Flink学习系列之五 DataStream API之Sink

sink是将数据源最终写入文件或者数据库或者其他中间件当中。

1 DataStream API之Data Sink

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】

上述1在之前写过。将数据写入txt。print为打印到控制台。第三则是将数据写入kafka和redis中。

2 内置Connectors

   可以将数据写入如下的消息中间件或者Nosql数据库当中。下面中间件是对Flink sink的支持。

• Apache Kafka (source/sink)
• Apache Cassandra (sink)
• Elasticsearch (sink)
• Hadoop FileSystem (sink)
• RabbitMQ (source/sink)
• Apache ActiveMQ (source/sink)
• Redis (sink)

3 Sink 容错性保证

4 自定义sink

实现自定义的sink 实现SinkFunction接口 或者继承RichSinkFunction 参考org.apache.flink.streaming.connectors.redis.RedisSink

下面的demo是将数据写入redis当中。通过网络socket获取数据源。

添加redis flink的依赖

        
            org.apache.bahir
            flink-connector-redis_2.11
            1.0
        

代码如下：

package com.caozg.stream.sink;

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.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;

/**
 * 接收socket数据，把数据保存到redis中
 *
 * list
 *
 * lpush list_key value
 *
 */
public class StreamingDemoToRedis {

    public static void main(String[] args) throws Exception{
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        DataStreamSource text = env.socketTextStream("127.0.0.1", 15000, "\n");

        //lpsuh l_words word

        //对数据进行组装,把string转化为tuple2
        DataStream> l_wordsData = text.map(new MapFunction>() {

            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("127.0.0.1")
                .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

        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

        public String getValueFromData(Tuple2 data) {
            return data.f1;
        }


        public RedisCommandDescription getCommandDescription() {
            return new RedisCommandDescription(RedisCommand.LPUSH);
        }
    }

}

通过nc -l 15000。然后生产单词数据。打开redis客户端，使用monitor监控命令，可以看到数据的变化：

caozg@caozg-PC:/usr/local/redis/bin$ ./redis-cli 
127.0.0.1:6379> 
127.0.0.1:6379> monitor
OK
1572572362.919833 [0 127.0.0.1:37358] "LPUSH" "l_words" "heloo"
1572572373.034903 [0 127.0.0.1:37366] "LPUSH" "l_words" "kkk"
1572572376.342446 [0 127.0.0.1:37374] "LPUSH" "l_words" "mon"
1572572377.745541 [0 127.0.0.1:37376] "LPUSH" "l_words" "dhshf"
1572572401.888554 [0 127.0.0.1:37388] "auth" "123456"

最后使用工具可以看下redis: