公告

Apache Flink是什么？

Apache Flink 是一个分布式大数据处理引擎，可对有限数据流和无限数据流进行有状态计算。可部署在各种集群环境，对各种大小的数据规模进行快速计算。上面是非常官方的描述，说白了我们为什么选择Flink，是因为他在社区口碑非常不错。在国内的话有阿里这种大数据大流量的公司一直在输出，当然像腾讯、华为、饿了么、滴滴等也都有使用Apache Flink。

进入正题

本篇博文涉及到的软件工具以及下载地址：

Apache Flink ：https://flink.apache.org/downloads.html

Netcat：https://eternallybored.org/misc/netcat/

Netcat是一个有“瑞士军刀”美誉的网络工具，这里用来绑定端口等待Apache Flink的连接

第一步：启动Flink

从上面的地址下载Flink后是一个压缩包，解压后的目录结构如下：

/conf/flink-conf.yaml里有一些Flink的基本配置信息，如，jobmanager、taskmanager的端口和jvm内存（默认1024M）大小，web控制台的端口（默认8081）等。我们可以不该任何配置，然后进入到bin下，执行start-cluster.bat。这里要注意不是并不是flink.bat。flink.bat是用来提交job的。还有要确保相关的端口没有被占用

运行成功后会有两个java黑窗口（一个TaskManager、一个JobManager），如果只有一个java黑窗口，很可能是你的TaskManager因为端口占用没有启动起来，成功后访问：http://localhost:8081.就会看到如下的web管理控制台了：

如果启动失败的话，上面箭头所指向的地方应该是0.

第二步：job任务编写

1.首先需要新建一个maven工程，然后导入Flink的接口依赖

<groupId>org.apache.flink</groupId>
<artifactId>flink-java</artifactId>
<version>1.7.1</version>

<groupId>org.apache.flink</groupId>
<artifactId>flink-streaming-java_2.11</artifactId>
<version>1.7.1</version>

<groupId>org.apache.flink</groupId>
<artifactId>flink-clients_2.11</artifactId>
<version>1.7.1</version>

2.编写具体的job，官方提供了一个单词统计的demo

package com.kl;
import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.ReduceFunction;
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.streaming.api.windowing.time.Time;
import org.apache.flink.util.Collector;
public class SocketWindowWordCount {
public static void main(String[] args) throws Exception {

// the host and the port to connect to
final String hostname;
final int port;
try {
final ParameterTool params = ParameterTool.fromArgs(args);
hostname = params.has("hostname") ? params.get("hostname") : "localhost";
port = params.has("port") ? params.getInt("port"):9000;
} catch (Exception e) {
System.err.println("No port specified. Please run 'SocketWindowWordCount " +
"--hostname <hostname> --port <port>', where hostname (localhost by default) " +
"and port is the address of the text server");
System.err.println("To start a simple text server, run 'netcat -l <port>' and " +
"type the input text into the command line");
return;
}
// get the execution environment
final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
// get input data by connecting to the socket
DataStream<String> text = env.socketTextStream(hostname, port, "\\n");
// parse the data, group it, window it, and aggregate the counts
DataStream<WordWithCount> windowCounts = text
.flatMap(new FlatMapFunction<String, WordWithCount>() {
public void flatMap(String value, Collector<WordWithCount> out) {
for (String word : value.split("\\\s")) {
out.collect(new WordWithCount(word, 1L));
} }})
.keyBy("word")
.timeWindow(Time.seconds(5))
.reduce(new ReduceFunction<WordWithCount>() {
public WordWithCount reduce(WordWithCount a, WordWithCount b) {
return new WordWithCount(a.word, a.count + b.count);
}});
// print the results with a single thread, rather than in parallel
windowCounts.print().setParallelism(1);
env.execute("Socket Window WordCount");

}
/**

\* Data type for words with count.
*/

public static class WordWithCount {

public String word;
public long count;
public WordWithCount() {}
public WordWithCount(String word, long count) {
this.word = word;
this.count = count;
}
@Override
public String toString() {
return word + " : " + count;
}

}
}

上面demo实现了从启动参数中获取ip和端口，然后连接从输入流接收文本信息，然后统计文本里单词出现的次数。因为要打成可运行的jar，所以，还需要引入maven的jar打包插件，如下：

<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-shade-plugin</artifactId>
<version>1.2.1</version>
<executions>
<execution>
<phase>package</phase>
<goals>
<goal>shade</goal>
</goals>
<configuration>
<transformers>
<transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
<mainClass>com.kl.SocketWindowWordCount</mainClass>
</transformer>
</transformers>
</configuration>
</execution>
</executions>
</plugin>
</plugins>