This documentation is for an unreleased version of Apache Flink. We recommend you use the latest stable version.

Starting with Flink 1.12 the DataSet API has been soft deprecated.

We recommend that you use the Table API and SQL to run efficient batch pipelines in a fully unified API. Table API is well integrated with common batch connectors and catalogs.

Alternatively, you can also use the DataStream API with BATCH execution mode. The linked section also outlines cases where it makes sense to use the DataSet API but those cases will become rarer as development progresses and the DataSet API will eventually be removed. Please also see FLIP-131 for background information on this decision.

集群执行 #

Flink 程序可以分布式运行在多机器集群上。有两种方式可以将程序提交到集群上执行：

命令行界面（Interface） #

命令行界面使你可以将打包的程序（JARs）提交到集群（或单机设置）。

有关详细信息，请参阅命令行界面文档。

远程环境（Remote Environment） #

远程环境使你可以直接在集群上执行 Flink Java 程序。远程环境指向你要执行程序的集群。

Maven Dependency #

如果将程序作为 Maven 项目开发，则必须添加 flink-clients 模块的依赖：

<dependency>
  <groupId>org.apache.flink</groupId>
  <artifactId>flink-clients</artifactId>
  <version>1.18-SNAPSHOT</version>
</dependency>

示例 #

下面演示了 RemoteEnvironment 的用法：

public static void main(String[] args) throws Exception {
    ExecutionEnvironment env = ExecutionEnvironment
        .createRemoteEnvironment("flink-jobmanager", 8081, "/home/user/udfs.jar");

    DataSet<String> data = env.readTextFile("hdfs://path/to/file");

    data
        .filter(new FilterFunction<String>() {
            public boolean filter(String value) {
                return value.startsWith("http://");
            }
        })
        .writeAsText("hdfs://path/to/result");

    env.execute();
}

请注意，该程序包含用户自定义代码，因此需要一个带有附加代码类的 JAR 文件。远程环境的构造函数使用 JAR 文件的路径进行构造。