flink on yarn 如何通过sql 执行

项目方案: Flink on YARN SQL 执行

1. 简介

Flink on YARN 是 Apache Flink 的一种运行模式，它能够在 Hadoop YARN 集群上运行 Flink 应用程序。Flink on YARN 提供了一种强大的方式来执行 Flink SQL 查询，通过将 SQL 语句转换为 Flink DataStream 或 DataSet API，实现对流数据或批量数据的处理。本文将介绍如何通过 Flink on YARN 来执行 SQL 查询的方案。

2. 安装与配置

首先需要安装配置好 Flink 和 Hadoop YARN 环境。详细的安装与配置步骤可以参考官方文档。

3. 编写 SQL 查询代码

3.1 创建 Flink SQL 环境

我们需要创建一个 Flink SQL 环境，用于执行查询。以下是创建 Flink SQL 环境的示例代码：

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;

StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);

3.2 注册输入表

我们需要将输入数据注册为一个表，以便能够在 SQL 中使用。以下是注册输入表的示例代码：

tableEnv.executeSql("CREATE TABLE orders (\n" +
        "  order_id INT,\n" +
        "  product_id INT,\n" +
        "  order_amount DOUBLE\n" +
        ") WITH (\n" +
        "  'connector' = 'kafka',\n" +
        "  'topic' = 'orders',\n" +
        "  'properties.bootstrap.servers' = 'localhost:9092',\n" +
        "  'format' = 'json'\n" +
        ")");

3.3 执行 SQL 查询

我们可以使用 Flink SQL 语法来编写查询语句，并通过 Flink SQL 环境来执行查询。以下是执行 SQL 查询的示例代码：

tableEnv.executeSql("CREATE VIEW popular_products AS\n" +
        "SELECT product_id, SUM(order_amount) as total_amount\n" +
        "FROM orders\n" +
        "GROUP BY product_id\n" +
        "HAVING SUM(order_amount) > 1000");

tableEnv.executeSql("SELECT * FROM popular_products").print();

4. 提交 Flink on YARN 任务

4.1 编写 Flink YARN 客户端代码

我们需要编写一个客户端程序，用于将 Flink SQL 代码提交给 YARN 集群执行。以下是一个简单的客户端代码示例：

import org.apache.flink.client.deployment.ClusterSpecification;
import org.apache.flink.client.program.ClusterClient;
import org.apache.flink.client.program.PackagedProgram;
import org.apache.flink.client.program.ProgramInvocationException;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.configuration.GlobalConfiguration;
import org.apache.flink.runtime.jobgraph.SavepointRestoreSettings;
import org.apache.flink.yarn.YarnClusterDescriptor;

public class FlinkYarnClient {

    public static void main(String[] args) throws Exception {
        // 创建 YARN 集群描述符
        Configuration globalConfig = GlobalConfiguration.loadConfiguration();
        YarnClusterDescriptor clusterDescriptor = new YarnClusterDescriptor(globalConfig);

        // 配置 YARN 集群描述符
        clusterDescriptor.setName("Flink-on-YARN");
        clusterDescriptor.setLocalJarPath(new Path("/path/to/flink-on-yarn.jar"));
        clusterDescriptor.setFlinkConfiguration(globalConfig);
        clusterDescriptor.setConfiguration(getYarnConfiguration());

        // 创建 Flink 程序包
        PackagedProgram program = new PackagedProgram(new File("/path/to/flink-on-yarn.jar"));

        // 创建集群规格
        ClusterSpecification clusterSpecification = new ClusterSpecification.ClusterSpecificationBuilder()
                .setMasterMemoryMB(1024)
                .setTaskManagerMemoryMB(2048)
                .setNumberTaskManagers(2)
                .setSlotsPerTaskManager(2)
                .createClusterSpecification();

        try {
            // 提交 Flink 程序到 YARN 集群
            ClusterClient<?> clusterClient = clusterDescriptor.deployJobCluster(
                    clusterSpecification,
                    program,
                    false,
                    SavepointRestoreSettings.none());
            
            // 等待任务完成
            clusterClient.waitForClusterToBeReady();
            clusterClient.shutdown();
        } catch (ProgramInvocationException e) {
            e.printStackTrace();
        }
    }

    private static org.apache.hadoop.conf.Configuration getYarnConfiguration() {
        org.apache.hadoop.conf.Configuration yarnConf = new org.apache.hadoop.conf.Configuration();
        yarnConf.addResource(new Path