【06】Flink之DataStreamAPI（四）：DataSink和自定义Sink

1、DataStream API（四）：Data Sink

1.1、Data Sink介绍

• sink是程序的数据输出，可以通过StreamExecutionEnvironment.addSink(sinkFunction)来为程序添加一个sink。
• flink提供了大量的已经实现好的sink方法，也可以自定义sink

1.2、Data Sink类型

1. writeAsText()：将元素以字符串形式逐行写入，这些字符串通过调用每个元素的toString()方法来获取
2. print() / printToErr()：打印每个元素的toString()方法的值到标准输出或者标准错误输出流中
3. 自定义输出addSink【kafka、redis】

1.3、Source容错性保证

1.4、Flink内置Connectors

• A p a c h e K a f k a ( s o u r c e / s i n k ) \color{red}{Apache Kafka (source/sink)}ApacheKafka(source/sink)
• Apache Cassandra (sink)
• Elasticsearch (sink)
• Hadoop FileSystem (sink)
• RabbitMQ (source/sink)
• Apache ActiveMQ (source/sink)
• R e d i s ( s i n k ) \color{red}{Redis (sink)}Redis(sink)

2、自定义Sink

2.1、实现方法即依赖

• 实现自定义的sink
  (1)、实现SinkFunction接口
  (2)、或者继承RichSinkFunction
• 参考org.apache.flink.streaming.connectors.redis.RedisSink

2.2、实例：Redis Sink

2.2.1、相关依赖

需要添加依赖
Maven仓库
搜索 flink-connector-redis 依赖：


org.apache.bahir
flink-connector-redis_${scala.version}
1.0


2.2.2、Java代码实现

完整程序:

package com.Streaming.custormSink;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
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;
import org.apache.flink.streaming.connectors.redis.common.mapper.RedisCommandDescription;
import org.apache.flink.streaming.connectors.redis.common.mapper.RedisMapper;
/**
* @Author: Henry
* @Description: 接收socket数据，把数据保存到redis中（list格式）
* 保存到Redis中数据一般采用两种格式：list或hashmap
*
* lpush list_key value
* @Date: Create in 2019/5/12 22:29
**/
public class StreamingDemoToRedis {
public static void main(String[] args) throws Exception{
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
DataStreamSource text = env.socketTextStream(
"master", 9000, "\n");
//input: l_words word , 其中 l_words 代表 list 类型
//对数据进行组装,把string转化为tuple2
DataStream> l_wordsData = text.map(
new MapFunction>() {
@Override
public Tuple2 map(String value) throws Exception {
return new Tuple2<>("l_words", value);
}
});
//创建redis的配置
FlinkJedisPoolConfig cOnf= new FlinkJedisPoolConfig.Builder()
.setHost("master").setPort(6379).build();
//创建redissink
RedisSink> redisSink = new RedisSink<>(
conf, new MyRedisMapper());
l_wordsData.addSink(redisSink);
env.execute("StreamingDemoToRedis");
}
public static class MyRedisMapper implements RedisMapper> {
//表示从接收的数据中获取需要操作的redis key
@Override
public String getKeyFromData(Tuple2 data) {
return data.f0;
}
//表示从接收的数据中获取需要操作的redis value
@Override
public String getValueFromData(Tuple2 data) {
return data.f1;
}
@Override
public RedisCommandDescription getCommandDescription() {
return new RedisCommandDescription(RedisCommand.LPUSH);
}
}
}

2.2.3、Scala代码实现

完整代码如下：

package cn.Streaming.custormSink
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
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}
/**
* @Author: Henry
* @Description:
* @Date: Create in 2019/5/14 22:37
**/
object StreamingDataToRedisScala {
def main(args: Array[String]): Unit = {
//获取socket端口号
val port = 9000
//获取运行环境
val env: StreamExecutiOnEnvironment= StreamExecutionEnvironment.getExecutionEnvironment
//链接socket获取输入数据
val text = env.socketTextStream("master",port,'\n')
//注意：必须要添加这一行隐式转行，否则下面的flatmap方法执行会报错
import org.apache.flink.api.scala._
val l_wordsData = text.map(line=>
("l_words_scala",line))
val cOnf= new FlinkJedisPoolConfig.Builder()
.setHost("master")
.setPort(6379)
.build()
val redisSink = new RedisSink[Tuple2[String,String]](conf,new MyRedisMapper)
l_wordsData.addSink(redisSink)
//执行任务
env.execute("Socket window count")
}
class MyRedisMapper extends RedisMapper[Tuple2[String,String]]{
override def getKeyFromData(data: (String, String)) = {
data._1
}
override def getValueFromData(data: (String, String)) = {
data._2
}
override def getCommandDescription = {
new RedisCommandDescription(RedisCommand.LPUSH) // 具体操作命令
}
}
}

2.2.4、运行结果

先在一个终端启动redis server服务：

./src/redis-server

再在另一个终端连接服务：

./src/redis-cli

开启socket终端：

nc -l 9000

在IDEA中点击“Run”运行代码：

通过 nc 终端输入数据，查询 redis 数据库：