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IoTDB 与 Apache Flinkopen in new window 的集成。此模块包含了 iotdb sink,允许 flink job 将时序数据写入 IoTDB。

IoTDBSink

使用 IoTDBSink ,您需要定义一个 IoTDBOptions 和一个 IoTSerializationSchema 实例。 IoTDBSink 默认每次发送一个数据,可以通过调用 withBatchSize(int) 进行调整。

示例

该示例演示了如下从一个 Flink job 中发送数据到 IoTDB server 的场景:

  • 一个模拟的 Source SensorSource 每秒钟产生一个数据点。

  • Flink 使用 IoTDBSink 消费产生的数据并写入 IoTDB 。

    import org.apache.iotdb.tsfile.file.metadata.enums.CompressionType;
    import org.apache.iotdb.tsfile.file.metadata.enums.TSDataType;
    import org.apache.iotdb.tsfile.file.metadata.enums.TSEncoding;
    
    import com.google.common.collect.Lists;
    import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
    import org.apache.flink.streaming.api.functions.source.SourceFunction;
    
    import java.security.SecureRandom;
    import java.util.HashMap;
    import java.util.Map;
    import java.util.Random;
    
    public class FlinkIoTDBSink {
      public static void main(String[] args) throws Exception {
        // run the flink job on local mini cluster
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
    
        IoTDBOptions options = new IoTDBOptions();
        options.setHost("127.0.0.1");
        options.setPort(6667);
        options.setUser("root");
        options.setPassword("root");
        options.setStorageGroup("root.sg");
    
        // If the server enables auto_create_schema, then we do not need to register all timeseries
        // here.
        options.setTimeseriesOptionList(
            Lists.newArrayList(
                new IoTDBOptions.TimeseriesOption(
                    "root.sg.d1.s1", TSDataType.DOUBLE, TSEncoding.GORILLA, CompressionType.SNAPPY)));
    
        IoTSerializationSchema serializationSchema = new DefaultIoTSerializationSchema();
        IoTDBSink ioTDBSink =
            new IoTDBSink(options, serializationSchema)
                // enable batching
                .withBatchSize(10)
                // how many connectons to the server will be created for each parallelism
                .withSessionPoolSize(3);
    
        env.addSource(new SensorSource())
            .name("sensor-source")
            .setParallelism(1)
            .addSink(ioTDBSink)
            .name("iotdb-sink");
    
        env.execute("iotdb-flink-example");
      }
    
      private static class SensorSource implements SourceFunction<Map<String, String>> {
        boolean running = true;
        Random random = new SecureRandom();
    
        @Override
        public void run(SourceContext context) throws Exception {
          while (running) {
            Map<String, String> tuple = new HashMap();
            tuple.put("device", "root.sg.d1");
            tuple.put("timestamp", String.valueOf(System.currentTimeMillis()));
            tuple.put("measurements", "s1");
            tuple.put("types", "DOUBLE");
            tuple.put("values", String.valueOf(random.nextDouble()));
    
            context.collect(tuple);
            Thread.sleep(1000);
          }
        }
    
        @Override
        public void cancel() {
          running = false;
        }
      }
    }
    
    

运行方法

  • 启动 IoTDB server
  • 运行 org.apache.iotdb.flink.FlinkIoTDBSink.java 将 Flink job 运行在本地的集群上。

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