Apache FlinkCEP 实现超时状态监控的步骤详解

CEP - Complex Event Processing复杂事件处理。

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Apache FlinkCEP API

CEPTimeoutEventJob

FlinkCEP源码简析

DataStream和PatternStream

DataStream 一般由相同类型事件或元素组成，一个DataStream可以通过一系列的转换操作如Filter、Map等转换为另一个DataStream。

PatternStream 是对CEP模式匹配的流的抽象，把DataStream和Pattern组合在一块，然后对外提供select和flatSelect等方法。PatternStream并不是DataStream，它提供方法把匹配的模式序列和与其相关联的事件组成的映射（就是Map<模式名称,List<事件>>）发出去，发到SingleOutputStreamOperator里面，SingleOutputStreamOperator是DataStream。

CEPOperatorUtils工具类里的方法和变量使用了「PatternStream」来命名，比如：

public

static
 <IN, OUT>
SingleOutputStreamOperator
<OUT> createPatternStream(...){...}
public

static
 <IN, OUT1, OUT2>
SingleOutputStreamOperator
<OUT1> createTimeoutPatternStream(...){...}

final

SingleOutputStreamOperator
<OUT> patternStream;

SingleOutputStreamOperator

@Public

public

class

SingleOutputStreamOperator
<T>
extends

DataStream
<T> {...}

PatternStream的构造方法：

PatternStream
(
final

DataStream
<T> inputStream,
final

Pattern
<T, ?> pattern) {

this
.inputStream = inputStream;

this
.pattern = pattern;

this
.comparator =
null
;

}

PatternStream
(
final

DataStream
<T> inputStream,
final

Pattern
<T, ?> pattern,
final

EventComparator
<T> comparator) {

this
.inputStream = inputStream;

this
.pattern = pattern;

this
.comparator = comparator;

}

Pattern、Quantifier和EventComparator

Pattern是模式定义的Base Class，Builder模式，定义好的模式会被NFACompiler用来生成NFA。

如果想要自己实现类似next和followedBy这种方法，比如timeEnd，对Pattern进行扩展重写应该是可行的。

public
class
Pattern
<T, F
extends
 T> {
/** 模式名称 */
private
final
String
 name;
/** 前面一个模式 */
private
final
Pattern
<T, ?
extends
 T> previous;
/** 一个事件如果要被当前模式匹配到，必须满足的约束条件 */
private
IterativeCondition
<F> condition;
/** 时间窗口长度，在时间长度内进行模式匹配 */
private
Time
 windowTime;
/** 模式量词，意思是一个模式匹配几个事件等 默认是匹配到一个 */
private
Quantifier
 quantifier =
Quantifier
.one(
ConsumingStrategy
.STRICT);
/** 停止将事件收集到循环状态时，事件必须满足的条件 */
private
IterativeCondition
<F> untilCondition;
/**
   * 适用于{@code times}模式，用来维护模式里事件可以连续发生的次数
   */
private
Times
 times;
// 匹配到事件之后的跳过策略
private
final
AfterMatchSkipStrategy
 afterMatchSkipStrategy;
  ...
}

Quantifier是用来描述具体模式行为的，主要有三大类：

Single-单一匹配、Looping-循环匹配、Times-一定次数或者次数范围内都能匹配到。

每一个模式Pattern可以是optional可选的（单一匹配或循环匹配），并可以设置ConsumingStrategy。

循环和次数也有一个额外的内部ConsumingStrategy，用在模式中接收的事件之间。

public
class
Quantifier
 {
  ...
/**
   * 5个属性，可以组合，但并非所有的组合都是有效的
   */
public
enum
QuantifierProperty
 {
    SINGLE,
    LOOPING,
    TIMES,
    OPTIONAL,
    GREEDY
  }
/**
   * 描述在此模式中匹配哪些事件的策略
   */
public
enum
ConsumingStrategy
 {
    STRICT,
    SKIP_TILL_NEXT,
    SKIP_TILL_ANY,
    NOT_FOLLOW,
    NOT_NEXT
  }
/**
   * 描述当前模式里事件可以连续发生的次数；举个例子，模式条件无非就是boolean，满足true条件的事件连续出现times次，或者一个次数范围，比如2~4次，2次，3次，4次都会被当前模式匹配出来，因此同一个事件会被重复匹配到
   */
public
static
class
Times
 {
private
final
int
 from;
private
final
int
 to;
private
Times
(
int
 from,
int
 to) {
Preconditions
.checkArgument(from >
0
,
"The from should be a positive number greater than 0."
);
Preconditions
.checkArgument(to >= from,
"The to should be a number greater than or equal to from: "
 + from +
"."
);
this
.from = from;
this
.to = to;
    }
public
int
 getFrom() {
return
 from;
    }
public
int
 getTo() {
return
 to;
    }
// 次数范围
public
static
Times
 of(
int
 from,
int
 to) {
return
new
Times
(from, to);
    }
// 指定具体次数
public
static
Times
 of(
int
 times) {
return
new
Times
(times, times);
    }
@Override
public
boolean
 equals(
Object
 o) {
if
 (
this
 == o) {
return
true
;
      }
if
 (o ==
null
 || getClass() != o.getClass()) {
return
false
;
      }
Times
 times = (
Times
) o;
return
 from == times.from &&
        to == times.to;
    }
@Override
public
int
 hashCode() {
return
Objects
.hash(from, to);
    }
  }
  ...
}

EventComparator，自定义事件比较器，实现EventComparator接口。

public

interface

EventComparator
<T>
extends

Comparator
<T>,
Serializable
 {
long
 serialVersionUID =
1L
;
}

NFACompiler和NFA

NFACompiler提供将Pattern编译成NFA或者NFAFactory的方法，使用NFAFactory可以创建多个NFA。

public
class
NFACompiler
 {
  ...
/**
   * NFAFactory 创建NFA的接口
   *
   * @param <T> Type of the input events which are processed by the NFA
   */
public
interface
NFAFactory
<T>
extends
Serializable
 {
    NFA<T> createNFA();
  }

/**
   * NFAFactory的具体实现NFAFactoryImpl
   *
   * <p>The implementation takes the input type serializer, the window time and the set of
   * states and their transitions to be able to create an NFA from them.
   *
   * @param <T> Type of the input events which are processed by the NFA
   */
private
static
class
NFAFactoryImpl
<T>
implements
NFAFactory
<T> {

private
static
final
long
 serialVersionUID =
8939783698296714379L
;

private
final
long
 windowTime;
private
final
Collection
<
State
<T>> states;
private
final
boolean
 timeoutHandling;

private
NFAFactoryImpl
(
long
 windowTime,
Collection
<
State
<T>> states,
boolean
 timeoutHandling) {

this
.windowTime = windowTime;
this
.states = states;
this
.timeoutHandling = timeoutHandling;
    }

@Override
public
 NFA<T> createNFA() {
// 一个NFA由状态集合、时间窗口的长度和是否处理超时组成
return
new
 NFA<>(states, windowTime, timeoutHandling);
    }
  }
}

NFA：Non-deterministic finite automaton - 非确定的有限（状态）自动机。

更多内容参见

https://zh.wikipedia.org/wiki/非确定有限状态自动机

public
class
 NFA<T> {
/**
   * NFACompiler返回的所有有效的NFA状态集合
   * These are directly derived from the user-specified pattern.
   */
private
final
Map
<
String
,
State
<T>> states;

/**
   * Pattern.within(Time)指定的时间窗口长度
   */
private
final
long
 windowTime;

/**
   * 一个超时匹配的标记
   */
private
final
boolean
 handleTimeout;
  ...
}

PatternSelectFunction和PatternFlatSelectFunction

当一个包含被匹配到的事件的映射能够通过模式名称访问到的时候，PatternSelectFunction的select()方法会被调用。模式名称是由Pattern定义的时候指定的。select()方法恰好返回一个结果，如果需要返回多个结果，则可以实现PatternFlatSelectFunction。

public

interface

PatternSelectFunction
<IN, OUT>
extends

Function
,
Serializable
 {

/**

   * 从给到的事件映射中生成一个结果。这些事件使用他们关联的模式名称作为唯一标识

   */

  OUT select(
Map
<
String
,
List
<IN>> pattern)
throws

Exception
;

}

PatternFlatSelectFunction，不是返回一个OUT，而是使用Collector 把匹配到的事件收集起来。

public
interface
PatternFlatSelectFunction
<IN, OUT>
extends
Function
,
Serializable
 {

/**
   * 生成一个或多个结果
   */
void
 flatSelect(
Map
<
String
,
List
<IN>> pattern,
Collector
<OUT> out)
throws
Exception
;
}

SelectTimeoutCepOperator、PatternTimeoutFunction

SelectTimeoutCepOperator是在CEPOperatorUtils中调用createTimeoutPatternStream()方法时创建出来。

SelectTimeoutCepOperator中会被算子迭代调用的方法是processMatchedSequences()和processTimedOutSequences()。

模板方法...对应到抽象类AbstractKeyedCEPPatternOperator中processEvent()方法和advanceTime()方法。

还有FlatSelectTimeoutCepOperator和对应的PatternFlatTimeoutFunction。

public
class
SelectTimeoutCepOperator
<IN, OUT1, OUT2, KEY>
extends
AbstractKeyedCEPPatternOperator
<IN, KEY, OUT1,
SelectTimeoutCepOperator
.
SelectWrapper
<IN, OUT1, OUT2>> {
private
OutputTag
<OUT2> timedOutOutputTag;
public
SelectTimeoutCepOperator
(
TypeSerializer
<IN> inputSerializer,
boolean
 isProcessingTime,
NFACompiler
.
NFAFactory
<IN> nfaFactory,
final
EventComparator
<IN> comparator,
AfterMatchSkipStrategy
 skipStrategy,
// 参数命名混淆了flat...包括SelectWrapper类中的成员命名...
PatternSelectFunction
<IN, OUT1> flatSelectFunction,
PatternTimeoutFunction
<IN, OUT2> flatTimeoutFunction,
OutputTag
<OUT2> outputTag,
OutputTag
<IN> lateDataOutputTag) {
super
(
      inputSerializer,
      isProcessingTime,
      nfaFactory,
      comparator,
      skipStrategy,
new
SelectWrapper
<>(flatSelectFunction, flatTimeoutFunction),
      lateDataOutputTag);
this
.timedOutOutputTag = outputTag;
  }
  ...
}
public
interface
PatternTimeoutFunction
<IN, OUT>
extends
Function
,
Serializable
 {
  OUT timeout(
Map
<
String
,
List
<IN>> pattern,
long
 timeoutTimestamp)
throws
Exception
;
}
public
interface
PatternFlatTimeoutFunction
<IN, OUT>
extends
Function
,
Serializable
 {
void
 timeout(
Map
<
String
,
List
<IN>> pattern,
long
 timeoutTimestamp,
Collector
<OUT> out)
throws
Exception
;
}

CEP和CEPOperatorUtils

CEP是创建PatternStream的工具类，PatternStream只是DataStream和Pattern的组合。

public
class
 CEP {

public
static
 <T>
PatternStream
<T> pattern(
DataStream
<T> input,
Pattern
<T, ?> pattern) {
return
new
PatternStream
<>(input, pattern);
  }

public
static
 <T>
PatternStream
<T> pattern(
DataStream
<T> input,
Pattern
<T, ?> pattern,
EventComparator
<T> comparator) {
return
new
PatternStream
<>(input, pattern, comparator);
  }
}

CEPOperatorUtils是在PatternStream的select()方法和flatSelect()方法被调用的时候，去创建SingleOutputStreamOperator（DataStream）。

public
class
CEPOperatorUtils
 {
  ...
private
static
 <IN, OUT, K>
SingleOutputStreamOperator
<OUT> createPatternStream(
final
DataStream
<IN> inputStream,
final
Pattern
<IN, ?> pattern,
final
TypeInformation
<OUT> outTypeInfo,
final
boolean
 timeoutHandling,
final
EventComparator
<IN> comparator,
final
OperatorBuilder
<IN, OUT> operatorBuilder) {
final
TypeSerializer
<IN> inputSerializer = inputStream.getType().createSerializer(inputStream.getExecutionConfig());

// check whether we use processing time
final
boolean
 isProcessingTime = inputStream.getExecutionEnvironment().getStreamTimeCharacteristic() ==
TimeCharacteristic
.
ProcessingTime
;

// compile our pattern into a NFAFactory to instantiate NFAs later on
final
NFACompiler
.
NFAFactory
<IN> nfaFactory =
NFACompiler
.compileFactory(pattern, timeoutHandling);

final
SingleOutputStreamOperator
<OUT> patternStream;

if
 (inputStream
instanceof
KeyedStream
) {
KeyedStream
<IN, K> keyedStream = (
KeyedStream
<IN, K>) inputStream;
      patternStream = keyedStream.transform(
        operatorBuilder.getKeyedOperatorName(),
        outTypeInfo,
        operatorBuilder.build(
          inputSerializer,
          isProcessingTime,
          nfaFactory,
          comparator,
          pattern.getAfterMatchSkipStrategy()));
    }
else
 {
KeySelector
<IN,
Byte
> keySelector =
new
NullByteKeySelector
<>();
      patternStream = inputStream.keyBy(keySelector).transform(
        operatorBuilder.getOperatorName(),
        outTypeInfo,
        operatorBuilder.build(
          inputSerializer,
          isProcessingTime,
          nfaFactory,
          comparator,
          pattern.getAfterMatchSkipStrategy()
        )).forceNonParallel();
    }

return
 patternStream;
  }
  ...
}

FlinkCEP实现步骤

1. IN: DataSource -> DataStream -> Transformations -> DataStream
2. Pattern: Pattern.begin.where.next.where...times...
3. PatternStream: CEP.pattern(DataStream, Pattern)
4. DataStream: PatternStream.select(PatternSelectFunction) PatternStream.flatSelect(PatternSelectFunction)
5. OUT: DataStream -> Transformations -> DataStream -> DataSink

FlinkCEP匹配超时实现步骤

TimeoutCEP的流需要keyBy，即KeyedStream，如果inputStream不是KeyedStream，会new一个0字节的Key（上面CEPOperatorUtils源码里有提到）。

KeySelector
<IN,
Byte
> keySelector =
new

NullByteKeySelector
<>();

Pattern最后调用within设置窗口时间。 如果是对主键进行分组，一个时间窗口内最多只会匹配出一个超时事件，使用PatternStream.select(...)就可以了。

1. IN: DataSource -> DataStream -> Transformations -> DataStream -> keyBy -> KeyedStream
2. Pattern: Pattern.begin.where.next.where...within(Time windowTime)
3. PatternStream: CEP.pattern(KeyedStream, Pattern)
4. OutputTag: new OutputTag(...)
5. SingleOutputStreamOperator: PatternStream.flatSelect(OutputTag, PatternFlatTimeoutFunction, PatternFlatSelectFunction)
6. DataStream: SingleOutputStreamOperator.getSideOutput(OutputTag)
7. OUT: DataStream -> Transformations -> DataStream -> DataSink

FlinkCEP超时不足

和Flink窗口聚合类似，如果使用事件时间和依赖事件生成的水印向前推进，需要后续的事件到达，才会触发窗口进行计算和输出结果。

FlinkCEP超时完整demo

public
class
CEPTimeoutEventJob
 {
private
static
final
String
 LOCAL_KAFKA_BROKER =
"localhost:9092"
;
private
static
final
String
 GROUP_ID =
CEPTimeoutEventJob
.
class
.getSimpleName();
private
static
final
String
 GROUP_TOPIC = GROUP_ID;

public
static
void
 main(
String
[] args)
throws
Exception
 {
// 参数
ParameterTool
 params =
ParameterTool
.fromArgs(args);

StreamExecutionEnvironment
 env =
StreamExecutionEnvironment
.getExecutionEnvironment();
// 使用事件时间
    env.setStreamTimeCharacteristic(
TimeCharacteristic
.
EventTime
);
    env.enableCheckpointing(
5000
);
    env.getCheckpointConfig().enableExternalizedCheckpoints(
CheckpointConfig
.
ExternalizedCheckpointCleanup
.RETAIN_ON_CANCELLATION);
    env.getConfig().disableSysoutLogging();
    env.getConfig().setRestartStrategy(
RestartStrategies
.fixedDelayRestart(
5
,
10000
));

// 不使用POJO的时间
final
AssignerWithPeriodicWatermarks
 extractor =
new
IngestionTimeExtractor
<POJO>();

// 与Kafka Topic的Partition保持一致
    env.setParallelism(
3
);

Properties
 kafkaProps =
new
Properties
();
    kafkaProps.setProperty(
"bootstrap.servers"
, LOCAL_KAFKA_BROKER);
    kafkaProps.setProperty(
"group.id"
, GROUP_ID);

// 接入Kafka的消息
FlinkKafkaConsumer011
<POJO> consumer =
new
FlinkKafkaConsumer011
<>(GROUP_TOPIC,
new
POJOSchema
(), kafkaProps);
DataStream
<POJO> pojoDataStream = env.addSource(consumer)
        .assignTimestampsAndWatermarks(extractor);
    pojoDataStream.print();

// 根据主键aid分组 即对每一个POJO事件进行匹配检测【不同类型的POJO，可以采用不同的within时间】
// 1.
DataStream
<POJO> keyedPojos = pojoDataStream
        .keyBy(
"aid"
);

// 从初始化到终态-一个完整的POJO事件序列
// 2.
Pattern
<POJO, POJO> completedPojo =
Pattern
.<POJO>begin(
"init"
)
            .where(
new
SimpleCondition
<POJO>() {
private
static
final
long
 serialVersionUID = -
6847788055093903603L
;

@Override
public
boolean
 filter(POJO pojo)
throws
Exception
 {
return
"02"
.equals(pojo.getAstatus());
              }
            })
            .followedBy(
"end"
)
//            .next("end")
            .where(
new
SimpleCondition
<POJO>() {
private
static
final
long
 serialVersionUID = -
2655089736460847552L
;

@Override
public
boolean
 filter(POJO pojo)
throws
Exception
 {
return
"00"
.equals(pojo.getAstatus()) ||
"01"
.equals(pojo.getAstatus());
              }
            });

// 找出1分钟内【便于测试】都没有到终态的事件aid
// 如果针对不同类型有不同within时间，比如有的是超时1分钟，有的可能是超时1个小时 则生成多个PatternStream
// 3.
PatternStream
<POJO> patternStream = CEP.pattern(keyedPojos, completedPojo.within(
Time
.minutes(
1
)));

// 定义侧面输出timedout
// 4.
OutputTag
<POJO> timedout =
new
OutputTag
<POJO>(
"timedout"
) {
private
static
final
long
 serialVersionUID =
773503794597666247L
;
    };

// OutputTag<L> timeoutOutputTag, PatternFlatTimeoutFunction<T, L> patternFlatTimeoutFunction, PatternFlatSelectFunction<T, R> patternFlatSelectFunction
// 5.
SingleOutputStreamOperator
<POJO> timeoutPojos = patternStream.flatSelect(
        timedout,
new
POJOTimedOut
(),
new
FlatSelectNothing
()
    );

// 打印输出超时的POJO
// 6.7.
    timeoutPojos.getSideOutput(timedout).print();
    timeoutPojos.print();
    env.execute(
CEPTimeoutEventJob
.
class
.getSimpleName());
  }

/**
   * 把超时的事件收集起来
   */
public
static
class
POJOTimedOut
implements
PatternFlatTimeoutFunction
<POJO, POJO> {
private
static
final
long
 serialVersionUID = -
4214641891396057732L
;

@Override
public
void
 timeout(
Map
<
String
,
List
<POJO>> map,
long
 l,
Collector
<POJO> collector)
throws
Exception
 {
if
 (
null
 != map.get(
"init"
)) {
for
 (POJO pojoInit : map.get(
"init"
)) {
System
.out.println(
"timeout init:"
 + pojoInit.getAid());
          collector.collect(pojoInit);
        }
      }
// 因为end超时了，还没收到end，所以这里是拿不到end的
System
.out.println(
"timeout end: "
 + map.get(
"end"
));
    }
  }

/**
   * 通常什么都不做，但也可以把所有匹配到的事件发往下游；如果是宽松临近，被忽略或穿透的事件就没办法选中发往下游了
   * 一分钟时间内走完init和end的数据
   *
   * @param <T>
   */
public
static
class
FlatSelectNothing
<T>
implements
PatternFlatSelectFunction
<T, T> {
private
static
final
long
 serialVersionUID = -
3029589950677623844L
;

@Override
public
void
 flatSelect(
Map
<
String
,
List
<T>> pattern,
Collector
<T> collector) {
System
.out.println(
"flatSelect: "
 + pattern);
    }
  }
}

测试结果（followedBy）：

3
> POJO{aid=
'ID000-0'
, astyle=
'STYLE000-0'
, aname=
'NAME-0'
, logTime=
1563419728242
, energy=
529.00
, age=
0
, tt=
2019
-
07
-
18
, astatus=
'02'
, createTime=
null
, updateTime=
null
}
3
> POJO{aid=
'ID000-1'
, astyle=
'STYLE000-2'
, aname=
'NAME-1'
, logTime=
1563419728783
, energy=
348.00
, age=
26
, tt=
2019
-
07
-
18
, astatus=
'02'
, createTime=
null
, updateTime=
null
}
3
> POJO{aid=
'ID000-0'
, astyle=
'STYLE000-0'
, aname=
'NAME-0'
, logTime=
1563419749259
, energy=
492.00
, age=
0
, tt=
2019
-
07
-
18
, astatus=
'00'
, createTime=
null
, updateTime=
null
}
flatSelect: {init=[POJO{aid=
'ID000-0'
, astyle=
'STYLE000-0'
, aname=
'NAME-0'
, logTime=
1563419728242
, energy=
529.00
, age=
0
, tt=
2019
-
07
-
18
, astatus=
'02'
, createTime=
null
, updateTime=
null
}],
end
=[POJO{aid=
'ID000-0'
, astyle=
'STYLE000-0'
, aname=
'NAME-0'
, logTime=
1563419749259
, energy=
492.00
, age=
0
, tt=
2019
-
07
-
18
, astatus=
'00'
, createTime=
null
, updateTime=
null
}]}
timeout init:ID000-
1
3
> POJO{aid=
'ID000-1'
, astyle=
'STYLE000-2'
, aname=
'NAME-1'
, logTime=
1563419728783
, energy=
348.00
, age=
26
, tt=
2019
-
07
-
18
, astatus=
'02'
, createTime=
null
, updateTime=
null
}
timeout
end
:
null
3
> POJO{aid=
'ID000-2'
, astyle=
'STYLE000-0'
, aname=
'NAME-0'
, logTime=
1563419829639
, energy=
467.00
, age=
0
, tt=
2019
-
07
-
18
, astatus=
'03'
, createTime=
null
, updateTime=
null
}
3
> POJO{aid=
'ID000-2'
, astyle=
'STYLE000-0'
, aname=
'NAME-0'
, logTime=
1563419841394
, energy=
107.00
, age=
0
, tt=
2019
-
07
-
18
, astatus=
'00'
, createTime=
null
, updateTime=
null
}
3
> POJO{aid=
'ID000-3'
, astyle=
'STYLE000-0'
, aname=
'NAME-0'
, logTime=
1563419967721
, energy=
431.00
, age=
0
, tt=
2019
-
07
-
18
, astatus=
'02'
, createTime=
null
, updateTime=
null
}
3
> POJO{aid=
'ID000-3'
, astyle=
'STYLE000-2'
, aname=
'NAME-0'
, logTime=
1563419979567
, energy=
32.00
, age=
26
, tt=
2019
-
07
-
18
, astatus=
'03'
, createTime=
null
, updateTime=
null
}
3
> POJO{aid=
'ID000-3'
, astyle=
'STYLE000-2'
, aname=
'NAME-0'
, logTime=
1563419993612
, energy=
542.00
, age=
26
, tt=
2019
-
07
-
18
, astatus=
'01'
, createTime=
null
, updateTime=
null
}
flatSelect: {init=[POJO{aid=
'ID000-3'
, astyle=
'STYLE000-0'
, aname=
'NAME-0'
, logTime=
1563419967721
, energy=
431.00
, age=
0
, tt=
2019
-
07
-
18
, astatus=
'02'
, createTime=
null
, updateTime=
null
}],
end
=[POJO{aid=
'ID000-3'
, astyle=
'STYLE000-2'
, aname=
'NAME-0'
, logTime=
1563419993612
, energy=
542.00
, age=
26
, tt=
2019
-
07
-
18
, astatus=
'01'
, createTime=
null
, updateTime=
null
}]}
3
> POJO{aid=
'ID000-4'
, astyle=
'STYLE000-0'
, aname=
'NAME-0'
, logTime=
1563420063760
, energy=
122.00
, age=
0
, tt=
2019
-
07
-
18
, astatus=
'02'
, createTime=
null
, updateTime=
null
}
3
> POJO{aid=
'ID000-4'
, astyle=
'STYLE000-0'
, aname=
'NAME-0'
, logTime=
1563420078008
, energy=
275.00
, age=
0
, tt=
2019
-
07
-
18
, astatus=
'03'
, createTime=
null
, updateTime=
null
}
timeout init:ID000-
4
3
> POJO{aid=
'ID000-4'
, astyle=
'STYLE000-0'
, aname=
'NAME-0'
, logTime=
1563420063760
, energy=
122.00
, age=
0
, tt=
2019
-
07
-
18
, astatus=
'02'
, createTime=
null
, updateTime=
null
}
timeout
end
:
null

总结

以上所述是小编给大家介绍的Apache FlinkCEP 实现超时状态监控的步骤，希望对大家有所帮助，如果大家有任何疑问欢迎给我留言，小编会及时回复大家的！