Flink中的多source+event watermark測試

時間  2019-12-11
標籤 flink source+event source event watermark 測試 简体版
原文   原文鏈接

此次須要作一個監控項目，全網日誌的指標計算，上線的話，計算量應該是百億/天java

單個source對應的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`)

---先作技術論證，寫了下面一個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`)

而後拉起flink任務,觀察是否可順利啓動---果真報錯了api

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

定位一下，看看是什麼問題致使的,看了下以前寫的sql，猜想是由於UNION的時候，沒有在每一個表裏帶上SPT時間屬性字段以及其它字段，補上後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`)

再重啓看看，此次應該差很少了吧---sql能夠順利編譯，可是仍是有錯oop

奇怪了，以前並無這樣的錯誤，贊，咱們來看看問題在哪！測試

 

咱們打開類的層次圖以下.net

借這個機會增強對這些類的理解！線程

---通過個人調試，發現問題出如今union上，不加這個Union，啥事沒有；加了就報錯，下面咱們再回到調用棧看看scala

一我的調試了一個下午，-_-||，最終發現知道修改一個地方就行

union -> union all

厲害了，給大佬低頭！

----好，既然解決了，咱們繼續來debug原理層！

測試了一下，發現多source跟單source相比，單source的watermark很好理解，可是多source就稍微複雜些，下面咱們來研究下原理！

首先，觀察一下現有的圖，以下所示：

下面再來研究一下線程，jstack一把

咱們來分析上面的線程，看看有沒有收穫！挑幾個重點線程講解

"VM Periodic Task Thread" os_prio=0 tid=0x00007f366825e800 nid=0x63d waiting on condition 
百度能夠知道
該線程是JVM週期性任務調度的線程，它由WatcherThread建立，是一個單例對象。該線程在JVM內使用得比較頻繁，好比：按期的內存監控、JVM運行情況監控。
下面幾個是GC線程
"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]
主線程，在flink內部等待全部事情結束
"New I/O worker #1" #24 prio=5 os_prio=0 tid=0x00007f366995f000 nid=0x648 runnable [0x00007f3642cd1000]
內部netty線程

---

"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個線程是用來獲取消息，對於這2個線程來講，這2個消息不是直接讀取kafka,而是其它線程讀取kafka餵給這2個線程

---

"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)
這個線程對應了咱們sql裏的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)
這個對應了group by算子

---生產者

"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)
對應着生產者，直連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)
每一個流對應着一個水印定時發送線程，由於我這邊的輸入是2個流
因此有2個水印發送線程

---

"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個自動分區發現線程

---

"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)
對應着2個直連kafka的生產者線程

線程debug完了，下面咱們來看每一個線程作什麼事情！這裏先簡單交代一下消息記錄和watermark的背景

對於每一個流，有1個消費者線程來讀取kafka的消息
而後經過本地內存交換，餵給另一個線程，就是文中Handover字樣的線程，這個線程會把消息往下游發送，同時，有1個水印線程定時探測是否有更大時間戳出現，出現的話，把這個時間戳放在一個水印事件裏下廣播給下游.

---下面先來debug下Handover線程，看看是如何消息餵給unionInputGate線程的

斷點在

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

跑起來！

而後，發送一條消息到kafka,斷點順利命中

接下來就是具體看消息的流轉過程！

消息處理過程當中，會記錄下當前事件的時間戳，位置在

做用是若是時間戳比當前值更大，則更新這個時間戳，後面會有水印線程定時讀取這個值決定是否須要發送水印信息

好，繼續觀察消息的流動，執行到了下面這個地方

[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)

看一下這裏的即將執行的代碼

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

這裏的print numChannels
 numChannels = 1 --->由於咱們有一個union操做，union天然是全部源歸一！這就對了！

---最後放入消息並提醒消費線程，完整的調用棧以下：

[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)

---水印的處理應該也是相似的，因此接下來，咱們來看Union所在的線程

咱們再來複習下上面裏提到的這個線程的調用棧

"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)
這個線程對應了咱們sql裏的union算子

上面這個圖，是等待有消息過來就提取消息，任何一個源有消息都會觸發消息提取，不然wait

---注意：這裏的消息有4種類型，通常咱們只須要關注record+watermark便可

具體地點是：

---這裏講一下，關於LatencyMarker，默認2秒鐘發送一次，截圖以下

其它的不論是record仍是watermark都會往下發送！

下面咱們來在union裏同時針對record和watermark打斷點，猜一猜哪一個斷點先被觸發？

斷點位於【針對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

觸發的順序以下：

---跟想的是同樣的！ 下面就去研究下groupby線程

"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)
這個對應了group by算子

針對group by來講，最重要的環節，這個其實跟union線程同樣的，也是在

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

這裏面來作事件的分發，因此斷點都是同樣的

---

這裏主要強調，在groupby處理watermark時的位置以下：【尤爲是針對多個source來講，很容易出問題】

這個時候，我意識到在groupby線程中來觀察watermark還早了點，由於在union線程中針對watermark的處理還有一些祕密

因此咱們回到union線程來挖這些祕密，把groupby線程用suspend命令掛起來，專門debug union線程便可！

---打個斷點【針對flink-1.5】

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

研究了一把，大體明白原理了，這麼說吧，線程模型以下

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

其中，流1和流2---每次都發送本身看到的最大時間戳發送個下游(看到小的就什麼都不作)

union這裏會動態更新流1和流2的各自所看到的最大時間戳，同時取Min（流1的最大時間戳，流2的最大時間戳），跟上一次的值比較

若是>上一次的Min值，則發送給group by.

---我以爲讀者看到這裏，確定已經懵逼了，我來解釋下思想

強調一下：消息在中間過程當中不攔截，直達最後的windowoperator那裏作windowLate判斷決定是否丟棄！
===========================================================================================
對於流1來講，它每次發送本身已知的最大時間戳給下游，就是說「你好，下游，對我來講小於這個時間戳的就算是延遲消息，你看着辦」
對於流2來講，它每次發送本身已知的最大時間戳給下游，就是說「你好，下游，對我來講小於這個時間戳的就算是延遲消息，你看着辦」
---對於union來講，這裏複雜些
它取值min( 流1的max時間戳，流2的max時間戳）跟上一次的min( 流1的max時間戳，流2的max時間戳）比較，
若是發現遞增了，就把當前較大的這個min值發送給下游，說「你好，下游，全局來講，對我來講小於這個時間戳的就算是延遲消息，我只能幫到這裏了，已經盡力拖住時間戳了,你看着辦」

---對於groupby來講，它收到時間戳，每次保留最大值，而後參考最大值來快速決定每一個消息是否是延遲消息(最大值-可容忍的延遲消息)。


因此，在多源狀況下，判斷全局一個消息是否是延遲消息，實際上由min( 流1的max時間戳，流2的max時間戳）這個值來參與決定
---
咱們再跳出來想想這個事情，我估計讀者最懵逼的地方就是union爲啥取每一個流的最小值，而不是最大值
咱們就這麼理解吧，若是取最大值，那消費慢的流的數據大部分都成爲了late數據被丟棄，union就會被打
因此union爲了防止被打，它不想惹衆怒，就取了min(每一個流），這樣全部人都無話可說了
union旁白：我都取了大家每一個流的各自的時間戳最大值的全局最小值，還要我怎麼樣，
最慢的那個流也不會說啥了，由於取的就是它這個流上報的自身最大值。

上面都是從技術角度來闡述這個事情，那麼咱們再拔高一下，從更高的層次來看這個事情
其實就是讓更多的數據沒有成爲late數據，歸入正常運算範圍內，由min( 流1的max時間戳，流2的max時間戳）的遞增來推進全局windowoperator的計算輸出結果. 相應的，消費最慢的流會拖累最終業務數據的延遲生成.

 

---讀者能夠再細細琢磨裏面的門道，下面咱們來作邏輯測試！驗證咱們是否真正理解了這個遊戲規則!

背景：容忍延遲3000毫秒
下面每行的格式就是：流名稱 + 時間戳 ，每次只輸出1條
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 --->這個不會觸發windowOperator的輸出，由於流1的最小值還不夠
9)流1 + 1545703902000-1 --->這個纔會觸發windowOperator的輸出
正確輸出了，記住，必定要2個流
【齊頭並進，理實交融】

可是，其實，僅僅研究到這一步，並無徹底結束，欲知後事如何請聽下回分解 :)

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