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flink Data Source数据源

news 2025/8/4 17:47:33

flink

Data Source数据源

Source

并行度
- 非并行：并行度只能为1
- 并行
基于集合的Source
- fromElements
  - package com.pxj.sx.flink;

import org.apache.flink.configuration.Configuration;
import org.apache.flink.configuration.RestOptions;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;public class FromElementDemo {public static void main(String[] args) throws Exception {Configuration configuration = new Configuration();configuration.setInteger(RestOptions.PORT, 8081);StreamExecutionEnvironment env = StreamExecutionEnvironment.createLocalEnvironmentWithWebUI(configuration);
//        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();DataStreamSource<String> daat = env.fromElements("flink", "spark", "hive");daat.print();Thread.sleep(2000000);}
}

	- fromElements(T ...) 方法是一个非并行的Source，可以将一到多个数据作为可变参数传入到该方法中，返回DataStreamSource。该方法返回的DataStream是一个有限数据流，数据读完后，程序退出，通常用于开发测试。-  fromCollection- fromCollection可以从一个结合读取数据，返回DataStream，该方法返回的DataStream是一个有限数据流，数据读完后，程序退出，通常用于开发测试。- package com.pxj.sx.flink;

import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;import java.util.Arrays;
import java.util.List;public class FromCollectionDemo {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();List<String> wordList = Arrays.asList("flink", "spark", "hadoop", "flink");DataStreamSource<String> source = env.fromCollection(wordList);source.print();env.execute("pxj");}
}

- fromParallelCollection- fromParallelCollection(SplittableIterator, Class) 方法是一个并行的Source（并行度可以使用env的setParallelism来设置），该方法需要传入两个参数，第一个是继承SplittableIterator的实现类的迭代器，第二个是迭代器中数据的类型。该方法返回的DataStream是一个有限数据流，数据读完后，程序退出，通常用于开发测试。- package com.pxj.sx.flink;

import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.types.LongValue;
import org.apache.flink.util.LongValueSequenceIterator;public class FromParallelCollectionDemo {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();//Source是多个并行的DataStreamSource<LongValue> dataSource = env.fromParallelCollection(new LongValueSequenceIterator(1, 10), LongValue.class);dataSource.print();env.execute("pxj");}
}

- generateSequence- generateSequence(long from, long to) 方法是一个并行的Source（并行度也可以通过调用该方法后，再调用setParallelism来设置）该方法需要传入两个long类型的参数，第一个是起始值，第二个是结束值，返回一个DataStreamSource。该方法返回的DataStream是一个有限数据流，数据读完后，程序退出，通常用于开发测试。

基于Socket网络端口
- socketTextStream(String hostname, int port) 方法是一个非并行的Source，该方法需要传入两个参数，第一个是指定的IP地址或主机名，第二个是端口号，即从指定的Socket读取数据创建DataStream。该方法还有多个重载的方法，其中一个是socketTextStream(String hostname, int port, String delimiter, long maxRetry)，这个重载的方法可以指定行分隔符和最大重新连接次数。这两个参数，默认行分隔符是"\n"，最大重新连接次数为0。
  - package com.pxj.sx.flink;

import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;public class SocktDemo {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();DataStreamSource<String> source = env.socketTextStream("pxj62", 8889);source.print();env.execute("pxj");}
}

基于文件
- readFile
  - package com.pxj.sx.flink;

import org.apache.flink.api.java.io.TextInputFormat;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.FileProcessingMode;public class ReadFlie {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();env.readFile(new TextInputFormat(null), "data/a.txt",FileProcessingMode.PROCESS_CONTINUOUSLY, 2000).print();env.execute("pxj");}
}

- readTextFile- package com.pxj.sx.flink;

import org.apache.flink.api.common.functions.FlatMapFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.tuple.Tuple2;
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 ReadFlieDemo {public static void main(String[] args) throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();DataStreamSource<String> source = env.readTextFile("data/a.txt");SingleOutputStreamOperator<Tuple2<String, Integer>> datas = source.flatMap(new FlatMapFunction<String, Tuple2<String, Integer>>() {@Overridepublic void flatMap(String value, Collector<Tuple2<String, Integer>> out) throws Exception {String[] strings = value.split(",");for (String s : strings) {out.collect(Tuple2.of(s, 1));}}});SingleOutputStreamOperator<Tuple2<String, Integer>> summed = datas.keyBy(0).sum(1);summed.print();env.execute("pxj");}
}

自定义Source
- 单并行度
  - 可以实现 SourceFunction 或者 RichSourceFunction , 这两者都是非并行的source算子
    - package com.pxj.sx.flink;

import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.RichParallelSourceFunction;
import org.apache.flink.streaming.api.functions.source.RichSourceFunction;public class MySource2{public static void main(String[] args)throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();DataStreamSource<String> source = env.addSource(new MySource3());source.print();env.execute("pxj");}
}
class  MySource3 extends RichSourceFunction<String> {private int i=0; //定义一个int类型的变量，从1开始private boolean flag=true;  //定义一个flag标标志//run方法就是用来读取外部的数据或产生数据的逻辑@Overridepublic void run(SourceContext<String> ctx) throws Exception {while (i<=100 && flag){Thread.sleep(1000); //为避免太快，睡眠1秒ctx.collect("data:"+i++);}}@Overridepublic void cancel() {flag=false;}
}

- 多并行度-    也可继承   ParallelSourceFunction  或者 RichParallelSourceFunction , 这两者都是可并行的source算子- 带 Rich的，都拥有 open() ,close() ,getRuntimeContext() 方法

带 Parallel的，都可多实例并行执行source

				- package com.pxj.sx.flink;import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.RichParallelSourceFunction;
import org.apache.flink.streaming.api.functions.source.SourceFunction;public class MySource1{public static void main(String[] args)throws Exception {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();DataStreamSource<String> source = env.addSource(new MySource());source.print();env.execute("pxj");}
}
class  MySource extends RichParallelSourceFunction<String> {private int i=0; //定义一个int类型的变量，从1开始private boolean flag=true;  //定义一个flag标标志//run方法就是用来读取外部的数据或产生数据的逻辑@Overridepublic void run(SourceContext<String> ctx) throws Exception {while (i<=100 && flag){Thread.sleep(1000); //为避免太快，睡眠1秒ctx.collect("data:"+i++);}}@Overridepublic void cancel() {flag=false;}
}