Flink Watermark

原创转载请注明出处：https://www.cnblogs.com/agilestyle/p/15161679.html

Event Time & Processing Time

Event Time：事件创建的时间
Processing Time：执行操作算子的当前机器的本地时间

官网权威解释可以参考 https://ci.apache.org/projects/flink/flink-docs-release-1.13/docs/concepts/time/#notions-of-time-event-time-and-processing-time

真实业务场景中，我们往往更关心事件时间（Event Time），Flink 从1.12起流的时间特性默认设置为 TimeCharacteristic.EventTime

Watermark

当 Flink 以 Event Time 模式处理数据流时，会根据数据里的时间戳来处理基于时间的算子，通常系统由于网络抖动、分布式架构等原因，会导致乱序数据的产生，从而导致窗口计算不精确。

Fink 为了避免乱序数据带来的窗口计算不精确的问题，引入了 Watermark 机制。

Watermark 用于标记 Event Time 的前进过程
Watermark 跟随 DataStream Event Time 变动，并自身携带 TimeStamp
Watermark 用于表明所有较早的事件已经（可能）到达
Watermark 本身也属于特殊的事件

官网权威解释可以参考 https://ci.apache.org/projects/flink/flink-docs-release-1.13/docs/concepts/time/#event-time-and-watermarks

在 Flink 中，Watermark 由应用程序开发人员生成，这通常需要开发人员对业务的上下游数据乱序的程度有一定的了解；如果 Watermark 设置的延迟太久，收到结果的速度可能就会很慢，解决办法是在水位线到达之前输出一个近似结果；而如果 Watermark 到达的太早，则可能收到错误结果，不过可以通过 Flink 处理迟到数据的机制来解决这个问题。

Demo

Maven Dependency

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <groupId>org.fool</groupId>
    <artifactId>flink</artifactId>
    <version>1.0-SNAPSHOT</version>

    <properties>
        <maven.compiler.source>8</maven.compiler.source>
        <maven.compiler.target>8</maven.compiler.target>
    </properties>

    <dependencies>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>1.12.5</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java_2.12</artifactId>
            <version>1.12.5</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_2.12</artifactId>
            <version>1.12.5</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-kafka_2.12</artifactId>
            <version>1.12.5</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-elasticsearch7_2.12</artifactId>
            <version>1.12.5</version>
        </dependency>

        <dependency>
            <groupId>org.apache.bahir</groupId>
            <artifactId>flink-connector-redis_2.11</artifactId>
            <version>1.0</version>
        </dependency>

        <dependency>
            <groupId>org.projectlombok</groupId>
            <artifactId>lombok</artifactId>
            <version>1.18.20</version>
        </dependency>

        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>8.0.26</version>
        </dependency>
    </dependencies>

</project>

SRC

src/main/java/org/fool/flink/contract/Sensor.java

package org.fool.flink.contract;

import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;

@Data
@NoArgsConstructor
@AllArgsConstructor
public class Sensor {
    private String id;
    private Long timestamp;
    private Double temperature;
}

src/main/java/org/fool/flink/window/WindowWatermarkTest.java

package org.fool.flink.window;

import org.apache.flink.api.common.eventtime.SerializableTimestampAssigner;
import org.apache.flink.api.common.eventtime.Watermark;
import org.apache.flink.api.common.eventtime.WatermarkGenerator;
import org.apache.flink.api.common.eventtime.WatermarkGeneratorSupplier;
import org.apache.flink.api.common.eventtime.WatermarkOutput;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.api.java.functions.KeySelector;
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.windowing.assigners.TumblingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.util.OutputTag;
import org.fool.flink.contract.Sensor;

public class WindowWatermarkTest {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment environment = StreamExecutionEnvironment.getExecutionEnvironment();
        environment.setParallelism(1);
        // environment.setParallelism(4);

        DataStream<String> inputStream = environment.socketTextStream("localhost", 7878);

        DataStream<Sensor> dataStream = inputStream.map(new MapFunction<String, Sensor>() {
            @Override
            public Sensor map(String value) throws Exception {
                String[] fields = value.split(",");
                return new Sensor(fields[0], new Long(fields[1]), new Double(fields[2]));
            }
        }).assignTimestampsAndWatermarks(new WatermarkStrategy<Sensor>() {
            @Override
            public WatermarkGenerator<Sensor> createWatermarkGenerator(WatermarkGeneratorSupplier.Context context) {
                return new WatermarkGenerator<Sensor>() {
                    private final long maxOutOfOrderness = 2000; // 2 seconds

                    private long currentMaxTimestamp;

                    @Override
                    public void onEvent(Sensor sensor, long eventTimestamp, WatermarkOutput output) {
                        // System.out.println("sensor.getTimestamp(): " + sensor.getTimestamp() * 1000L);
                        // System.out.println("eventTimestamp: " + eventTimestamp);
                        currentMaxTimestamp = Math.max(sensor.getTimestamp() * 1000L, eventTimestamp);
                        // System.out.println("currentMaxTimestamp1: " + currentMaxTimestamp);
                    }

                    @Override
                    public void onPeriodicEmit(WatermarkOutput output) {
                        // System.out.println("currentMaxTimestamp2: " + currentMaxTimestamp);
                        output.emitWatermark(new Watermark(currentMaxTimestamp - maxOutOfOrderness - 1));
                    }
                };
            }
        }.withTimestampAssigner(new SerializableTimestampAssigner<Sensor>() {
            @Override
            public long extractTimestamp(Sensor sensor, long recordTimestamp) {
                return sensor.getTimestamp() * 1000L;
            }
        }));

        OutputTag<Sensor> lateTag = new OutputTag<>("late", TypeInformation.of(Sensor.class));

        SingleOutputStreamOperator<Sensor> minStream = dataStream.keyBy(new KeySelector<Sensor, String>() {
            @Override
            public String getKey(Sensor sensor) throws Exception {
                return sensor.getId();
            }
        }).window(TumblingEventTimeWindows.of(Time.seconds(15)))
                .allowedLateness(Time.minutes(1))
                .sideOutputLateData(lateTag)
                .minBy("temperature");

        minStream.print("min temp");

        minStream.getSideOutput(lateTag).print("late");
        environment.execute();
    }

}

Note: 当前并行度是 1，Watermark 设置为 2 秒

environment.setParallelism(1);

Run

Socket Input

1,1628754405,35.8
1,1628754420,34.8
1,1628754422,33.8

Note：1628754422 这个时间点会触发窗口 [05, 20) 这个窗口计算

Console Output

min temp> Sensor(id=1, timestamp=1628754405, temperature=35.8)

Socket Input

1,1628754406,30.8
1,1628754407,31.8

Note：在 1628754422 这个时间点后继续输入, 1628754406、1628754407 后仍旧会触发窗口计算

Console Output

min temp> Sensor(id=1, timestamp=1628754406, temperature=30.8)
min temp> Sensor(id=1, timestamp=1628754406, temperature=30.8)

Note：因为设置了 1 分钟的 allowedLateness，1628754406、1628754407 这两个迟到的事件在 [05, 20) 这个窗口已经触发过计算后仍旧会触发窗口计算

allowedLateness(Time.minutes(1))

Socket Input

1,1628754482,28.8

Note：在 1628754407 这个时间点后继续输入

Console Output

min temp> Sensor(id=1, timestamp=1628754422, temperature=33.8)

Note：1628754482 这个时间点，1 分钟的 allowedLateness 的窗口会关闭，触发窗口计算

Socket Input

1,1628754411,30.3
1,1628754412,31.3

Note：在 1628754482 这个时间点后继续输入，即 1 分钟的 allowedLateness 的窗口已经关闭

Console Output

late> Sensor(id=1, timestamp=1628754411, temperature=30.3)
late> Sensor(id=1, timestamp=1628754412, temperature=31.3)

Note：1 分钟的 allowedLateness 的窗口关闭后，1628754411、1628754412 这两个迟到的事件会进入 side output

完整的 Socket Input

完整的 Console Output

Key Point

以上操作都是基于并行度为 1 的情况下进行的，当设置设置并行度不为 1 时，比如设置并行度为 4，结果会不一样。

environment.setParallelism(4);

并行度不为 1 的时候，测试输出的时候，Watermark 在上下游任务之间传递的规则：必须是每一个分区的 Watermark 都要上升，取最小的值才是当前的 Watermark，才会触发窗口聚合计算

Socket Input

Note：4 个分区的 Watermark 都到了 1628754422，才会触发窗口聚合计算

Console Output

Reference

https://ci.apache.org/projects/flink/flink-docs-release-1.13/docs/dev/datastream/event-time/generating_watermarks/

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posted @ 2021-08-19 17:28 李白与酒阅读(221) 评论(0) 编辑收藏举报

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TechStyle

精通高等数学、线性代数、概率论与数理统计 => 高阶IT从业大神

Flink Watermark

Event Time & Processing Time

Watermark

Demo

Maven Dependency

SRC

Run

Key Point

Reference

公告

TechStyle

精通 高等数学、线性代数、概率论与数理统计 => 高阶IT从业大神

Flink Watermark

Event Time & Processing Time

Watermark

Demo

Maven Dependency

SRC

Run

Key Point

Reference

公告

精通高等数学、线性代数、概率论与数理统计 => 高阶IT从业大神