Flink：下沉Sink常用API_flink sink api

依赖

<!--flink核心包-->
<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-java</artifactId>
    <version>1.7.2</version>
</dependency>
<!--flink流处理包-->
<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-streaming-java_2.12</artifactId>
    <version>1.7.2</version>
    <scope>provided</scope>
</dependency>	

DataStream和DataSet的用法一样

// DataStream
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
 
 
// DataSet
ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

Flink针对DataStream提供了大量的已经实现的数据目的地（Sink），具体如下所示

• writeAsText()：讲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

• 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

• 自定义输出：addSink可以实现把数据输出到第三方存储介质中

  Flink提供了一批内置的Connector，其中有的Connector会提供对应的Sink支持

案例：将流数据下沉到redis中

1、依赖：

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-redis_2.11</artifactId>
            <version>1.1.5</version>
        </dependency>

 2、代码：

import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
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;
 
public class StreamToRedis {
 
    public static void main(String[] args) throws Exception {
 
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
 
        String hostname = "192.168.25.128";
        int port = 7777;
        DataStreamSource<String> data = env.socketTextStream(hostname, port);
        SingleOutputStreamOperator<Tuple2<String, String>> m_word = data.map(new MapFunction<String, Tuple2<String, String>>() {
            public Tuple2<String, String> map(String value) throws Exception {
                return new Tuple2<String, String>("m_word", value);
            }
        });
 
        FlinkJedisPoolConfig conf = new FlinkJedisPoolConfig.Builder().setHost("192.168.25.129").setPort(6379).build();
        // 参数一：配置信息
        // 参数二：下沉mapper映射器用于指明谁是key，谁是value
        RedisSink<Tuple2<String, String>> redisSink = new RedisSink<Tuple2<String, String>>(conf, new MyMapper());
        // 将结果保存到redis中
        m_word.addSink(redisSink);
        env.execute("to redis");
    }
 
    /**
     * RedisMapper<Tuple2<String,String>> 泛型为Tuple2<String,String>为将来传递过来的数据类型
     * 将数据其映射为key和value
     */
    public static class MyMapper implements RedisMapper<Tuple2<String,String>>{
 
        public RedisCommandDescription getCommandDescription() {
 
            // redis命令就是怎样插入key和value
            return new RedisCommandDescription(RedisCommand.LPUSH);
        }
 
        public String getKeyFromData(Tuple2<String, String> data) {
            // 指定key
            return data.f0;
        }
 
        public String getValueFromData(Tuple2<String, String> data) {
            // 指定value
            return data.f1;
        }
    }
}

案例：统计一个文件中各个单词出现的次数，把统计结果输出到文件

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.java.DataSet;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.operators.AggregateOperator;
import org.apache.flink.api.java.operators.DataSource;
import org.apache.flink.api.java.operators.FlatMapOperator;
import org.apache.flink.api.java.operators.UnsortedGrouping;
import org.apache.flink.api.java.tuple.Tuple2;
 
import org.apache.flink.util.Collector;
 
 
public class WordCountBatch {
 
    public static void main(String[] args) throws Exception {
        // 1、获取flink的运行环境
       StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
 
        // 2、用flink的运行环境，去获取待分析数据
        String input = "C:\\data\\input\\hello.txt";
        String output = "C:\\data\\output";
        DataSource<String> lines = env.readTextFile(input);
 
        // 3、处理数据
        //将lines中的每一行元素都施加一个算法
        FlatMapOperator<String, Tuple2<String, Integer>> wordAndOne = lines.flatMap(new SplitFun());
 
        // (单词，1)
        // b。把相同的单词聚合到一起
        UnsortedGrouping<Tuple2<String, Integer>> groupByWord = wordAndOne.groupBy(0);
        // c。把聚合到一起的数据累加处理
        DataSet<Tuple2<String, Integer>> result = groupByWord.sum(1);
        // 4、保存处理结果
        result.writeAsText(output);
        result.print();
        // 5、触发程序执行
        env.execute("wordcount batch process");
 
 
    }
 
    /**
     * 作用:将输入进来的每一行元素根据空格切分，切分成一个一个的单词
     * 再把切分好的单词变成《单词，1)这样的组合，元组中
     */
    static class SplitFun implements FlatMapFunction<String, Tuple2<String ,Integer>>{
 
        @Override
        public void flatMap(String in, Collector<Tuple2<String, Integer>> out) throws Exception {
            // a.将文本内容打散成一个一个单词
            String[] words = in.split(" "); //hello you ==> (hello,1) (you,1)
            for (String word : words) {
                Tuple2<String, Integer> wordAndOne = new Tuple2<>();
                out.collect(wordAndOne);
            }
        }
    }
}