Flink笔记9：Flink流处理API之Sink

Flink 没有类似于spark 中foreach 方法，让用户进行迭代的操作。虽有对外的输出操作都要利用Sink 完成。最后通过类似如下方式完成整个任务最终输出操作。

stream.addSink(new MySink(xxxx))

官方提供了一部分的框架的sink。除此以外，需要用户自定义实现sink。

1、Kafka

pom.xml

<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-connector-kafka-0.11_2.12</artifactId>
    <version>1.10.1</version>
</dependency>

主函数中添加sink：

val union = high.union(low).map(_.temperature.toString)
union.addSink(new FlinkKafkaProducer011[String]("localhost:9092","test", new SimpleStringSchema()))

注：我们要输出数据到kafka，所以使用的是生产者。

2、Redis

pom.xml

<dependency>
    <groupId>org.apache.bahir</groupId>
    <artifactId>flink-connector-redis_2.11</artifactId>
    <version>1.0</version>
</dependency>

定义一个redis 的mapper 类，用于定义保存到redis 时调用的命令：

// 定义一个RedisMapper
class MyRedisMapper extends RedisMapper[SensorReading]{
  // 定义保存数据写入redis的命令，HSET 表名 key value
  override def getCommandDescription: RedisCommandDescription = {
    new RedisCommandDescription(RedisCommand.HSET, "sensor_temp")
  }

  // 将温度值指定为value
  override def getValueFromData(data: SensorReading): String = data.temperature.toString

  // 将id指定为key
  override def getKeyFromData(data: SensorReading): String = data.id
}

注：sensor_temp是表名

在主函数中调用：

// 定义一个FlinkJedisConfigBase
val conf = new FlinkJedisPoolConfig.Builder()
  .setHost("localhost")
  .setPort(6379)
  .build()

dataStream.addSink( new RedisSink[SensorReading]( conf, new MyRedisMapper ) )

3、Elasticsearch

pom.xml

<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-connector-elasticsearch6_2.12</artifactId>
    <version>1.10.1</version>
</dependency>

在主函数中调用：

	// 定义HttpHosts
    val httpHosts = new util.ArrayList[HttpHost]()
    httpHosts.add(new HttpHost("localhost", 9200))

    // 自定义写入es的EsSinkFunction
    val myEsSinkFunc = new ElasticsearchSinkFunction[SensorReading] {
      override def process(t: SensorReading, runtimeContext: RuntimeContext, requestIndexer: RequestIndexer): Unit = {
        // 包装一个Map作为data source
        val dataSource = new util.HashMap[String, String]()
        dataSource.put("id", t.id)
        dataSource.put("temperature", t.temperature.toString)
        dataSource.put("ts", t.timestamp.toString)

        // 创建index request，用于发送http请求
        val indexRequest = Requests.indexRequest()
          .index("sensor")
          .`type`("readingdata")
          .source(dataSource)

        // 用indexer发送请求
        requestIndexer.add(indexRequest)

      }
    }

    dataStream.addSink(new ElasticsearchSink
      .Builder[SensorReading](httpHosts, myEsSinkFunc)
      .build()
    )

4、JDBC 自定义sink

<dependency>
    <groupId>mysql</groupId>
    <artifactId>mysql-connector-java</artifactId>
    <version>5.1.44</version>
</dependency>

添加MyJdbcSink

class MyJdbcSinkFunc() extends RichSinkFunction[SensorReading]{
  // 定义连接、预编译语句
  var conn: Connection = _
  var insertStmt: PreparedStatement = _
  var updateStmt: PreparedStatement = _

  override def open(parameters: Configuration): Unit = {
    conn = DriverManager.getConnection("jdbc:mysql://localhost:3306/test", "root", "123456")
    insertStmt = conn.prepareStatement("insert into sensor_temp (id, temp) values (?, ?)")
    updateStmt = conn.prepareStatement("update sensor_temp set temp = ? where id = ?")
  }

  override def invoke(value: SensorReading, context: SinkFunction.Context[_]): Unit = {
    // 先执行更新操作，查到就更新
    updateStmt.setDouble(1, value.temperature)
    updateStmt.setString(2, value.id)
    updateStmt.execute()
    // 如果更新没有查到数据，那么就插入
    if( updateStmt.getUpdateCount == 0 ){
      insertStmt.setString(1, value.id)
      insertStmt.setDouble(2, value.temperature)
      insertStmt.execute()`在这里插入代码片`
    }
  }

  override def close(): Unit = {
    insertStmt.close()
    updateStmt.close()
    conn.close()
  }
}

在main 方法中增加，把明细保存到mysql 中

dataStream.addSink(new MyJdbcSinkFunc())

5、文件

	//方法一
    dataStream.writeAsCsv("D:\\Projects\\BigData\\FlinkTutorial\\src\\main\\resources\\out.txt")
    //方法二，推荐
    dataStream.addSink(
      StreamingFileSink.forRowFormat(
        new Path("D:\\Projects\\BigData\\\\FlinkTutorial\\src\\main\\resources\\out1.txt"),
        new SimpleStringEncoder[SensorReading]()
      ).build()
    )