Flink流处理API——Sink_flink sink

原文链接：https://www.toutiao.com/i6859235904779715076/

本文主要从以下几个方面介绍Flink的流处理API——Sink

一、输出到Kafka

二、输出到Redis

三、输出到MySQL

数据处理的过程基本可以分为三个阶段分别是，数据从来哪里，做什么业务逻辑，落地到哪里去。

这三部分在Flink中分别被称为Source、Transform和Sink

其中Source部分可以参考这篇：Flink流处理API——Source

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。

Flink支持的Sink

版本：

scala：2.11.12

Kafka：0.8.2.2

Flink：1.7.2

Redis：3.2.9

MySQL：5.7.30

pom.xml依赖部分(log日志的依赖一定要加上，否则当Flink链接Kafka0.8时会报Failed to instantiate SLF4J LoggerFactory Reported exception错误)

<dependencies>
 
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-scala_2.11</artifactId>
            <version>1.7.2</version>
        </dependency>
 
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-scala_2.11</artifactId>
            <version>1.7.2</version>
            <scope>provided</scope>
        </dependency>
 
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_2.11</artifactId>
            <version>1.7.2</version>
        </dependency>
 
        <!-- https://mvnrepository.com/artifact/org.apache.flink/flink-connector-kafka-0.10 -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-kafka-0.8_2.11</artifactId>
            <version>1.7.2</version>
        </dependency>
 
 
        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-api</artifactId>
            <version>1.7.22</version>
        </dependency>
 
        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-log4j12</artifactId>
            <version>1.7.22</version>
        </dependency>
 
        <dependency>
            <groupId>org.apache.bahir</groupId>
            <artifactId>flink-connector-redis_2.11</artifactId>
            <version>1.0</version>
        </dependency>
 
        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>5.1.38</version>
        </dependency>
 
 
    </dependencies>

一、输出到Kafka

Flink和Kafka天生是一对，Sink到Kafka相当方便

package xxx
 
import org.apache.flink.api.common.serialization.SimpleStringSchemaimport org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.connectors.kafka._
 
object SinkDataToKafka {
  def main(args: Array[String]): Unit = {
 
    val environment: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment    
    // source    
    val value: DataStream[String] = environment.readTextFile("src\\main\\resources\\sensor.txt")
 
    // transform操作    
    val maped: DataStream[String] = value.map(line => {
      val fildes: Array[String] = line.split(",")  // 这里的split是scala的split方法      
      SensorReading(fildes(0).trim, fildes(1).trim.toLong, fildes(2).trim.toDouble).toString   // 转成String方便输出    
    })
 
    // FlinkKafkaProducer08参数：brokerList，topicID，序列化机制    
  maped.addSink(new FlinkKafkaProducer08[String](
    "slave1:9092,slave2:9092,slave3:9092", "out", new SimpleStringSchema()))
 
    environment.execute()
  }
}

二、输出到Redis

redis：在slave4，端口：6379，密码;123

package xxx
 
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.connectors.redis.RedisSink
import org.apache.flink.streaming.connectors.redis.common.config.FlinkJedisPoolConfig
import org.apache.flink.streaming.connectors.redis.common.mapper.{RedisCommand, RedisCommandDescription, RedisMapper}
 
object SinkDataToRedis {
  def main(args: Array[String]): Unit = {
    val environment: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
 
    // source
    val value: DataStream[String] = environment.readTextFile("src\\main\\resources\\sensor.txt")
 
    // transform操作
    val maped: DataStream[SensorReading] = value.map(line => {
      val fildes: Array[String] = line.split(",")  // 这里的split是scala的split方法
      SensorReading(fildes(0).trim, fildes(1).trim.toLong, fildes(2).trim.toDouble)
    })
 
    // redis的链接参数
    val conf = new FlinkJedisPoolConfig.Builder().setHost("slave4").setPort(6379).setPassword("123").build()
 
    maped.addSink(new RedisSink[SensorReading](conf, new MyRedisMapper())) // 用hgetall sensor 命令查看值
 
    environment.execute()
  }
}
 
class MyRedisMapper extends RedisMapper[SensorReading]{
  // 定义保存数据到redis的命令
  override def getCommandDescription: RedisCommandDescription = {
    new RedisCommandDescription(RedisCommand.HSET, "sensor")
  }
 
  // 保存到redis的key
  override def getKeyFromData(t: SensorReading): String = {
    t.id
  }
 
  // 保存到redis的value
  override def getValueFromData(t: SensorReading): String = {
    t.temperature.toString
  }
}

三、输出到MySQL

表信息：slave3,端口：3306，用户名：root 密码：root

库名：sensor_db 表名：sensor

字段信息：

package xxx
 
import java.sql.{Connection, DriverManager, PreparedStatement}
 
import org.apache.flink.configuration.Configuration
import org.apache.flink.streaming.api.functions.sink.{RichSinkFunction, SinkFunction}
import org.apache.flink.streaming.api.scala._
 
object SinkDataToMySQL {
  def main(args: Array[String]): Unit = {
    val environment: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
 
    // source
    val value: DataStream[String] = environment.readTextFile("src\\main\\resources\\sensor.txt")
 
    // transform操作
    val maped: DataStream[SensorReading] = value.map(line => {
      val fildes: Array[String] = line.split(",")  // 这里的split是scala的split方法
      SensorReading(fildes(0).trim, fildes(1).trim.toLong, fildes(2).trim.toDouble)
    })
 
    maped.addSink(new MyJDBCSink())
 
    environment.execute()
  }
 
}
 
class MyJDBCSink() extends RichSinkFunction[SensorReading]{
  // 定义SQL连接、定义预编译器
  var connect: Connection = _
  var insertStrmt: PreparedStatement = _
  var updateStrmt : PreparedStatement = _
 
  // 初始化，创建链接和预编译语句
  override def open(parameters: Configuration): Unit = {
    super.open(parameters)
    connect = DriverManager.getConnection("jdbc:mysql://slave3:3306/sensor_db?useUnicode=true&characterEncoding=UTF-8", "root", "root")
    insertStrmt = connect.prepareStatement("INSERT  INTO sensors(id, temp) VALUES (?, ?)")
    updateStrmt = connect.prepareStatement("UPDATE sensors set temp = ? where id = ?")
  }
 
  // 调用链接，执行sql
  override def invoke(value: SensorReading, context: SinkFunction.Context[_]): Unit = {
    // 执行更新语句
    updateStrmt.setDouble(1, value.temperature)  // setDouble表示数据的类型，1表示第几个？，value.temperature表示赋值
    updateStrmt.setString(2, value.id)
    updateStrmt.execute()
 
    // update没有查到数据，那么执行插入语句
    if (updateStrmt.getUpdateCount == 0){
      insertStrmt.setDouble(2, value.temperature)
      insertStrmt.setString(1, value.id)
      insertStrmt.execute()
    }
  }
  
// 关闭链接
  override def close(): Unit = {
    insertStrmt.close()
    updateStrmt.close()
    connect.close()
  }
 
}