1. 场景
随机产生数据然后将产生的数据写入到hdfs 中。
2. 随机数据源
代码：
package com.wudl.flink.hdfs.source;
import org.apache.flink.api.java.tuple.Tuple;
import org.apache.flink.api.java.tuple.Tuple4;
import org.apache.flink.streaming.api.functions.source.SourceFunction;
import java.text.SimpleDateFormat;
import java.util.Date;
import java.util.Random;
/**
* @author ：wudl
* @date ：Created in 2021-12-27 0:29
* @description：
* @modified By：
* @version: 1.0
*/
public class MySource implements SourceFunction<String> {
private boolean isRunning = true;
String[] citys = {"北京","广东","山东","江苏","河南","上海","河北","浙江","香港","山西","陕西","湖南","重庆","福建","天津","云南","四川","广西","安徽","海南","江西","湖北","山西","辽宁","内蒙古"};
int i = 0;
@Override
public void run(SourceContext<String> ctx) throws Exception {
Random random = new Random();
SimpleDateFormat df = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
while (isRunning) {
int number = random.nextInt(4) + 1;
Integer id = i++;
String eventTime = df.format(new Date());
String address = citys[random.nextInt(citys.length)];
int productId = random.nextInt(25);
ctx.collect(id+","+eventTime +","+ address +","+ productId);
Thread.sleep(500);
}
}
@Override
public void cancel() {
isRunning = false;
}
}
3. hdfssink
需要注意的怎么设置文件的前缀和后缀以及 文件的大小 。
package com.wudl.flink.hdfs.sink;
import org.apache.flink.api.common.serialization.SimpleStringEncoder;
import org.apache.flink.api.connector.sink.Sink;
import org.apache.flink.api.java.tuple.Tuple4;
import org.apache.flink.connector.file.sink.FileSink;
import org.apache.flink.core.fs.Path;
import org.apache.flink.streaming.api.functions.sink.filesystem.OutputFileConfig;
import org.apache.flink.streaming.api.functions.sink.filesystem.bucketassigners.DateTimeBucketAssigner;
import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.DefaultRollingPolicy;
import java.util.concurrent.TimeUnit;
/**
* @author ：wudl
* @date ：Created in 2021-12-26 23:49
* @description：HdfsSink
* @modified By：
* @version: 1.0
*/
public class HdfsSink {
public static FileSink<String> getHdfsSink() {
OutputFileConfig config = OutputFileConfig
.builder()
.withPartPrefix("wudl")
.withPartSuffix(".txt")
.build();
FileSink<String> finkSink = FileSink.forRowFormat(new Path("hdfs://192.168.1.161:8020/FlinkFileSink/wudlfile"), new SimpleStringEncoder<String>("UTF-8"))
.withRollingPolicy(DefaultRollingPolicy.builder()
////每隔15分钟生成一个新文件
.withRolloverInterval(TimeUnit.MINUTES.toMinutes(1))
//每隔5分钟没有新数据到来,也把之前的生成一个新文件
.withInactivityInterval(TimeUnit.MINUTES.toMinutes(5))
.withMaxPartSize(1024 * 1024 * 1024)
.build())
.withOutputFileConfig(config)
.withBucketAssigner(new DateTimeBucketAssigner("yyyy-MM-dd"))
.build();
return finkSink;
}
}
4.主类
package com.wudl.flink.hdfs;
import com.wudl.flink.hdfs.sink.HdfsSink;
import com.wudl.flink.hdfs.source.MySource;
import org.apache.commons.lang3.SystemUtils;
import org.apache.flink.api.common.restartstrategy.RestartStrategies;
import org.apache.flink.api.common.time.Time;
import org.apache.flink.api.java.tuple.Tuple4;
import org.apache.flink.connector.file.sink.FileSink;
import org.apache.flink.runtime.state.filesystem.FsStateBackend;
import org.apache.flink.streaming.api.CheckpointingMode;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import java.util.concurrent.TimeUnit;
/**
* @author ：wudl
* @date ：Created in 2021-12-27 0:12
* @description：
* @modified By：
* @version: 1.0
*/
public class Application {
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(1);
env.getCheckpointConfig().setCheckpointStorage("hdfs://192.168.1.161:8020/flink-checkpoint/checkpoint");
//设置两个Checkpoint 之间最少等待时间,如设置Checkpoint之间最少是要等 500ms(为了避免每隔1000ms做一次Checkpoint的时候,前一次太慢和后一次重叠到一起去了 --//默认是0
env.getCheckpointConfig().setMinPauseBetweenCheckpoints(500);
//设置Checkpoint的时间间隔为1000ms做一次Checkpoint/其实就是每隔1000ms发一次Barrier!
env.getCheckpointConfig().setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);
//设置checkpoint的超时时间,如果 Checkpoint在 60s内尚未完成说明该次Checkpoint失败,则丢弃。
env.getCheckpointConfig().setCheckpointTimeout(10000L);
//设置同一时间有多少个checkpoint可以同时执行
env.getCheckpointConfig().setMaxConcurrentCheckpoints(2);
// 设置重启策略
// 一个时间段内的最大失败次数
env.setRestartStrategy(RestartStrategies.failureRateRestart(3,
// 衡量失败次数的是时间段
Time.of(5, TimeUnit.MINUTES),
// 间隔
Time.of(10, TimeUnit.SECONDS)
));
DataStreamSource<String> mySouceData = env.addSource(new MySource());
FileSink<String> hdfsSink = HdfsSink.getHdfsSink();
mySouceData.sinkTo(hdfsSink );
mySouceData.print();
env.execute();
}
}
结果
hdfs-flink.png
