旁路输出
[读取数据]---->[处理逻辑1]---------->[数据输出]---->[HDFS]
\--------->[数据输出]----->[mysql]
\-------->[数据输出]----->[clickhouse]
1) 对数据流进行分割,但又不会复制数据流的一种分流机制。 2) 对延迟迟到的数据进行处理,这样就可以不用丢弃迟到的数据。 3) 能有效解决Split算子不能进行连续分流的问题。
1)ProcessFunction; 2)KeyedProcessFunction; 3)CoProcessFunction; 4)ProcessWindowFunction; 5)ProcessAllWindowFunction; 6)ProcessJoinFunction; 7)KeyedCoProcessFunction。
窗口window 的作用是为了周期性的获取数据。 watermark的作用是防止数据出现乱序(经常),事件时间内获取不到指定的全部数据,而做的一种保险方法。 allowLateNess是将窗口关闭时间再延迟一段时间。 sideOutPut是最后兜底操作,所有过期延迟数据,指定窗口已经彻底关闭了,就会把数据放到侧输出流。
package org.bigdatatechcir.learn_flink.part5_flink_watermark;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.api.java.tuple.Tuple3;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.configuration.RestOptions;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.RichParallelSourceFunction;
import org.apache.flink.streaming.api.functions.windowing.ProcessWindowFunction;
import org.apache.flink.streaming.api.watermark.Watermark;
import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;
import org.apache.flink.util.Collector;
import org.apache.flink.util.OutputTag;
import java.time.Duration;
import java.time.Instant;
import java.time.ZoneId;
import java.time.ZonedDateTime;
import java.time.format.DateTimeFormatter;
import java.util.Random;
public class SideOutputDemo {
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
conf.setString(RestOptions.BIND_PORT, "8081");
final StreamExecutionEnvironment env = StreamExecutionEnvironment.createLocalEnvironmentWithWebUI(conf);
env.setParallelism(1);
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
DataStream<String> text = env.addSource(new RichParallelSourceFunction<String>() {
private volatile boolean running = true;
private volatile long count = 0; // 计数器用于跟踪已生成的数据条数
private final Random random = new Random();
public void run(SourceContext<String> ctx) throws Exception {
while (running) {
int randomNum = random.nextInt(5) + 1;
long timestamp = System.currentTimeMillis();
// 如果生成的是 key2,则在一个新线程中处理延迟
if (randomNum == 2) {
new Thread(() -> {
try {
int delay = random.nextInt(10) + 1; // 随机数范围从1到10
Thread.sleep(delay * 1000); // 增加1到10秒的延迟
ctx.collectWithTimestamp("key" + randomNum + "," + 1 + "," + timestamp, timestamp);
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
}
}).start();
} else {
ctx.collectWithTimestamp("key" + randomNum + "," + 1 + "," + timestamp, timestamp);
}
if (++count % 200 == 0) {
ctx.emitWatermark(new Watermark(timestamp));
//System.out.println("Manual Watermark emitted: " + timestamp);
}
ZonedDateTime generateDataDateTime = Instant.ofEpochMilli(timestamp).atZone(ZoneId.systemDefault());
DateTimeFormatter formatter = DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss.SSS");
String formattedGenerateDataDateTime = generateDataDateTime.format(formatter);
//System.out.println("Generated data: " + "key" + randomNum + "," + 1 + "," + timestamp + " at " + formattedGenerateDataDateTime);
Thread.sleep(1000); // 每次循环后等待1秒
}
}
public void cancel() {
running = false;
}
});
DataStream<Tuple3<String, Integer, Long>> tuplesWithTimestamp = text.map(new MapFunction<String, Tuple3<String, Integer, Long>>() {
public Tuple3<String, Integer, Long> map(String value) {
String[] words = value.split(",");
return new Tuple3<>(words[0], Integer.parseInt(words[1]), Long.parseLong(words[2]));
}
}).returns(Types.TUPLE(Types.STRING, Types.INT, Types.LONG));
// 设置 Watermark 策略
DataStream<Tuple3<String, Integer, Long>> withWatermarks = tuplesWithTimestamp.assignTimestampsAndWatermarks(
WatermarkStrategy.<Tuple3<String, Integer, Long>>forBoundedOutOfOrderness(Duration.ofSeconds(5))
.withTimestampAssigner((element, recordTimestamp) -> element.f2)
);
final OutputTag<Tuple3<String, Integer, Long>> outputTag = new OutputTag<Tuple3<String, Integer, Long>>("side-output"){};
// 窗口逻辑
SingleOutputStreamOperator<Tuple2<String, Integer>> keyedStream = withWatermarks
.keyBy(value -> value.f0)
.window(TumblingEventTimeWindows.of(Time.seconds(5)))
.sideOutputLateData(outputTag)
.process(new ProcessWindowFunction<Tuple3<String, Integer, Long>, Tuple2<String, Integer>, String, TimeWindow>() {
public void process(String s, Context context, Iterable<Tuple3<String, Integer, Long>> elements, Collector<Tuple2<String, Integer>> out) throws Exception {
int count = 0;
for (Tuple3<String, Integer, Long> element : elements) {
count++;
}
long start = context.window().getStart();
long end = context.window().getEnd();
ZonedDateTime startDateTime = Instant.ofEpochMilli(start).atZone(ZoneId.systemDefault());
ZonedDateTime endDateTime = Instant.ofEpochMilli(end).atZone(ZoneId.systemDefault());
DateTimeFormatter formatter = DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss.SSS");
String formattedStart = startDateTime.format(formatter);
String formattedEnd = endDateTime.format(formatter);
//System.out.println("Tumbling Window [start " + formattedStart + ", end " + formattedEnd + ") for key " + s);
// 输出窗口结束时的Watermark
long windowEndWatermark = context.currentWatermark();
ZonedDateTime windowEndDateTime = Instant.ofEpochMilli(windowEndWatermark).atZone(ZoneId.systemDefault());
String formattedWindowEndWatermark = windowEndDateTime.format(formatter);
//System.out.println("Watermark at the end of window: " + formattedWindowEndWatermark);
out.collect(new Tuple2<>(s, count));
}
});
// 输出结果
keyedStream.print();
DataStream<Tuple3<String, Integer, Long>> lateStream = keyedStream.getSideOutput(outputTag);
lateStream.print();
// 执行任务
env.execute("Side Output Demo");
}
}
这或许是一个对你有用的开源项目,data-warehouse-learning 项目是一套基于 MySQL + Kafka + Hadoop + Hive + Dolphinscheduler + Doris + Seatunnel + Paimon + Hudi + Iceberg + Flink + Dinky + DataRT + SuperSet 实现的实时离线数仓(数据湖)系统,以大家最熟悉的电商业务为切入点,详细讲述并实现了数据产生、同步、数据建模、数仓(数据湖)建设、数据服务、BI报表展示等数据全链路处理流程。
https://gitee.com/wzylzjtn/data-warehouse-learning
https://github.com/Mrkuhuo/data-warehouse-learning
https://bigdatacircle.top/
项目演示:
03
代码获取
https://gitee.com/wzylzjtn/data-warehouse-learning
https://github.com/Mrkuhuo/data-warehouse-learning
04
文档获取
05
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