Flink中的多source+event watermark测试

时间  2019-12-11
标签 flink source+event source event watermark 测试 繁體版
原文   原文链接

此次须要作一个监控项目，全网日志的指标计算，上线的话，计算量应该是百亿/天java

单个source对应的sql以下sql

最原始的sql

select pro,throwable,level,ip,`count`,id,`time`,firstl,lastl  

from 
(

select pro,throwable,level,ip,
count(*) as `count`,
lastStrInGroupSkipNull(CONCAT_WS('_',KAFKA_TOPIC,CAST(KAFKA_PARTITION AS VARCHAR),CAST(KAFKA_OFFSET as VARCHAR))) as id,
firstLong(l) as firstl,
lastLong(l) as lastl,
TUMBLE_END(SPT, INTERVAL '3' SECOND) as `time` 

from input.`ymm-appmetric-dev-self1` 

where 
pro IS NOT NULL and throwable IS NOT NULL and level IS NOT NULL and level='ERROR' and ip IS NOT NULL 
group by pro,throwable,level,ip,TUMBLE(SPT,INTERVAL '3' SECOND)

) 

where 1=uniqueWithin100MS(pro,throwable,level,ip,`time`)

---先作技术论证，写了下面一个sqlapache

select pro,throwable,level,ip,`count`,id,`time`,firstl,lastl  

from (

select pro,throwable,level,ip,count(*) as `count`,
lastStrInGroupSkipNull(CONCAT_WS('_',KAFKA_TOPIC,CAST(KAFKA_PARTITION AS VARCHAR),CAST(KAFKA_OFFSET as VARCHAR))) as id,
firstLong(l) as firstl,
lastLong(l) as lastl,
TUMBLE_END(SPT, INTERVAL '3' SECOND) as `time` 
from (

select pro,throwable,level,ip
from input.`ymm-appmetric-dev-self1` 
where pro IS NOT NULL and throwable IS NOT NULL and level IS NOT NULL and level='ERROR' and ip IS NOT NULL 
union
select pro,throwable,level,ip
from input.`ymm-appmetric-dev-self2` 
where pro IS NOT NULL and throwable IS NOT NULL and level IS NOT NULL and level='ERROR' and ip IS NOT NULL 

)

group by pro,throwable,level,ip,TUMBLE(SPT,INTERVAL '3' SECOND)

)

where 1=uniqueWithin100MS(pro,throwable,level,ip,`time`)

而后拉起flink任务,观察是否可顺利启动---果真报错了api

Caused by: org.apache.calcite.sql.validate.SqlValidatorException: Column 'SPT' not found in any table

定位一下，看看是什么问题致使的,看了下以前写的sql，猜想是由于UNION的时候，没有在每一个表里带上SPT时间属性字段以及其它字段，补上后sql以下app

select pro,throwable,level,ip,`count`,id,`time`,firstl,lastl  

from (

select pro,throwable,level,ip,count(*) as `count`,
lastStrInGroupSkipNull(CONCAT_WS('_',KAFKA_TOPIC,CAST(KAFKA_PARTITION AS VARCHAR),CAST(KAFKA_OFFSET as VARCHAR))) as id,
firstLong(l) as firstl,
lastLong(l) as lastl,
TUMBLE_END(SPT, INTERVAL '3' SECOND) as `time` 
from (

select pro,throwable,level,ip,l,KAFKA_TOPIC,KAFKA_PARTITION,KAFKA_OFFSET,SPT
from input.`ymm-appmetric-dev-self1` 
where pro IS NOT NULL and throwable IS NOT NULL and level IS NOT NULL and level='ERROR' and ip IS NOT NULL 
union
select pro,throwable,level,ip,l,KAFKA_TOPIC,KAFKA_PARTITION,KAFKA_OFFSET,SPT
from input.`ymm-appmetric-dev-self2` 
where pro IS NOT NULL and throwable IS NOT NULL and level IS NOT NULL and level='ERROR' and ip IS NOT NULL 

)

group by pro,throwable,level,ip,TUMBLE(SPT,INTERVAL '3' SECOND)

)

where 1=uniqueWithin100MS(pro,throwable,level,ip,`time`)

再重启看看，此次应该差很少了吧---sql能够顺利编译，可是仍是有错oop

奇怪了，以前并无这样的错误，赞，咱们来看看问题在哪！测试

 

咱们打开类的层次图以下.net

借这个机会增强对这些类的理解！线程

---通过个人调试，发现问题出如今union上，不加这个Union，啥事没有；加了就报错，下面咱们再回到调用栈看看scala

一我的调试了一个下午，-_-||，最终发现知道修改一个地方就行

union -> union all

厉害了，给大佬低头！

----好，既然解决了，咱们继续来debug原理层！

测试了一下，发现多source跟单source相比，单source的watermark很好理解，可是多source就稍微复杂些，下面咱们来研究下原理！

首先，观察一下现有的图，以下所示：

下面再来研究一下线程，jstack一把

咱们来分析上面的线程，看看有没有收获！挑几个重点线程讲解

"VM Periodic Task Thread" os_prio=0 tid=0x00007f366825e800 nid=0x63d waiting on condition 
百度能够知道
该线程是JVM周期性任务调度的线程，它由WatcherThread建立，是一个单例对象。该线程在JVM内使用得比较频繁，好比：按期的内存监控、JVM运行情况监控。
下面几个是GC线程
"Gang worker#0 (Parallel GC Threads)" os_prio=0 tid=0x00007f3668031800 nid=0x626 runnable 

"Gang worker#1 (Parallel GC Threads)" os_prio=0 tid=0x00007f3668033800 nid=0x627 runnable 

"Gang worker#2 (Parallel GC Threads)" os_prio=0 tid=0x00007f3668035800 nid=0x628 runnable 

"Gang worker#3 (Parallel GC Threads)" os_prio=0 tid=0x00007f3668037800 nid=0x629 runnable 

"Gang worker#4 (Parallel GC Threads)" os_prio=0 tid=0x00007f3668039800 nid=0x62a runnable 

"Gang worker#5 (Parallel GC Threads)" os_prio=0 tid=0x00007f366803b000 nid=0x62b runnable 

"Gang worker#6 (Parallel GC Threads)" os_prio=0 tid=0x00007f366803d000 nid=0x62c runnable 

"Gang worker#7 (Parallel GC Threads)" os_prio=0 tid=0x00007f366803f000 nid=0x62d runnable 

"Concurrent Mark-Sweep GC Thread" os_prio=0 tid=0x00007f36680b7000 nid=0x630 runnable 

"Gang worker#0 (Parallel CMS Threads)" os_prio=0 tid=0x00007f36680b2800 nid=0x62e runnable 

"Gang worker#1 (Parallel CMS Threads)" os_prio=0 tid=0x00007f36680b4800 nid=0x62f runnable

---

"main" #1 prio=5 os_prio=0 tid=0x00007f3668019800 nid=0x625 waiting on condition [0x00007f3670010000]
主线程，在flink内部等待全部事情结束
"New I/O worker #1" #24 prio=5 os_prio=0 tid=0x00007f366995f000 nid=0x648 runnable [0x00007f3642cd1000]
内部netty线程

---

"Source: MyKafka010JsonTableSource -> from: (l, KAFKA_TOPIC, KAFKA_PARTITION, KAFKA_OFFSET, pro, throwable, level, ip, SPT) -> Timestamps/Watermarks -> where: (AND(=(level, _UTF-16LE'ERROR'), IS NOT NULL(pro), IS NOT NULL(throwable), IS NOT NULL(ip))), select: (pro, throwable, CAST(_UTF-16LE'ERROR') AS level, ip, SPT, CONCAT_WS(_UTF-16LE'_', KAFKA_TOPIC, CAST(KAFKA_PARTITION), CAST(KAFKA_OFFSET)) AS $f5, l) (1/1)" #51 prio=5 os_prio=0 tid=0x00007f363d11a800 nid=0x65e in Object.wait() [0x00007f3641ac3000]
   java.lang.Thread.State: WAITING (on object monitor)
    at java.lang.Object.wait(Native Method)
    at java.lang.Object.wait(Object.java:502)
    at org.apache.flink.streaming.connectors.kafka.internal.Handover.pollNext(Handover.java:74)
    - locked <0x00000000e6ee2df0> (a java.lang.Object)
    at org.apache.flink.streaming.connectors.kafka.internal.Kafka09Fetcher.runFetchLoop(Kafka09Fetcher.java:133)
    at org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerBase.run(FlinkKafkaConsumerBase.java:721)
    at org.apache.flink.streaming.api.operators.StreamSource.run(StreamSource.java:87)
    at org.apache.flink.streaming.api.operators.StreamSource.run(StreamSource.java:56)
    at org.apache.flink.streaming.runtime.tasks.SourceStreamTask.run(SourceStreamTask.java:99)
    at org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(StreamTask.java:306)
    at org.apache.flink.runtime.taskmanager.Task.run(Task.java:703)
    at java.lang.Thread.run(Thread.java:748)

"Source: MyKafka010JsonTableSource -> from: (l, KAFKA_TOPIC, KAFKA_PARTITION, KAFKA_OFFSET, pro, throwable, level, ip, SPT) -> Timestamps/Watermarks -> where: (AND(=(level, _UTF-16LE'ERROR'), IS NOT NULL(pro), IS NOT NULL(throwable), IS NOT NULL(ip))), select: (pro, throwable, CAST(_UTF-16LE'ERROR') AS level, ip, SPT, CONCAT_WS(_UTF-16LE'_', KAFKA_TOPIC, CAST(KAFKA_PARTITION), CAST(KAFKA_OFFSET)) AS $f5, l) (1/1)" #50 prio=5 os_prio=0 tid=0x00007f363d120800 nid=0x65d in Object.wait() [0x00007f3641bc4000]
   java.lang.Thread.State: WAITING (on object monitor)
    at java.lang.Object.wait(Native Method)
    at java.lang.Object.wait(Object.java:502)
    at org.apache.flink.streaming.connectors.kafka.internal.Handover.pollNext(Handover.java:74)
    - locked <0x00000000e6ee2e98> (a java.lang.Object)
    at org.apache.flink.streaming.connectors.kafka.internal.Kafka09Fetcher.runFetchLoop(Kafka09Fetcher.java:133)
    at org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerBase.run(FlinkKafkaConsumerBase.java:721)
    at org.apache.flink.streaming.api.operators.StreamSource.run(StreamSource.java:87)
    at org.apache.flink.streaming.api.operators.StreamSource.run(StreamSource.java:56)
    at org.apache.flink.streaming.runtime.tasks.SourceStreamTask.run(SourceStreamTask.java:99)
    at org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(StreamTask.java:306)
    at org.apache.flink.runtime.taskmanager.Task.run(Task.java:703)
    at java.lang.Thread.run(Thread.java:748)

有2个线程是用来获取消息，对于这2个线程来讲，这2个消息不是直接读取kafka,而是其它线程读取kafka喂给这2个线程

---

"time attribute: (SPT) (1/1)" #53 prio=5 os_prio=0 tid=0x00007f363d8e4000 nid=0x662 in Object.wait() [0x00007f36418c1000]
   java.lang.Thread.State: WAITING (on object monitor)
    at java.lang.Object.wait(Native Method)
    at java.lang.Object.wait(Object.java:502)
    at org.apache.flink.runtime.io.network.partition.consumer.UnionInputGate.waitAndGetNextInputGate(UnionInputGate.java:205)
    - locked <0x00000000e6ee8210> (a java.util.ArrayDeque)
    at org.apache.flink.runtime.io.network.partition.consumer.UnionInputGate.getNextBufferOrEvent(UnionInputGate.java:163)
    at org.apache.flink.streaming.runtime.io.BarrierTracker.getNextNonBlocked(BarrierTracker.java:94)
    at org.apache.flink.streaming.runtime.io.StreamInputProcessor.processInput(StreamInputProcessor.java:209)
    at org.apache.flink.streaming.runtime.tasks.OneInputStreamTask.run(OneInputStreamTask.java:103)
    at org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(StreamTask.java:306)
    at org.apache.flink.runtime.taskmanager.Task.run(Task.java:703)
    at java.lang.Thread.run(Thread.java:748)
这个线程对应了咱们sql里的union算子

---

"groupBy: (pro, throwable, level, ip), window: (TumblingGroupWindow('w$, 'SPT, 3000.millis)), select: (pro, throwable, level, ip, COUNT(*) AS count, lastStrInGroupSkipNull($f5) AS id, firstLong(l) AS firstl, lastLong(l) AS lastl, start('w$) AS w$start, end('w$) AS w$end, rowtime('w$) AS w$rowtime, proctime('w$) AS w$proctime) -> where: (=(1, uniqueWithin100MS(pro, throwable, _UTF-16LE'ERROR', ip, w$end))), select: (pro, throwable, level, ip, count, id, w$end AS time, firstl, lastl) -> to: Row -> Sink: Kafka010JsonTableSink(pro, throwable, level, ip, count, id, time, firstl, lastl) (1/1)" #54 prio=5 os_prio=0 tid=0x00007f363fde3800 nid=0x664 in Object.wait() [0x00007f3641127000]
   java.lang.Thread.State: WAITING (on object monitor)
    at java.lang.Object.wait(Native Method)
    at java.lang.Object.wait(Object.java:502)
    at org.apache.flink.runtime.io.network.partition.consumer.SingleInputGate.getNextBufferOrEvent(SingleInputGate.java:533)
    - locked <0x00000000e6ee2d48> (a java.util.ArrayDeque)
    at org.apache.flink.runtime.io.network.partition.consumer.SingleInputGate.getNextBufferOrEvent(SingleInputGate.java:502)
    at org.apache.flink.streaming.runtime.io.BarrierTracker.getNextNonBlocked(BarrierTracker.java:94)
    at org.apache.flink.streaming.runtime.io.StreamInputProcessor.processInput(StreamInputProcessor.java:209)
    at org.apache.flink.streaming.runtime.tasks.OneInputStreamTask.run(OneInputStreamTask.java:103)
    at org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(StreamTask.java:306)
    at org.apache.flink.runtime.taskmanager.Task.run(Task.java:703)
    at java.lang.Thread.run(Thread.java:748)
这个对应了group by算子

---生产者

"kafka-producer-network-thread | producer-1" #55 daemon prio=5 os_prio=0 tid=0x00007f364d0f0800 nid=0x667 runnable [0x00007f3640a26000]
   java.lang.Thread.State: RUNNABLE
    at sun.nio.ch.EPollArrayWrapper.epollWait(Native Method)
    at sun.nio.ch.EPollArrayWrapper.poll(EPollArrayWrapper.java:269)
    at sun.nio.ch.EPollSelectorImpl.doSelect(EPollSelectorImpl.java:93)
    at sun.nio.ch.SelectorImpl.lockAndDoSelect(SelectorImpl.java:86)
    - locked <0x00000000e6ef3358> (a sun.nio.ch.Util$3)
    - locked <0x00000000e6ef3340> (a java.util.Collections$UnmodifiableSet)
    - locked <0x00000000e6eedbd8> (a sun.nio.ch.EPollSelectorImpl)
    at sun.nio.ch.SelectorImpl.select(SelectorImpl.java:97)
    at org.apache.kafka.common.network.Selector.select(Selector.java:489)
    at org.apache.kafka.common.network.Selector.poll(Selector.java:298)
    at org.apache.kafka.clients.NetworkClient.poll(NetworkClient.java:349)
    at org.apache.kafka.clients.producer.internals.Sender.run(Sender.java:225)
    at org.apache.kafka.clients.producer.internals.Sender.run(Sender.java:126)
    at java.lang.Thread.run(Thread.java:748)
对应着生产者，直连kafka

---

"Time Trigger for Source: MyKafka010JsonTableSource -> from: (l, KAFKA_TOPIC, KAFKA_PARTITION, KAFKA_OFFSET, pro, throwable, level, ip, SPT) -> Timestamps/Watermarks -> where: (AND(=(level, _UTF-16LE'ERROR'), IS NOT NULL(pro), IS NOT NULL(throwable), IS NOT NULL(ip))), select: (pro, throwable, CAST(_UTF-16LE'ERROR') AS level, ip, SPT, CONCAT_WS(_UTF-16LE'_', KAFKA_TOPIC, CAST(KAFKA_PARTITION), CAST(KAFKA_OFFSET)) AS $f5, l) (1/1)" #57 daemon prio=5 os_prio=0 tid=0x00007f364d264800 nid=0x669 waiting on condition [0x00007f3640624000]
   java.lang.Thread.State: TIMED_WAITING (parking)
    at sun.misc.Unsafe.park(Native Method)
    - parking to wait for  <0x00000000e6ef84c0> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
    at java.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:215)
    at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.awaitNanos(AbstractQueuedSynchronizer.java:2078)
    at java.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:1093)
    at java.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:809)
    at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1067)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1127)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:748)

"Time Trigger for Source: MyKafka010JsonTableSource -> from: (l, KAFKA_TOPIC, KAFKA_PARTITION, KAFKA_OFFSET, pro, throwable, level, ip, SPT) -> Timestamps/Watermarks -> where: (AND(=(level, _UTF-16LE'ERROR'), IS NOT NULL(pro), IS NOT NULL(throwable), IS NOT NULL(ip))), select: (pro, throwable, CAST(_UTF-16LE'ERROR') AS level, ip, SPT, CONCAT_WS(_UTF-16LE'_', KAFKA_TOPIC, CAST(KAFKA_PARTITION), CAST(KAFKA_OFFSET)) AS $f5, l) (1/1)" #56 daemon prio=5 os_prio=0 tid=0x00007f363e937800 nid=0x668 waiting on condition [0x00007f3640725000]
   java.lang.Thread.State: TIMED_WAITING (parking)
    at sun.misc.Unsafe.park(Native Method)
    - parking to wait for  <0x00000000e6ee2bc8> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject)
    at java.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:215)
    at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.awaitNanos(AbstractQueuedSynchronizer.java:2078)
    at java.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:1093)
    at java.util.concurrent.ScheduledThreadPoolExecutor$DelayedWorkQueue.take(ScheduledThreadPoolExecutor.java:809)
    at java.util.concurrent.ThreadPoolExecutor.getTask(ThreadPoolExecutor.java:1067)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1127)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:748)
每一个流对应着一个水印定时发送线程，由于我这边的输入是2个流
因此有2个水印发送线程

---

"Kafka Partition Discovery for Source: MyKafka010JsonTableSource -> from: (l, KAFKA_TOPIC, KAFKA_PARTITION, KAFKA_OFFSET, pro, throwable, level, ip, SPT) -> Timestamps/Watermarks -> where: (AND(=(level, _UTF-16LE'ERROR'), IS NOT NULL(pro), IS NOT NULL(throwable), IS NOT NULL(ip))), select: (pro, throwable, CAST(_UTF-16LE'ERROR') AS level, ip, SPT, CONCAT_WS(_UTF-16LE'_', KAFKA_TOPIC, CAST(KAFKA_PARTITION), CAST(KAFKA_OFFSET)) AS $f5, l) (1/1)" #61 prio=5 os_prio=0 tid=0x00007f364d25f000 nid=0x66c waiting on condition [0x00007f3640121000]
   java.lang.Thread.State: TIMED_WAITING (sleeping)
    at java.lang.Thread.sleep(Native Method)
    at org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerBase$2.run(FlinkKafkaConsumerBase.java:701)
    at java.lang.Thread.run(Thread.java:748)
    
    
"Kafka Partition Discovery for Source: MyKafka010JsonTableSource -> from: (l, KAFKA_TOPIC, KAFKA_PARTITION, KAFKA_OFFSET, pro, throwable, level, ip, SPT) -> Timestamps/Watermarks -> where: (AND(=(level, _UTF-16LE'ERROR'), IS NOT NULL(pro), IS NOT NULL(throwable), IS NOT NULL(ip))), select: (pro, throwable, CAST(_UTF-16LE'ERROR') AS level, ip, SPT, CONCAT_WS(_UTF-16LE'_', KAFKA_TOPIC, CAST(KAFKA_PARTITION), CAST(KAFKA_OFFSET)) AS $f5, l) (1/1)" #59 prio=5 os_prio=0 tid=0x00007f363f4bc800 nid=0x66a waiting on condition [0x00007f3640323000]
   java.lang.Thread.State: TIMED_WAITING (sleeping)
    at java.lang.Thread.sleep(Native Method)
    at org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerBase$2.run(FlinkKafkaConsumerBase.java:701)
    at java.lang.Thread.run(Thread.java:748)
2个自动分区发现线程

---

"Kafka 0.10 Fetcher for Source: MyKafka010JsonTableSource -> from: (l, KAFKA_TOPIC, KAFKA_PARTITION, KAFKA_OFFSET, pro, throwable, level, ip, SPT) -> Timestamps/Watermarks -> where: (AND(=(level, _UTF-16LE'ERROR'), IS NOT NULL(pro), IS NOT NULL(throwable), IS NOT NULL(ip))), select: (pro, throwable, CAST(_UTF-16LE'ERROR') AS level, ip, SPT, CONCAT_WS(_UTF-16LE'_', KAFKA_TOPIC, CAST(KAFKA_PARTITION), CAST(KAFKA_OFFSET)) AS $f5, l) (1/1)" #60 daemon prio=5 os_prio=0 tid=0x00007f364d269800 nid=0x66d runnable [0x00007f363bffe000]
   java.lang.Thread.State: RUNNABLE
    at sun.nio.ch.EPollArrayWrapper.epollWait(Native Method)
    at sun.nio.ch.EPollArrayWrapper.poll(EPollArrayWrapper.java:269)
    at sun.nio.ch.EPollSelectorImpl.doSelect(EPollSelectorImpl.java:93)
    at sun.nio.ch.SelectorImpl.lockAndDoSelect(SelectorImpl.java:86)
    - locked <0x00000000e73f0888> (a sun.nio.ch.Util$3)
    - locked <0x00000000e73f0870> (a java.util.Collections$UnmodifiableSet)
    - locked <0x00000000e7279b20> (a sun.nio.ch.EPollSelectorImpl)
    at sun.nio.ch.SelectorImpl.select(SelectorImpl.java:97)
    at org.apache.kafka.common.network.Selector.select(Selector.java:489)
    at org.apache.kafka.common.network.Selector.poll(Selector.java:298)
    at org.apache.kafka.clients.NetworkClient.poll(NetworkClient.java:349)
    at org.apache.kafka.clients.consumer.internals.ConsumerNetworkClient.poll(ConsumerNetworkClient.java:226)
    - locked <0x00000000e7497ec0> (a org.apache.kafka.clients.consumer.internals.ConsumerNetworkClient)
    at org.apache.kafka.clients.consumer.KafkaConsumer.pollOnce(KafkaConsumer.java:1047)
    at org.apache.kafka.clients.consumer.KafkaConsumer.poll(KafkaConsumer.java:995)
    at org.apache.flink.streaming.connectors.kafka.internal.KafkaConsumerThread.run(KafkaConsumerThread.java:257)



"Kafka 0.10 Fetcher for Source: MyKafka010JsonTableSource -> from: (l, KAFKA_TOPIC, KAFKA_PARTITION, KAFKA_OFFSET, pro, throwable, level, ip, SPT) -> Timestamps/Watermarks -> where: (AND(=(level, _UTF-16LE'ERROR'), IS NOT NULL(pro), IS NOT NULL(throwable), IS NOT NULL(ip))), select: (pro, throwable, CAST(_UTF-16LE'ERROR') AS level, ip, SPT, CONCAT_WS(_UTF-16LE'_', KAFKA_TOPIC, CAST(KAFKA_PARTITION), CAST(KAFKA_OFFSET)) AS $f5, l) (1/1)" #58 daemon prio=5 os_prio=0 tid=0x00007f363f4be800 nid=0x66b runnable [0x00007f3640222000]
   java.lang.Thread.State: RUNNABLE
    at sun.nio.ch.EPollArrayWrapper.epollWait(Native Method)
    at sun.nio.ch.EPollArrayWrapper.poll(EPollArrayWrapper.java:269)
    at sun.nio.ch.EPollSelectorImpl.doSelect(EPollSelectorImpl.java:93)
    at sun.nio.ch.SelectorImpl.lockAndDoSelect(SelectorImpl.java:86)
    - locked <0x00000000e6ef0758> (a sun.nio.ch.Util$3)
    - locked <0x00000000e6ef0740> (a java.util.Collections$UnmodifiableSet)
    - locked <0x00000000e6ee0248> (a sun.nio.ch.EPollSelectorImpl)
    at sun.nio.ch.SelectorImpl.select(SelectorImpl.java:97)
    at org.apache.kafka.common.network.Selector.select(Selector.java:489)
    at org.apache.kafka.common.network.Selector.poll(Selector.java:298)
    at org.apache.kafka.clients.NetworkClient.poll(NetworkClient.java:349)
    at org.apache.kafka.clients.consumer.internals.ConsumerNetworkClient.poll(ConsumerNetworkClient.java:226)
    - locked <0x00000000e6f03398> (a org.apache.kafka.clients.consumer.internals.ConsumerNetworkClient)
    at org.apache.kafka.clients.consumer.KafkaConsumer.pollOnce(KafkaConsumer.java:1047)
    at org.apache.kafka.clients.consumer.KafkaConsumer.poll(KafkaConsumer.java:995)
    at org.apache.flink.streaming.connectors.kafka.internal.KafkaConsumerThread.run(KafkaConsumerThread.java:257)
对应着2个直连kafka的生产者线程

线程debug完了，下面咱们来看每一个线程作什么事情！这里先简单交代一下消息记录和watermark的背景

对于每一个流，有1个消费者线程来读取kafka的消息
而后经过本地内存交换，喂给另一个线程，就是文中Handover字样的线程，这个线程会把消息往下游发送，同时，有1个水印线程定时探测是否有更大时间戳出现，出现的话，把这个时间戳放在一个水印事件里下广播给下游.

---下面先来debug下Handover线程，看看是如何消息喂给unionInputGate线程的

断点在

stop at org.apache.flink.streaming.connectors.kafka.internal.Kafka09Fetcher:154

跑起来！

而后，发送一条消息到kafka,断点顺利命中

接下来就是具体看消息的流转过程！

消息处理过程当中，会记录下当前事件的时间戳，位置在

做用是若是时间戳比当前值更大，则更新这个时间戳，后面会有水印线程定时读取这个值决定是否须要发送水印信息

好，继续观察消息的流动，执行到了下面这个地方

[1] org.apache.flink.runtime.io.network.api.writer.RecordWriter.emit (RecordWriter.java:104)
  [2] org.apache.flink.streaming.runtime.io.StreamRecordWriter.emit (StreamRecordWriter.java:81)
  [3] org.apache.flink.streaming.runtime.io.RecordWriterOutput.pushToRecordWriter (RecordWriterOutput.java:107)
  [4] org.apache.flink.streaming.runtime.io.RecordWriterOutput.collect (RecordWriterOutput.java:89)
  [5] org.apache.flink.streaming.runtime.io.RecordWriterOutput.collect (RecordWriterOutput.java:45)
  [6] org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect (AbstractStreamOperator.java:679)
  [7] org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect (AbstractStreamOperator.java:657)
  [8] org.apache.flink.streaming.api.operators.TimestampedCollector.collect (TimestampedCollector.java:51)
  [9] org.apache.flink.table.runtime.CRowWrappingCollector.collect (CRowWrappingCollector.scala:37)
  [10] org.apache.flink.table.runtime.CRowWrappingCollector.collect (CRowWrappingCollector.scala:28)
  [11] DataStreamCalcRule$69.processElement (null)
  [12] org.apache.flink.table.runtime.CRowProcessRunner.processElement (CRowProcessRunner.scala:66)
  [13] org.apache.flink.table.runtime.CRowProcessRunner.processElement (CRowProcessRunner.scala:35)
  [14] org.apache.flink.streaming.api.operators.ProcessOperator.processElement (ProcessOperator.java:66)
  [15] org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.pushToOperator (OperatorChain.java:560)
  [16] org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect (OperatorChain.java:535)
  [17] org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect (OperatorChain.java:515)
  [18] org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect (AbstractStreamOperator.java:679)
  [19] org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect (AbstractStreamOperator.java:657)
  [20] org.apache.flink.streaming.runtime.operators.TimestampsAndPeriodicWatermarksOperator.processElement (TimestampsAndPeriodicWatermarksOperator.java:67)
  [21] org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.pushToOperator (OperatorChain.java:560)
  [22] org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect (OperatorChain.java:535)
  [23] org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect (OperatorChain.java:515)
  [24] org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect (AbstractStreamOperator.java:679)
  [25] org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect (AbstractStreamOperator.java:657)
  [26] org.apache.flink.streaming.api.operators.TimestampedCollector.collect (TimestampedCollector.java:51)
  [27] org.apache.flink.table.runtime.CRowWrappingCollector.collect (CRowWrappingCollector.scala:37)
  [28] org.apache.flink.table.runtime.CRowWrappingCollector.collect (CRowWrappingCollector.scala:28)
  [29] DataStreamSourceConversion$23.processElement (null)
  [30] org.apache.flink.table.runtime.CRowOutputProcessRunner.processElement (CRowOutputProcessRunner.scala:67)
  [31] org.apache.flink.streaming.api.operators.ProcessOperator.processElement (ProcessOperator.java:66)
  [32] org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.pushToOperator (OperatorChain.java:560)
  [33] org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect (OperatorChain.java:535)
  [34] org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect (OperatorChain.java:515)
  [35] org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect (AbstractStreamOperator.java:679)
  [36] org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect (AbstractStreamOperator.java:657)
  [37] org.apache.flink.streaming.api.operators.StreamSourceContexts$ManualWatermarkContext.processAndCollectWithTimestamp (StreamSourceContexts.java:310)
  [38] org.apache.flink.streaming.api.operators.StreamSourceContexts$WatermarkContext.collectWithTimestamp (StreamSourceContexts.java:409)
  [39] org.apache.flink.streaming.connectors.kafka.internals.AbstractFetcher.emitRecordWithTimestamp (AbstractFetcher.java:398)
  [40] org.apache.flink.streaming.connectors.kafka.internal.Kafka010Fetcher.emitRecord (Kafka010Fetcher.java:89)
  [41] org.apache.flink.streaming.connectors.kafka.internal.Kafka09Fetcher.runFetchLoop (Kafka09Fetcher.java:154)
  [42] org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerBase.run (FlinkKafkaConsumerBase.java:721)
  [43] org.apache.flink.streaming.api.operators.StreamSource.run (StreamSource.java:87)
  [44] org.apache.flink.streaming.api.operators.StreamSource.run (StreamSource.java:56)
  [45] org.apache.flink.streaming.runtime.tasks.SourceStreamTask.run (SourceStreamTask.java:99)
  [46] org.apache.flink.streaming.runtime.tasks.StreamTask.invoke (StreamTask.java:306)
  [47] org.apache.flink.runtime.taskmanager.Task.run (Task.java:703)
  [48] java.lang.Thread.run (Thread.java:748)

看一下这里的即将执行的代码

public void emit(T record) throws IOException, InterruptedException {
        for (int targetChannel : channelSelector.selectChannels(record, numChannels)) {
            sendToTarget(record, targetChannel);
        }
    }

这里的print numChannels
 numChannels = 1 --->由于咱们有一个union操做，union天然是全部源归一！这就对了！

---最后放入消息并提醒消费线程，完整的调用栈以下：

[1] org.apache.flink.runtime.io.network.partition.consumer.SingleInputGate.queueChannel (SingleInputGate.java:623)
  [2] org.apache.flink.runtime.io.network.partition.consumer.SingleInputGate.notifyChannelNonEmpty (SingleInputGate.java:612)
  [3] org.apache.flink.runtime.io.network.partition.consumer.InputChannel.notifyChannelNonEmpty (InputChannel.java:121)
  [4] org.apache.flink.runtime.io.network.partition.consumer.LocalInputChannel.notifyDataAvailable (LocalInputChannel.java:202)
  [5] org.apache.flink.runtime.io.network.partition.PipelinedSubpartitionView.notifyDataAvailable (PipelinedSubpartitionView.java:56)
  [6] org.apache.flink.runtime.io.network.partition.PipelinedSubpartition.notifyDataAvailable (PipelinedSubpartition.java:290)
  [7] org.apache.flink.runtime.io.network.partition.PipelinedSubpartition.flush (PipelinedSubpartition.java:76)
  [8] org.apache.flink.runtime.io.network.partition.ResultPartition.flush (ResultPartition.java:269)
  [9] org.apache.flink.runtime.io.network.api.writer.RecordWriter.sendToTarget (RecordWriter.java:149)
  [10] org.apache.flink.runtime.io.network.api.writer.RecordWriter.emit (RecordWriter.java:105)
  [11] org.apache.flink.streaming.runtime.io.StreamRecordWriter.emit (StreamRecordWriter.java:81)
  [12] org.apache.flink.streaming.runtime.io.RecordWriterOutput.pushToRecordWriter (RecordWriterOutput.java:107)
  [13] org.apache.flink.streaming.runtime.io.RecordWriterOutput.collect (RecordWriterOutput.java:89)
  [14] org.apache.flink.streaming.runtime.io.RecordWriterOutput.collect (RecordWriterOutput.java:45)
  [15] org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect (AbstractStreamOperator.java:679)
  [16] org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect (AbstractStreamOperator.java:657)
  [17] org.apache.flink.streaming.api.operators.TimestampedCollector.collect (TimestampedCollector.java:51)
  [18] org.apache.flink.table.runtime.CRowWrappingCollector.collect (CRowWrappingCollector.scala:37)
  [19] org.apache.flink.table.runtime.CRowWrappingCollector.collect (CRowWrappingCollector.scala:28)
  [20] DataStreamCalcRule$69.processElement (null)
  [21] org.apache.flink.table.runtime.CRowProcessRunner.processElement (CRowProcessRunner.scala:66)
  [22] org.apache.flink.table.runtime.CRowProcessRunner.processElement (CRowProcessRunner.scala:35)
  [23] org.apache.flink.streaming.api.operators.ProcessOperator.processElement (ProcessOperator.java:66)
  [24] org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.pushToOperator (OperatorChain.java:560)
  [25] org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect (OperatorChain.java:535)
  [26] org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect (OperatorChain.java:515)
  [27] org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect (AbstractStreamOperator.java:679)
  [28] org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect (AbstractStreamOperator.java:657)
  [29] org.apache.flink.streaming.runtime.operators.TimestampsAndPeriodicWatermarksOperator.processElement (TimestampsAndPeriodicWatermarksOperator.java:67)
  [30] org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.pushToOperator (OperatorChain.java:560)
  [31] org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect (OperatorChain.java:535)
  [32] org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect (OperatorChain.java:515)
  [33] org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect (AbstractStreamOperator.java:679)
  [34] org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect (AbstractStreamOperator.java:657)
  [35] org.apache.flink.streaming.api.operators.TimestampedCollector.collect (TimestampedCollector.java:51)
  [36] org.apache.flink.table.runtime.CRowWrappingCollector.collect (CRowWrappingCollector.scala:37)
  [37] org.apache.flink.table.runtime.CRowWrappingCollector.collect (CRowWrappingCollector.scala:28)
  [38] DataStreamSourceConversion$23.processElement (null)
  [39] org.apache.flink.table.runtime.CRowOutputProcessRunner.processElement (CRowOutputProcessRunner.scala:67)
  [40] org.apache.flink.streaming.api.operators.ProcessOperator.processElement (ProcessOperator.java:66)
  [41] org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.pushToOperator (OperatorChain.java:560)
  [42] org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect (OperatorChain.java:535)
  [43] org.apache.flink.streaming.runtime.tasks.OperatorChain$CopyingChainingOutput.collect (OperatorChain.java:515)
  [44] org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect (AbstractStreamOperator.java:679)
  [45] org.apache.flink.streaming.api.operators.AbstractStreamOperator$CountingOutput.collect (AbstractStreamOperator.java:657)
  [46] org.apache.flink.streaming.api.operators.StreamSourceContexts$ManualWatermarkContext.processAndCollectWithTimestamp (StreamSourceContexts.java:310)
  [47] org.apache.flink.streaming.api.operators.StreamSourceContexts$WatermarkContext.collectWithTimestamp (StreamSourceContexts.java:409)
  [48] org.apache.flink.streaming.connectors.kafka.internals.AbstractFetcher.emitRecordWithTimestamp (AbstractFetcher.java:398)
  [49] org.apache.flink.streaming.connectors.kafka.internal.Kafka010Fetcher.emitRecord (Kafka010Fetcher.java:89)
  [50] org.apache.flink.streaming.connectors.kafka.internal.Kafka09Fetcher.runFetchLoop (Kafka09Fetcher.java:154)
  [51] org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumerBase.run (FlinkKafkaConsumerBase.java:721)
  [52] org.apache.flink.streaming.api.operators.StreamSource.run (StreamSource.java:87)
  [53] org.apache.flink.streaming.api.operators.StreamSource.run (StreamSource.java:56)
  [54] org.apache.flink.streaming.runtime.tasks.SourceStreamTask.run (SourceStreamTask.java:99)
  [55] org.apache.flink.streaming.runtime.tasks.StreamTask.invoke (StreamTask.java:306)
  [56] org.apache.flink.runtime.taskmanager.Task.run (Task.java:703)
  [57] java.lang.Thread.run (Thread.java:748)

---水印的处理应该也是相似的，因此接下来，咱们来看Union所在的线程

咱们再来复习下上面里提到的这个线程的调用栈

"time attribute: (SPT) (1/1)" #53 prio=5 os_prio=0 tid=0x00007f363d8e4000 nid=0x662 in Object.wait() [0x00007f36418c1000]
   java.lang.Thread.State: WAITING (on object monitor)
    at java.lang.Object.wait(Native Method)
    at java.lang.Object.wait(Object.java:502)
    at org.apache.flink.runtime.io.network.partition.consumer.UnionInputGate.waitAndGetNextInputGate(UnionInputGate.java:205)
    - locked <0x00000000e6ee8210> (a java.util.ArrayDeque)
    at org.apache.flink.runtime.io.network.partition.consumer.UnionInputGate.getNextBufferOrEvent(UnionInputGate.java:163)
    at org.apache.flink.streaming.runtime.io.BarrierTracker.getNextNonBlocked(BarrierTracker.java:94)
    at org.apache.flink.streaming.runtime.io.StreamInputProcessor.processInput(StreamInputProcessor.java:209)
    at org.apache.flink.streaming.runtime.tasks.OneInputStreamTask.run(OneInputStreamTask.java:103)
    at org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(StreamTask.java:306)
    at org.apache.flink.runtime.taskmanager.Task.run(Task.java:703)
    at java.lang.Thread.run(Thread.java:748)
这个线程对应了咱们sql里的union算子

上面这个图，是等待有消息过来就提取消息，任何一个源有消息都会触发消息提取，不然wait

---注意：这里的消息有4种类型，通常咱们只须要关注record+watermark便可

具体地点是：

---这里讲一下，关于LatencyMarker，默认2秒钟发送一次，截图以下

其它的不论是record仍是watermark都会往下发送！

下面咱们来在union里同时针对record和watermark打断点，猜一猜哪一个断点先被触发？

断点位于【针对flink-1.5版本】

Breakpoints set:
    breakpoint org.apache.flink.streaming.runtime.io.StreamInputProcessor:184
    breakpoint org.apache.flink.streaming.runtime.io.StreamInputProcessor:198

触发的顺序以下：

---跟想的是同样的！ 下面就去研究下groupby线程

"groupBy: (pro, throwable, level, ip), window: (TumblingGroupWindow('w$, 'SPT, 3000.millis)), select: (pro, throwable, level, ip, COUNT(*) AS count, lastStrInGroupSkipNull($f5) AS id, firstLong(l) AS firstl, lastLong(l) AS lastl, start('w$) AS w$start, end('w$) AS w$end, rowtime('w$) AS w$rowtime, proctime('w$) AS w$proctime) -> where: (=(1, uniqueWithin100MS(pro, throwable, _UTF-16LE'ERROR', ip, w$end))), select: (pro, throwable, level, ip, count, id, w$end AS time, firstl, lastl) -> to: Row -> Sink: Kafka010JsonTableSink(pro, throwable, level, ip, count, id, time, firstl, lastl) (1/1)" #54 prio=5 os_prio=0 tid=0x00007f363fde3800 nid=0x664 in Object.wait() [0x00007f3641127000]
   java.lang.Thread.State: WAITING (on object monitor)
    at java.lang.Object.wait(Native Method)
    at java.lang.Object.wait(Object.java:502)
    at org.apache.flink.runtime.io.network.partition.consumer.SingleInputGate.getNextBufferOrEvent(SingleInputGate.java:533)
    - locked <0x00000000e6ee2d48> (a java.util.ArrayDeque)
    at org.apache.flink.runtime.io.network.partition.consumer.SingleInputGate.getNextBufferOrEvent(SingleInputGate.java:502)
    at org.apache.flink.streaming.runtime.io.BarrierTracker.getNextNonBlocked(BarrierTracker.java:94)
    at org.apache.flink.streaming.runtime.io.StreamInputProcessor.processInput(StreamInputProcessor.java:209)
    at org.apache.flink.streaming.runtime.tasks.OneInputStreamTask.run(OneInputStreamTask.java:103)
    at org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(StreamTask.java:306)
    at org.apache.flink.runtime.taskmanager.Task.run(Task.java:703)
    at java.lang.Thread.run(Thread.java:748)
这个对应了group by算子

针对group by来讲，最重要的环节，这个其实跟union线程同样的，也是在

org.apache.flink.streaming.runtime.io.StreamInputProcessor.processInput

这里面来作事件的分发，因此断点都是同样的

---

这里主要强调，在groupby处理watermark时的位置以下：【尤为是针对多个source来讲，很容易出问题】

这个时候，我意识到在groupby线程中来观察watermark还早了点，由于在union线程中针对watermark的处理还有一些秘密

因此咱们回到union线程来挖这些秘密，把groupby线程用suspend命令挂起来，专门debug union线程便可！

---打个断点【针对flink-1.5】

stop at org.apache.flink.streaming.runtime.io.StreamInputProcessor:184

研究了一把，大体明白原理了，这么说吧，线程模型以下

流1-------
         |
         |
         |
         |
         |
         |---------->union线程的watermark--------->groupby线程的watermark
         |
         |
         |
         |
流2-------

其中，流1和流2---每次都发送本身看到的最大时间戳发送个下游(看到小的就什么都不作)

union这里会动态更新流1和流2的各自所看到的最大时间戳，同时取Min（流1的最大时间戳，流2的最大时间戳），跟上一次的值比较

若是>上一次的Min值，则发送给group by.

---我以为读者看到这里，确定已经懵逼了，我来解释下思想

强调一下：消息在中间过程当中不拦截，直达最后的windowoperator那里作windowLate判断决定是否丢弃！
===========================================================================================
对于流1来讲，它每次发送本身已知的最大时间戳给下游，就是说“你好，下游，对我来讲小于这个时间戳的就算是延迟消息，你看着办”
对于流2来讲，它每次发送本身已知的最大时间戳给下游，就是说“你好，下游，对我来讲小于这个时间戳的就算是延迟消息，你看着办”
---对于union来讲，这里复杂些
它取值min( 流1的max时间戳，流2的max时间戳）跟上一次的min( 流1的max时间戳，流2的max时间戳）比较，
若是发现递增了，就把当前较大的这个min值发送给下游，说“你好，下游，全局来讲，对我来讲小于这个时间戳的就算是延迟消息，我只能帮到这里了，已经尽力拖住时间戳了,你看着办”

---对于groupby来讲，它收到时间戳，每次保留最大值，而后参考最大值来快速决定每一个消息是否是延迟消息(最大值-可容忍的延迟消息)。


因此，在多源状况下，判断全局一个消息是否是延迟消息，实际上由min( 流1的max时间戳，流2的max时间戳）这个值来参与决定
---
咱们再跳出来想想这个事情，我估计读者最懵逼的地方就是union为啥取每一个流的最小值，而不是最大值
咱们就这么理解吧，若是取最大值，那消费慢的流的数据大部分都成为了late数据被丢弃，union就会被打
因此union为了防止被打，它不想惹众怒，就取了min(每一个流），这样全部人都无话可说了
union旁白：我都取了大家每一个流的各自的时间戳最大值的全局最小值，还要我怎么样，
最慢的那个流也不会说啥了，由于取的就是它这个流上报的自身最大值。

上面都是从技术角度来阐述这个事情，那么咱们再拔高一下，从更高的层次来看这个事情
其实就是让更多的数据没有成为late数据，归入正常运算范围内，由min( 流1的max时间戳，流2的max时间戳）的递增来推进全局windowoperator的计算输出结果. 相应的，消费最慢的流会拖累最终业务数据的延迟生成.

 

---读者能够再细细琢磨里面的门道，下面咱们来作逻辑测试！验证咱们是否真正理解了这个游戏规则!

背景：容忍延迟3000毫秒
下面每行的格式就是：流名称 + 时间戳 ，每次只输出1条
1)流1 + 1545703896000
2)流1 + 1545703896000
3)流2 + 1545703896000
4)流2 + 1545703898999
5)流2 + 1545703899000
6)流1 + 1545703899000
7)流1 + 1545703900000
8)流2 + 1545703902000-1 --->这个不会触发windowOperator的输出，由于流1的最小值还不够
9)流1 + 1545703902000-1 --->这个才会触发windowOperator的输出
正确输出了，记住，必定要2个流
【齐头并进，理实交融】

可是，其实，仅仅研究到这一步，并无彻底结束，欲知后事如何请听下回分解 :)

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