Apache Flink® - 数据流上的有状态计算



所有流式场景
  • 事件驱动应用
  • 流批分析
  • 数据管道 & ETL
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正确性保证
  • Exactly-once 状态一致性
  • 事件时间处理
  • 成熟的迟到数据处理
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分层 API
  • SQL on Stream & Batch Data
  • DataStream API & DataSet API
  • ProcessFunction (Time & State)
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聚焦运维
  • 灵活部署
  • 高可用
  • 保存点
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大规模计算
  • 水平扩展架构
  • 支持超大状态
  • 增量检查点机制
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性能卓越
  • 低延迟
  • 高吞吐
  • 内存计算
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Pravega Flink Connector 101
A brief introduction to the Pravega Flink Connector
Apache Flink 1.14.3 Release Announcement
The Apache Flink community released the second bugfix version of the Apache Flink 1.14 series.
Apache Flink ML 2.0.0 Release Announcement
The Apache Flink community is excited to announce the release of Flink ML 2.0.0! This release involves a major refactor of the earlier Flink ML library and introduces major features that extend the Flink ML API and the iteration runtime, such as supporting stages with multi-input multi-output, graph-based stage composition, and a new stream-batch unified iteration library.
How We Improved Scheduler Performance for Large-scale Jobs - Part Two
Part one of this blog post briefly introduced the optimizations we’ve made to improve the performance of the scheduler; compared to Flink 1.12, the time cost and memory usage of scheduling large-scale jobs in Flink 1.14 is significantly reduced. In part two, we will elaborate on the details of these optimizations.
How We Improved Scheduler Performance for Large-scale Jobs - Part One
To improve the performance of the scheduler for large-scale jobs, several optimizations were introduced in Flink 1.13 and 1.14. In this blog post we'll take a look at them.