flink学习（6）——自定义source和kafka

概述

SourceFunction:非并行数据源(并行度只能=1) --接口

RichSourceFunction:多功能非并行数据源(并行度只能=1) --类

ParallelSourceFunction:并行数据源(并行度能够>=1) --接口

RichParallelSourceFunction:多功能并行数据源(并行度能够>=1) --类 【建议使用的】

——Rich 字样代表富有，在编程中，富有代表可以调用的方法很多，功能很全的意思。

 基础案例

package com.bigdata.day02;//1、SourceFunction
// public class ZidingyiSource implements SourceFunction<Student> {
//2、RichSourceFunction
// public class ZidingyiSource extends RichSourceFunction<Student> {
//3、ParallelSourceFunction
//public class ZidingyiSource implements ParallelSourceFunction<Student> {
//4、RichParallelSourceFunction
//public class ZidingyiSource extends RichParallelSourceFunction<Student> {
// 推荐的
public class ZidingyiSource extends RichParallelSourceFunction<Student> {// ctrl + oprivate final Random random = new Random();private boolean flag = true;// 现在不用@Overridepublic void open(Configuration parameters) throws Exception {System.out.println("实现一些资源的开启");}// 现在不用@Overridepublic void close() throws Exception {System.out.println("实现一些资源的关闭");}@Overridepublic void run(SourceContext<Student> sourceContext) throws Exception {while (flag){String stu_id = UUID.randomUUID().toString();String stu_name = "Student_"+stu_id;int stu_age = random.nextInt(8)+10;long stu_timestamp = System.currentTimeMillis();Student student = new Student(stu_id,stu_name,stu_age,stu_timestamp);sourceContext.collect(student);Thread.sleep(1000);}}// 具体什么时候 会调用还不知道@Overridepublic void cancel() {flag = false;System.out.println("停止运行");}
}//调用
public class ZiDingYi {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();// add + new DataStreamSource<Student> studentDataStreamSource = env.addSource(new ZidingyiSource());int parallelism = studentDataStreamSource.getParallelism();System.out.println(parallelism);// print之前与之后的并行度是不同的studentDataStreamSource.print().setParallelism(1);env.execute();}
}

cancel+open+close的调用时机

package com.bigdata.day02;import java.util.Objects;/*
* 1、这几个方法都会按照并行度调用多次 调度的次数 按照studentDataStreamSource的并行度
*
*/public class ZiDingYi {public static void main(String[] args) throws Exception {// 在上面案例的基础上实现StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();DataStreamSource<Student> studentDataStreamSource = env.addSource(new ZidingyiSource());// 此时就只会调用一次了studentDataStreamSource.setParallelism(1);// 此时打印也会有多个并行度（8个cpu）studentDataStreamSource.print();// 异步调用 此时会调用open方法JobExecutionResult execute = env.execute();JobClient flink_job = env.executeAsync("Flink Job");Thread.sleep(3000);// 此时会调用 cancel 和 close flink_job.cancel();}
}

 kafkaSource

package com.bigdata.day02;import java.util.Properties;public class KafkaSource {public static void main(String[] args) throws Exception{//envStreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();// properties Properties properties = new Properties();properties.setProperty("bootstrap.servers", "bigdata01:9092");properties.setProperty("group.id", "g1");// consumerFlinkKafkaConsumer<String> consumer= new FlinkKafkaConsumer<String>("yhedu",new SimpleStringSchema(),properties);// sourceDataStreamSource<String> dataStreamSource = env.addSource(consumer);dataStreamSource.filter(new FilterFunction<String>() {@Overridepublic boolean filter(String s) throws Exception {return s.contains("success");}}).print();env.execute();}
}