in Sources - Simpler out-of-the box support in sources *Checkpointing* - Consistency of Side Effects: suspend / end with savepoint (FLIP-34) - Failed checkpoints explicitly aborted on TaskManagers (not only on coordinator) *Automatic scaling (adjusting parallelism)* - Reactive scaling - Active scaling policies *Kubernetes Integration* - Active Kubernetes Integration (Flink actively manages containers) *SQL Ecosystem* - Extended Metadata Stores / Catalog / Schema Registries support - DDL support - Integration with Hive Ecosystem *Simpler Handling of Dependencies* - Scala in the APIs, but not in the core (hide in separate class loader) - Hadoop-free by default
