Apache FlinkCEP 實現(xiàn)超時狀態(tài)監(jiān)控的步驟詳解

更新時間：2019年10月27日 09:57:13 作者：知了小巷

這篇文章主要介紹了Apache FlinkCEP 實現(xiàn)超時狀態(tài)監(jiān)控的步驟，本文給大家介紹的非常詳細，具有一定的參考借鑒價值,需要的朋友可以參考下

CEP - Complex Event Processing復雜事件處理。

訂單下單后超過一定時間還未進行支付確認。

打車訂單生成后超過一定時間沒有確認上車。

外賣超過預定送達時間一定時限還沒有確認送達。

Apache FlinkCEP API

CEPTimeoutEventJob

FlinkCEP源碼簡析

DataStream和PatternStream

DataStream 一般由相同類型事件或元素組成，一個DataStream可以通過一系列的轉(zhuǎn)換操作如Filter、Map等轉(zhuǎn)換為另一個DataStream。

PatternStream 是對CEP模式匹配的流的抽象，把DataStream和Pattern組合在一塊，然后對外提供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與其相關(guān)聯(lián)的事件組成的映射（就是Map<模式名稱,List<事件>>）發(fā)出去，發(fā)到SingleOutputStreamOperator里面，SingleOutputStreamOperator是DataStream。

CEPOperatorUtils工具類里的方法和變量使用了「PatternStream」來命名，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的構(gòu)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是模式定義的Base Class，Builder模式，定義好的模式會被NFACompiler用來生成NFA。

如果想要自己實現(xiàn)類似next和followedBy這種方法，比如timeEnd，對Pattern進行擴展重寫應(yīng)該是可行的。

public
class
Pattern
<T, F 
extends
 T> {
/** 模式名稱 */
private
final
String
 name;
/** 前面一個模式 */
private
final
Pattern
<T, ? 
extends
 T> previous;
/** 一個事件如果要被當前模式匹配到，必須滿足的約束條件 */
private
IterativeCondition
<F> condition;
/** 時間窗口長度，在時間長度內(nèi)進行模式匹配 */
private
Time
 windowTime;
/** 模式量詞，意思是一個模式匹配幾個事件等 默認是匹配到一個 */
private
Quantifier
 quantifier = 
Quantifier
.one(
ConsumingStrategy
.STRICT);
/** 停止將事件收集到循環(huán)狀態(tài)時，事件必須滿足的條件 */
private
IterativeCondition
<F> untilCondition;
/**
   * 適用于{@code times}模式，用來維護模式里事件可以連續(xù)發(fā)生的次數(shù)
   */
private
Times
 times;
// 匹配到事件之后的跳過策略
private
final
AfterMatchSkipStrategy
 afterMatchSkipStrategy;
  ...
}

Quantifier是用來描述具體模式行為的，主要有三大類：

Single-單一匹配、Looping-循環(huán)匹配、Times-一定次數(shù)或者次數(shù)范圍內(nèi)都能匹配到。

每一個模式Pattern可以是optional可選的（單一匹配或循環(huán)匹配），并可以設(shè)置ConsumingStrategy。

循環(huán)和次數(shù)也有一個額外的內(nèi)部ConsumingStrategy，用在模式中接收的事件之間。

public
class
Quantifier
 {
  ...
/**
   * 5個屬性，可以組合，但并非所有的組合都是有效的
   */
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
  }
/**
   * 描述當前模式里事件可以連續(xù)發(fā)生的次數(shù)；舉個例子，模式條件無非就是boolean，滿足true條件的事件連續(xù)出現(xiàn)times次，或者一個次數(shù)范圍，比如2~4次，2次，3次，4次都會被當前模式匹配出來，因此同一個事件會被重復匹配到
   */
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;
    }
// 次數(shù)范圍
public
static
Times
 of(
int
 from, 
int
 to) {
return
new
Times
(from, to);
    }
// 指定具體次數(shù)
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，自定義事件比較器，實現(xiàn)EventComparator接口。

public
 
interface
 
EventComparator
<T> 
extends
 
Comparator
<T>, 
Serializable
 {
long
 serialVersionUID = 
1L
;
}

NFACompiler和NFA

NFACompiler提供將Pattern編譯成NFA或者NFAFactory的方法，使用NFAFactory可以創(chuàng)建多個NFA。

public
class
NFACompiler
 {
  ...
/**
   * NFAFactory 創(chuàng)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的具體實現(xiàn)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() {
// 一個NFA由狀態(tài)集合、時間窗口的長度和是否處理超時組成
return
new
 NFA<>(states, windowTime, timeoutHandling);
    }
  }
}

NFA：Non-deterministic finite automaton - 非確定的有限（狀態(tài)）自動機。

更多內(nèi)容參見

https://zh.wikipedia.org/wiki/非確定有限狀態(tài)自動機

public
class
 NFA<T> {
/**
   * NFACompiler返回的所有有效的NFA狀態(tài)集合
   * These are directly derived from the user-specified pattern.
   */
private
final
Map
<
String
, 
State
<T>> states;
  
/**
   * Pattern.within(Time)指定的時間窗口長度
   */
private
final
long
 windowTime;
  
/**
   * 一個超時匹配的標記
   */
private
final
boolean
 handleTimeout;
  ...
}

PatternSelectFunction和PatternFlatSelectFunction

當一個包含被匹配到的事件的映射能夠通過模式名稱訪問到的時候，PatternSelectFunction的select()方法會被調(diào)用。模式名稱是由Pattern定義的時候指定的。select()方法恰好返回一個結(jié)果，如果需要返回多個結(jié)果，則可以實現(xiàn)PatternFlatSelectFunction。

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



  
/**

   * 從給到的事件映射中生成一個結(jié)果。這些事件使用他們關(guān)聯(lián)的模式名稱作為唯一標識

   */

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

}

PatternFlatSelectFunction，不是返回一個OUT，而是使用Collector 把匹配到的事件收集起來。

public
interface
PatternFlatSelectFunction
<IN, OUT> 
extends
Function
, 
Serializable
 {
  
/**
   * 生成一個或多個結(jié)果
   */
void
 flatSelect(
Map
<
String
, 
List
<IN>> pattern, 
Collector
<OUT> out) 
throws
Exception
;
}

SelectTimeoutCepOperator、PatternTimeoutFunction

SelectTimeoutCepOperator是在CEPOperatorUtils中調(diào)用createTimeoutPatternStream()方法時創(chuàng)建出來。

SelectTimeoutCepOperator中會被算子迭代調(diào)用的方法是processMatchedSequences()和processTimedOutSequences()。

模板方法...對應(yīng)到抽象類AbstractKeyedCEPPatternOperator中processEvent()方法和advanceTime()方法。

還有FlatSelectTimeoutCepOperator和對應(yīng)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,
// 參數(shù)命名混淆了flat...包括SelectWrapper類中的成員命名...
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是創(chuàng)建PatternStream的工具類，PatternStream只是DataStream和Pattern的組合。

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()方法被調(diào)用的時候，去創(chuàng)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實現(xiàn)步驟

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

FlinkCEP匹配超時實現(xiàn)步驟

TimeoutCEP的流需要keyBy，即KeyedStream，如果inputStream不是KeyedStream，會new一個0字節(jié)的Key（上面CEPOperatorUtils源碼里有提到）。

KeySelector
<IN, 
Byte
> keySelector = 
new
 
NullByteKeySelector
<>();

Pattern最后調(diào)用within設(shè)置窗口時間。如果是對主鍵進行分組，一個時間窗口內(nèi)最多只會匹配出一個超時事件，使用PatternStream.select(...)就可以了。

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

FlinkCEP超時不足

和Flink窗口聚合類似，如果使用事件時間和依賴事件生成的水印向前推進，需要后續(xù)的事件到達，才會觸發(fā)窗口進行計算和輸出結(jié)果。

FlinkCEP超時完整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
 {
// 參數(shù)
ParameterTool
 params = 
ParameterTool
.fromArgs(args);
    
StreamExecutionEnvironment
 env = 
StreamExecutionEnvironment
.getExecutionEnvironment();
// 使用事件時間
    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的時間
final
AssignerWithPeriodicWatermarks
 extractor = 
new
IngestionTimeExtractor
<POJO>();
    
// 與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();
    
// 根據(jù)主鍵aid分組 即對每一個POJO事件進行匹配檢測【不同類型的POJO，可以采用不同的within時間】
// 1.
DataStream
<POJO> keyedPojos = pojoDataStream
        .keyBy(
"aid"
);
    
// 從初始化到終態(tài)-一個完整的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分鐘內(nèi)【便于測試】都沒有到終態(tài)的事件aid
// 如果針對不同類型有不同within時間，比如有的是超時1分鐘，有的可能是超時1個小時 則生成多個PatternStream
// 3.
PatternStream
<POJO> patternStream = CEP.pattern(keyedPojos, completedPojo.within(
Time
.minutes(
1
)));
    
// 定義側(cè)面輸出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
()
    );
    
// 打印輸出超時的POJO
// 6.7.
    timeoutPojos.getSideOutput(timedout).print();
    timeoutPojos.print();
    env.execute(
CEPTimeoutEventJob
.
class
.getSimpleName());
  }
  
/**
   * 把超時的事件收集起來
   */
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);
        }
      }
// 因為end超時了，還沒收到end，所以這里是拿不到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發(fā)往下游；如果是寬松臨近，被忽略或穿透的事件就沒辦法選中發(fā)往下游了
   * 一分鐘時間內(nèi)走完init和end的數(shù)據(jù)
   *
   * @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);
    }
  }
}

測試結(jié)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

總結(jié)

以上所述是小編給大家介紹的Apache FlinkCEP 實現(xiàn)超時狀態(tài)監(jiān)控的步驟，希望對大家有所幫助，如果大家有任何疑問歡迎給我留言，小編會及時回復大家的！

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