flink-table_2.11-1.7.0-sources.jar!/org/apache/flink/table/api/table.scala

flink-table_2.11-1.7.0-sources.jar!/org/apache/flink/table/api/table.scala

flink-table_2.11-1.7.0-sources.jar!/org/apache/flink/table/api/table.scala

flink-table_2.11-1.7.0-sources.jar!/org/apache/flink/table/api/TableEnvironment.scala

序 本文主要研究一下flink的Table API及SQL Programs 实例 {代码...} 本实例展示了flink的Table API及SQL Programs的基本用法 Table API实例 {代码...} 通过tableEnv.scan方法来创建Table，之后使用Table的各种查询api sqlQuery实例 {代码...} sqlQuery内部是使用Apache Calcite来实现的 sqlUpdate实例(TableSink) {代码...

flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/api/operators/async/AsyncWaitOperator.java

本实例展示了flink Async I/O的基本用法，首先是实现AsyncFunction接口，用于编写异步请求逻辑及将结果或异常设置到resultFuture，然后就是使用AsyncDataStream的unorderedWait或orderedWait方法将AsyncFunction作用到DataStream作为transformation；AsyncDataStream的unorderedWait或orderedWait有两个关于async operat...

flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/api/operators/InternalTimeServiceManager.java

flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/api/TimerService.java

本实例展示了如何在ProcessFunction里头使用keyed state以及timer；process方法使用的ProcessFunction是CountWithTimeoutFunction

本实例展示了IterativeStream的一些基本用法，使用iterate创建IterativeStream，使用IterativeStream的closeWith方法来关闭feedbackStream

flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/api/datastream/DataStream.java

flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/api/datastream/DataStream.java

flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/api/datastream/DataStream.java

flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/api/datastream/KeyedStream.java

这里首先调用join，与另外一个stream合并，返回的是JoinedStreams，之后就可以调用JoinedStreams的where操作来构建Where对象构造条件；Where有equalTo操作可以构造EqualTo，而EqualTo有window操作可以构造WithWindow，而WithWindow可以设置windowAssigner、trigger、evictor、allowedLateness，它提供apply操作

本实例首先根据key进行partition，然后再按指定的window对这些key进行计数，之后对该dataStream进行windowAll操作，其时间WindowAssigner与前面的相同，这样可以达到在同样的时间窗口内先partition汇总，再全局汇总的效果(可以解决类似top-k elements的问题)

flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/api/datastream/WindowedStream.java

flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/api/windowing/evictors/Evictor.java

flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/api/windowing/triggers/Trigger.java

flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/api/windowing/windows/GlobalWindow.java

flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/api/windowing/assigners/MergingWindowAssigner.java

flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/api/windowing/assigners/SlidingEventTimeWindows.java

flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/api/windowing/assigners/WindowAssigner.java

flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/api/datastream/DataStream.java

整体而言，今年技术层面稍微有点拓宽，跨入了外表看上去高大上的流式计算领域，打开了另外一扇窗；而基于java的分布式/微服务领域，今年变化比较大，spring cloud netflix的部分组件宣布将要进入维护阶段，而国内spring cloud alibaba组件逐渐活跃起来，目前看来处于PublicEvolving阶段；而java自身也处在不断进化中，今...

flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/api/datastream/KeyedStream.java

flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/api/datastream/KeyedStream.java

flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/api/datastream/KeyedStream.java

CountWindowAverage通过ValueStateDescriptor的setQueryable("query-name")方法，将state声明为是queryable的