Flink读写Redis（一）-写入Redis

项目pom文件

<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <groupId>com.jike.flink</groupId>
    <artifactId>flink-demo</artifactId>
    <version>1.0-SNAPSHOT</version>

    <properties>
        <maven.compiler.source>1.8</maven.compiler.source>
        <maven.compiler.target>1.8</maven.compiler.target>
        <encoding>UTF-8</encoding>
        <flink.version>1.10.0</flink.version>
    </properties>

    <dependencies>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>${flink.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java_2.11</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>

        <!-- flink 11中需要手动添加
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_2.11</artifactId>
            <version>1.11.2</version>
        </dependency>
        -->

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-redis_2.11</artifactId>
            <version>1.1.5</version>
            <scope>system</scope>
            <systemPath>${basedir}/lib/flink-connector-redis_2.11-1.1.5.jar</systemPath>
        </dependency>

        <dependency>
            <groupId>redis.clients</groupId>
            <artifactId>jedis</artifactId>
            <version>2.8.0</version>
            <scope>compile</scope>
        </dependency>

    </dependencies>

</project>

实现flink写入redis

实现wordcount功能，并将结果实时写入redis，这里使用了第三方依赖flink-connector-redis_2.11，该依赖提供了RedisSink可以直接使用，具体代码如下：

代码

首先定义数据源处理实现类LineSplitter，该类将一行数据分词，输出<单词，1>元祖

package com.jike.flink.examples.redis;

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.util.Collector;

public class LineSplitter implements FlatMapFunction<String, Tuple2<String,Integer>> {
    public void flatMap(String s, Collector<Tuple2<String, Integer>> collector) throws Exception {
        String[] tokens = s.toLowerCase().split("\W+");
        for(String token : tokens){
            if(token.length() > 0){
                collector.collect(new Tuple2<String,Integer>(token,1));
            }
        }
    }
}

然后定义数据写入Redis的配置类，这里面将统计后的所有信息词频写入一个哈希表，哈希表的key为"flink"，作为测试使用，哈希表中每个元素key为单词，value为词频

package com.jike.flink.examples.redis;

import org.apache.flink.api.java.tuple.Tuple2;
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;

public class SinkRedisMapper implements RedisMapper<Tuple2<String,Integer>> {
    @Override
    public RedisCommandDescription getCommandDescription() {
        //hset
        return new RedisCommandDescription(RedisCommand.HSET,"flink");
    }

    @Override
    public String getKeyFromData(Tuple2<String, Integer> stringIntegerTuple2) {
        return stringIntegerTuple2.f0;
    }

    @Override
    public String getValueFromData(Tuple2<String, Integer> stringIntegerTuple2) {
        return stringIntegerTuple2.f1.toString();
    }
}

最后编写主程序类，该类中使用了socketTextStream数据源，通过前面定义LineSplitter完成解析，然后根据单词进行分组统计，最后写入redis

package com.jike.flink.examples.redis;

import org.apache.flink.api.java.functions.KeySelector;
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;

public class Sink2Redis {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment executionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment();
        DataStreamSource<String> dataStreamSource = executionEnvironment.socketTextStream("实际IP",12345);
        DataStream<Tuple2<String,Integer>> counts = dataStreamSource.flatMap(new LineSplitter()).keyBy(new KeySelector<Tuple2<String, Integer>, String>() {
            public String getKey(Tuple2<String, Integer> stringIntegerTuple2) throws Exception {
                return stringIntegerTuple2.f0;
            }
        }).sum(1);
         //控制台打印
        counts.print().setParallelism(1);
        //定义redis服务器信息
        FlinkJedisPoolConfig conf = new FlinkJedisPoolConfig.Builder().setHost("redis服务器ip").setPort(redis服务端口).setPassword("redis服务密码").build();
        counts.addSink(new RedisSink<>(conf,new SinkRedisMapper()));
        executionEnvironment.execute();
    }
}

运行效果

通过nc -l 12345，命令模拟数据源，并输入一些数据

IDEA中查看打印记录

查看redis

可以发现数据已写入redis

总结

flink-connector-redis_2.11中提供了RedisSink类，该类实现了RichSinkFunction，可以直接使用，如果有特殊需求，可以自定义Sink类，继承RichSinkFunction，实现特殊处理。flink-connector-redis_2.11的源码比较简洁，下一篇打算分析学习下。