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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: 2022-04-19
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: dev@flink.apache.org
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.
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
Platform Infrastructure
Support for Common Languages, Formats, Catalogs
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.
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.
Continuous work to keep improving performance and recovery speed.
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.
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 mentionend FLIP-27 and FLIP-143 a new default for connectors has been established.
There are various dedicated efforts to simplify the maintenance and structure (more intuitive navigation/reading) of the documentation.
The Stateful Functions subproject has its own roadmap published under statefun.io.
The Flink Kubernetes Operator subproject has its own roadmap under the documentation.
The Flink Table Store subproject has its own roadmap under the documentation.