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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Apache Flink 1.8.1 Released

The Apache Flink community released the first bugfix version of the Apache Flink 1.8 series.

A Practical Guide to Broadcast State in Apache Flink
Apache Flink has multiple types of operator state, one of which is called Broadcast State. In this post, we explain what Broadcast State is, and show an example of how it can be applied to an application that evaluates dynamic patterns on an event stream.
A Deep-Dive into Flink's Network Stack
Flink’s network stack is one of the core components that make up Apache Flink's runtime module sitting at the core of every Flink job. In this post, which is the first in a series of posts about the network stack, we look at the abstractions exposed to the stream operators and detail their physical implementation and various optimisations in Apache Flink.
State TTL in Flink 1.8.0: How to Automatically Cleanup Application State in Apache Flink
A common requirement for many stateful streaming applications is to automatically cleanup application state for effective management of your state size, or to control how long the application state can be accessed. State TTL enables application state cleanup and efficient state size management in Apache Flink
Flux capacitor, huh? Temporal Tables and Joins in Streaming SQL
Apache Flink natively supports temporal table joins since the 1.7 release for straightforward temporal data handling. In this blog post, we provide an overview of how this new concept can be leveraged for effective point-in-time analysis in streaming scenarios.