flink入门到实战（6）flink批处理从0到1

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,"\n"," ").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,"\n"," ").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,"\n"," ").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)); } } } } }