通过使用 Flink DataStream Connectors 数据流连接器连接到 Redis 缓存数据库，并提供数据流输入与输出操作；

示例环境

java.version: 1.8.x
flink.version: 1.11.1
redis：3.2

示例数据源 （项目码云下载）

Flink 系例 之 搭建开发环境与数据

示例模块 （pom.xml）

Flink 系例 之 DataStream Connectors 与 示例模块

数据流输入

DataStreamSource.java

package com.flink.examples.redis;import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.RichSourceFunction;
import redis.clients.jedis.Jedis;
import redis.clients.jedis.JedisPool;
import redis.clients.jedis.JedisPoolConfig;
import redis.clients.jedis.Protocol;/*** @Description 从redis中读取数据输出到DataStream数据流中*/publicclass DataStreamSource {/*** 官方文档：https://bahir.apache.org/docs/flink/current/flink-streaming-redis/*/publicstaticvoid main(String[] args) throws Exception {final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();String key = "props";//实现RichSourceFunction抽象方法，加载数据源数据到流中DataStream<Tuple2<String, String>> dataStream = env.addSource(new RichSourceFunction<Tuple2<String, String>>(){private JedisPool jedisPool = null;@Overridepublicvoid run(SourceContext<Tuple2<String, String>> ctx) throws Exception {jedisPool = new JedisPool(new JedisPoolConfig(), "127.0.0.1", 6379, Protocol.DEFAULT_TIMEOUT);Jedis jedis = jedisPool.getResource();try{ctx.collect(Tuple2.of(key, jedis.get(key)));}catch(Exception e){e.printStackTrace();}finally{if (jedis != null){//用完即关，内部会做判断，如果存在数据源与池，则回滚到池中jedis.close();}}}@Overridepublicvoid cancel() {try {super.close();}catch(Exception e){}if (jedisPool != null){jedisPool.close();jedisPool = null;}}});dataStream.print();env.execute("flink redis source");}}

数据流输出

DataStreamSink.java

package com.flink.examples.redis;import org.apache.commons.lang3.RandomUtils;
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.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;/*** @Description 将数据流写入到redis中*/publicclass DataStreamSink {/*** 官方文档：https://bahir.apache.org/docs/flink/current/flink-streaming-redis/*/publicstaticvoid main(String[] args) throws Exception {final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();//1.写入数据到流中String [] words = newString[]{"props","student","build","name","execute"};DataStream<Tuple2<String, Integer>> sourceStream = env.fromElements(words).map(new MapFunction<String, Tuple2<String, Integer>>() {@Overridepublic Tuple2<String, Integer> map(String v) throws Exception {return Tuple2.of(v, RandomUtils.nextInt(1000, 9999));}});sourceStream.print();//2.实例化FlinkJedisPoolConfig 配置redisFlinkJedisPoolConfig conf = new FlinkJedisPoolConfig.Builder().setHost("127.0.0.1").setPort(6379).build();//3.写入到redis，实例化RedisSink，并通过flink的addSink的方式将flink计算的结果插入到redissourceStream.addSink(new RedisSink<>(conf, new RedisMapper<Tuple2<String, Integer>>(){@Overridepublic RedisCommandDescription getCommandDescription() {returnnew RedisCommandDescription(RedisCommand.SET, null);//通过实例化传参，设置hash值的key//return new RedisCommandDescription(RedisCommand.HSET, key);}@OverridepublicString getKeyFromData(Tuple2<String, Integer> tuple2) {return tuple2.f0;}@OverridepublicString getValueFromData(Tuple2<String, Integer> tuple2) {return tuple2.f1.toString();}}));env.execute("flink redis sink");}}

数据展示