Flink学习连载第二篇-使用flink编写WordCount(多种情况演示)

使用Flink编写代码，步骤非常固定，大概分为以下几步，只要牢牢抓住步骤，基本轻松拿下：

1. env-准备环境

2. source-加载数据

3. transformation-数据处理转换

4. sink-数据输出

5. execute-执行

DataStream API开发

//nightlies.apache.org/flink/flink-docs-release-1.13/docs/dev/datastream/overview/

0. 添加依赖

<properties><flink.version>1.13.6</flink.version>
</properties><dependencies><dependency><groupId>org.apache.flink</groupId><artifactId>flink-streaming-java_2.11</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-java</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-clients_2.11</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-table-api-java-bridge_2.11</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-table-planner-blink_2.11</artifactId><version>${flink.version}</version></dependency><dependency><groupId>org.apache.flink</groupId><artifactId>flink-shaded-hadoop-2-uber</artifactId><version>2.7.5-10.0</version></dependency><dependency><groupId>log4j</groupId><artifactId>log4j</artifactId><version>1.2.17</version></dependency><dependency><groupId>org.projectlombok</groupId><artifactId>lombok</artifactId><version>1.18.24</version></dependency></dependencies><build><extensions><extension><groupId>org.apache.maven.wagon</groupId><artifactId>wagon-ssh</artifactId><version>2.8</version></extension></extensions><plugins><plugin><groupId>org.codehaus.mojo</groupId><artifactId>wagon-maven-plugin</artifactId><version>1.0</version><configuration><!--上传的本地jar的位置--><fromFile>target/${project.build.finalName}.jar</fromFile><!--远程拷贝的地址--><url>scp://root:root@bigdata01:/opt/app</url></configuration></plugin></plugins></build>

1. 编写代码

package com.bigdata.day01;import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.api.java.tuple.Tuple2;
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.util.Collector;public class WordCount01 {/*** 1. env-准备环境* 2. source-加载数据* 3. transformation-数据处理转换* 4. sink-数据输出* 5. execute-执行*/public static void main(String[] args) throws Exception {// 导入常用类时要注意   不管是在本地开发运行还是在集群上运行，都这么写，非常方便StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();// 这个是 自动 ，根据流的性质，决定是批处理还是流处理//env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);// 批处理流， 一口气把数据算出来// env.setRuntimeMode(RuntimeExecutionMode.BATCH);// 流处理，默认是这个  可以通过打印批和流的处理结果，体会流和批的含义env.setRuntimeMode(RuntimeExecutionMode.STREAMING);// 获取数据  多态的写法 DataStreamSource 它是 DataStream 的子类DataStream<String> dataStream01 = env.fromElements("spark flink kafka", "spark sqoop flink", "kakfa hadoop flink");DataStream<String> flatMapStream = dataStream01.flatMap(new FlatMapFunction<String, String>() {@Overridepublic void flatMap(String line, Collector<String> collector) throws Exception {String[] arr = line.split(" ");for (String word : arr) {// 循环遍历每一个切割完的数据，放入到收集器中，就可以形成一个新的DataStreamcollector.collect(word);}}});//flatMapStream.print();// Tuple2 指的是2元组DataStream<Tuple2<String, Integer>> mapStream = flatMapStream.map(new MapFunction<String, Tuple2<String, Integer>>() {@Overridepublic Tuple2<String, Integer> map(String word) throws Exception {return Tuple2.of(word, 1); // ("hello",1)}});DataStream<Tuple2<String, Integer>> sumResult = mapStream.keyBy(new KeySelector<Tuple2<String, Integer>, String>() {@Overridepublic String getKey(Tuple2<String, Integer> tuple2) throws Exception {return tuple2.f0;}// 此处的1 指的是元组的第二个元素，进行相加的意思}).sum(1);sumResult.print();// 执行env.execute();}
}

查看本机的CPU的逻辑处理器的数量，逻辑处理器的数量就是你的分区数量。

12> spark
13> kakfa
11> spark
11> flink
11> kafka
13> hadoop
12> sqoop
13> flink
12> flink前面的数字是分区数，默认跟逻辑处理器的数量有关系。

对结果进行解释：

什么是批，什么是流？

批处理结果：前面的序号代表分区

流处理结果：

也可以通过如下方式修改分区数量：

 env.setParallelism(2);

关于并行度的代码演示：

系统以及算子都可以设置并行度，或者获取并行度

package com.bigdata.day01;import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.api.java.tuple.Tuple2;
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.util.Collector;public class WordCount01 {/*** 1. env-准备环境* 2. source-加载数据* 3. transformation-数据处理转换* 4. sink-数据输出* 5. execute-执行*/public static void main(String[] args) throws Exception {// 导入常用类时要注意   不管是在本地开发运行还是在集群上运行，都这么写，非常方便StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();// 这个是 自动 ，根据流的性质，决定是批处理还是流处理//env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);// 批处理流， 一口气把数据算出来// env.setRuntimeMode(RuntimeExecutionMode.BATCH);// 流处理，默认是这个  可以通过打印批和流的处理结果，体会流和批的含义env.setRuntimeMode(RuntimeExecutionMode.STREAMING);// 将任务的并行度设置为2// env.setParallelism(2);// 通过这个获取系统的并行度int parallelism = env.getParallelism();System.out.println(parallelism);// 获取数据  多态的写法 DataStreamSource 它是 DataStream 的子类DataStream<String> dataStream01 = env.fromElements("spark flink kafka", "spark sqoop flink", "kakfa hadoop flink");DataStream<String> flatMapStream = dataStream01.flatMap(new FlatMapFunction<String, String>() {@Overridepublic void flatMap(String line, Collector<String> collector) throws Exception {String[] arr = line.split(" ");for (String word : arr) {// 循环遍历每一个切割完的数据，放入到收集器中，就可以形成一个新的DataStreamcollector.collect(word);}}});// 每一个算子也有自己的并行度，一般跟系统保持一致System.out.println("flatMap的并行度："+flatMapStream.getParallelism());//flatMapStream.print();// Tuple2 指的是2元组DataStream<Tuple2<String, Integer>> mapStream = flatMapStream.map(new MapFunction<String, Tuple2<String, Integer>>() {@Overridepublic Tuple2<String, Integer> map(String word) throws Exception {return Tuple2.of(word, 1); // ("hello",1)}});DataStream<Tuple2<String, Integer>> sumResult = mapStream.keyBy(new KeySelector<Tuple2<String, Integer>, String>() {@Overridepublic String getKey(Tuple2<String, Integer> tuple2) throws Exception {return tuple2.f0;}// 此处的1 指的是元组的第二个元组，进行相加的意思}).sum(1);sumResult.print();// 执行env.execute();}
}

1. 打包、上传

文件夹不需要提前准备好，它可以帮我创建

1. 提交我们自己开发打包的任务

flink run -c com.bigdata.day01.WordCount01 /opt/app/FlinkDemo-1.0-SNAPSHOT.jar

去界面中查看运行结果：

因为你这个是集群运行的，所以标准输出流中查看，假如第一台没有，去第二台查看，一直点。

获取主函数参数工具类

可以通过外部传参的方式给定一个路径

以下代码可以做到，假如给定路径，就获取路径的数据，假如没给，就读取默认数据：

package com.bigdata.day01;import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;public class WordCount02 {/*** 1. env-准备环境* 2. source-加载数据* 3. transformation-数据处理转换* 4. sink-数据输出* 5. execute-执行*/public static void main(String[] args) throws Exception {// 导入常用类时要注意   不管是在本地开发运行还是在集群上运行，都这么写，非常方便StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();// 这个是 自动 ，根据流的性质，决定是批处理还是流处理//env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);// 批处理流， 一口气把数据算出来// env.setRuntimeMode(RuntimeExecutionMode.BATCH);// 流处理，默认是这个  可以通过打印批和流的处理结果，体会流和批的含义env.setRuntimeMode(RuntimeExecutionMode.STREAMING);// 将任务的并行度设置为2// env.setParallelism(2);// 通过这个获取系统的并行度int parallelism = env.getParallelism();System.out.println(parallelism);// 获取数据  多态的写法 DataStreamSource 它是 DataStream 的子类// 连着写的本质就是 因为每一个算子的返回值都是DataStream的子类，所以可以这么写// 以下代码中路径是写死的，能不能通过外部传参进来，当然可以！ agrsDataStream<String> dataStream = null;System.out.println(args.length);if(args.length !=0){String path = args[0];dataStream =  env.readTextFile(path);}else{dataStream =  env.fromElements("spark flink kafka", "spark sqoop flink", "kakfa hadoop flink");}dataStream.flatMap(new FlatMapFunction<String, String>() {@Overridepublic void flatMap(String line, Collector<String> collector) throws Exception {String[] arr = line.split(" ");for (String word : arr) {// 循环遍历每一个切割完的数据，放入到收集器中，就可以形成一个新的DataStreamcollector.collect(word);}}}).map(new MapFunction<String, Tuple2<String, Integer>>() {@Overridepublic Tuple2<String, Integer> map(String word) throws Exception {return Tuple2.of(word, 1); // ("hello",1)}}).keyBy(new KeySelector<Tuple2<String, Integer>, String>() {@Overridepublic String getKey(Tuple2<String, Integer> tuple2) throws Exception {return tuple2.f0;}// 此处的1 指的是元组的第二个元组，进行相加的意思}).sum(1).print();// 执行env.execute();}
}

flink run -c com.bigdata.day01.Demo02 FlinkDemo-1.0-SNAPSHOT.jar /home/wc.txt

这样做，跟我们以前的做法还是不一样。以前的运行方式是这样的

flink run /opt/installs/flink/examples/batch/WordCount.jar --input /home/wc.txt

这个写法，传递参数的时候，带有--字样，而我们的没有。

以上代码进行升级，我想将参数前面追加一个 --input 这样，怎么写？

ParameterTool parameterTool = ParameterTool.fromArgs(args);
if(parameterTool.has("output")){path = parameterTool.get("output");
}在代码中的使用：
ParameterTool parameterTool = ParameterTool.fromArgs(args);String output = "";if (parameterTool.has("output")) {output = parameterTool.get("output");System.out.println("指定了输出路径使用:" + output);} else {output = "hdfs://node01:9820/wordcount/output47_";System.out.println("可以指定输出路径使用 --output ,没有指定使用默认的:" + output);}

升级过的代码：

package com.bigdata.day01;import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;public class WordCount02 {/*** 1. env-准备环境* 2. source-加载数据* 3. transformation-数据处理转换* 4. sink-数据输出* 5. execute-执行*/public static void main(String[] args) throws Exception {// 导入常用类时要注意   不管是在本地开发运行还是在集群上运行，都这么写，非常方便StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();// 这个是 自动 ，根据流的性质，决定是批处理还是流处理//env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);// 批处理流， 一口气把数据算出来// env.setRuntimeMode(RuntimeExecutionMode.BATCH);// 流处理，默认是这个  可以通过打印批和流的处理结果，体会流和批的含义env.setRuntimeMode(RuntimeExecutionMode.STREAMING);// 将任务的并行度设置为2// env.setParallelism(2);// 通过这个获取系统的并行度int parallelism = env.getParallelism();System.out.println(parallelism);// 获取数据  多态的写法 DataStreamSource 它是 DataStream 的子类// 连着写的本质就是 因为每一个算子的返回值都是DataStream的子类，所以可以这么写// 以下代码中路径是写死的，能不能通过外部传参进来，当然可以！ agrsDataStream<String> dataStream = null;System.out.println(args.length);if(args.length !=0){String path ;ParameterTool parameterTool = ParameterTool.fromArgs(args);if(parameterTool.has("input")){path = parameterTool.get("input");}else{path = args[0];}dataStream =  env.readTextFile(path);}else{dataStream =  env.fromElements("spark flink kafka", "spark sqoop flink", "kakfa hadoop flink");}dataStream.flatMap(new FlatMapFunction<String, String>() {@Overridepublic void flatMap(String line, Collector<String> collector) throws Exception {String[] arr = line.split(" ");for (String word : arr) {// 循环遍历每一个切割完的数据，放入到收集器中，就可以形成一个新的DataStreamcollector.collect(word);}}}).map(new MapFunction<String, Tuple2<String, Integer>>() {@Overridepublic Tuple2<String, Integer> map(String word) throws Exception {return Tuple2.of(word, 1); // ("hello",1)}}).keyBy(new KeySelector<Tuple2<String, Integer>, String>() {@Overridepublic String getKey(Tuple2<String, Integer> tuple2) throws Exception {return tuple2.f0;}// 此处的1 指的是元组的第二个元组，进行相加的意思}).sum(1).print();// 执行env.execute();}
}

DataStream (Lambda表达式-扩展 了解)

import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.typeinfo.Types;
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;import java.util.Arrays;/*** Desc 演示Flink-DataStream-流批一体API完成批处理WordCount* 使用Java8的lambda表示完成函数式风格的WordCount*/
public class WordCount02 {public static void main(String[] args) throws Exception {//TODO 1.env-准备环境StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();//env.setRuntimeMode(RuntimeExecutionMode.STREAMING);//指定计算模式为流//env.setRuntimeMode(RuntimeExecutionMode.BATCH);//指定计算模式为批env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);//自动//不设置的话默认是流模式defaultValue(RuntimeExecutionMode.STREAMING)//TODO 2.source-加载数据DataStream<String> dataStream = env.fromElements("flink hadoop spark", "flink hadoop spark", "flink hadoop", "flink");//TODO 3.transformation-数据转换处理//3.1对每一行数据进行分割并压扁/*public interface FlatMapFunction<T, O> extends Function, Serializable {void flatMap(T value, Collector<O> out) throws Exception;}*//*DataStream<String> wordsDS = dataStream.flatMap(new FlatMapFunction<String, String>() {@Overridepublic void flatMap(String value, Collector<String> out) throws Exception {String[] words = value.split(" ");for (String word : words) {out.collect(word);}}});*///注意:Java8的函数的语法/lambda表达式的语法: (参数)->{函数体}DataStream<String> wordsDS = dataStream.flatMap((String value, Collector<String> out) -> {String[] words = value.split(" ");for (String word : words) {out.collect(word);}}).returns(Types.STRING);//3.2 每个单词记为<单词,1>/*public interface MapFunction<T, O> extends Function, Serializable {O map(T value) throws Exception;}*//*DataStream<Tuple2<String, Integer>> wordAndOneDS = wordsDS.map(new MapFunction<String, Tuple2<String, Integer>>() {@Overridepublic Tuple2<String, Integer> map(String value) throws Exception {return Tuple2.of(value, 1);}});*/DataStream<Tuple2<String, Integer>> wordAndOneDS = wordsDS.map((String value) -> Tuple2.of(value, 1)).returns(Types.TUPLE(Types.STRING, Types.INT));//3.3分组//注意:DataSet中分组用groupBy,DataStream中分组用keyBy//KeyedStream<Tuple2<String, Integer>, Tuple> keyedDS = wordAndOneDS.keyBy(0);/*public interface KeySelector<IN, KEY> extends Function, Serializable {KEY getKey(IN value) throws Exception;}*//*KeyedStream<Tuple2<String, Integer>, String> keyedDS = wordAndOneDS.keyBy(new KeySelector<Tuple2<String, Integer>, String>() {@Overridepublic String getKey(Tuple2<String, Integer> value) throws Exception {return value.f0;}});*/KeyedStream<Tuple2<String, Integer>, String> keyedDS = wordAndOneDS.keyBy((Tuple2<String, Integer> value) -> value.f0);//3.4聚合SingleOutputStreamOperator<Tuple2<String, Integer>> result = keyedDS.sum(1);//TODO 4.sink-数据输出result.print();//TODO 5.execute-执行env.execute();}
}

此处有一个大坑，就是使用完lambda表达式以后，需要添加一个returns(Types.STRING); 否则报错，这样的话，使用lambda也不是特别快了。

连着写的版本如下：

package com.bigdata.day01;import org.apache.flink.api.common.RuntimeExecutionMode;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.util.Collector;public class WordCount03 {/*** 1. env-准备环境* 2. source-加载数据* 3. transformation-数据处理转换* 4. sink-数据输出* 5. execute-执行*/public static void main(String[] args) throws Exception {// 导入常用类时要注意   不管是在本地开发运行还是在集群上运行，都这么写，非常方便StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();// 这个是 自动 ，根据流的性质，决定是批处理还是流处理//env.setRuntimeMode(RuntimeExecutionMode.AUTOMATIC);// 批处理流， 一口气把数据算出来// env.setRuntimeMode(RuntimeExecutionMode.BATCH);// 流处理，默认是这个  可以通过打印批和流的处理结果，体会流和批的含义//env.setRuntimeMode(RuntimeExecutionMode.STREAMING);// 将任务的并行度设置为2// env.setParallelism(2);// 通过这个获取系统的并行度int parallelism = env.getParallelism();System.out.println(parallelism);// 获取数据  多态的写法 DataStreamSource 它是 DataStream 的子类// 连着写的本质就是 因为每一个算子的返回值都是DataStream的子类，所以可以这么写// 以下代码中路径是写死的，能不能通过外部传参进来，当然可以！ agrsDataStream<String> dataStream = null;System.out.println(args.length);if(args.length !=0){String path ;ParameterTool parameterTool = ParameterTool.fromArgs(args);if(parameterTool.has("input")){path = parameterTool.get("input");}else{path = args[0];}dataStream =  env.readTextFile(path);}else{dataStream =  env.fromElements("spark flink kafka", "spark sqoop flink", "kakfa hadoop flink");}dataStream.flatMap((String line, Collector<String> collector) -> {String[] arr = line.split(" ");for (String word : arr) {// 循环遍历每一个切割完的数据，放入到收集器中，就可以形成一个新的DataStreamcollector.collect(word);}}).returns(Types.STRING).map((String word)-> {return Tuple2.of(word, 1); // ("hello",1)}).returns(Types.TUPLE(Types.STRING, Types.INT)).keyBy((Tuple2<String, Integer> tuple2)-> {return tuple2.f0;}).sum(1).print();// 执行env.execute();}
}