Roadmap #

Preamble: This roadmap means to provide user and contributors with a high-level summary of ongoing efforts, grouped by the major threads to which the efforts belong. With so much that is happening in Flink, we hope that this helps with understanding the direction of the project. The roadmap contains both efforts in early stages as well as nearly completed efforts, so that users may get a better impression of the overall status and direction of those developments.

More details and various smaller changes can be found in the FLIPs

The roadmap is continuously updated. New features and efforts should be added to the roadmap once there is consensus that they will happen and what they will roughly look like for the user.

Last Update: 2022-11-14

Feature Radar #

The feature radar is meant to give users guidance regarding feature maturity, as well as which features are approaching end-of-life. For questions, please contact the developer mailing list:

Feature Stages #

  • MVP: Have a look, consider whether this can help you in the future.
  • Beta: You can benefit from this, but you should carefully evaluate the feature.
  • Ready and Evolving: Ready to use in production, but be aware you may need to make some adjustments to your application and setup in the future, when you upgrade Flink.
  • Stable: Unrestricted use in production
  • Reaching End-of-Life: Stable, still feel free to use, but think about alternatives. Not a good match for new long-lived projects.
  • Deprecated: Start looking for alternatives now

Unified Analytics: Where Batch and Streaming come Together; SQL and beyond. #

Flink is a streaming data system in its core, that executes “batch as a special case of streaming”. Efficient execution of batch jobs is powerful in its own right; but even more so, batch processing capabilities (efficient processing of bounded streams) open the way for a seamless unification of batch and streaming applications.

Unified streaming/batch up-levels the streaming data paradigm: It gives users consistent semantics across their real-time and lag-time applications. Furthermore, streaming applications often need to be complemented by batch (bounded stream) processing, for example when reprocessing data after bugs or data quality issues, or when bootstrapping new applications. A unified API and system make this much easier.

A unified SQL Platform #

The community has been building Flink to a powerful basis for a unified (batch and streaming) SQL analytics platform, and is continuing to do so.

SQL has very strong cross-batch-streaming semantics, allowing users to use the same queries for ad-hoc analytics and as continuous queries. Flink already contains an efficient unified query engine, and a wide set of integrations. With user feedback, those are continuously improved.

Going Beyond a SQL Stream/Batch Processing Engine

  • To extend the capability of a pure stream processor and make Flink ready for future use cases, FLIP-188 has been announced adding built in dynamic table storage.
  • The experience of updating Flink SQL based jobs has been rather cumbersome as it could have lead to new job graphs making restoring from savepoints/checkpoints impossible. FLIP-190 that already has been shipped as MVP is targeting this.

Platform Infrastructure

  • After FLIP-163 the community is working again on a set of SQL Client usability improvements (FLIP-189) which is aiming at improving the user experience, when using the SQL client.

Support for Common Languages, Formats, Catalogs

  • With FLIP-216 there’s now the initiative to introduce pluggable dialects on the example of the Hive connector. Including so many dependencies to make dialects work has lead to an overhead for contributors and users.

Flink has a broad SQL coverage for batch (full TPC-DS support) and a state-of-the-art set of supported operations in streaming. There is continuous effort to add more functions and cover more SQL operations.

Deep Batch / Streaming Unification for the DataStream API #

The DataStream API is Flink’s physical API, for use cases where users need very explicit control over data types, streams, state, and time. This API is evolving to support efficient batch execution on bounded data.

DataStream API executes the same dataflow shape in batch as in streaming, keeping the same operators. That way users keep the same level of control over the dataflow, and our goal is to mix and switch between batch/streaming execution in the future to make it a seamless experience.

Unified Sources and Sinks

  • The first APIs and implementations of sources were specific to either streaming programs in the DataStream API (SourceFunction), or to batch programs in the DataSet API (InputFormat).

    In this effort, we are creating sources that work across batch and streaming execution. The aim is to give users a consistent experience across both modes, and to allow them to easily switch between streaming and batch execution for their unbounded and bounded streaming applications. The interface for this New Source API is done and available, and we are working on migrating more source connectors to this new model, see FLIP-27.

  • Similar to the sources, the original sink APIs are also specific to streaming (SinkFunction) and batch (OutputFormat) APIs and execution.

    We have introduced a new API for sinks that consistently handles result writing and committing (Transactions) across batch and streaming. The first iteration of the API exists, and we are porting sinks and refining the API in the process. See FLIP-143.

The goal of these efforts is to make it feel natural to deploy (long running streaming) Flink applications. Instead of starting a cluster and submitting a job to that cluster, these efforts support deploying a streaming job as a self contained application.

For example as a simple Kubernetes deployment; deployed and scaled like a regular application without extra workflows.

  • There is currently a Kubernetes Operator being developed by the community. See FLIP-212.

Performance #

Continuous work to keep improving performance and recovery speed.

Faster Checkpoints and Recovery #

The community is continuously working on improving checkpointing and recovery speed. Checkpoints and recovery are stable and have been a reliable workhorse for years. We are still trying to make it faster, more predictable, and to remove some confusions and inflexibility in some areas.

  • FLIP-183 is targeting size of checkpoints by debloating the buffers. A first beta is already available.
  • With FLIP-151 there is an ongoing effort to implement a heap based state backend.

There is almost no use case in which Apache Flink is used on its own. It has established itself as part of many data related reference architectures. In fact you’ll find the squirrel logo covering several aspects.

The community has added a lot of connectors and formats. With the already mentioned FLIP-27 and FLIP-143 a new default for connectors has been established.

  • There are efforts to revise the formats API with FLIP-219
  • There is ongoing work on new connectors (e.g. Pinot)
  • Connectors will be hosted in an external repository going forward. See the ML thread

The Flink community has removed Gelly, it’s old graph-processing library.

Documentation #

There are various dedicated efforts to simplify the maintenance and structure (more intuitive navigation/reading) of the documentation.

The Flink Kubernetes Operator subproject has its own roadmap under the documentation.

The Flink Table Store subproject has its own roadmap under the documentation.