【原创】大叔问题定位分享（7）Spark任务中Job进度卡住不动

Spark2.1.1

最近运行spark任务时会发现任务经常运行很久，具体job如下：

Job Id  ▾	Description	Submitted	Duration	Stages: Succeeded/Total	Tasks (for all stages): Succeeded/Total
16	(kill)treeReduce at CRFWithLBFGS.scala:160	2018/12/03 12:39:50	2.3 h	0/5	196/4723

job中正在运行的stage如下：

Stage Id  ▾	Description	Submitted	Duration	Tasks: Succeeded/Total	Input	Output	Shuffle Read	Shuffle Write
60	(kill)treeReduce at CRFWithLBFGS.scala:160+details	2018/12/03 12:39:57	2.3 h	196/200	4.5 GB			1455.1 MB

该stage中有4个task一直处于running状态，这些task的统计信息异常（Input Size / Records和Shuffle Write Size / Records均为0.0B/0），并且这4个task都位于同一个executor上：

33

8938

0

RUNNING

PROCESS_LOCAL

12 / $executor_server_ip stdout stderr

2018/12/03 12:39:57

2.3 h

0.0 B / 0

0.0 B / 0

有问题的task所在的executor统计信息也有异常（Total Tasks为0），该executor如下：

12 stdout stderr

$executor_server_ip:36755

0 ms

0

0

0

0

0.0 B / 0

0.0 B / 0

此时Driver堆栈信息如下：

"Driver" #26 prio=5 os_prio=0 tid=0x00007f163a116000 nid=0x5192 waiting on condition [0x00007f15bb9a0000]

java.lang.Thread.State: WAITING (parking)

at sun.misc.Unsafe.park(Native Method)

- parking to wait for  <0x00000001a8c4f9e0> (a scala.concurrent.impl.Promise$CompletionLatch)

at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)

at java.util.concurrent.locks.AbstractQueuedSynchronizer.parkAndCheckInterrupt(AbstractQueuedSynchronizer.java:836)

at java.util.concurrent.locks.AbstractQueuedSynchronizer.doAcquireSharedInterruptibly(AbstractQueuedSynchronizer.java:997)

at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquireSharedInterruptibly(AbstractQueuedSynchronizer.java:1304)

at scala.concurrent.impl.Promise$DefaultPromise.tryAwait(Promise.scala:202)

at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:218)

at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:153)

at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:619)

at org.apache.spark.SparkContext.runJob(SparkContext.scala:1925)

at org.apache.spark.SparkContext.runJob(SparkContext.scala:1988)

at org.apache.spark.rdd.RDD$$anonfun$reduce$1.apply(RDD.scala:1026)

at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)

at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)

at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)

at org.apache.spark.rdd.RDD.reduce(RDD.scala:1008)

at org.apache.spark.rdd.RDD$$anonfun$treeAggregate$1.apply(RDD.scala:1151)

at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)

at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)

at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)

at org.apache.spark.rdd.RDD.treeAggregate(RDD.scala:1128)

at org.apache.spark.rdd.RDD$$anonfun$treeReduce$1.apply(RDD.scala:1059)

at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)

at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)

at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)

at org.apache.spark.rdd.RDD.treeReduce(RDD.scala:1037)

at breeze.optimize.CachedDiffFunction.calculate(CachedDiffFunction.scala:23)

at breeze.optimize.LineSearch$$anon$1.calculate(LineSearch.scala:41)

at breeze.optimize.LineSearch$$anon$1.calculate(LineSearch.scala:30)

at breeze.optimize.StrongWolfeLineSearch.breeze$optimize$StrongWolfeLineSearch$$phi$1(StrongWolfe.scala:69)

at breeze.optimize.StrongWolfeLineSearch$$anonfun$minimize$1.apply$mcVI$sp(StrongWolfe.scala:142)

at scala.collection.immutable.Range.foreach$mVc$sp(Range.scala:160)

at breeze.optimize.StrongWolfeLineSearch.minimize(StrongWolfe.scala:141)

at breeze.optimize.LBFGS.determineStepSize(LBFGS.scala:78)

at breeze.optimize.LBFGS.determineStepSize(LBFGS.scala:40)

at breeze.optimize.FirstOrderMinimizer$$anonfun$infiniteIterations$1.apply(FirstOrderMinimizer.scala:64)

at breeze.optimize.FirstOrderMinimizer$$anonfun$infiniteIterations$1.apply(FirstOrderMinimizer.scala:62)

at scala.collection.Iterator$$anon$7.next(Iterator.scala:129)

at breeze.util.IteratorImplicits$RichIterator$$anon$2.next(Implicits.scala:71)

at scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:733)

at scala.collection.immutable.Range.foreach(Range.scala:160)

at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:732)

at app.package.AppClass.main(AppClass.scala)

at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)

at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)

at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)

at java.lang.reflect.Method.invoke(Method.java:497)

at org.apache.spark.deploy.yarn.ApplicationMaster$$anon$2.run(ApplicationMaster.scala:637)

可见正在runJob，并且等待executor执行结果；

有问题的executor上堆栈信息有一个可疑的thread长时间一直在running:

"shuffle-client-5-4" #94 daemon prio=5 os_prio=0 tid=0x00007fbae0e42800 nid=0x2a3a runnable [0x00007fbae4760000]

java.lang.Thread.State: RUNNABLE

at io.netty.util.Recycler$Stack.scavengeSome(Recycler.java:476)

at io.netty.util.Recycler$Stack.scavenge(Recycler.java:454)

at io.netty.util.Recycler$Stack.pop(Recycler.java:435)

at io.netty.util.Recycler.get(Recycler.java:144)

at io.netty.buffer.PooledUnsafeDirectByteBuf.newInstance(PooledUnsafeDirectByteBuf.java:39)

at io.netty.buffer.PoolArena$DirectArena.newByteBuf(PoolArena.java:727)

at io.netty.buffer.PoolArena.allocate(PoolArena.java:140)

at io.netty.buffer.PooledByteBufAllocator.newDirectBuffer(PooledByteBufAllocator.java:271)

at io.netty.buffer.AbstractByteBufAllocator.directBuffer(AbstractByteBufAllocator.java:177)

at io.netty.buffer.AbstractByteBufAllocator.directBuffer(AbstractByteBufAllocator.java:168)

at io.netty.buffer.AbstractByteBufAllocator.ioBuffer(AbstractByteBufAllocator.java:129)

at io.netty.channel.AdaptiveRecvByteBufAllocator$HandleImpl.allocate(AdaptiveRecvByteBufAllocator.java:104)

at io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:117)

at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:652)

at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:575)

at io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:489)

at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:451)

at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:140)

at io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:144)

at java.lang.Thread.run(Thread.java:745)

ps：出问题的executor上当时的内存资源很空闲，进程状态也正常：

-bash-4.2$ free -m

total        used        free      shared  buff/cache   available

Mem:         257676       29251        5274         517      223150      226669

Swap:             0           0           0

怀疑此处可能有死循环，spark2.1.1使用的netty版本是4.0.42，查看netty代码：

io.netty.util.Recycler

        boolean scavengeSome() {

            WeakOrderQueue cursor = this.cursor;

            if (cursor == null) {

                cursor = head;

                if (cursor == null) {

                    return false;

                }

            }

            boolean success = false;

            WeakOrderQueue prev = this.prev;

            do {

                if (cursor.transfer(this)) {

                    success = true;

                    break;

                }

                WeakOrderQueue next = cursor.next;

                if (cursor.owner.get() == null) {

                    // If the thread associated with the queue is gone, unlink it, after

                    // performing a volatile read to confirm there is no data left to collect.

                    // We never unlink the first queue, as we don't want to synchronize on updating the head.

                    if (cursor.hasFinalData()) {

                        for (;;) {

                            if (cursor.transfer(this)) {

                                success = true;

                            } else {

                                break;

                            }

                        }

                    }

                    if (prev != null) {

                        prev.next = next;

                    }

                } else {

                    prev = cursor;

                }

                cursor = next;

            } while (cursor != null && !success);

            this.prev = prev;

            this.cursor = cursor;

            return success;

        }

问题在于cursor初始化的时候没有清空prev：

if (cursor == null) {

cursor = head;

该问题在4.0.43中被修复，升级spark2.1.1中的netty到4.0.43或以上版本可以修复问题；

官方issues位于：https://github.com/netty/netty/issues/6153