Flink Join样例程序（Java）

功能介绍

在Flink应用中，调用flink-connector-kafka模块的接口，生产并消费数据。

代码样例

用户在开发前需要使用对接安全模式的Kafka，则需要引入kafka-clients-*.jar，该jar包可在client目录下获取。

下面列出producer和consumer，以及Flink Stream SQL Join使用主要逻辑代码作为演示。

完整代码参见com.huawei.bigdata.flink.examples.WriteIntoKafka和com.huawei.bigdata.flink.examples.SqlJoinWithSocket

1. 每秒钟往Kafka中生产一条用户信息，用户信息有姓名、年龄、性别组成。

   //producer代码
   public class WriteIntoKafka {
   
         public static void main(String[] args) throws Exception {
   
         // 打印出执行flink run的参考命令
           System.out.println("use command as: ");
   
           System.out.println("./bin/flink run --class com.huawei.bigdata.flink.examples.WriteIntoKafka" +
   
              " /opt/test.jar --topic topic-test -bootstrap.servers 10.91.8.218:9092");
   
           System.out.println("./bin/flink run --class com.huawei.bigdata.flink.examples.WriteIntoKafka" +
   
              " /opt/test.jar --topic topic-test -bootstrap.servers 10.91.8.218:21007 --security.protocol SASL_PLAINTEXT --sasl.kerberos.service.name kafka");
   
   
           System.out.println("******************************************************************************************");
   
           System.out.println("<topic> is the kafka topic name");
   
           System.out.println("<bootstrap.servers> is the ip:port list of brokers");
   
           System.out.println("******************************************************************************************");
          
           // 构造执行环境
           StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
           // 设置并发度
           env.setParallelism(1);
           // 解析运行参数
           ParameterTool paraTool = ParameterTool.fromArgs(args);
           // 构造流图，将自定义Source生成的数据写入Kafka
           DataStream<String> messageStream = env.addSource(new SimpleStringGenerator());
   
           FlinkKafkaProducer<String> producer = new FlinkKafkaProducer<>(paraTool.get("topic"),
   
              new SimpleStringSchema(),
   
              paraTool.getProperties());
   
           producer.setWriteTimestampToKafka(true);
   
           messageStream.addSink(producer);
   
           // 调用execute触发执行
           env.execute();
        }
   
   // 自定义Source，每隔1s持续产生消息
   public static class SimpleStringGenerator implements SourceFunction<String> {
           static final String[] NAME = {"Carry", "Alen", "Mike", "Ian", "John", "Kobe", "James"};
   
           static final String[] SEX = {"MALE", "FEMALE"};
   
           static final int COUNT = NAME.length;   
   
           boolean running = true;
   
           Random rand = new Random(47);
   
          @Override
           //rand随机产生名字，性别，年龄的组合信息
            public void run(SourceContext<String> ctx) throws Exception {
   
               while (running) {
   
                   int i = rand.nextInt(COUNT);
   
                   int age = rand.nextInt(70);
   
                   String sexy = SEX[rand.nextInt(2)];
   
                   ctx.collect(NAME[i] + "," + age + "," + sexy);
   
                   thread.sleep(1000);
   
               }
   
       }
   
          @Override
   
          public void cancel() {
   
            running = false;
   
          }
   
        }
   
      }

2. 生成Table1和Table2，并使用Join对Table1和Table2进行联合查询，打印输出结果。

   public class SqlJoinWithSocket {
       public static void main(String[] args) throws Exception{
   
           final String hostname;
   
           final int port;
   
           System.out.println("use command as: ");
   
           System.out.println("flink run --class com.huawei.bigdata.flink.examples.SqlJoinWithSocket" +
                   " /opt/test.jar --topic topic-test -bootstrap.servers xxxx.xxx.xxx.xxx:9092 --hostname xxx.xxx.xxx.xxx --port xxx");
   
           System.out.println("flink run --class com.huawei.bigdata.flink.examples.SqlJoinWithSocket" +
                   " /opt/test.jar --topic topic-test -bootstrap.servers xxxx.xxx.xxx.xxx:21007 --security.protocol SASL_PLAINTEXT --sasl.kerberos.service.name kafka"
                   + "--hostname xxx.xxx.xxx.xxx --port xxx");
   
           System.out.println("******************************************************************************************");
           System.out.println("<topic> is the kafka topic name");
           System.out.println("<bootstrap.servers> is the ip:port list of brokers");
           System.out.println("******************************************************************************************");
   
           try {
               final ParameterTool params = ParameterTool.fromArgs(args);
   
               hostname = params.has("hostname") ? params.get("hostname") : "localhost";
   
               port = params.getInt("port");
   
           } catch (Exception e) {
               System.err.println("No port specified. Please run 'FlinkStreamSqlJoinExample " +
                       "--hostname <hostname> --port <port>', where hostname (localhost by default) " +
                       "and port is the address of the text server");
   
               System.err.println("To start a simple text server, run 'netcat -l -p <port>' and " +
                       "type the input text into the command line");
   
               return;
           }
           
           EnvironmentSettings fsSettings = EnvironmentSettings.newInstance().useOldPlanner().inStreamingMode().build();
           StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
           StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env, fsSettings);
   
           //基于EventTime进行处理
           env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
   
           env.setParallelism(1);
   
           ParameterTool paraTool = ParameterTool.fromArgs(args);
   
           //Stream1，从Kafka中读取数据
           DataStream<Tuple3<String, String, String>> kafkaStream = env.addSource(new FlinkKafkaConsumer<>(paraTool.get("topic"),
                   new SimpleStringSchema(),
                   paraTool.getProperties())).map(new MapFunction<String, Tuple3<String, String, String>>() {
               @Override
               public Tuple3<String, String, String> map(String s) throws Exception {
                   String[] word = s.split(",");
   
                   return new Tuple3<>(word[0], word[1], word[2]);
               }
           });
   
           //将Stream1注册为Table1
           tableEnv.registerDataStream("Table1", kafkaStream, "name, age, sexy, proctime.proctime");
   
           //Stream2，从Socket中读取数据
           DataStream<Tuple2<String, String>> socketStream = env.socketTextStream(hostname, port, "\n").
                   map(new MapFunction<String, Tuple2<String, String>>() {
                       @Override
                       public Tuple2<String, String> map(String s) throws Exception {
                           String[] words = s.split("\\s");
                           if (words.length < 2) {
                               return new Tuple2<>();
                           }
   
                           return new Tuple2<>(words[0], words[1]);
                       }
                   });
   
           //将Stream2注册为Table2
           tableEnv.registerDataStream("Table2", socketStream, "name, job, proctime.proctime");
   
           //执行SQL Join进行联合查询
           Table result = tableEnv.sqlQuery("SELECT t1.name, t1.age, t1.sexy, t2.job, t2.proctime as shiptime\n" +
                   "FROM Table1 AS t1\n" +
                   "JOIN Table2 AS t2\n" +
                   "ON t1.name = t2.name\n" +
                   "AND t1.proctime BETWEEN t2.proctime - INTERVAL '1' SECOND AND t2.proctime + INTERVAL '1' SECOND");
   
           //将查询结果转换为Stream，并打印输出
           tableEnv.toAppendStream(result, Row.class).print();
   
           env.execute();
       }
   }