DataStream

Function接口

计算逻辑
Map 等算子都是
�

org.apache.flink.streaming.api.operators包

StreamOperator接口

AbstractStreamOperator#initializeState类。

双流JOIN StreamingJoinOperator类中。

状态

程序入口

Client

执行bin/flink run xxx会调用CliFrontend类的run方法。

FlinkYarnSessionCli

YarnSessionClusterEntrypoint

YarnJobClusterEntrypoint

1.12 以上

 env.execute("Streaming WordCount SideOutput");

下面代码会进行分流

        CompletableFuture<JobClient> jobClientFuture =
                executorFactory
                        .getExecutor(configuration)
                        .execute(streamGraph, configuration, userClassloader);

到底是使用yarn或k8s的什么模式。一个模式实现一个PipelineExecutor接口。

public interface PipelineExecutor {
    /**
     * Executes a {@link Pipeline} based on the provided configuration and returns a {@link
     * JobClient} which allows to interact with the job being executed, e.g. cancel it or take a
     * savepoint.
     *
     * <p><b>ATTENTION:</b> The caller is responsible for managing the lifecycle of the returned
     * {@link JobClient}. This means that e.g. {@code close()} should be called explicitly at the
     * call-site.
     *
     * @param pipeline the {@link Pipeline} to execute
     * @param configuration the {@link Configuration} with the required execution parameters
     * @param userCodeClassloader the {@link ClassLoader} to deserialize usercode
     * @return a {@link CompletableFuture} with the {@link JobClient} corresponding to the pipeline.
     */
    CompletableFuture<JobClient> execute(
            final Pipeline pipeline,
            final Configuration configuration,
            final ClassLoader userCodeClassloader)
            throws Exception;
}

SQL

http://aitozi.com/BinaryRow-implement.html

源码解释

https://blog.csdn.net/hxcaifly/article/details/84558346