flink 1.11.2 学习笔记(1)-wordCount

一、pom依赖

  1 <?xml version="1.0" encoding="UTF-8"?>
  2 <project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  3          xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd">
  4     <modelVersion>4.0.0</modelVersion>
  5
  6     <groupId>com.cnblogs.yjmyzz</groupId>
  7     <artifactId>spring-boot-demo</artifactId>
  8     <version>0.0.1-SNAPSHOT</version>
  9
 10     <properties>
 11         <java.version>1.8</java.version>
 12         <flink.version>1.11.2</flink.version>
 13     </properties>
 14
 15     <dependencies>
 16
 17         <!--lombok可选-->
 18         <dependency>
 19             <groupId>org.projectlombok</groupId>
 20             <artifactId>lombok</artifactId>
 21             <version>1.18.12</version>
 22         </dependency>
 23
 24         <!-- spring应用最小依赖-可选 -->
 25         <dependency>
 26             <groupId>org.springframework</groupId>
 27             <artifactId>spring-context</artifactId>
 28             <version>5.2.4.RELEASE</version>
 29         </dependency>
 30
 31         <!-- flink -->
 32         <dependency>
 33             <groupId>org.apache.flink</groupId>
 34             <artifactId>flink-core</artifactId>
 35             <version>${flink.version}</version>
 36         </dependency>
 37
 38         <dependency>
 39             <groupId>org.apache.flink</groupId>
 40             <artifactId>flink-java</artifactId>
 41             <version>${flink.version}</version>
 42         </dependency>
 43
 44         <dependency>
 45             <groupId>org.apache.flink</groupId>
 46             <artifactId>flink-scala_2.12</artifactId>
 47             <version>${flink.version}</version>
 48         </dependency>
 49
 50         <dependency>
 51             <groupId>org.apache.flink</groupId>
 52             <artifactId>flink-clients_2.12</artifactId>
 53             <version>${flink.version}</version>
 54         </dependency>
 55
 56         <dependency>
 57             <groupId>org.apache.flink</groupId>
 58             <artifactId>flink-test-utils-junit</artifactId>
 59             <version>${flink.version}</version>
 60         </dependency>
 61     </dependencies>
 62
 63     <build>
 64         <plugins>
 65             <plugin>
 66                 <artifactId>maven-compiler-plugin</artifactId>
 67                 <version>3.1</version>
 68                 <configuration>
 69                     <source>1.8</source>
 70                     <target>1.8</target>
 71                 </configuration>
 72             </plugin>
 73
 74             <!-- Scala Compiler -->
 75             <plugin>
 76                 <groupId>net.alchim31.maven</groupId>
 77                 <artifactId>scala-maven-plugin</artifactId>
 78                 <version>4.4.0</version>
 79                 <executions>
 80                     <!-- Run scala compiler in the process-resources phase, so that dependencies on
 81                         scala classes can be resolved later in the (Java) compile phase -->
 82                     <execution>
 83                         <id>scala-compile-first</id>
 84                         <phase>process-resources</phase>
 85                         <goals>
 86                             <goal>compile</goal>
 87                         </goals>
 88                     </execution>
 89                 </executions>
 90                 <configuration>
 91                     <jvmArgs>
 92                         <jvmArg>-Xms128m</jvmArg>
 93                         <jvmArg>-Xmx512m</jvmArg>
 94                     </jvmArgs>
 95                 </configuration>
 96             </plugin>
 97
 98         </plugins>
 99     </build>
100
101 </project>

二、WordCount(批处理版本)

 1 import org.apache.flink.api.common.functions.FlatMapFunction;
 2 import org.apache.flink.api.java.DataSet;
 3 import org.apache.flink.api.java.ExecutionEnvironment;
 4 import org.apache.flink.api.java.tuple.Tuple2;
 5 import org.apache.flink.util.Collector;
 6
 7 /**
 8  * WordCount批处理版本
 9  */
10 public class BatchWordCount {
11
12     public static void main(String[] args) throws Exception {
13
14         // 1 设置环境
15         final ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
16
17         // 2. 定义数据
18         String wordFilePath = "/Users/jimmy/Downloads/word.txt";
19         DataSet<String> text = env.readTextFile(wordFilePath);
20
21         // 3. 处理逻辑
22         DataSet<Tuple2<String, Integer>> counts =
23
24                 text.flatMap(new FlatMapFunction<String, Tuple2<String, Integer>>() {
25                     @Override
26                     public void flatMap(String value, Collector<Tuple2<String, Integer>> out) throws Exception {
27                         //将每行按word拆分
28                         String[] tokens = value.toLowerCase().split("\\W+");
29
30                         //收集(类似:map-reduce思路)
31                         for (String token : tokens) {
32                             if (token.length() > 0) {
33                                 out.collect(new Tuple2<>(token, 1));
34                             }
35                         }
36                     }
37                 })
38                         //按Tuple2里的第0项，即：word分组
39                         .groupBy(0)
40                         //然后对Tuple2里的第1项求合
41                         .sum(1);
42
43         // 4. 打印结果
44         counts.print();
45
46     }
47 }

注：数据文件/Users/jimmy/Downloads/word.txt的位置，大家可根据实际情况调整，该文件的内容类似：

hello java
hello flink

　　

三、WordCount(流式处理版本)

 1 import org.apache.flink.api.common.functions.FlatMapFunction;
 2 import org.apache.flink.api.java.tuple.Tuple2;
 3 import org.apache.flink.streaming.api.datastream.DataStream;
 4 import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
 5 import org.apache.flink.util.Collector;
 6
 7 public class StreamWordCount {
 8
 9     public static void main(String[] args) throws Exception {
10         // 1 设置环境
11         final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
12
13         // 2. 定义数据
14         DataStream<String> text = env.socketTextStream("127.0.0.1", 9999);
15
16         // 3. 处理逻辑
17         DataStream<Tuple2<String, Integer>> counts =
18
19                 text.flatMap(new FlatMapFunction<String, Tuple2<String, Integer>>() {
20                     @Override
21                     public void flatMap(String value, Collector<Tuple2<String, Integer>> out) throws Exception {
22                         //将每行按word拆分
23                         String[] tokens = value.toLowerCase().split("\\b+");
24
25                         //收集(类似:map-reduce思路)
26                         for (String token : tokens) {
27                             if (token.trim().length() > 0) {
28                                 out.collect(new Tuple2<>(token.trim(), 1));
29                             }
30                         }
31                     }
32                 })
33                         //按Tuple2里的第0项，即：word分组
34                         .keyBy(value -> value.f0)
35                         //然后对Tuple2里的第1项求合
36                         .sum(1);
37
38         // 4. 打印结果
39         counts.print();
40
41         // execute program
42         env.execute("Streaming WordCount");
43
44     }
45 }

注：运行前，先在终端命令行输入nc -l 9999，开启一个tcp server作为流式处理的数据源，然后再运行上面的代码，然后命令行输入一些文本，即可看到输出