Spark:加入2个大型DF时，大小超过Integer.max_value

folks，

我试图加入2个大数据范围（每行100GB ）时遇到这个问题。

我在EMR上使用Spark 1.6，这是我在做的：

val df1 = sqlContext.read.json("hdfs:///df1/")
val df2 = sqlContext.read.json("hdfs:///df2/")
// clean up and filter steps later 
df1.registerTempTable("df1")
df2.registerTempTable("df2")
val df3 = sql("select df1.*, df2.col1 from df1 left join df2 on df1.col3 = df2.col4")
df3.write.json("hdfs:///df3/")

这基本上是我正在做的事情，除了其他清理和过滤步骤中，最终加入了DF1和DF2。

我看到的错误是：

java.lang.IllegalArgumentException: Size exceeds Integer.MAX_VALUE
    at sun.nio.ch.FileChannelImpl.map(FileChannelImpl.java:860)
    at org.apache.spark.storage.DiskStore$$anonfun$getBytes$2.apply(DiskStore.scala:127)
    at org.apache.spark.storage.DiskStore$$anonfun$getBytes$2.apply(DiskStore.scala:115)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1250)
    at org.apache.spark.storage.DiskStore.getBytes(DiskStore.scala:129)
    at org.apache.spark.storage.DiskStore.getBytes(DiskStore.scala:136)
    at org.apache.spark.storage.BlockManager.doGetLocal(BlockManager.scala:503)
    at org.apache.spark.storage.BlockManager.getLocal(BlockManager.scala:420)
    at org.apache.spark.storage.BlockManager.get(BlockManager.scala:625)
    at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:44)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:268)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
    at org.apache.spark.scheduler.Task.run(Task.scala:89)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
    at java.lang.Thread.run(Thread.java:745)

配置和参考：
我使用的是13个节点60GB，每个群集与执行者和驱动程序内存相应地设置了开销。我尝试调整的事情：

spark.sql.broadcastTimeout
spark.sql.shuffle.partitions

我也尝试使用更大的群集，但没有帮助。此链接说，如果洗牌分区尺寸超过2GB，则会丢弃此错误。但是我尝试将数量的分区数量增加到很高的价值，但仍然没有运气。

我怀疑这可能与懒惰加载有关。当我在DF上进行10次操作时，它们仅在最后一步执行。我尝试在DF的各个存储级别上添加.persist()，但它仍然没有成功。我还尝试丢弃临时桌，清空所有早期的DF以进行清理。

但是，如果我将其分解为2个部分，则代码可以工作 - 将最终的临时数据（2个数据帧）写入磁盘，退出。重新启动以加入两个DFS。

我较早遇到此错误：

Exception in thread "main" java.util.concurrent.TimeoutException: Futures timed out after [300 seconds]
    at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:219)
    at scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223)
    at scala.concurrent.Await$$anonfun$result$1.apply(package.scala:107)
    at scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(BlockContext.scala:53)
    at scala.concurrent.Await$.result(package.scala:107)
    at org.apache.spark.sql.execution.joins.BroadcastHashOuterJoin.doExecute(BroadcastHashOuterJoin.scala:113)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
    at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
    at org.apache.spark.sql.execution.Project.doExecute(basicOperators.scala:46)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
    at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
    at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:55)
    at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:55)
    at org.apache.spark.sql.DataFrame.toJSON(DataFrame.scala:1724)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:606)
    at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:731)
    at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)
    at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)
    at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)
    at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

但是，当我调整spark.sql.broadcastTimeout时，我开始遇到第一个错误。

在这种情况下，请感谢任何帮助。如果需要，我可以添加更多信息。

在Spark中，您的洗牌块大于2GB。这是因为，Spark将洗牌块存储为字节卷。这是您分配的方式：

ByteBuffer.allocate(int capacity)

as Bytebuffer受到Integer.max_size（2GB）的限制，因此混乱块也是如此！解决方案是通过在SparkSQL中使用spark.sql.shuffle.partitions或通过rdd.partition() or rdd.colease()来增加分区数量，以使每个分区大小均为＆lt; = 2GB。

您提到您试图增加分区数，但仍然失败。您可以检查分区尺寸是否> 2GB。只需确保指定的分区数量足以使每个块大小＆lt;2GB

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