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quick-flink\nApache Flink 学习\n\n### 基本部分\n\nFlink程序看起来像是转换数据集合的普通程序。每个程序包含相同的基本部分：\n\n```json\n1、获取执行环境；\n2、加载/创建初始数据；\n3、指定程序的转换；\n4、指定放置计算结果的位置；\n5、触发程序计算；\n```\n\n\n#### 获取执行环境\n\n在`StreamExecutionEnvironment`(所有Flink执行的基类)，可以使用以下静态方法获取一个StreamExecutionEnvironment\n\n```java\ngetExecutionEnvironment()\n\ncreateLocalEnvironment()\n\ncreateRemoteEnvironment(String host, int port, String... jarFiles)\n```\n\n```scala\ngetExecutionEnvironment()\n\ncreateLocalEnvironment()\n\ncreateRemoteEnvironment(host: String, port: Int, jarFiles: String*)\n```\n\n通常，您只需要使用`getExecutionEnvironment()`，因为这会根据上下文作出正确选择：\n- 如果您在IDE中执行程序或作为普通Java程序，它将创建一个本地环境，将在本地计算机上执行您的程序。\n- 如果把程序打包成JAR包，并通过命令行调用它 ，则Flink集群管理器将执行您的main方法并`getExecutionEnvironment()`返回一个执行环境，以便在集群上执行您的程序。\n\n文件行读取：\n```java\nfinal StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();\n\nDataStream\u003cString\u003e text = env.readTextFile(\"file:///path/to/file\");\n\n```\n\n```scala\nval env = StreamExecutionEnvironment.getExecutionEnvironment()\n\nval text: DataStream[String] = env.readTextFile(\"file:///path/to/file\")\n```\n\n可做一些转换\n```java\nDataStream\u003cString\u003e input = ...;\n\nDataStream\u003cInteger\u003e parsed = input.map(new MapFunction\u003cString, Integer\u003e() {\n    @Override\n    public Integer map(String value) {\n        return Integer.parseInt(value);\n    }\n});\n```\n\n```scala\nval input: DataSet[String] = ...\n\nval mapped = input.map { x =\u003e x.toInt }\n```\n\n只要生成最终结果的DataStream，就可以创建接收器将其写入外部系统，如\n```java\nwriteAsText(String path)\n\nprint()\n```\n\n```scala\nwriteAsText(path: String)\n\nprint()\n```\n\n最后，需要触发程序调用`StreamExecutionEnvironment`的`execute()`,它会根据ExecutionEnvironment执行类型，\n将在本地计算机上触发执行或提交程序以在群集上执行。\n\n该`execute()`方法返回一个`JobExecutionResult`，它包含执行时间和累加器结果。\n\n#### 支持的数据类型\n```text\nJava Tuples and Scala Case Classes\nJava POJOs\nPrimitive Types\nRegular Classes\nValues\nHadoop Writables\nSpecial Types\n```\n\n#### 学习建议\n\n![mark](http://cdn.wangxc.club/blog/20190709/kW6eUgepjfm2.png?imageslim)\n\n#### 分布式缓存\nFlink提供了一个分布式缓存，类似于Apache Hadoop，可以在本地访问用户函数的并行实例。此功能可用于共享包含静态外部数据的文件，如词典或机器学习回归模型。","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvector4wang%2Fquick-flink","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvector4wang%2Fquick-flink","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvector4wang%2Fquick-flink/lists"}