1.11.Flink DataSetAPI、DataSet API之Data Sources、DataSet API之Transformations、DataSet Sink部分详解
2024-05-15 18:29:43
1.11.Flink DataSetAPI
1.11.1.DataSet API之Data Sources
1.11.2.DataSet API之Transformations
1.11.3.DataSet Sink部分详解
1.11.Flink DataSetAPI
1.11.1.DataSet API之Data Sources
基于文件
readTextFile(path)
基于集合
fromCollection(Collection)
1.11.2.DataSet API之Transformations
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。
FlatMap：输入一个元素，可以返回零个，一个或者多个元素。
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】
import org.apache.flink.api.common.functions.MapPartitionFunction;
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 xxxx on 2020/10/09 .*/
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>>() {@Overridepublic 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>>() {@Overridepublic 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();}}
import org.apache.flink.api.scala.ExecutionEnvironment
import scala.collection.mutable.ListBuffer/*** Created by xxxx on 2020/10/09*/
object BatchDemoHashRangePartitionScala {def main(args: Array[String]): Unit = {val env = ExecutionEnvironment.getExecutionEnvironmentimport org.apache.flink.api.scala._val data1 = ListBuffer[Tuple2[Int,String]]()data1.append((1,"hello1"))data1.append((2,"hello2"))data1.append((2,"hello3"))data1.append((3,"hello4"))data1.append((3,"hello5"))data1.append((3,"hello6"))data1.append((4,"hello7"))data1.append((4,"hello8"))data1.append((4,"hello9"))data1.append((4,"hello10"))data1.append((5,"hello11"))data1.append((5,"hello12"))data1.append((5,"hello13"))data1.append((5,"hello14"))data1.append((5,"hello15"))data1.append((6,"hello16"))data1.append((6,"hello17"))data1.append((6,"hello18"))data1.append((6,"hello19"))data1.append((6,"hello20"))data1.append((6,"hello21"))val text = env.fromCollection(data1)/*text.partitionByHash(0).mapPartition(it=>{while (it.hasNext){val tu = it.next()println("当前线程id："+Thread.currentThread().getId+","+tu)}it}).print()*/text.partitionByRange(0).mapPartition(it=>{while (it.hasNext){val tu = it.next()println("当前线程id："+Thread.currentThread().getId+","+tu)}it}).print()}}
再例如：
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.util.Collector;import java.util.ArrayList;
import java.util.Iterator;/*** Created by xxxx on 2020/10/09 .*/
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>() {@Overridepublic 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>() {@Overridepublic 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();}}
import org.apache.flink.api.scala.ExecutionEnvironment
import scala.collection.mutable.ListBuffer/*** Created by xxxx on 2020/10/09*/
object BatchDemoMapPartitionScala {def main(args: Array[String]): Unit = {val env = ExecutionEnvironment.getExecutionEnvironmentimport org.apache.flink.api.scala._val data = ListBuffer[String]()data.append("hello you")data.append("hello me")val text = env.fromCollection(data)text.mapPartition(it=>{//创建数据库连接，建议吧这块代码放到try-catch代码块中val res = ListBuffer[String]()while(it.hasNext){val line = it.next()val words = line.split("\\W+")for(word <- words){res.append(word)}}res//关闭连接}).print()}}
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
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
Aggregation:sum、max、min等。
Distinct:返回一个数据集中去重之后的元素，data.distinct()
import org.apache.flink.api.common.functions.FlatMapFunction;
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;/*** Created by xxxx on 2020/10/09 .*/
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>() {@Overridepublic 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();}
}
import org.apache.flink.api.scala.ExecutionEnvironmentimport scala.collection.mutable.ListBuffer/*** Created by xxxx on 2020/10/09*/
object BatchDemoDistinctScala {def main(args: Array[String]): Unit = {val env = ExecutionEnvironment.getExecutionEnvironmentimport org.apache.flink.api.scala._val data = ListBuffer[String]()data.append("hello you")data.append("hello me")val text = env.fromCollection(data)val flatMapData = text.flatMap(line=>{val words = line.split("\\W+")for(word <- words){println("单词："+word)}words})flatMapData.distinct().print()}}
Join:内连接
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;/*** Created by xxxx on 2020/10/09 .*/
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>>() {@Overridepublic 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>>() {@Overridepublic 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();*/}
}
import org.apache.flink.api.scala.ExecutionEnvironment
import scala.collection.mutable.ListBuffer/*** Created by xxxx on 2020/10/09*/
object BatchDemoJoinScala {def main(args: Array[String]): Unit = {val env = ExecutionEnvironment.getExecutionEnvironmentimport org.apache.flink.api.scala._val data1 = ListBuffer[Tuple2[Int,String]]()data1.append((1,"zs"))data1.append((2,"ls"))data1.append((3,"ww"))val data2 = ListBuffer[Tuple2[Int,String]]()data2.append((1,"beijing"))data2.append((2,"shanghai"))data2.append((3,"guangzhou"))val text1 = env.fromCollection(data1)val text2 = env.fromCollection(data2)text1.join(text2).where(0).equalTo(0).apply((first,second)=>{(first._1,first._2,second._2)}).print()}
}
OuterJoin:外链接
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** Created by xxxx on 2020/10/09 .*/
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>>() {@Overridepublic 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>>() {@Overridepublic 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>>() {@Overridepublic 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();}
}
import org.apache.flink.api.scala.ExecutionEnvironment
import scala.collection.mutable.ListBuffer/*** Created by xxxx on 2020/10/09*/
object BatchDemoOuterJoinScala {def main(args: Array[String]): Unit = {val env = ExecutionEnvironment.getExecutionEnvironmentimport org.apache.flink.api.scala._val data1 = ListBuffer[Tuple2[Int,String]]()data1.append((1,"zs"))data1.append((2,"ls"))data1.append((3,"ww"))val data2 = ListBuffer[Tuple2[Int,String]]()data2.append((1,"beijing"))data2.append((2,"shanghai"))data2.append((4,"guangzhou"))val text1 = env.fromCollection(data1)val text2 = env.fromCollection(data2)text1.leftOuterJoin(text2).where(0).equalTo(0).apply((first,second)=>{if(second==null){(first._1,first._2,"null")}else{(first._1,first._2,second._2)}}).print()println("===============================")text1.rightOuterJoin(text2).where(0).equalTo(0).apply((first,second)=>{if(first==null){(second._1,"null",second._2)}else{(first._1,first._2,second._2)}}).print()println("===============================")text1.fullOuterJoin(text2).where(0).equalTo(0).apply((first,second)=>{if(first==null){(second._1,"null",second._2)}else if(second==null){(first._1,first._2,"null")}else{(first._1,first._2,second._2)}}).print()}}
Cross:获取两个数据集的笛卡尔积
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.api.java.operators.CrossOperator;
import org.apache.flink.api.java.operators.DataSource;import java.util.ArrayList;/*** 获取笛卡尔积** Created by xxxx on 2020/10/09 .*/
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();/*** 输出结果为:* (zs,1)* (zs,2)* (ww,1)* (ww,2)*/}
}
import org.apache.flink.api.scala.ExecutionEnvironment/*** Created by xxxx on 2020/10/09 on 2018/10/30.*/
object BatchDemoCrossScala {def main(args: Array[String]): Unit = {val env = ExecutionEnvironment.getExecutionEnvironmentimport org.apache.flink.api.scala._val data1 = List("zs","ww")val data2 = List(1,2)val text1 = env.fromCollection(data1)val text2 = env.fromCollection(data2)text1.cross(text2).print()}
}
Union:返回两个数据集的总和，数据类型需要一致。
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 java.util.ArrayList;/*** Created by xxxx on 2020/10/09 .*/
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();
}}
import org.apache.flink.api.scala.ExecutionEnvironment
import scala.collection.mutable.ListBuffer/*** Created by xxxx on 2020/10/09*/
object BatchDemoUnionScala {def main(args: Array[String]): Unit = {val env = ExecutionEnvironment.getExecutionEnvironmentimport org.apache.flink.api.scala._val data1 = ListBuffer[Tuple2[Int,String]]()data1.append((1,"zs"))data1.append((2,"ls"))data1.append((3,"ww"))val data2 = ListBuffer[Tuple2[Int,String]]()data2.append((1,"jack"))data2.append((2,"lili"))data2.append((3,"jessic"))val text1 = env.fromCollection(data1)val text2 = env.fromCollection(data2)text1.union(text2).print()}}
First-n:获取集合中的前N个元素。
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 java.util.ArrayList;/*** 获取集合中的前N个元素* Created by xxxx on 2020/10/09 .*/
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();}}
import org.apache.flink.api.common.operators.Order
import org.apache.flink.api.scala.ExecutionEnvironment
import scala.collection.mutable.ListBuffer/*** Created by xxxx on 2020/10/09*/
object BatchDemoFirstNScala {def main(args: Array[String]): Unit = {val env = ExecutionEnvironment.getExecutionEnvironmentimport org.apache.flink.api.scala._val data = ListBuffer[Tuple2[Int,String]]()data.append((2,"zs"))data.append((4,"ls"))data.append((3,"ww"))data.append((1,"xw"))data.append((1,"aw"))data.append((1,"mw"))val text = env.fromCollection(data)//获取前3条数据，按照数据插入的顺序text.first(3).print()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()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()println("==============================")//不分组，全局排序获取集合中的前3个元素，text.sortPartition(0,Order.ASCENDING).sortPartition(1,Order.DESCENDING).first(3).print()}}
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。
Rebalance:对数据集进行再平衡，重分区，消除数据倾斜。
Hash-Partition:根据指定key的哈希值对数据集进行分区（例如：partitionByHash（））
Range-Partiton:根据指定的key对数据集进行范围分区（例如：partitionByRange（））
Custom Partitioning:自定义分区规则
一、自定义分区需要实现Partitioner接口
二、partitionCustom(partitioner, “someKey”)
三、或者partitionCustom(partitioner, 0)
1.11.3.DataSet Sink部分详解
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
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
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。
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