flink批处理从0到1学习

一、DataSet API之Data Sources（消费者之数据源）

介绍：

flink提供了大量的已经实现好的source方法，你也可以自定义source 通过实现sourceFunction接口来自定义无并行度的source， 或者你也可以通过实现ParallelSourceFunction 接口 or 继承RichParallelSourceFunction 来自定义有并行度的source。

类型：

基于文件

readTextFile(path) 读取文本文件，文件遵循TextInputFormat 读取规则，逐行读取并返回。

基于集合

fromCollection(Collection) 通过java 的collection集合创建一个数据流，集合中的所有元素必须是相同类型的。

代码实现：

1、fromCollection

import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
object StreamingFromCollectionScala {

  def main(args: Array[String]): Unit = {

    val env = StreamExecutionEnvironment.getExecutionEnvironment

    //隐式转换
    import org.apache.flink.api.scala._

    val data = List(10,15,20)

    val text = env.fromCollection(data)

    //针对map接收到的数据执行加1的操作
    val num = text.map(_+1)

    num.print().setParallelism(1)

    env.execute("StreamingFromCollectionScala")

  }

}
package xuwei.tech.batch;

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.DataSource;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.util.Collector;

/**
 */
public class BatchWordCountJava {

    public static void main(String[] args) throws Exception{
         val data = List(10,15,20)
        String outPath = "D:\data\result";
        //获取运行环境
        ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
        //获取文件中的内容
        val text = env.fromCollection(data)
        DataSet<Tuple2<String, Integer>> counts = text.flatMap(new Tokenizer()).groupBy(0).sum(1);
        counts.writeAsCsv(outPath,"
"," ").setParallelism(1);
        env.execute("batch word count");

    }


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

2、readTextFile

package xuwei.tech.batch;

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.DataSource;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.util.Collector;

/**
 * Created by xuwei.tech on 2018/10/8.
 */
public class BatchWordCountJava {

    public static void main(String[] args) throws Exception{
        String inputPath = "D:\data\file";
        String outPath = "D:\data\result";

        //获取运行环境
        ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
        //获取文件中的内容
        DataSource<String> text = env.readTextFile(inputPath);

        DataSet<Tuple2<String, Integer>> counts = text.flatMap(new Tokenizer()).groupBy(0).sum(1);
        counts.writeAsCsv(outPath,"
"," ").setParallelism(1);
        env.execute("batch word count");

    }


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

二、DataSet API之Transformations

介绍：

---

1. Map：输入一个元素，然后返回一个元素，中间可以做一些清洗转换等操作
2. FlatMap：输入一个元素，可以返回零个，一个或者多个元素
3. MapPartition：类似map，一次处理一个分区的数据【如果在进行map处理的时候需要获取第三方资源链接，建议使用MapPartition】
4. Filter：过滤函数，对传入的数据进行判断，符合条件的数据会被留下
5. Reduce：对数据进行聚合操作，结合当前元素和上一次reduce返回的值进行聚合操作，然后返回一个新的值
6. Aggregate：sum、max、min等
7. Distinct：返回一个数据集中去重之后的元素，data.distinct()
8. Join：内连接
9. OuterJoin：外链接
10. Cross：获取两个数据集的笛卡尔积
11. Union：返回两个数据集的总和，数据类型需要一致
12. First-n：获取集合中的前N个元素
13. Sort Partition：在本地对数据集的所有分区进行排序，通过sortPartition()的链接调用来完成对多个字段的排序

代码实现：

1、broadcast（广播变量）

package xuwei.tech.batch.batchAPI;

import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.functions.RichMapFunction;
import org.apache.flink.api.java.DataSet;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.operators.DataSource;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.configuration.Configuration;

import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;

/**
 * broadcast广播变量
 *
 *
 *
 * 需求：
 * flink会从数据源中获取到用户的姓名
 *
 * 最终需要把用户的姓名和年龄信息打印出来
 *
 * 分析：
 * 所以就需要在中间的map处理的时候获取用户的年龄信息
 *
 * 建议吧用户的关系数据集使用广播变量进行处理
 *
 *
 *
 *
 * 注意：如果多个算子需要使用同一份数据集，那么需要在对应的多个算子后面分别注册广播变量
 */
public class BatchDemoBroadcast {

    public static void main(String[] args) throws Exception{

        //获取运行环境
        ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

        //1：准备需要广播的数据
        ArrayList<Tuple2<String, Integer>> broadData = new ArrayList<>();
        broadData.add(new Tuple2<>("zs",18));
        broadData.add(new Tuple2<>("ls",20));
        broadData.add(new Tuple2<>("ww",17));
        DataSet<Tuple2<String, Integer>> tupleData = env.fromCollection(broadData);


        //1.1:处理需要广播的数据,把数据集转换成map类型，map中的key就是用户姓名，value就是用户年龄
        DataSet<HashMap<String, Integer>> toBroadcast = tupleData.map(new MapFunction<Tuple2<String, Integer>, HashMap<String, Integer>>() {
            @Override
            public HashMap<String, Integer> map(Tuple2<String, Integer> value) throws Exception {
                HashMap<String, Integer> res = new HashMap<>();
                res.put(value.f0, value.f1);
                return res;
            }
        });

        //源数据
        DataSource<String> data = env.fromElements("zs", "ls", "ww");

        //注意：在这里需要使用到RichMapFunction获取广播变量
        DataSet<String> result = data.map(new RichMapFunction<String, String>() {

            List<HashMap<String, Integer>> broadCastMap = new ArrayList<HashMap<String, Integer>>();
            HashMap<String, Integer> allMap = new HashMap<String, Integer>();

            /**
             * 这个方法只会执行一次
             * 可以在这里实现一些初始化的功能
             *
             * 所以，就可以在open方法中获取广播变量数据
             *
             */
            @Override
            public void open(Configuration parameters) throws Exception {
                super.open(parameters);
                //3:获取广播数据
                this.broadCastMap = getRuntimeContext().getBroadcastVariable("broadCastMapName");
                for (HashMap map : broadCastMap) {
                    allMap.putAll(map);
                }

            }

            @Override
            public String map(String value) throws Exception {
                Integer age = allMap.get(value);
                return value + "," + age;
            }
        }).withBroadcastSet(toBroadcast, "broadCastMapName");//2：执行广播数据的操作
        result.print();
    }

}

2、IntCounter（累加器）

package xuwei.tech.batch.batchAPI;

import org.apache.flink.api.common.JobExecutionResult;
import org.apache.flink.api.common.accumulators.IntCounter;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.functions.RichMapFunction;
import org.apache.flink.api.java.DataSet;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.operators.DataSource;
import org.apache.flink.api.java.operators.MapOperator;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.configuration.Configuration;

import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;

/**
 * 全局累加器
 *
 * counter 计数器
 *
 * 需求：
 * 计算map函数中处理了多少数据
 *
 *
 * 注意：只有在任务执行结束后，才能获取到累加器的值
 *
 *
 *
 * Created by xuwei.tech on 2018/10/8.
 */
public class BatchDemoCounter {

    public static void main(String[] args) throws Exception{

        //获取运行环境
        ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

        DataSource<String> data = env.fromElements("a", "b", "c", "d");

        DataSet<String> result = data.map(new RichMapFunction<String, String>() {

            //1:创建累加器
           private IntCounter numLines = new IntCounter();

            @Override
            public void open(Configuration parameters) throws Exception {
                super.open(parameters);
                //2:注册累加器
                getRuntimeContext().addAccumulator("num-lines",this.numLines);

            }

            //int sum = 0;
            @Override
            public String map(String value) throws Exception {
                //如果并行度为1，使用普通的累加求和即可，但是设置多个并行度，则普通的累加求和结果就不准了
                //sum++;
                //System.out.println("sum："+sum);
                this.numLines.add(1);
                return value;
            }
        }).setParallelism(8);

        //result.print();

        result.writeAsText("d:\data\count10");

        JobExecutionResult jobResult = env.execute("counter");
        //3：获取累加器
        int num = jobResult.getAccumulatorResult("num-lines");
        System.out.println("num:"+num);

    }



}

3、cross（获取笛卡尔积）

package xuwei.tech.batch.batchAPI;

import org.apache.flink.api.common.functions.JoinFunction;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.operators.CrossOperator;
import org.apache.flink.api.java.operators.DataSource;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;

import java.util.ArrayList;

/**
 * 获取笛卡尔积
 *
 * Created by xuwei.tech on 2018/10/8.
 */
public class BatchDemoCross {

    public static void main(String[] args) throws Exception{

        //获取运行环境
        ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

        //tuple2<用户id，用户姓名>
        ArrayList<String> data1 = new ArrayList<>();
        data1.add("zs");
        data1.add("ww");

        //tuple2<用户id，用户所在城市>
        ArrayList<Integer> data2 = new ArrayList<>();
        data2.add(1);
        data2.add(2);

        DataSource<String> text1 = env.fromCollection(data1);
        DataSource<Integer> text2 = env.fromCollection(data2);

        CrossOperator.DefaultCross<String, Integer> cross = text1.cross(text2);

        cross.print();


    }



}

4、registerCachedFile（Distributed Cache）

package xuwei.tech.batch.batchAPI;

import org.apache.commons.io.FileUtils;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.functions.RichMapFunction;
import org.apache.flink.api.java.DataSet;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.operators.DataSource;
import org.apache.flink.api.java.operators.MapOperator;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.configuration.Configuration;

import java.io.File;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;

/**
 * Distributed Cache
 */
public class BatchDemoDisCache {

    public static void main(String[] args) throws Exception{

        //获取运行环境
        ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

        //1：注册一个文件,可以使用hdfs或者s3上的文件
        env.registerCachedFile("d:\data\file\a.txt","a.txt");

        DataSource<String> data = env.fromElements("a", "b", "c", "d");

        DataSet<String> result = data.map(new RichMapFunction<String, String>() {
            private ArrayList<String> dataList = new ArrayList<String>();

            @Override
            public void open(Configuration parameters) throws Exception {
                super.open(parameters);
                //2：使用文件
                File myFile = getRuntimeContext().getDistributedCache().getFile("a.txt");
                List<String> lines = FileUtils.readLines(myFile);
                for (String line : lines) {
                    this.dataList.add(line);
                    System.out.println("line:" + line);
                }
            }

            @Override
            public String map(String value) throws Exception {
                //在这里就可以使用dataList
                return value;
            }
        });

        result.print();

    }

}

5、distinct

package xuwei.tech.batch.batchAPI;

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.functions.MapPartitionFunction;
import org.apache.flink.api.java.DataSet;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.operators.DataSource;
import org.apache.flink.api.java.operators.FlatMapOperator;
import org.apache.flink.util.Collector;

import java.util.ArrayList;
import java.util.Iterator;
public class BatchDemoDistinct {

    public static void main(String[] args) throws Exception{

        //获取运行环境
        ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

        ArrayList<String> data = new ArrayList<>();
        data.add("hello you");
        data.add("hello me");

        DataSource<String> text = env.fromCollection(data);

        FlatMapOperator<String, String> flatMapData = text.flatMap(new FlatMapFunction<String, String>() {
            @Override
            public void flatMap(String value, Collector<String> out) throws Exception {
                String[] split = value.toLowerCase().split("\W+");
                for (String word : split) {
                    System.out.println("单词："+word);
                    out.collect(word);
                }
            }
        });

        flatMapData.distinct()// 对数据进行整体去重
                .print();


    }



}

6、排序（first）

package xuwei.tech.batch.batchAPI;

import org.apache.flink.api.common.functions.JoinFunction;
import org.apache.flink.api.common.operators.Order;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.operators.DataSource;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;

import java.util.ArrayList;

/**
 * 获取集合中的前N个元素
 * Created by xuwei.tech on 2018/10/8.
 */
public class BatchDemoFirstN {

    public static void main(String[] args) throws Exception{

        //获取运行环境
        ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

        ArrayList<Tuple2<Integer, String>> data = new ArrayList<>();
        data.add(new Tuple2<>(2,"zs"));
        data.add(new Tuple2<>(4,"ls"));
        data.add(new Tuple2<>(3,"ww"));
        data.add(new Tuple2<>(1,"xw"));
        data.add(new Tuple2<>(1,"aw"));
        data.add(new Tuple2<>(1,"mw"));


        DataSource<Tuple2<Integer, String>> text = env.fromCollection(data);


        //获取前3条数据，按照数据插入的顺序
        text.first(3).print();
        System.out.println("==============================");

        //根据数据中的第一列进行分组，获取每组的前2个元素
        text.groupBy(0).first(2).print();
        System.out.println("==============================");

        //根据数据中的第一列分组，再根据第二列进行组内排序[升序]，获取每组的前2个元素
        text.groupBy(0).sortGroup(1, Order.ASCENDING).first(2).print();
        System.out.println("==============================");

        //不分组，全局排序获取集合中的前3个元素，针对第一个元素升序，第二个元素倒序
        text.sortPartition(0,Order.ASCENDING).sortPartition(1,Order.DESCENDING).first(3).print();

    }



}

7、join

package xuwei.tech.batch.batchAPI;

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.JoinFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.operators.DataSource;
import org.apache.flink.api.java.operators.FlatMapOperator;
import org.apache.flink.api.java.tuple.Tuple1;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.util.Collector;

import java.util.ArrayList;
public class BatchDemoJoin {

    public static void main(String[] args) throws Exception{

        //获取运行环境
        ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

        //tuple2<用户id，用户姓名>
        ArrayList<Tuple2<Integer, String>> data1 = new ArrayList<>();
        data1.add(new Tuple2<>(1,"zs"));
        data1.add(new Tuple2<>(2,"ls"));
        data1.add(new Tuple2<>(3,"ww"));


        //tuple2<用户id，用户所在城市>
        ArrayList<Tuple2<Integer, String>> data2 = new ArrayList<>();
        data2.add(new Tuple2<>(1,"beijing"));
        data2.add(new Tuple2<>(2,"shanghai"));
        data2.add(new Tuple2<>(3,"guangzhou"));


        DataSource<Tuple2<Integer, String>> text1 = env.fromCollection(data1);
        DataSource<Tuple2<Integer, String>> text2 = env.fromCollection(data2);


        text1.join(text2).where(0)//指定第一个数据集中需要进行比较的元素角标
                        .equalTo(0)//指定第二个数据集中需要进行比较的元素角标
                        .with(new JoinFunction<Tuple2<Integer,String>, Tuple2<Integer,String>, Tuple3<Integer,String,String>>() {
                            @Override
                            public Tuple3<Integer, String, String> join(Tuple2<Integer, String> first, Tuple2<Integer, String> second)
                                    throws Exception {
                                return new Tuple3<>(first.f0,first.f1,second.f1);
                            }
                        }).print();

        System.out.println("==================================");

        //注意，这里用map和上面使用的with最终效果是一致的。
        /*text1.join(text2).where(0)//指定第一个数据集中需要进行比较的元素角标
                .equalTo(0)//指定第二个数据集中需要进行比较的元素角标
                .map(new MapFunction<Tuple2<Tuple2<Integer,String>,Tuple2<Integer,String>>, Tuple3<Integer,String,String>>() {
                    @Override
                    public Tuple3<Integer, String, String> map(Tuple2<Tuple2<Integer, String>, Tuple2<Integer, String>> value) throws Exception {
                        return new Tuple3<>(value.f0.f0,value.f0.f1,value.f1.f1);
                    }
                }).print();*/
    }
}

8、outerJoin

package xuwei.tech.batch.batchAPI;

import org.apache.flink.api.common.functions.JoinFunction;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.operators.DataSource;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;

import java.util.ArrayList;

/**
 * 外连接
 *
 * 左外连接
 * 右外连接
 * 全外连接
 */
public class BatchDemoOuterJoin {

    public static void main(String[] args) throws Exception{

        //获取运行环境
        ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

        //tuple2<用户id，用户姓名>
        ArrayList<Tuple2<Integer, String>> data1 = new ArrayList<>();
        data1.add(new Tuple2<>(1,"zs"));
        data1.add(new Tuple2<>(2,"ls"));
        data1.add(new Tuple2<>(3,"ww"));


        //tuple2<用户id，用户所在城市>
        ArrayList<Tuple2<Integer, String>> data2 = new ArrayList<>();
        data2.add(new Tuple2<>(1,"beijing"));
        data2.add(new Tuple2<>(2,"shanghai"));
        data2.add(new Tuple2<>(4,"guangzhou"));


        DataSource<Tuple2<Integer, String>> text1 = env.fromCollection(data1);
        DataSource<Tuple2<Integer, String>> text2 = env.fromCollection(data2);

        /**
         * 左外连接
         *
         * 注意：second这个tuple中的元素可能为null
         *
         */
        text1.leftOuterJoin(text2)
                .where(0)
                .equalTo(0)
                .with(new JoinFunction<Tuple2<Integer,String>, Tuple2<Integer,String>, Tuple3<Integer,String,String>>() {
                    @Override
                    public Tuple3<Integer, String, String> join(Tuple2<Integer, String> first, Tuple2<Integer, String> second) throws Exception {
                        if(second==null){
                            return new Tuple3<>(first.f0,first.f1,"null");
                        }else{
                            return new Tuple3<>(first.f0,first.f1,second.f1);
                        }

                    }
                }).print();

        System.out.println("=============================================================================");

        /**
         * 右外连接
         *
         * 注意：first这个tuple中的数据可能为null
         *
         */
        text1.rightOuterJoin(text2)
                .where(0)
                .equalTo(0)
                .with(new JoinFunction<Tuple2<Integer,String>, Tuple2<Integer,String>, Tuple3<Integer,String,String>>() {
                    @Override
                    public Tuple3<Integer, String, String> join(Tuple2<Integer, String> first, Tuple2<Integer, String> second) throws Exception {
                        if(first==null){
                            return new Tuple3<>(second.f0,"null",second.f1);
                        }
                        return new Tuple3<>(first.f0,first.f1,second.f1);
                    }
                }).print();



        System.out.println("=============================================================================");

        /**
         * 全外连接
         *
         * 注意：first和second这两个tuple都有可能为null
         *
         */

        text1.fullOuterJoin(text2)
                .where(0)
                .equalTo(0)
                .with(new JoinFunction<Tuple2<Integer,String>, Tuple2<Integer,String>, Tuple3<Integer,String,String>>() {
                    @Override
                    public Tuple3<Integer, String, String> join(Tuple2<Integer, String> first, Tuple2<Integer, String> second) throws Exception {
                        if(first==null){
                            return new Tuple3<>(second.f0,"null",second.f1);
                        }else if(second == null){
                            return new Tuple3<>(first.f0,first.f1,"null");
                        }else{
                            return new Tuple3<>(first.f0,first.f1,second.f1);
                        }
                    }
                }).print();


    }

}

9、union

package xuwei.tech.batch.batchAPI;

import org.apache.flink.api.common.functions.JoinFunction;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.operators.DataSource;
import org.apache.flink.api.java.operators.UnionOperator;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;

import java.util.ArrayList;

/**
 * Created by xuwei.tech on 2018/10/8.
 */
public class BatchDemoUnion {

    public static void main(String[] args) throws Exception{

        //获取运行环境
        ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

        ArrayList<Tuple2<Integer, String>> data1 = new ArrayList<>();
        data1.add(new Tuple2<>(1,"zs"));
        data1.add(new Tuple2<>(2,"ls"));
        data1.add(new Tuple2<>(3,"ww"));


        ArrayList<Tuple2<Integer, String>> data2 = new ArrayList<>();
        data2.add(new Tuple2<>(1,"lili"));
        data2.add(new Tuple2<>(2,"jack"));
        data2.add(new Tuple2<>(3,"jessic"));


        DataSource<Tuple2<Integer, String>> text1 = env.fromCollection(data1);
        DataSource<Tuple2<Integer, String>> text2 = env.fromCollection(data2);

        UnionOperator<Tuple2<Integer, String>> union = text1.union(text2);

        union.print();



    }



}

三、DataStream API之partition

介绍：

1. Rebalance：对数据集进行再平衡，重分区，消除数据倾斜
2. Hash-Partition：根据指定key的哈希值对数据集进行分区
3. partitionByHash()
4. Range-Partition：根据指定的key对数据集进行范围分区
5. .partitionByRange()
6. Custom Partitioning：自定义分区规则
7. 自定义分区需要实现Partitioner接口
8. partitionCustom(partitioner, "someKey")
9. 或者partitionCustom(partitioner, 0)

代码实现：

1、partitionByRange或partitionByHash

package xuwei.tech.batch.batchAPI;

import org.apache.flink.api.common.functions.MapPartitionFunction;
import org.apache.flink.api.common.operators.Order;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.operators.DataSource;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.util.Collector;

import java.util.ArrayList;
import java.util.Iterator;

/**
 * Hash-Partition
 *
 * Range-Partition
 *
 *
 * Created by xuwei.tech on 2018/10/8.
 */
public class BatchDemoHashRangePartition {

    public static void main(String[] args) throws Exception{

        //获取运行环境
        ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

        ArrayList<Tuple2<Integer, String>> data = new ArrayList<>();
        data.add(new Tuple2<>(1,"hello1"));
        data.add(new Tuple2<>(2,"hello2"));
        data.add(new Tuple2<>(2,"hello3"));
        data.add(new Tuple2<>(3,"hello4"));
        data.add(new Tuple2<>(3,"hello5"));
        data.add(new Tuple2<>(3,"hello6"));
        data.add(new Tuple2<>(4,"hello7"));
        data.add(new Tuple2<>(4,"hello8"));
        data.add(new Tuple2<>(4,"hello9"));
        data.add(new Tuple2<>(4,"hello10"));
        data.add(new Tuple2<>(5,"hello11"));
        data.add(new Tuple2<>(5,"hello12"));
        data.add(new Tuple2<>(5,"hello13"));
        data.add(new Tuple2<>(5,"hello14"));
        data.add(new Tuple2<>(5,"hello15"));
        data.add(new Tuple2<>(6,"hello16"));
        data.add(new Tuple2<>(6,"hello17"));
        data.add(new Tuple2<>(6,"hello18"));
        data.add(new Tuple2<>(6,"hello19"));
        data.add(new Tuple2<>(6,"hello20"));
        data.add(new Tuple2<>(6,"hello21"));


        DataSource<Tuple2<Integer, String>> text = env.fromCollection(data);

        /*text.partitionByHash(0).mapPartition(new MapPartitionFunction<Tuple2<Integer,String>, Tuple2<Integer,String>>() {
            @Override
            public void mapPartition(Iterable<Tuple2<Integer, String>> values, Collector<Tuple2<Integer, String>> out) throws Exception {
                Iterator<Tuple2<Integer, String>> it = values.iterator();
                while (it.hasNext()){
                    Tuple2<Integer, String> next = it.next();
                    System.out.println("当前线程id："+Thread.currentThread().getId()+","+next);
                }

            }
        }).print();*/


        text.partitionByRange(0).mapPartition(new MapPartitionFunction<Tuple2<Integer,String>, Tuple2<Integer,String>>() {
            @Override
            public void mapPartition(Iterable<Tuple2<Integer, String>> values, Collector<Tuple2<Integer, String>> out) throws Exception {
                Iterator<Tuple2<Integer, String>> it = values.iterator();
                while (it.hasNext()){
                    Tuple2<Integer, String> next = it.next();
                    System.out.println("当前线程id："+Thread.currentThread().getId()+","+next);
                }

            }
        }).print();
    }

}

2、mapPartition

package xuwei.tech.batch.batchAPI;

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.functions.MapPartitionFunction;
import org.apache.flink.api.java.DataSet;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.operators.DataSource;
import org.apache.flink.api.java.operators.MapPartitionOperator;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.util.Collector;

import java.util.ArrayList;
import java.util.Iterator;

/**
 * Created by xuwei.tech on 2018/10/8.
 */
public class BatchDemoMapPartition {

    public static void main(String[] args) throws Exception{

        //获取运行环境
        ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();

        ArrayList<String> data = new ArrayList<>();
        data.add("hello you");
        data.add("hello me");

        DataSource<String> text = env.fromCollection(data);

        /*text.map(new MapFunction<String, String>() {
            @Override
            public String map(String value) throws Exception {
                //获取数据库连接--注意，此时是每过来一条数据就获取一次链接
                //处理数据
                //关闭连接
                return value;
            }
        });*/


        DataSet<String> mapPartitionData = text.mapPartition(new MapPartitionFunction<String, String>() {
            @Override
            public void mapPartition(Iterable<String> values, Collector<String> out) throws Exception {
                //获取数据库连接--注意，此时是一个分区的数据获取一次连接【优点，每个分区获取一次链接】
                //values中保存了一个分区的数据
                //处理数据
                Iterator<String> it = values.iterator();
                while (it.hasNext()) {
                    String next = it.next();
                    String[] split = next.split("\W+");
                    for (String word : split) {
                        out.collect(word);
                    }
                }
                //关闭链接
            }
        });

        mapPartitionData.print();


    }



}

四、DataSet API之Data Sink（数据落地）

介绍：

1. writeAsText()：将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
2. writeAsCsv()：将元组以逗号分隔写入文件中，行及字段之间的分隔是可配置的。每个字段的值来自对象的toString()方法
3. print()：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

代码：

1、writeAsCsv

package xuwei.tech.batch;

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.DataSource;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.util.Collector;

/**
 * Created by xuwei.tech on 2018/10/8.
 */
public class BatchWordCountJava {

    public static void main(String[] args) throws Exception{
        String inputPath = "D:\data\file";
        String outPath = "D:\data\result";

        //获取运行环境
        ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
        //获取文件中的内容
        DataSource<String> text = env.readTextFile(inputPath);

        DataSet<Tuple2<String, Integer>> counts = text.flatMap(new Tokenizer()).groupBy(0).sum(1);
        counts.writeAsCsv(outPath,"
"," ").setParallelism(1);
        env.execute("batch word count");

    }


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