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问题背景：cep模板 对数据源设置分组条件后，告警的数据，和分组条件对不上， 掺杂了，其他的不同组的数据，产生了告警

策略条件：

选择了两个kafka的的topic的数据作为数据源，

对A 数据源 test-topic1， 进行条件过滤， 过滤条件为：login_type  = 1

对B 数据源 test-topic2，进行条件过滤，过滤条件为：login_type =  2

分组条件 为   src_ip，hostname两个字段进行分组

进行followby 关联。时间关联的最大时间间隔为  60秒

运行并行度设置为3

通过SourceStream打印的原始数据：

2> {"src_ip":"172.11.11.1","hostname":"hostname1","as":"A","create_time":1666859021060,"create_time_desc":"2022-10-27 16:23:41","event_type_value":"single","id":"67d32010-1f66-4850-b110-a7087e419c64_0","login_type":"1"}
2> {"src_ip":"172.11.11.1","hostname":"hostname1","as":"A","create_time":1666859020192,"create_time_desc":"2022-10-27 16:23:40","event_type_value":"single","id":"67d32010-1f66-4850-b110-a7087e419c64_0","login_type":"1"}
1> {"src_ip":"172.11.11.1","hostname":"hostname2","as":"B","create_time":1666859021231,"create_time_desc":"2022-10-27 16:23:41","event_type_value":"single","id":"67d32010-1f66-4850-b110-a7087e419c64_0","login_type ":"2"}

经过cep处理后，产了告警

产生告警:{A=[{"src_ip":"172.11.11.1","hostname":"hostname1","as":"A","create_time":1666859021060,"create_time_desc":"2022-10-27 16:23:41","event_type_value":"single","id":"67d32010-1f66-4850-b110-a7087e419c64_0","login_type":"1"}, {"src_ip":"172.11.11.1","hostname":"hostname1","as":"A","create_time":1666859020192,"create_time_desc":"2022-10-27 16:23:40","event_type_value":"single","id":"67d32010-1f66-4850-b110-a7087e419c64_0","login_type":"1"}], B=[{"src_ip":"172.11.11.1","hostname":"hostname2","as":"B","create_time":1666859021231,"create_time_desc":"2022-10-27 16:23:41","event_type_value":"single","id":"67d32010-1f66-4850-b110-a7087e419c64_0","login_type":"2"}]}

经过src_ip，和hostname分组后， 理论上应该只分组后的相同的 scr_ip，hostname进行事件关联告警

结果其他的分组数据也参和进来关联告警了。 

期望的是  login_type = 1 出现至少两次， 接着login_type=2的至少出现1次，且相同的src_ip和hostname

然后结果是下面数据也产生了告警。

{"src_ip":"172.11.11.1","hostname":"hostname1","login_type":1}
{"src_ip":"172.11.11.1","hostname":"hostname1","login_type":1}
{"src_ip":"172.11.11.1","hostname":"hostname1","login_type":2}

怀疑是分组没生效。

然后debug数据源那块的方法kafkaStreamSource（） 里面有进行分组，debug后发现确实也进行了keyby

后来找不到其他问题，纠结了下， 怀疑是不是 KeyedSteam.union(KeyedStream)后得到的就不是一个KeyedSteam了。 所以

出现问题的原始代码数据源代码：

//程序具体执行流程DataStream<JSONObject> sourceStream = SourceProcess.getKafkaStream(env, rule);DataStream<JSONObject> resultStream = TransformProcess.process(sourceStream, rule);SinkProcess.sink(resultStream, rule);public static DataStream<JSONObject> getKafkaStream(StreamExecutionEnvironment env, Rule rule) {DataStream<JSONObject> inputStream = null;List<Event> events = rule.getEvents();if (events.size() > SharingConstant.NUMBER_ZERO) {for (Event event : events) {FlinkKafkaConsumer<JSONObject> kafkaConsumer =new KafkaSourceFunction(rule, event).init();if (inputStream != null) {// 多条 stream 合成一条 streaminputStream = inputStream.union(kafkaStreamSource(env, event, rule, kafkaConsumer));} else {// 只有一条 streaminputStream = kafkaStreamSource(env, event, rule, kafkaConsumer);}}}return inputStream;}private static DataStream<JSONObject> kafkaStreamSource(StreamExecutionEnvironment env,Event event,Rule rule,FlinkKafkaConsumer<JSONObject> kafkaConsumer) {DataStream<JSONObject> inputStream = env.addSource(kafkaConsumer);// 对多个黑白名单查询进行循环String conditions = event.getConditions();while (conditions.contains(SharingConstant.ARGS_NAME)) {// 使用新的redis 数据结构，进行 s.include 过滤inputStream = AsyncDataStream.orderedWait(inputStream,new RedisNameListFilterSourceFunction(s,rule.getSettings().getRedis()),30,TimeUnit.SECONDS,2000);conditions = conditions.replace(s, "");}// 一般过滤处理inputStream = AsyncDataStream.orderedWait(inputStream,new Redis3SourceFunction(event, rule.getSettings().getRedis()), 30, TimeUnit.SECONDS, 2000);// kafka source 进行 keyBy 处理return KeyedByStream.keyedBy(inputStream, rule.getGroupBy());}public static DataStream<JSONObject> keyedBy(DataStream<JSONObject> input, Map<String, String> groupBy) {if (null == groupBy || groupBy.isEmpty() ||"".equals(groupBy.values().toArray()[SharingConstant.NUMBER_ZERO])){return input;}return input.keyBy(new TwoEventKeySelector(groupBy.values().toArray()[SharingConstant.NUMBER_ZERO].toString()));}public class TwoEventKeySelector implements KeySelector<JSONObject, String> {private static final long serialVersionUID = 8534968406068735616L;private final String groupBy;public TwoEventKeySelector(String groupBy) {this.groupBy = groupBy;}@Overridepublic String getKey(JSONObject event) {StringBuilder keys = new StringBuilder();for (String key : groupBy.split(SharingConstant.DELIMITER_COMMA)) {keys.append(event.getString(key));}return keys.toString();}
}

问题出现在这里：

// 多条 stream 合成一条 stream
                    inputStream = inputStream.union(kafkaStreamSource(env, event, rule, kafkaConsumer));

kafkaStreamSource()这个方法返回的是 KeyedStream ,

两个KeyedStream unio合并后，  本来以为返回时KeyedStream，结果确是DataStream类型，

结果导致cep分组不生效，一个告警中出现了其他分组的数据。

解决方法， 就是在cep pattern前 根据是否有分组条件再KeyedBy一次

  private static DataStream<JSONObject> patternProcess(DataStream<JSONObject> inputStream, Rule rule) {PatternGen patternGenerator = new PatternGen(rule.getPatterns(), rule.getWindow().getSize());Pattern<JSONObject, JSONObject> pattern = patternGenerator.getPattern();if (!rule.getGroupBy().isEmpty()){inputStream = KeyedByStream.keyedBy(inputStream, rule.getGroupBy());}PatternStream<JSONObject> patternStream = CEP.pattern(inputStream, pattern);return patternStream.inProcessingTime().select(new RuleSelectFunction(rule.getAlarmInfo(), rule.getSelects()));

输入数据：

 {"src_ip":"172.11.11.1","hostname":"hostname1","as":"A","create_time":1666860300012,"create_time_desc":"2022-10-27 16:45:00","event_type_value":"single","id":"1288a709-d2b3-41c9-b7b7-e45149084514_0","login_type":"1"}{"src_ip":"172.11.11.1","hostname":"hostname1","as":"A","create_time":1666860299272,"create_time_desc":"2022-10-27 16:44:59","event_type_value":"single","id":"1288a709-d2b3-41c9-b7b7-e45149084514_0","login_type":"1"}{"src_ip":"172.11.11.1","hostname":"hostname2","as":"B","create_time":1666860300196,"create_time_desc":"2022-10-27 16:45:00","event_type_value":"single","id":"1288a709-d2b3-41c9-b7b7-e45149084514_0","login_type":"2"}

不产生告警，符合预期

再次输入同分组的数据：

2> {"src_ip":"172.11.11.1","hostname":"hostname1","as":"A","create_time":1666860369307,"create_time_desc":"2022-10-27 16:46:09","event_type_value":"single","id":"61004dd6-69ec-4d67-845c-8c15e7cc4bf7_0","app_id":"1"}
2> {"src_ip":"172.11.11.1","hostname":"hostname1","as":"A","create_time":1666860368471,"create_time_desc":"2022-10-27 16:46:08","event_type_value":"single","id":"61004dd6-69ec-4d67-845c-8c15e7cc4bf7_0","app_id":"1"}
2> {"src_ip":"172.11.11.1","hostname":"hostname1","as":"B","create_time":1666860369478,"create_time_desc":"2022-10-27 16:46:09","event_type_value":"single","id":"61004dd6-69ec-4d67-845c-8c15e7cc4bf7_0","app_id":"2"}
产生告警:{A=[{"src_ip":"172.11.11.1","hostname":"hostname1","as":"A","create_time":1666860368471,"create_time_desc":"2022-10-27 16:46:08","event_type_value":"single","id":"61004dd6-69ec-4d67-845c-8c15e7cc4bf7_0","app_id":"1"}, {"src_ip":"172.11.11.1","hostname":"hostname1","as":"A","create_time":1666860369307,"create_time_desc":"2022-10-27 16:46:09","event_type_value":"single","id":"61004dd6-69ec-4d67-845c-8c15e7cc4bf7_0","app_id":"1"}], B=[{"src_ip":"172.11.11.1","hostname":"hostname1","as":"B","create_time":1666860369478,"create_time_desc":"2022-10-27 16:46:09","event_type_value":"single","id":"61004dd6-69ec-4d67-845c-8c15e7cc4bf7_0","app_id":"2"}]}
告警输出:{"org_log_id":"61004dd6-69ec-4d67-845c-8c15e7cc4bf7_0,61004dd6-69ec-4d67-845c-8c15e7cc4bf7_0,61004dd6-69ec-4d67-845c-8c15e7cc4bf7_0","event_category_id":1,"event_technique_type":"无","event_description":"1","alarm_first_time":1666860368471,"src_ip":"172.11.11.1","hostname":"hostname1","intelligence_id":"","strategy_category_id":"422596451785379862","intelligence_type":"","id":"cc1cd8cd-a626-4916-bdd3-539ea57e898f","event_nums":3,"event_category_label":"资源开发","severity":"info","create_time":1666860369647,"strategy_category_name":"网络威胁分析","rule_name":"ceptest","risk_score":1,"data_center":"guo-sen","baseline":[],"sop_id":"","event_device_type":"无","rule_id":214,"policy_type":"pattern","strategy_category":"/NetThreatAnalysis","internal_event":"1","event_name":"ceptest","event_model_source":"/RuleEngine/OnLine","alarm_last_time":1666860369478}

产生告警符合预期