大数据-131 - Flink CEP 案例：检测交易活跃用户、超时未交付-阿里云开发者社区

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章节内容

上节我们完成了如下的内容：

Flink CEP 开发的流程

CEP 开发依赖

CEP 案例：恶意登录检测实现

Fline CEP

之前已经介绍过，但是防止大家没看到，这里再简单介绍以下。

基本概念

Flink CEP（Complex Event Processing）是Apache Flink提供的一个扩展库，用于实时复杂事件处理。通过Flink CEP，开发者可以从流数据中识别出特定的事件模式。这在欺诈检测、网络安全、实时监控、物联网等场景中非常有用。

Flink CEP的核心是通过定义事件模式，从流中检测复杂事件序列。

具体来说，CEP允许用户：

定义事件模式：用户可以描述感兴趣的事件组合（如连续事件、延迟事件等）。

匹配模式：Flink CEP从流中搜索与定义模式相匹配的事件序列。

处理匹配结果：一旦找到符合模式的事件序列，用户可以定义如何处理这些匹配。

基本组成部分

Pattern（模式）：描述要在事件流中匹配的事件序列。可以是单个事件或多个事件的组合。常用的模式操作包括next（紧邻）、followedBy（接续）等。

PatternStream（模式流）：通过应用模式定义，将事件流转变为模式流。

Select函数：用于从模式流中提取匹配的事件序列

CEP开发步骤

开发Flink CEP应用的基本步骤包括：

定义事件流：创建一个DataStream，表示原始的事件流。

定义事件模式：使用Flink CEP的API定义事件模式，例如连续事件、迟到事件等。

将模式应用到流中：将定义好的模式应用到事件流上，生成模式流PatternStream。

提取匹配事件：使用select函数提取匹配模式的事件，并定义如何处理这些事件。

使用场景

欺诈检测：可以通过CEP识别连续发生的异常行为，如频繁的登录尝试等。

网络监控：检测一段时间内的特定网络攻击模式。

物联网：分析传感器数据，检测设备异常、温度异常等。

用户行为分析：分析用户在某一时间段内的行为序列，从而作出预测或检测异常。

案例2：检测交易活跃用户

业务需求

业务上需要找出24小时内，至少5次有效交易的用户。

数据源如下：

new CepActiveUserBean("100XX", 0.0D, 1597905234000L),
new CepActiveUserBean("100XX", 100.0D, 1597905235000L),
new CepActiveUserBean("100XX", 200.0D, 1597905236000L),
new CepActiveUserBean("100XX", 300.0D, 1597905237000L),
new CepActiveUserBean("100XX", 400.0D, 1597905238000L),
new CepActiveUserBean("100XX", 500.0D, 1597905239000L),
new CepActiveUserBean("101XX", 0.0D, 1597905240000L),
new CepActiveUserBean("101XX", 100.0D, 1597905241000L)

获取数据源
Watermark转化
keyBy转化
至少5次：timeOrMore(5)
24小时之内：within(Time.hours(24))
模式匹配
提取匹配成功的数据

编写代码

package icu.wzk;

import org.apache.flink.api.common.eventtime.*;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.cep.CEP;
import org.apache.flink.cep.PatternStream;
import org.apache.flink.cep.functions.PatternProcessFunction;
import org.apache.flink.cep.pattern.Pattern;
import org.apache.flink.cep.pattern.conditions.SimpleCondition;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.util.Collector;

import java.util.List;
import java.util.Map;


public class FlinkCepActiveUser {

    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
        env.setParallelism(1);
        DataStreamSource<CepActiveUserBean> data = env.fromElements(
                new CepActiveUserBean("100XX", 0.0D, 1597905234000L),
                new CepActiveUserBean("100XX", 100.0D, 1597905235000L),
                new CepActiveUserBean("100XX", 200.0D, 1597905236000L),
                new CepActiveUserBean("100XX", 300.0D, 1597905237000L),
                new CepActiveUserBean("100XX", 400.0D, 1597905238000L),
                new CepActiveUserBean("100XX", 500.0D, 1597905239000L),
                new CepActiveUserBean("101XX", 0.0D, 1597905240000L),
                new CepActiveUserBean("101XX", 100.0D, 1597905241000L)
        );
        SingleOutputStreamOperator<CepActiveUserBean> watermark = data
                .assignTimestampsAndWatermarks(new WatermarkStrategy<CepActiveUserBean>() {
                    @Override
                    public WatermarkGenerator<CepActiveUserBean> createWatermarkGenerator(WatermarkGeneratorSupplier.Context context) {
                        return new WatermarkGenerator<CepActiveUserBean>() {

                            long maxTimestamp = Long.MAX_VALUE;
                            long maxOutOfOrderness = 500L;

                            @Override
                            public void onEvent(CepActiveUserBean event, long eventTimestamp, WatermarkOutput output) {
                                maxTimestamp = Math.max(event.getTimestamp(), maxTimestamp);
                            }

                            @Override
                            public void onPeriodicEmit(WatermarkOutput output) {
                                output.emitWatermark(new Watermark(maxTimestamp - maxOutOfOrderness));
                            }
                        };
                    }
                }.withTimestampAssigner((element, recordTimes) -> element.getTimestamp())
                );
        KeyedStream<CepActiveUserBean, String> keyed = watermark
                .keyBy(new KeySelector<CepActiveUserBean, String>() {
                    @Override
                    public String getKey(CepActiveUserBean value) throws Exception {
                        return value.getUsername();
                    }
                });
        Pattern<CepActiveUserBean, CepActiveUserBean> pattern = Pattern
                .<CepActiveUserBean>begin("start")
                .where(new SimpleCondition<CepActiveUserBean>() {
                    @Override
                    public boolean filter(CepActiveUserBean value) throws Exception {
                        return value.getPrice() > 0;
                    }
                })
                .timesOrMore(5)
                .within(Time.hours(24));
        PatternStream<CepActiveUserBean> parentStream = CEP.pattern(keyed, pattern);
        SingleOutputStreamOperator<CepActiveUserBean> process = parentStream
                .process(new PatternProcessFunction<CepActiveUserBean, CepActiveUserBean>() {
                    @Override
                    public void processMatch(Map<String, List<CepActiveUserBean>> map, Context context, Collector<CepActiveUserBean> collector) throws Exception {
                        System.out.println("map: " + map);
                    }
                });
        process.print();
        env.execute("FlinkCepActiveUser");
    }

}


class CepActiveUserBean {
    private String username;
    private Double price;
    private Long timestamp;

    public CepActiveUserBean(String username, Double price, Long timestamp) {
        this.username = username;
        this.price = price;
        this.timestamp = timestamp;
    }

    public String getUsername() {
        return username;
    }

    public void setUsername(String username) {
        this.username = username;
    }

    public Double getPrice() {
        return price;
    }

    public void setPrice(Double price) {
        this.price = price;
    }

    public Long getTimestamp() {
        return timestamp;
    }

    public void setTimestamp(Long timestamp) {
        this.timestamp = timestamp;
    }

    @Override
    public String toString() {
        return "CepActiveUserBean{" +
                "username='" + username + '\'' +
                ", price=" + price +
                ", timestamp=" + timestamp +
                '}';
    }
}

运行结果

map: {start=[CepActiveUserBean{username='100XX', price=100.0, timestamp=1597905235000}, CepActiveUserBean{username='100XX', price=200.0, timestamp=1597905236000}, CepActiveUserBean{username='100XX', price=300.0, timestamp=1597905237000}, CepActiveUserBean{username='100XX', price=400.0, timestamp=1597905238000}, CepActiveUserBean{username='100XX', price=500.0, timestamp=1597905239000}]}

Process finished with exit code 0

运行结果如下图所示：

案例3：超时未支付

业务需求

找出下单后10分钟没有支付的订单，数据源如下：

new TimeOutPayBean(1L, "create", 1597905234000L),
new TimeOutPayBean(1L, "pay", 1597905235000L),
new TimeOutPayBean(2L, "create", 1597905236000L),
new TimeOutPayBean(2L, "pay", 1597905237000L),
new TimeOutPayBean(3L, "create", 1597905239000L)

获取数据源
转 Watermark
keyBy 转化
做出 Pattern （下单以后10分钟未支付）
模式匹配
取出匹配成功的数据

编写代码

package icu.wzk;

import org.apache.flink.api.common.eventtime.*;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.cep.CEP;
import org.apache.flink.cep.PatternSelectFunction;
import org.apache.flink.cep.PatternStream;
import org.apache.flink.cep.PatternTimeoutFunction;
import org.apache.flink.cep.pattern.Pattern;
import org.apache.flink.cep.pattern.conditions.IterativeCondition;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.util.OutputTag;

import java.util.List;
import java.util.Map;


public class FlinkCepTimeOutPay {

    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
        env.setParallelism(1);
        DataStreamSource<TimeOutPayBean> data = env.fromElements(
                new TimeOutPayBean(1L, "create", 1597905234000L),
                new TimeOutPayBean(1L, "pay", 1597905235000L),
                new TimeOutPayBean(2L, "create", 1597905236000L),
                new TimeOutPayBean(2L, "pay", 1597905237000L),
                new TimeOutPayBean(3L, "create", 1597905239000L)
        );
        DataStream<TimeOutPayBean> watermark = data
                .assignTimestampsAndWatermarks(new WatermarkStrategy<TimeOutPayBean>() {
                    @Override
                    public WatermarkGenerator<TimeOutPayBean> createWatermarkGenerator(WatermarkGeneratorSupplier.Context context) {
                        return new WatermarkGenerator<TimeOutPayBean>() {

                            long maxTimestamp = Long.MAX_VALUE;
                            long maxOutOfOrderness = 500L;

                            @Override
                            public void onEvent(TimeOutPayBean event, long eventTimestamp, WatermarkOutput output) {
                                maxTimestamp = Math.max(maxTimestamp, event.getTimestamp());
                            }

                            @Override
                            public void onPeriodicEmit(WatermarkOutput output) {
                                output.emitWatermark(new Watermark(maxTimestamp - maxOutOfOrderness));
                            }
                        };
                    }
                }.withTimestampAssigner((element, recordTimestamp) -> element.getTimestamp())
                );
        KeyedStream<TimeOutPayBean, Long> keyedStream = watermark
                .keyBy(new KeySelector<TimeOutPayBean, Long>() {
                    @Override
                    public Long getKey(TimeOutPayBean value) throws Exception {
                        return value.getUserId();
                    }
                });
        // 逻辑处理代码
        OutputTag<TimeOutPayBean> orderTimeoutOutput = new OutputTag<>("orderTimeout") {};
        Pattern<TimeOutPayBean, TimeOutPayBean> pattern = Pattern
                .<TimeOutPayBean>begin("begin")
                .where(new IterativeCondition<TimeOutPayBean>() {
                    @Override
                    public boolean filter(TimeOutPayBean timeOutPayBean, Context<TimeOutPayBean> context) throws Exception {
                        return timeOutPayBean.getOperation().equals("create");
                    }
                })
                .followedBy("pay")
                .where(new IterativeCondition<TimeOutPayBean>() {
                    @Override
                    public boolean filter(TimeOutPayBean timeOutPayBean, Context<TimeOutPayBean> context) throws Exception {
                        return timeOutPayBean.getOperation().equals("pay");
                    }
                })
                .within(Time.seconds(600));
        PatternStream<TimeOutPayBean> patternStream = CEP.pattern(keyedStream, pattern);
        SingleOutputStreamOperator<TimeOutPayBean> result = patternStream
                .select(orderTimeoutOutput, new PatternTimeoutFunction<TimeOutPayBean, TimeOutPayBean>() {
                    @Override
                    public TimeOutPayBean timeout(Map<String, List<TimeOutPayBean>> map, long l) throws Exception {
                        return map.get("begin").get(0);
                    }
                }, new PatternSelectFunction<TimeOutPayBean, TimeOutPayBean>() {
                    @Override
                    public TimeOutPayBean select(Map<String, List<TimeOutPayBean>> map) throws Exception {
                        return map.get("pay").get(0);
                    }
                });

        // 输出结果
        // result.print();
        System.out.println("==============");
        DataStream<TimeOutPayBean> sideOutput = result
                .getSideOutput(orderTimeoutOutput);
        sideOutput.print();

        // 执行
        env.execute("FlinkCepTimeOutPay");
    }

}


class TimeOutPayBean {

    private Long userId;

    private String operation;

    private Long timestamp;

    public TimeOutPayBean(Long userId, String operation, Long timestamp) {
        this.userId = userId;
        this.operation = operation;
        this.timestamp = timestamp;
    }

    public Long getUserId() {
        return userId;
    }

    public void setUserId(Long userId) {
        this.userId = userId;
    }

    public String getOperation() {
        return operation;
    }

    public void setOperation(String operation) {
        this.operation = operation;
    }

    public Long getTimestamp() {
        return timestamp;
    }

    public void setTimestamp(Long timestamp) {
        this.timestamp = timestamp;
    }

    @Override
    public String toString() {
        return "TimeOutPayBean{" +
                "userId=" + userId +
                ", operation='" + operation + '\'' +
                ", timestamp=" + timestamp +
                '}';
    }
}

运行结果

控制台输出为：

==============
TimeOutPayBean{userId=1, operation='pay', timestamp=1597905235000}
TimeOutPayBean{userId=3, operation='create', timestamp=1597905239000}
TimeOutPayBean{userId=2, operation='pay', timestamp=1597905237000}

Process finished with exit code 0

对应截图如下：

大数据-131 - Flink CEP 案例：检测交易活跃用户、超时未交付

编写代码

运行结果

案例3：超时未支付

业务需求

编写代码

运行结果

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