2、快速上手开发Flink应用程序

开发环境准备

1、JDK
2、Maven
3、IDEA

使用Flink开发一个批处理应用程序

以最简单的 word count 为案例
准备一个文本，路径为 src/main/test_files/test_file

hello,welcome
hello,world,welcome

开发流程

1. set up the batch execution environment
2. read
3. transform operations 开发的核心所在：开发业务逻辑
4. execute program

Java实现

官方网址
第一种创建项目的方式

mvn archetype:generate                               \
      -DarchetypeGroupId=org.apache.flink              \
      -DarchetypeArtifactId=flink-quickstart-java      \
      -DarchetypeVersion=1.9.0

第二种创建项目的方式
其实是一个shell脚本

$ curl https://flink.apache.org/q/quickstart.sh | bash -s 1.9.0

使用maven创建的时候会出现一个问题

[INFO] Generating project in Interactive mode

命令行会停留在这一行很久，如何解决？在mvn后加一个参数

mvn archetype:generate                               \
      -DarchetypeGroupId=org.apache.flink              \
      -DarchetypeArtifactId=flink-quickstart-java      \
      -DarchetypeVersion=1.9.0                            \
      -DarchetypeCatalog=local

之后分别输入红框中自定义名字

成功

java代码

package com.kun.flink.java.chapter02;

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.tuple.Tuple2;
import org.apache.flink.util.Collector;

/**
 * 使用java api来开发Flink的批处理应用程序
 */
public class BatchWCJavaAPP {
    public static void main(String[] args) throws Exception {
        String input = "src/main/test_files/test_file";
        //step1、获取运行环境
        ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
        //step2、read data
        DataSource<String> text = env.readTextFile(input);
        //step3、transform
        //每一行数据按照指定的分隔符拆分
        text.flatMap(new FlatMapFunction<String, Tuple2<String,Integer>>() {
            @Override
            public void flatMap(String s, Collector<Tuple2<String,Integer>> collector) throws Exception {
                String[] strings = s.toLowerCase().split(",");
                for(String string:strings){
                    if(string.length()>0){
                        //为每个单词附上次数1
                        collector.collect(new Tuple2<String,Integer>(string,1));
                    }
                }
            }
        })
        //合并操作
        .groupBy(0).sum(1).print()
        ;
    }
}

运行结果

19:29:13,205 INFO  org.apache.flink.runtime.rpc.akka.AkkaRpcService              - Stopped Akka RPC service.
(world,1)
(hello,2)
(welcome,2)

Scala实现

官方网址
有两种构建项目的方式 SBT和Maven
这里只测试maven

    $ mvn archetype:generate                               \
      -DarchetypeGroupId=org.apache.flink              \
      -DarchetypeArtifactId=flink-quickstart-scala     \
      -DarchetypeVersion=1.9.0  \
      -DarchetypeCatalog=local

步骤和上面java创建工程一样

scala代码

package com.kun.flink.scala.chapter02

import org.apache.flink.api.scala._

object BatchWCScalaAPP {
  def main(args: Array[String]): Unit = {
    val input ="src/test_files/test_file"
    val env = ExecutionEnvironment.getExecutionEnvironment
    val text=env.readTextFile(input)
    text.flatMap(_.toLowerCase.split(","))
      .filter(_.nonEmpty)
      .map((_,1))
      .groupBy(0)
      .sum(1).print()
  }
}

结果

19:53:00,164 INFO  org.apache.flink.runtime.rpc.akka.AkkaRpcService              - Stopped Akka RPC service.
(world,1)
(hello,2)
(welcome,2)

使用Flink开发一个流处理应用程序

测试流处理的时候先打开nc

java

package com.kun.flink.java.chapter02;

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.util.Collector;

/**
 * 使用java api来开发Flink的实时处理应用程序
 *
 * wc统计的数据源自socket
 */
public class StreamingWCJavaAPP {
    public static void main(String[] args) throws Exception {
        //step1:获取执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        //step2：读取数据
        DataStreamSource<String> text = env.socketTextStream("hadoop",9999);
        //step3:transform
        //每一行数据按照指定的分隔符拆分
        text.flatMap(new FlatMapFunction<String, Tuple2<String,Integer>>() {
            @Override
            public void flatMap(String s, Collector<Tuple2<String,Integer>> collector) throws Exception {
                String[] strings = s.toLowerCase().split(",");
                for(String string:strings){
                    if(string.length()>0){
                        //为每个单词附上次数1
                        collector.collect(new Tuple2<String,Integer>(string,1));
                    }
                }
            }
        })
        //合并操作
        .keyBy(0).timeWindow(Time.seconds(5)).sum(1).print()
        ;
        env.execute("StreamingWCJavaAPP");

    }
}

结果

2> (b,2)
6> (a,3)
4> (c,4)

重构程序手动传入参数

package com.kun.flink.java.chapter02;

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.util.Collector;

/**
 * 使用java api来开发Flink的实时处理应用程序
 *
 * wc统计的数据源自socket
 */
public class StreamingWCJava02APP {
    public static void main(String[] args) throws Exception {
        //获取参数
        int port = 0;
        //flink里专属类
        try {
            ParameterTool tool = ParameterTool.fromArgs(args);
            port=tool.getInt("port");
        }catch (Exception e){
            System.err.println("端口未设置");
            port = 9998;
        }

        //step1:获取执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        //step2：读取数据
        DataStreamSource<String> text = env.socketTextStream("hadoop",port);
        //step3:transform
        //每一行数据按照指定的分隔符拆分
        text.flatMap(new FlatMapFunction<String, Tuple2<String,Integer>>() {
            @Override
            public void flatMap(String s, Collector<Tuple2<String,Integer>> collector) throws Exception {
                String[] strings = s.toLowerCase().split(",");
                for(String string:strings){
                    if(string.length()>0){
                        //为每个单词附上次数1
                        collector.collect(new Tuple2<String,Integer>(string,1));
                    }
                }
            }
        })
        //合并操作
        .keyBy(0).timeWindow(Time.seconds(5)).sum(1).print()
        ;
        env.execute("StreamingWCJavaAPP");

    }
}

运行方法：（运行结果略）

scala

package com.kun.flink.scala.chapter02

import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
import org.apache.flink.streaming.api.windowing.time.Time

object StreamingWCScalaAPP {
  def main(args: Array[String]): Unit = {
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    val text=env.socketTextStream("hadoop",9999)
    import org.apache.flink.streaming.api.scala._
    text.flatMap(_.split(","))
      .map((_,1))
      .keyBy(0)
      .timeWindow(Time.seconds(5))
      .sum(1).print()
    env.execute("StreamingWCScalaAPP")
  }

}

运行结果

4> (c,1)
6> (a,2)
2> (b,3)