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导读： 此计划路线图旨在对Flink社区当前正在进行的项目进行总结摘要，并对这些项目根据工作内容进行分组。 鉴于Flink每个分组中现在都有非常多的工作正在进行，我们希望此计划书有助于用户和贡献者理解每个项目乃至于整个Flink的未来方向。 这个计划书既涵盖刚起步的项目，也包括接近完成的项目，这样可以使大家更好地了解各项目的发展方向以及当前的进展。
关于各个项目更多的细节讨论和其他较小的改动记录在 FLIPs 。
路线图会不断更新。一旦达成共识，新的特性和工作都会添加到路线图中。 这里的共识是指这些特性和工作将来确定会发生，以及这些工作对于用户来说大致是什么样的。
Last Update: 2022-04-19
功能图谱旨在为用户提供有关功能成熟度方面的引导，包括哪些功能正在积极推进，哪些功能即将寿终正寝。 如有任何疑问，请联系开发人员邮件列表：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.
Flink Kubernetes Operator 项目拥有自己的路线图，您可以在它的说明文档下面看到社区即将开展的工作。