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章节内容
上节我们完成了如下的内容：
Flink CEP 核心组件
CEP 的应用场景
CEP 的优势
超时事件提取
当一个模式通过within关键字定义了检测窗口时间时，部分事件序列可能因为超过窗口长度而被丢弃，为了能够处理这些超时的部分匹配，select和flatSelectAPI调用允许制定超时处理程序。
FlinkCEP开发流程
DataSource中的数据转换为DataStream
定义Pattern，并将DataStream和Pattern组合转换为PatternStream。
PatternStream 经过 Select、Process 等算子转换为 DataStream
再次转换为 DataStream 经过处理后，Sink到目标库。
SELECT 方法：
SingleOutputStreamOperator<PayEvent> result = patternStream.select(orderTimeoutOutput, new PatternTimeoutFunction<PayEvent, PayEvent>() { @Override public PayEvent timeout(Map<String, List<PayEvent>> map, long l) throws Exception { return map.get("begin").get(0); } }, new PatternSelectFunction<PayEvent, PayEvent>() { @Override public PayEvent select(Map<String, List<PayEvent>> map) throws Exception { return map.get("pay").get(0); } });
对检测到的序列模式序列应用选择函数，对于每个模式序列，调用提供的 PatternSelectFunction，模式选择函数只能产生一个结果元素。
对超时的部分模式序列应用超时函数，对于每个部分模式序列，调用提供的 PatternTimeoutFunction，模式超时函数只能产生一个结果元素。
你可以在使用相同 OutputTag 进行 Select 操作 SingleOutputStreamOperator上获得SingleOutputStreamOperator生成的超时数据流。
非确定有限自动机
FlinkCEP 在运行时会将用户的逻辑转换为这样一个 NFA Graph（NFA对象）
所以有限状态机的工作过程，就是从开始状态，根据不同的输入，自动进行转换的过程。
上图中的状态机的功能，是检测二进制数是否含有偶数个0。从图上可以看出，输入只有1和0两种。
从S1状态开始，只有输入0才会转换到S2状态，同样S2状态下只有输入0才会转换到S1。所以，二进制输入完毕，如果满足最终状态，也就是最后停在S1状态，那么输入的二进制数就含有偶数个0。
CEP开发流程
FlinkCEP开发流程：
DataSource中数据转换为DataStream、Watermark、keyby
定义Pattern，并将DataStream和Pattern组合转换为PatternStream
PatternStream经过select、process等算子转换为 DataStream
再次转换为 DataStream 经过处理后，Sink到目标库
添加依赖
<dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-cep_2.12</artifactId> <version>${flink.version}</version> </dependency>
案例1：恶意登录检测
找出5秒内，连续登录失败的账号
以下是数据：
new CepLoginBean(1L, "fail", 1597905234000L), new CepLoginBean(1L, "success", 1597905235000L), new CepLoginBean(2L, "fail", 1597905236000L), new CepLoginBean(2L, "fail", 1597905237000L), new CepLoginBean(2L, "fail", 1597905238000L), new CepLoginBean(3L, "fail", 1597905239000L), new CepLoginBean(3L, "success", 1597905240000L)
整体思路
- 获取到数据
- 在数据源上做Watermark
- 在Watermark上根据ID分组keyBy
- 做出模式Pattern
- 在数据流上进行模式匹配
- 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.IterativeCondition; 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 FlinkCepLoginTest { public static void main(String[] args) throws Exception { StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime); env.setParallelism(1); DataStreamSource<CepLoginBean> data = env.fromElements( new CepLoginBean(1L, "fail", 1597905234000L), new CepLoginBean(1L, "success", 1597905235000L), new CepLoginBean(2L, "fail", 1597905236000L), new CepLoginBean(2L, "fail", 1597905237000L), new CepLoginBean(2L, "fail", 1597905238000L), new CepLoginBean(3L, "fail", 1597905239000L), new CepLoginBean(3L, "success", 1597905240000L) ); SingleOutputStreamOperator<CepLoginBean> watermarks = data .assignTimestampsAndWatermarks(new WatermarkStrategy<CepLoginBean>() { @Override public WatermarkGenerator<CepLoginBean> createWatermarkGenerator(WatermarkGeneratorSupplier.Context context) { return new WatermarkGenerator<CepLoginBean>() { long maxTimestamp = Long.MAX_VALUE; long maxOutOfOrderness = 500L; @Override public void onEvent(CepLoginBean 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<CepLoginBean, Long> keyed = watermarks .keyBy(new KeySelector<CepLoginBean, Long>() { @Override public Long getKey(CepLoginBean value) throws Exception { return value.getUserId(); } }); Pattern<CepLoginBean, CepLoginBean> pattern = Pattern .<CepLoginBean>begin("start") .where(new IterativeCondition<CepLoginBean>() { @Override public boolean filter(CepLoginBean cepLoginBean, Context<CepLoginBean> context) throws Exception { return cepLoginBean.getOperation().equals("fail"); } }) .next("next") .where(new IterativeCondition<CepLoginBean>() { @Override public boolean filter(CepLoginBean cepLoginBean, Context<CepLoginBean> context) throws Exception { return cepLoginBean.getOperation().equals("fail"); } }) .within(Time.seconds(5)); PatternStream<CepLoginBean> patternStream = CEP.pattern(keyed, pattern); SingleOutputStreamOperator<CepLoginBean> process = patternStream .process(new PatternProcessFunction<CepLoginBean, CepLoginBean>() { @Override public void processMatch(Map<String, List<CepLoginBean>> map, Context context, Collector<CepLoginBean> collector) throws Exception { System.out.println("map: " + map); List<CepLoginBean> start = map.get("start"); collector.collect(start.get(0)); } }); process.print(); env.execute("FlinkCepLoginTest"); } } class CepLoginBean { private Long userId; private String operation; private Long timestamp; public CepLoginBean(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 "CepLoginBean{" + "userId=" + userId + ", operation='" + operation + '\'' + ", timestamp=" + timestamp + '}'; } }
运行结果
可以看到程序输出：
map: {start=[CepLoginBean{userId=2, operation='fail', timestamp=1597905236000}], next=[CepLoginBean{userId=2, operation='fail', timestamp=1597905237000}]} CepLoginBean{userId=2, operation='fail', timestamp=1597905236000} map: {start=[CepLoginBean{userId=2, operation='fail', timestamp=1597905237000}], next=[CepLoginBean{userId=2, operation='fail', timestamp=1597905238000}]} CepLoginBean{userId=2, operation='fail', timestamp=1597905237000} Process finished with exit code 0
运行截图如下所示：
