这篇文章主要介绍“Flink区分运行环境的方法是什么”，在日常操作中，相信很多人在Flink区分运行环境的方法是什么问题上存在疑惑，小编查阅了各式资料，整理出简单好用的操作方法，希望对大家解答”Flink区分运行环境的方法是什么”的疑惑有所帮助！接下来，请跟着小编一起来学习吧！
Flink判断运行环境（本地、集群）的逻辑如下：
（1）在任务的main方法中，通过 StreamExecutionEnvironment 获取运行环境
StreamExecutionEnvironment environment = StreamExecutionEnvironment.getExecutionEnvironment();
（2）生成运行环境的工厂类放在ThreadLocal中；threadLocalContextEnvironmentFactory 是StreamExecutionEnvironment类的静态属性
/** The ThreadLocal used to store {@link StreamExecutionEnvironmentFactory}. */
private static final ThreadLocal<StreamExecutionEnvironmentFactory> threadLocalContextEnvironmentFactory = new ThreadLocal<>();
①当是本地IDE直接运行任务main方法时，ThreadLocal中获取到的StreamExecutionEnvironmentFactory为空，此时生成本地运行环境LocalStreamEnvironment
public static StreamExecutionEnvironment getExecutionEnvironment() {
return Utils.resolveFactory(threadLocalContextEnvironmentFactory, contextEnvironmentFactory)
.map(StreamExecutionEnvironmentFactory::createExecutionEnvironment)
.orElseGet(StreamExecutionEnvironment::createLocalEnvironment);
}
当ThreadLocal中有StreamExecutionEnvironmentFactory时，则用其createExecutionEnvironment()方法来生成运行环境
②当集群环境时，是如何将StreamExecutionEnvironmentFactory放入到ThreadLocal中？
通过 bin/flink run .... 命令提交jar包到集群运行命令时，该脚本会调用 org.apache.flink.client.cli.CliFrontend 来运行用户程序，如下：
.......
.......
# Add HADOOP_CLASSPATH to allow the usage of Hadoop file systems
exec $JAVA_RUN $JVM_ARGS $FLINK_ENV_JAVA_OPTS "${log_setting[@]}" -classpath "`manglePathList "$CC_CLASSPATH:$INTERNAL_HADOOP_CLASSPATHS"`" org.apache.flink.client.cli.CliFrontend "$@"
在CliFrontend中依次执行以下方法 main() -> parseParameters() -> run() -> executeProgram()
protected void executeProgram(final Configuration configuration, final PackagedProgram program) throws ProgramInvocationException {
ClientUtils.executeProgram(new DefaultExecutorServiceLoader(), configuration, program, false, false);
}
在org.apache.flink.client.ClientUtils的executeProgram()中调用 StreamContextEnvironment.setAsContext(...)，StreamContextEnvironment继承自StreamExecutionEnvironment。setAsContext()代码如下
public static void setAsContext(
final PipelineExecutorServiceLoader executorServiceLoader,
final Configuration configuration,
final ClassLoader userCodeClassLoader,
final boolean enforceSingleJobExecution,
final boolean suppressSysout) {
StreamExecutionEnvironmentFactory factory = () -> new StreamContextEnvironment(
executorServiceLoader,
configuration,
userCodeClassLoader,
enforceSingleJobExecution,
suppressSysout);
initializeContextEnvironment(factory);
}
创建生成运行环境的工厂类实例，在initializeContextEnvironment()方法中把实例放到StreamExecutionEnvironment类的静态属性threadLocalContextEnvironmentFactory 中 ，代码如下
protected static void initializeContextEnvironment(StreamExecutionEnvironmentFactory ctx) {
contextEnvironmentFactory = ctx;
threadLocalContextEnvironmentFactory.set(contextEnvironmentFactory);
}
这样在用户程序 StreamExecutionEnvironment.getExecutionEnvironment() 时，获取到的运行环境就是StreamContextEnvironment类的setAsContext()方法中生成的
public static void setAsContext(
final PipelineExecutorServiceLoader executorServiceLoader,
final Configuration configuration,
final ClassLoader userCodeClassLoader,
final boolean enforceSingleJobExecution,
final boolean suppressSysout) {
StreamExecutionEnvironmentFactory factory = () -> new StreamContextEnvironment(
executorServiceLoader,
configuration,
userCodeClassLoader,
enforceSingleJobExecution,
suppressSysout);
......
}
本地运行环境LocalStreamEnvironment 和 独立集群、flink on yarn等运行环境StreamContextEnvironment 的主要区别在于，他们的成员属性 configuration 不同。LocalStreamEnvironment 中是创建的空键值对(new Configuration())，而StreamContextEnvironment 是通过 CliFrontend 生成的 Configuration 对象。
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