Spark:广播对象时内存不足

Question

我试图播放一个不那么大的地图(当保存到HDFS作为文本文件时大约70 MB),我出现了内存错误.我试图将驱动程序内存增加到11G并将执行程序内存增加到11G,但仍然会出现相同的错误.memory.fraction设置为0.3,并且缓存的数据也不多(小于1G).

当地图只有大约2 MB时,没有问题.我想知道在播放对象时是否存在大小限制.如何使用更大的地图解决这个问题？谢谢!

Exception in thread "main" java.lang.OutOfMemoryError: Java heap space
    at java.util.IdentityHashMap.resize(IdentityHashMap.java:469)
    at java.util.IdentityHashMap.put(IdentityHashMap.java:445)
    at org.apache.spark.util.SizeEstimator$SearchState.enqueue(SizeEstimator.scala:159)
    at org.apache.spark.util.SizeEstimator$.visitArray(SizeEstimator.scala:229)
    at org.apache.spark.util.SizeEstimator$.visitSingleObject(SizeEstimator.scala:194)
    at org.apache.spark.util.SizeEstimator$.org$apache$spark$util$SizeEstimator$$estimate(SizeEstimator.scala:186)
    at org.apache.spark.util.SizeEstimator$.estimate(SizeEstimator.scala:54)
    at org.apache.spark.util.collection.SizeTracker$class.takeSample(SizeTracker.scala:78)
    at org.apache.spark.util.collection.SizeTracker$class.afterUpdate(SizeTracker.scala:70)
    at org.apache.spark.util.collection.SizeTrackingVector.$plus$eq(SizeTrackingVector.scala:31)
    at org.apache.spark.storage.MemoryStore.unrollSafely(MemoryStore.scala:278)
    at org.apache.spark.storage.MemoryStore.putIterator(MemoryStore.scala:165)
    at org.apache.spark.storage.MemoryStore.putIterator(MemoryStore.scala:143)
    at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:801)
    at org.apache.spark.storage.BlockManager.putIterator(BlockManager.scala:648)
    at org.apache.spark.storage.BlockManager.putSingle(BlockManager.scala:1006)
    at org.apache.spark.broadcast.TorrentBroadcast.writeBlocks(TorrentBroadcast.scala:99)
    at org.apache.spark.broadcast.TorrentBroadcast.<init>(TorrentBroadcast.scala:85)
    at org.apache.spark.broadcast.TorrentBroadcastFactory.newBroadcast(TorrentBroadcastFactory.scala:34)
    at org.apache.spark.broadcast.BroadcastManager.newBroadcast(BroadcastManager.scala:63)
    at org.apache.spark.SparkContext.broadcast(SparkContext.scala:1327)

编辑: 根据评论添加更多信息:

• 我使用spark-submit以客户端模式提交已编译的jar文件.Spark 1.5.0
• spark.yarn.executor.memoryOverhead 600
• set("spark.kryoserializer.buffer.max","256m")
• set("spark.speculation","true")
• set("spark.storage.memoryFraction","0.3")
• set("spark.driver.memory","15G")
• set("spark.executor.memory","11G")
• 我试过set("spar.sql.tungsten.enabled","false")并没有帮助.
• 主机有60G内存.大约30G用于Spark/Yarn.我不确定我的工作堆大小是多少,但同时还没有其他进程.特别是地图只有70MB左右.

一些与广播相关的代码:

val mappingAllLocal: Map[String, Int] = mappingAll.rdd.map(r => (r.getAs[String](0), r.getAs[Int](1))).collectAsMap().toMap
// I can use the above mappingAll to HDFS, and it's around 70MB
val mappingAllBrd = sc.broadcast(mappingAllLocal) // <-- this is where the out of memory happens

Answer 1

使用set("spark.driver.memory", "15G")对客户端模式没有影响.--conf="spark.driver.memory=15G"提交应用程序时,必须使用命令行参数来增加驱动程序的堆大小.