這次需要做一個監控項目，全網日志的指標計算，上線的話，計算量應該是百億/天
單個source對應的sql如下
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`)
---先做技術論證，寫了下面一個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 (
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`)
然后拉起flink任務,觀察是否可順利啟動---果然報錯了
Caused by: org.apache.calcite.sql.validate.SqlValidatorException: Column 'SPT' not found in any table
定位一下，看看是什么問題導致的,看了下之前寫的sql，猜測是因為UNION的時候，沒有在每個表里帶上SPT時間屬性字段以及其它字段，補上后sql如下
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`)
再重啟看看，這次應該差不多了吧---sql可以順利編譯，但是還是有錯
奇怪了，之前並沒有這樣的錯誤，贊，我們來看看問題在哪！
我們打開類的層次圖如下
借這個機會加強對這些類的理解！
---經過我的調試，發現問題出現在union上，不加這個Union，啥事沒有；加了就報錯，下面我們再回到調用棧看看
一個人調試了一個下午，-_-||，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，我們繼續來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，但是多source就稍微復雜些，下面我們來研究下原理！
首先，觀察一下現有的圖，如下所示：
下面再來研究一下線程，jstack一把
我們來分析上面的線程，看看有沒有收獲！挑幾個重點線程講解
"VM Periodic Task Thread" os_prio=0 tid=0x00007f366825e800 nid=0x63d waiting on condition
百度可以知道
該線程是JVM周期性任務調度的線程，它由WatcherThread創建，是一個單例對象。該線程在JVM內使用得比較頻繁，比如：定期的內存監控、JVM運行狀況監控。
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內部等待所有事情結束
"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個線程來說，這2個消息不是直接讀取kafka,而是其它線程讀取kafka喂給這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)
這個線程對應了我們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)
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)
每個流對應着一個水印定時發送線程，因為我這邊的輸入是2個流
所以有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，下面我們來看每個線程做什么事情！這里先簡單交代一下消息記錄和watermark的背景
對於每個流，有1個消費者線程來讀取kafka的消息
然后通過本地內存交換，喂給另外一個線程，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里下廣播給下游.
---下面先來debug下Handover線程，看看是如何消息喂給unionInputGate線程的
斷點在
stop at org.apache.flink.streaming.connectors.kafka.internal.Kafka09Fetcher:154
跑起來！
然后，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！
消息處理過程中，會記錄下當前事件的時間戳，位置在
作用是如果時間戳比當前值更大，則更新這個時間戳，后面會有水印線程定時讀取這個值決定是否需要發送水印信息
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)
看一下這里的即將執行的代碼
public void emit(T record) throws IOException, InterruptedException {
for (int targetChannel : channelSelector.selectChannels(record, numChannels)) {
sendToTarget(record, targetChannel);
}
}
這里的print numChannels
numChannels = 1 --->因為我們有一個union操作，union自然是所有源歸一！這就對了！
---最后放入消息並提醒消費線程，完整的調用棧如下：
[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類似的，所以接下來，我們來看Union所在的線程
我們再來復習下上面里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)
這個線程對應了我們sql里的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，否則wait
---注意：這里的消息有4種類型，一般我們只需要關注record+watermark即可
具體地點是：
---這里講一下，關於LatencyMarker，默認2秒鍾發送一次，截圖如下
其它的不管是record還是watermark都會往下發送！
下面我們來在union里同時針對record和watermark打斷點，猜一猜哪個斷點先被觸發？
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
觸發的順序如下：
---跟想的是一樣的！ 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來說，最重要的環節，這個其實跟union線程一樣的，也是在
org.apache.flink.streaming.runtime.io.StreamInputProcessor.processInput
這里面來做事件的分發，所以斷點都是一樣的
---
這里主要強調，在groupby處理watermark時的位置如下：【尤其是針對多個source來說，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，因為在union線程中針對watermark的處理還有一些秘密
所以我們回到union線程來挖這些秘密，把groupby線程用suspend命令掛起來，專門debug union線程即可！
---打個斷點【針對flink-1.5】
stop at org.apache.flink.streaming.runtime.io.StreamInputProcessor:184
研究了一把，大致明白原理了，這么說吧，線程模型如下
流1-------
|
|
|
|
|
|---------->union線程的watermark--------->groupby線程的watermark
|
|
|
|
流2-------
其中，流1和流2---每次都發送自己看到的最大時間戳發送個下游(看到小的就什么都不做)
union這里會動態更新流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
如果>上一次的Min值，則發送給group by.
---我覺得讀者看到這里，肯定已經懵逼了，我來解釋下思想
強調一下：消息在中間過程中不攔截，直達最后的windowoperator那里做windowLate判斷決定是否丟棄！
===========================================================================================
對於流1來說，它每次發送自己已知的最大時間戳給下游，就是說“你好，下游，對我來說小於這個時間戳的就算是延遲消息，你看着辦”
對於流2來說，它每次發送自己已知的最大時間戳給下游，就是說“你好，下游，對我來說小於這個時間戳的就算是延遲消息，你看着辦”
---對於union來說，這里復雜些
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，
如果發現遞增了，就把當前較大的這個min值發送給下游，說“你好，下游，全局來說，對我來說小於這個時間戳的就算是延遲消息，我只能幫到這里了，已經盡力拖住時間戳了,你看着辦”
---對於groupby來說，它收到時間戳，每次保留最大值，然后參考最大值來快速決定每個消息是不是延遲消息(最大值-可容忍的延遲消息)。
所以，在多源情況下，判斷全局一個消息是不是延遲消息，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
---
我們再跳出來想一想這個事情，我估計讀者最懵逼的地方就是union為啥取每個流的最小值，而不是最大值
我們就這么理解吧，如果取最大值，那消費慢的流的數據大部分都成為了late數據被丟棄，union就會被打
所以union為了防止被打，它不想惹眾怒，就取了min(每個流），這樣所有人都無話可說了
union旁白：我都取了你們每個流的各自的時間戳最大值的全局最小值，還要我怎么樣，
最慢的那個流也不會說啥了，因為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，那么我們再拔高一下，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為late數據，納入正常運算范圍內，由min( 流1的max時間戳，流2的max時間戳）的遞增來推動全局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.
---讀者可以再細細琢磨里面的門道，下面我們來做邏輯測試！驗證我們是否真正理解了這個游戲規則!
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，因為流1的最小值還不夠
9)流1 + 1545703902000-1 --->這個才會觸發windowOperator的輸出
正確輸出了，記住，一定要2個流
【齊頭並進，理實交融】
但是，其實，僅僅研究到這一步，並沒有完全結束，欲知后事如何請聽下回分解 :)
原文鏈接：