| commit | d84c21a09618a63ca55ab5a4102f2cc82b462ba5 | [log] [tgz] |
|---|---|---|
| author | 阿洋 <xinzhuxiansheng@126.com> | Sun Sep 25 21:42:10 2022 +0800 |
| committer | GitHub <noreply@github.com> | Sun Sep 25 21:42:10 2022 +0800 |
| tree | 94b394ef0a8029e8a4aadee6b9b9155da58d8870 | |
| parent | 74542220290a47208bb10d236e5d1bb36521748c [diff] |
jobStatuses use appId as key (#1686)
Make stream processing easier
A magical framework that make stream processing easier!
Apache Flink and Apache Spark are widely used as the next generation of big data streaming computing engines. Based on a bench of excellent experiences combined with best practices, we extracted the task deployment and runtime parameters into the configuration files. In this way, an easy-to-use RuntimeContext with out-of-the-box connectors would bring easier and more efficient task development experience. It reduces the learning cost and development barriers, hence developers can focus on the business logic. On the other hand, It can be challenge for enterprises to use Flink & Spark if there is no professional management platform for Flink & Spark tasks during the deployment phase. StreamPark provides such a professional task management platform, including task development, scheduling, interactive query, deployment, operation, maintenance, etc.
click Document for more information
Various companies and organizations use StreamPark for research, production and commercial products. Are you using this project ? you can add your company
You can submit any ideas as pull requests or as GitHub issues.
If you're new to posting issues, we ask that you read How To Ask Questions The Smart Way (This guide does not provide actual support services for this project!), How to Report Bugs Effectively prior to posting. Well written bug reports help us help you!
Thank you to all the people who already contributed to StreamPark!
