这期内容当中小编将会给大家带来有关Flink中怎么自定义redis的Sink函数，文章内容丰富且以专业的角度为大家分析和叙述，阅读完这篇文章希望大家可以有所收获。
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1.添加redis对应pom依赖
org.apache.bahir flink-connector-redis_2.11 1.0
2.主函数代码：
package com.hadoop.ljs.flink110.redis;import org.apache.flink.api.common.functions.FilterFunction;import org.apache.flink.api.common.functions.MapFunction;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;import scala.Tuple2;/** * @author: Created By lujisen * @company ChinaUnicom Software JiNan * @date: 2020-05-02 10:30 * @version: v1.0 * @description: com.hadoop.ljs.flink110.redis */public class RedisSinkMain { public static void main(String[] args) throws Exception { StreamExecutionEnvironment senv =StreamExecutionEnvironment.getExecutionEnvironment();
DataStream source = senv.socketTextStream("localhost", 9000); DataStream filter = source.filter(new FilterFunction() { @Override public boolean filter(String value) throws Exception { if (null == value || value.split(",").length != 2) { return false; } return true; } }); DataStream> keyValue = filter.map(new MapFunction>() { @Override public Tuple2 map(String value) throws Exception {
String[] split = value.split(",");
return new Tuple2<>(split[0], split[1]); } }); //创建redis的配置 单机redis用FlinkJedisPoolConfig,集群redis需要用FlinkJedisClusterConfig FlinkJedisPoolConfig redisConf = new FlinkJedisPoolConfig.Builder().setHost("worker2.hadoop.ljs").setPort(6379).setPassword("123456a?").build();
keyValue.addSink(new RedisSink>(redisConf, new RedisMapper>() { @Override public RedisCommandDescription getCommandDescription() { return new RedisCommandDescription(RedisCommand.HSET,"table1"); } @Override public String getKeyFromData(Tuple2 data) { return data._1; } @Override public String getValueFromData(Tuple2 data) { return data._2; } })); /*启动执行*/ senv.execute(); }}
3.函数测试
1).window端scoket发送数据
2.redis结果验证
上述就是小编为大家分享的Flink中怎么自定义Redis的Sink函数了，如果刚好有类似的疑惑，不妨参照上述分析进行理解。如果想知道更多相关知识，欢迎关注创新互联行业资讯频道。
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