Spark Streaming 应用程序运行 24 小时后出现 OOM

Posted 2023-04-17

【中文标题】Spark Streaming 应用程序运行 24 小时后出现 OOM

【英文标题】：Spark Streaming application gives OOM after running for 24 hours

【发布时间】：2016-05-17 18:21:49

【问题描述】：

我正在使用 spark 1.5.0 并正在开发 spark 流应用程序。该应用程序从 HDFS 读取文件，将 rdd 转换为数据帧，并对每个数据帧执行多个查询。

应用程序完美运行了大约 24 小时，然后它崩溃了。 应用程序主日志/驱动程序日志显示：

Exception in thread "dag-scheduler-event-loop" java.lang.OutOfMemoryError: GC overhead limit exceeded
at java.lang.Class.getDeclaredMethods0(Native Method)
at java.lang.Class.privateGetDeclaredMethods(Class.java:2701)
at java.lang.Class.getDeclaredMethod(Class.java:2128)
at java.io.ObjectStreamClass.getInheritableMethod(ObjectStreamClass.java:1442)
at java.io.ObjectStreamClass.access$2200(ObjectStreamClass.java:72)
at java.io.ObjectStreamClass$2.run(ObjectStreamClass.java:508)
at java.io.ObjectStreamClass$2.run(ObjectStreamClass.java:472)
at java.security.AccessController.doPrivileged(Native Method)
at java.io.ObjectStreamClass.<init>(ObjectStreamClass.java:472)
at java.io.ObjectStreamClass.lookup(ObjectStreamClass.java:369)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1134)
at java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
    at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
    at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
    at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
    at java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
    at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
    at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
    at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
    at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:348)
    at scala.collection.immutable.$colon$colon.writeObject(List.scala:379)
    at sun.reflect.GeneratedMethodAccessor1511.invoke(Unknown Source)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:497)
    at java.io.ObjectStreamClass.invokeWriteObject(ObjectStreamClass.java:1028)
    at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1496)
    at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
    at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
    at java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
    at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
    at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
    at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
Exception in thread "JobGenerator" java.lang.OutOfMemoryError: GC overhead limit exceeded
    at java.util.zip.ZipCoder.getBytes(ZipCoder.java:80)
    at java.util.zip.ZipFile.getEntry(ZipFile.java:310)
    at java.util.jar.JarFile.getEntry(JarFile.java:240)
    at sun.net.www.protocol.jar.URLJarFile.getEntry(URLJarFile.java:128)
    at sun.net.www.protocol.jar.JarURLConnection.connect(JarURLConnection.java:132)
    at sun.net.www.protocol.jar.JarURLConnection.getInputStream(JarURLConnection.java:150)
    at java.net.URLClassLoader.getResourceAsStream(URLClassLoader.java:238)
    at java.lang.Class.getResourceAsStream(Class.java:2223)
    at org.apache.spark.util.ClosureCleaner$.getClassReader(ClosureCleaner.scala:38)
    at org.apache.spark.util.ClosureCleaner$.getInnerClosureClasses(ClosureCleaner.scala:81)
    at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:187)
    at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:122)
    at org.apache.spark.SparkContext.clean(SparkContext.scala:2032)
    at org.apache.spark.rdd.RDD$$anonfun$map$1.apply(RDD.scala:314)
    at org.apache.spark.rdd.RDD$$anonfun$map$1.apply(RDD.scala:313)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:147)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:108)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:306)
    at org.apache.spark.rdd.RDD.map(RDD.scala:313)
    at org.apache.spark.streaming.dstream.MappedDStream$$anonfun$compute$1.apply(MappedDStream.scala:35)
    at org.apache.spark.streaming.dstream.MappedDStream$$anonfun$compute$1.apply(MappedDStream.scala:35)
    at scala.Option.map(Option.scala:145)
    at org.apache.spark.streaming.dstream.MappedDStream.compute(MappedDStream.scala:35)
    at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:350)
    at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1$$anonfun$apply$7.apply(DStream.scala:350)
    at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
    at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:349)
    at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:349)
    at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:399)
    at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:344)
    at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:342)
    at scala.Option.orElse(Option.scala:257)

我收集了驱动堆转储，它说可能的内存泄漏来自org.apache.spark.sql.execution.ui.SQLListener

另外，在我的应用主 URL 中，我可以看到数千个 SQL tabs eg:-> SQL 1, SQL2 .. SQL 2000 并且这些选项卡的数量不断增加。

有谁知道为什么这些 SQL 选项卡不断增加并建议 GC 异常。 谢谢

【问题讨论】：

你能添加你的工作代码吗？

【参考方案1】：

1.5.0 中存在一些内存泄漏问题：SPARK-11126、SPARK-10155。

根据您的描述，您点击的是SPARK-11126。

您需要升级到 1.5.2 或为您的 Spark 应用补丁。