在采用log4j的kafka-appender收集spark任務運行日志時，發現提交到yarn上的任務始終ACCEPTED狀態，無法進入RUNNING狀態，并且會重試兩次后超時。期初認為是yarn資源不足導致，但在確認yarn資源充裕的時候問題依舊，而且基本上能穩定復現。
起初是這么配置spark日志輸出到kafka的：
log4j.rootCategory=INFO, console, kafka
log4j.appender.console=org.apache.log4j.ConsoleAppender
log4j.appender.console.target=System.err
log4j.appender.console.layout=org.apache.log4j.PatternLayout
log4j.appender.console.layout.ConversionPattern=%d{yyyy/MM/dd HH:mm:ss.SSS} %p %c{1}: [${log4j.pipelineId}] %m%n
# Kafka appender
log4j.appender.kafka=org.apache.kafka.log4jappender.KafkaLog4jAppender
# Set Kafka topic and brokerList
log4j.appender.kafka.topic=yarn_spark_log
log4j.appender.kafka.brokerList=localhost:9092
log4j.appender.kafka.compressionType=none
log4j.appender.kafka.syncSend=false
log4j.appender.kafka.maxBlockMs=10
log4j.appender.kafka.layout=org.apache.log4j.PatternLayout
log4j.appender.kafka.layout.ConversionPattern=%d{yyyy/MM/dd HH:mm:ss.SSS} %p %c{1}: [${log4j.pipelineId}] %m
這里用org.apache.kafka.log4jappender.KafkaLog4jAppender默認將所有日志都輸出到kafka，這個appender已經被kafka官方維護，穩定性應該是可以保障的。
問題定位
發現問題后，嘗試將輸出到kafka的規則去掉，問題解除！于是把問題定位到跟日志輸出到kafka有關。通過其他測試，證實目標kafka其實是正常的，這就非常奇怪了。
查看yarn的ResourceManager日志，發現有如下超時
2020-05-07 21:49:48,230 INFO org.apache.hadoop.yarn.util.AbstractLivelinessMonitor: Expired:appattempt_1578970174552_3204_000002 Timed out after 600 secs
2020-05-07 21:49:48,230 INFO org.apache.hadoop.yarn.server.resourcemanager.rmapp.attempt.RMAppAttemptImpl: Updating application attempt appattempt_1578970174552_3204_000002 with final
state: FAILED, and exit status: -1000
2020-05-07 21:49:48,231 INFO org.apache.hadoop.yarn.server.resourcemanager.rmapp.attempt.RMAppAttemptImpl: appattempt_1578970174552_3204_000002 State change from LAUNCHED to FINAL_SAV
ING on event = EXPIRE
表明，yarn本身是接收任務的，但是發現任務遲遲沒有啟動。在spark的場景下其實是指只有driver啟動了，但是沒有啟動executor。
而查看driver日志，發現日志輸出到一個地方就卡住了，不往下繼續了。通過對比成功運行和卡住的情況發現，日志卡在這條上：
2020/05/07 19:37:10.324 INFO SecurityManager: Changing view acls to: yarn,root
2020/05/07 19:37:10.344 INFO Metadata: Cluster ID: 6iG6WHA2SoK7FfgGgWHt_A
卡住的情況下，只會打出SecurityManager這行，而無法打出Metadata這行。
猜想Metadata這行是kafka-client本身打出來的，因為整個上下文只有yarn, spark, kafka-client可能會打出這個日志。
在kafka-client 2.2.0版本中找到這個日志是輸出位置：
public synchronized void update(MetadataResponse metadataResponse, long now) {
...
String newClusterId = cache.cluster().clusterResource().clusterId();
if (!Objects.equals(previousClusterId, newClusterId)) {
log.info("Cluster ID: {}", newClusterId);
}
...
}
看到synchronized，高度懷疑死鎖。于是考慮用jstack分析：
在yarn上運行spark任務的時候，driver進程叫ApplicationMaster，executor進程叫CoarseGrainedExecutorBackend。這里首先嘗試再復現過程中找到drvier最終在哪個節點上運行，然后快速使用jstack -F pid>打印堆棧
jstack果然不負眾望，報告了死鎖！這里我把結果貼的全一點
[root@node1 ~]# jstack 20136
20136: Unable to open socket file: target process not responding or HotSpot VM not loaded
The -F option can be used when the target process is not responding
[root@node1 ~]# jstack -F 20136
Attaching to process ID 20136, please wait...
Debugger attached successfully.
Server compiler detected.
JVM version is 25.231-b11
Deadlock Detection:
Found one Java-level deadlock:
=============================
"kafka-producer-network-thread | producer-1":
waiting to lock Monitor@0x00000000025fcc48 (Object@0x00000000ed680b60, a org/apache/kafka/log4jappender/KafkaLog4jAppender),
which is held by "main"
"main":
waiting to lock Monitor@0x00007fec9dbde038 (Object@0x00000000ee44de38, a org/apache/kafka/clients/Metadata),
which is held by "kafka-producer-network-thread | producer-1"
Found a total of 1 deadlock.
Thread 20157: (state = BLOCKED)
- org.apache.log4j.AppenderSkeleton.doAppend(org.apache.log4j.spi.LoggingEvent) @bci=0, line=231 (Interpreted frame)
- org.apache.log4j.helpers.AppenderAttachableImpl.appendLoopOnAppenders(org.apache.log4j.spi.LoggingEvent) @bci=41, line=66 (Interpreted frame)
- org.apache.log4j.Category.callAppenders(org.apache.log4j.spi.LoggingEvent) @bci=26, line=206 (Interpreted frame)
- org.apache.log4j.Category.forcedLog(java.lang.String, org.apache.log4j.Priority, java.lang.Object, java.lang.Throwable) @bci=14, line=391 (Interpreted frame)
- org.apache.log4j.Category.log(java.lang.String, org.apache.log4j.Priority, java.lang.Object, java.lang.Throwable) @bci=34, line=856 (Interpreted frame)
- org.slf4j.impl.Log4jLoggerAdapter.info(java.lang.String, java.lang.Object) @bci=34, line=324 (Interpreted frame)
- org.apache.kafka.clients.Metadata.update(org.apache.kafka.common.requests.MetadataResponse, long) @bci=317, line=365 (Interpreted frame)
- org.apache.kafka.clients.NetworkClient$DefaultMetadataUpdater.handleCompletedMetadataResponse(org.apache.kafka.common.requests.RequestHeader, long, org.apache.kafka.common.requests.MetadataResponse) @bci=184, line=1031 (Interpreted frame)
- org.apache.kafka.clients.NetworkClient.handleCompletedReceives(java.util.List, long) @bci=215, line=822 (Interpreted frame)
- org.apache.kafka.clients.NetworkClient.poll(long, long) @bci=132, line=544 (Interpreted frame)
- org.apache.kafka.clients.producer.internals.Sender.run(long) @bci=227, line=311 (Interpreted frame)
- org.apache.kafka.clients.producer.internals.Sender.run() @bci=28, line=235 (Interpreted frame)
- java.lang.Thread.run() @bci=11, line=748 (Interpreted frame)
Thread 20150: (state = BLOCKED)
Thread 20149: (state = BLOCKED)
- java.lang.Object.wait(long) @bci=0 (Interpreted frame)
- java.lang.ref.ReferenceQueue.remove(long) @bci=59, line=144 (Interpreted frame)
- java.lang.ref.ReferenceQueue.remove() @bci=2, line=165 (Interpreted frame)
- java.lang.ref.Finalizer$FinalizerThread.run() @bci=36, line=216 (Interpreted frame)
Thread 20148: (state = BLOCKED)
- java.lang.Object.wait(long) @bci=0 (Interpreted frame)
- java.lang.Object.wait() @bci=2, line=502 (Interpreted frame)
- java.lang.ref.Reference.tryHandlePending(boolean) @bci=54, line=191 (Interpreted frame)
- java.lang.ref.Reference$ReferenceHandler.run() @bci=1, line=153 (Interpreted frame)
Thread 20137: (state = BLOCKED)
- java.lang.Object.wait(long) @bci=0 (Interpreted frame)
- org.apache.kafka.clients.Metadata.awaitUpdate(int, long) @bci=63, line=261 (Interpreted frame)
- org.apache.kafka.clients.producer.KafkaProducer.waitOnMetadata(java.lang.String, java.lang.Integer, long) @bci=160, line=983 (Interpreted frame)
- org.apache.kafka.clients.producer.KafkaProducer.doSend(org.apache.kafka.clients.producer.ProducerRecord, org.apache.kafka.clients.producer.Callback) @bci=19, line=860 (Interpreted frame)
- org.apache.kafka.clients.producer.KafkaProducer.send(org.apache.kafka.clients.producer.ProducerRecord, org.apache.kafka.clients.producer.Callback) @bci=12, line=840 (Interpreted frame)
- org.apache.kafka.clients.producer.KafkaProducer.send(org.apache.kafka.clients.producer.ProducerRecord) @bci=3, line=727 (Interpreted frame)
- org.apache.kafka.log4jappender.KafkaLog4jAppender.append(org.apache.log4j.spi.LoggingEvent) @bci=69, line=283 (Interpreted frame)
- org.apache.log4j.AppenderSkeleton.doAppend(org.apache.log4j.spi.LoggingEvent) @bci=106, line=251 (Interpreted frame)
- org.apache.log4j.helpers.AppenderAttachableImpl.appendLoopOnAppenders(org.apache.log4j.spi.LoggingEvent) @bci=41, line=66 (Interpreted frame)
- org.apache.log4j.Category.callAppenders(org.apache.log4j.spi.LoggingEvent) @bci=26, line=206 (Interpreted frame)
- org.apache.log4j.Category.forcedLog(java.lang.String, org.apache.log4j.Priority, java.lang.Object, java.lang.Throwable) @bci=14, line=391 (Interpreted frame)
- org.apache.log4j.Category.log(java.lang.String, org.apache.log4j.Priority, java.lang.Object, java.lang.Throwable) @bci=34, line=856 (Interpreted frame)
- org.slf4j.impl.Log4jLoggerAdapter.info(java.lang.String) @bci=12, line=305 (Interpreted frame)
- org.apache.spark.internal.Logging$class.logInfo(org.apache.spark.internal.Logging, scala.Function0) @bci=29, line=54 (Interpreted frame)
- org.apache.spark.SecurityManager.logInfo(scala.Function0) @bci=2, line=44 (Interpreted frame)
- org.apache.spark.SecurityManager.setViewAcls(scala.collection.immutable.Set, java.lang.String) @bci=36, line=139 (Interpreted frame)
- org.apache.spark.SecurityManager.init>(org.apache.spark.SparkConf, scala.Option) @bci=158, line=81 (Interpreted frame)
- org.apache.spark.deploy.yarn.ApplicationMaster.init>(org.apache.spark.deploy.yarn.ApplicationMasterArguments) @bci=85, line=70 (Interpreted frame)
- org.apache.spark.deploy.yarn.ApplicationMaster$.main(java.lang.String[]) @bci=25, line=802 (Interpreted frame)
- org.apache.spark.deploy.yarn.ApplicationMaster.main(java.lang.String[]) @bci=4 (Interpreted frame)
到這里，已經確定是死鎖，導致driver一開始就運行停滯，那么當然無法提交executor執行。
具體的死鎖稍后分析，先考慮如何解決。從感性認識看，似乎只要不讓kafka-client的日志也輸出到kafka即可。實驗后，發現果然如此：如果只輸出org.apache.spark的日志就可以正常執行。
根因分析
從stack的結果看，造成死鎖的是如下兩個線程：
- kafka-client內部的網絡線程spark
- 主入口線程
兩個線程其實都是卡在打日志上了，觀察堆棧可以發現，兩個線程同時持有了同一個log對象。而這個log對象實際上是kafka-appender。而kafka-appender本質上持有kafka-client，及其內部的Metadata對象。log4j的doAppend為了保證線程安全也用synchronized修飾了：
public
synchronized
void doAppend(LoggingEvent event) {
if(closed) {
LogLog.error("Attempted to append to closed appender named ["+name+"].");
return;
}
if(!isAsSevereAsThreshold(event.level)) {
return;
}
Filter f = this.headFilter;
FILTER_LOOP:
while(f != null) {
switch(f.decide(event)) {
case Filter.DENY: return;
case Filter.ACCEPT: break FILTER_LOOP;
case Filter.NEUTRAL: f = f.next;
}
}
this.append(event);
}
于是事情開始了：
- main線程嘗試打日志，首先進入了synchronized的doAppend，即獲取了
kafka-appender的鎖
kafka-appender內部需要調用kafka-client發送日志到kafka，最終調用到Thread 20137展示的，運行到Metadata.awaitUpdate（也是個synchronized方法），內部的wait會嘗試獲取metadata的鎖。（詳見https://github.com/apache/kaf...）- 但此時，kafka-producer-network-thread線程剛好進入了上文提到的打
Cluster ID這個日志的這個階段（update方法也是synchronized的），也就是說kafka-producer-network-thread線程獲得了metadata對象的鎖
- kafka-producer-network-thread線程要打印日志同樣執行synchronized的doAppend，即獲取了
kafka-appender的鎖
上圖main-thread持有了log對象鎖，要求獲取metadata對象鎖；kafka-producer-network-thread持有了metadata對象鎖，要求獲取log對象鎖于是造成了死鎖。
總結
到此這篇關于spark通過kafka-appender指定日志輸出到kafka引發的死鎖的文章就介紹到這了,更多相關spark指定日志輸出內容請搜索腳本之家以前的文章或繼續瀏覽下面的相關文章希望大家以后多多支持腳本之家！
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