8.FLINK Transformation基本操作合并和连接拆分和选择rebalance重平衡分区其他分区操作API

Posted 2022-12-14 涂作权的博客

8.Transformation
8.1.基本操作
8.2.合并和连接
8.3.拆分和选择
8.4.rebalance重平衡分区
8.5.其他分区操作
8.5.1.API

8.Transformation

8.1.基本操作

map/flatMap/filter/keyBy/sum/reduce...

和之前学习的Scala/Spark里面的一样的意思。

需求
对流数据中的单词进行统计，排除敏感词TMD(Theater Missile Defense 战区导弹防御)

import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.FilterFunction;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.functions.ReduceFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;

/**
 * TODO
 *
 * @author tuzuoquan
 * @date 2022/4/17 22:22
 */
public class TransformationDemo01 

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

        //TODO 1.source
        DataStream<String> lines = env.socketTextStream("node1", 9999);

        //TODO 2.transformation
        DataStream<String> words = lines.flatMap(new FlatMapFunction<String, String>() 
            @Override
            public void flatMap(String value, Collector<String> out) throws Exception 
                String[] arr = value.split(" ");
                for (String word : arr) 
                    out.collect(word);
                
            
        );

        DataStream<String> filted = words.filter(new FilterFunction<String>() 
            @Override
            public boolean filter(String value) throws Exception 
                return !value.equals("TMD"); //如果是TMD则返回false表示过滤掉
            
        );

        SingleOutputStreamOperator<Tuple2<String, Integer>> wordAndOne = filted.map(
                new MapFunction<String, Tuple2<String, Integer>>() 
            @Override
            public Tuple2<String, Integer> map(String value) throws Exception 
                return Tuple2.of(value, 1);
            
        );

        KeyedStream<Tuple2<String, Integer>, String> grouped = wordAndOne.keyBy(t -> t.f0);

        //SingleOutputStreamOperator<Tuple2<String, Integer>> result = grouped.sum(1);
        SingleOutputStreamOperator<Tuple2<String, Integer>> result = grouped.reduce(
                new ReduceFunction<Tuple2<String, Integer>>() 
                    @Override
                    public Tuple2<String, Integer> reduce(Tuple2<String, Integer> value1, Tuple2<String, Integer> value2) throws Exception 
                        // Tuple2<String, Integer> value1 : 进来的(单词，历史值)
                        // Tuple2<String, Integer> value2 : 进来的(单词， 1)
                        // 需要返回(单词,数量)
                        return Tuple2.of(value1.f0, value1.f1 + value2.f1);
                    
                );

        //TODO 3.sink
        result.print();

        //TODO 4.execute
        env.execute();
    

8.2.合并和连接

package demo5;

import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.streaming.api.datastream.ConnectedStreams;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.co.CoMapFunction;
import sun.awt.windows.WPrinterJob;

/**
 * TODO
 *
 * @author tuzuoquan
 * @date 2022/4/17 23:59
 */
public class TransformationDemo02 

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

        //TODO 1.source
        DataStream<String> ds1 = env.fromElements("hadoop", "spark", "flink");
        DataStream<String> ds2 = env.fromElements("hadoop", "spark", "flink");
        DataStream<Long> ds3 = env.fromElements(1L, 2L, 3L);

        //TODO 2.transformation
        //注意union能合并同类型
        DataStream<String> result1 = ds1.union(ds2);
        //ds1.union(ds3); //注意union不可以合并不同类型
        //注意:connect可以合并同类型
        ConnectedStreams<String,String> result2 = ds1.connect(ds2);
        //注意connect可以合并不同类型
        ConnectedStreams<String,Long> result3 = ds1.connect(ds3);

        SingleOutputStreamOperator<String> result = result3.map(new CoMapFunction<String, Long, String>() 
            @Override
            public String map1(String value) throws Exception 
                return "String:" + value;
            

            @Override
            public String map2(Long value) throws Exception 
                return "Long:" + value;
            
        );

        //TODO 3.sink
//        System.out.println("result1.print:");
//        result1.print();
//        System.out.println("result2.print:");
        //result2.print();//注意:connect之后需要做其他的处理,不能直接输出
        //result3.print();//注意:connect之后需要做其他的处理,不能直接输出
        System.out.println("result.print");
        result.print();

        //TODO 4.execute
        env.execute();

    

8.3.拆分和选择

Split就是将一个流分成多个流
Select就是获取分流后对应的数据
注意：split函数已过期并移除

Side Outputs: 可以使用process方法对流中数据进行处理，并针对不同的处理结果将数据收集到不同的OutputTag中。

import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.streaming.api.datastream.DataStream;
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.api.functions.ProcessFunction;
import org.apache.flink.util.Collector;
import org.apache.flink.util.OutputTag;

/**
 * @author tuzuoquan
 * @date 2022/4/18 18:38
 */
public class TransformationDemo03 

    public static void main(String[] args) throws Exception 
        //TODO 0.env
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);

        //TODO 1.source
        DataStreamSource<Integer> ds = env.fromElements(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);

        //TODO 2.transformation
        //需求：对流中的数据按照奇数和偶数拆分并选择
        OutputTag<Integer> oddTag = new OutputTag<>("奇数", TypeInformation.of(Integer.class));
        OutputTag<Integer> evenTag = new OutputTag<>("偶数", TypeInformation.of(Integer.class));

        /*
        public abstract class ProcessFunction<I, O> extends AbstractRichFunction 
            public abstract void processElement(I value, ProcessFunction.Context ctx, Collector<O> out) throws Exception;
        
         */
        SingleOutputStreamOperator<Integer> result = ds.process(new ProcessFunction<Integer, Integer>() 
            @Override
            public void processElement(Integer value, ProcessFunction<Integer, Integer>.Context ctx, Collector<Integer> out) throws Exception 
                //out收集完的还是放在一起的,ctx可以将数据放到不同的OutputTag
                if (value % 2 == 0) 
                    ctx.output(evenTag, value);
                 else 
                    ctx.output(oddTag, value);
                
            
        );

        DataStream<Integer> oddResult = result.getSideOutput(oddTag);
        DataStream<Integer> evenResult = result.getSideOutput(evenTag);

        //TODO 3.sink
        //OutputTag(Integer, 奇数)
        System.out.println(oddTag);
        //OutputTag(Integer, 偶数)
        System.out.println(evenTag);
        oddResult.print("奇数:");
        evenResult.print("偶数:");

        //TODO 4.execute
        env.execute();
    

8.4.rebalance重平衡分区

解决数据倾斜的问题

package demo7;

import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.FilterFunction;
import org.apache.flink.api.common.functions.RichMapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.FileReadFunction;

/**
 * TODO
 *
 * @author tuzuoquan
 * @date 2022/4/18 19:16
 */
public class TransformationDemo04 

    public static void main(String[] args) throws Exception 
        //TODO 0.env
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);

        //TODO 1.source
        DataStream<Long> longDS = env.fromSequence(0, 100);
        //下面的操作相当于将数据随机分配一下，有可能出现数据倾斜
        DataStream<Long> filterDS = longDS.filter(new FilterFunction<Long>() 
            @Override
            public boolean filter(Long num) throws Exception 
                return num > 10;
            
        );

        //TODO 2.transformation
        //没有经过rebalance有可能出现数据倾斜
        SingleOutputStreamOperator<Tuple2<Integer, Integer>> result1 = filterDS
                .map(new RichMapFunction<Long, Tuple2<Integer, Integer>>() 
                    @Override
                    public Tuple2<Integer, Integer> map(Long value) throws Exception 
                        //子任务id/分区编号
                        int subTaskId = getRuntimeContext().getIndexOfThisSubtask();
                        return Tuple2.of(subTaskId, 1);
                    
                    //按照子任务id/分区编号分组,并统计每个子任务/分区中有几个元素
                ).keyBy(t -> t.f0).sum(1);

        //调用了rebalance解决了数据倾斜
        SingleOutputStreamOperator<Tuple2<Integer, Integer>> result2 = filterDS.rebalance()
                .map(new RichMapFunction<Long, Tuple2<Integer, Integer>>() 
                    @Override
                    public Tuple2<Integer, Integer> map(Long value) throws Exception 
                        //子任务id/分区编号
                        int subTaskId = getRuntimeContext().getIndexOfThisSubtask();
                        return Tuple2.of(subTaskId, 1);
                    
                    //按照子任务id/分区编号分组,并统计每个子任务/分区中有几个元素
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