Spark /标量大小的SQL查询超过Integer.MAX_VALUE [英] SQL query in Spark/scala Size exceeds Integer.MAX_VALUE
问题描述
我正在尝试使用Spark在S3事件上创建一个简单的SQL查询。我正在加载约30GB的JSON文件，如下所示：
I am trying to create a simple sql query on S3 events using Spark. I am loading ~30GB of JSON files as following:
val d2 = spark.read.json("s3n://myData/2017/02/01/1234");
d2.persist(org.apache.spark.storage.StorageLevel.MEMORY_AND_DISK);
d2.registerTempTable("d2");
然后我正尝试写出查询结果：
Then I am trying to write to file the result of my query:
val users_count = sql("select count(distinct data.user_id) from d2");
users_count.write.format("com.databricks.spark.csv").option("header", "true").save("s3n://myfolder/UsersCount.csv");
但是Spark抛出以下异常：
But Spark is throwing the following exception:
java.lang.IllegalArgumentException: Size exceeds Integer.MAX_VALUE
at sun.nio.ch.FileChannelImpl.map(FileChannelImpl.java:869)
at org.apache.spark.storage.DiskStore$$anonfun$getBytes$2.apply(DiskStore.scala:103)
at org.apache.spark.storage.DiskStore$$anonfun$getBytes$2.apply(DiskStore.scala:91)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1287)
at org.apache.spark.storage.DiskStore.getBytes(DiskStore.scala:105)
at org.apache.spark.storage.BlockManager.getLocalValues(BlockManager.scala:439)
at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:672)
at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:330)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:281)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47)
at org.apache.spark.scheduler.Task.run(Task.scala:85)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
请注意，同一查询适用于少量数据。
Note that the same query works for smaller amounts of data. What's the problem here?
推荐答案
Spark shuffle块不能大于2GB（Integer.MAX_VALUE字节），因此您需要更多/较小的分区。
No Spark shuffle block can be larger than 2GB (Integer.MAX_VALUE bytes) so you need more / smaller partitions.
您应调整spark.default.parallelism和spark.sql.shuffle.partitions（默认值为200），以使分区数可以容纳您的数据而不会达到2GB的上限（您可以尝试将256MB /分区作为目标，因此，对于200GB的分区，您可以获得800个分区）。成千上万个分区非常常见，因此请不要害怕将其分区为建议的1000。
You should adjust spark.default.parallelism and spark.sql.shuffle.partitions (default 200) such that the number of partitions can accommodate your data without reaching the 2GB limit (you could try aiming for 256MB / partition so for 200GB you get 800 partitions). Thousands of partitions is very common so don't be afraid to repartition to 1000 as suggested.
仅供参考，您可以使用rdd之类的命令检查RDD的分区数.getNumPartitions（即d2.rdd.getNumPartitions）
FYI, you may check the number of partitions for an RDD with something like rdd.getNumPartitions (i.e. d2.rdd.getNumPartitions)
有一个故事可以跟踪解决各种2GB限制的工作（现在已经开放了一段时间）： https://issues.apache.org/jira/browse/SPARK-6235
There's a story to track the effort of addressing the various 2GB limits (been open for a while now): https://issues.apache.org/jira/browse/SPARK-6235
请参见 http://www.slideshare.net/cloudera/top-5-mistakes-to-avoid-when-writing-apache-spark-applications/25 有关此的更多信息错误。
See http://www.slideshare.net/cloudera/top-5-mistakes-to-avoid-when-writing-apache-spark-applications/25 for more info on this error.
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