Spark/scala 中的 SQL 查询大小超过 Integer.MAX_VALUE

SQL query in Spark/scala Size exceeds Integer.MAX_VALUE

提问by eexxoo

I am trying to create a simple sql query on S3 events using Spark. I am loading ~30GB of JSON files as following:

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 ~30GB 的 JSON 文件，如下所示：

val d2 = spark.read.json("s3n://myData/2017/02/01/1234");
d2.persist(org.apache.spark.storage.StorageLevel.MEMORY_AND_DISK);
d2.registerTempTable("d2");

Then I am trying to write to file the result of my query:

然后我试图写入文件我的查询结果：

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");

But Spark is throwing the following exception:

但 Spark 抛出以下异常：

java.lang.IllegalArgumentException: Size exceeds Integer.MAX_VALUE
at sun.nio.ch.FileChannelImpl.map(FileChannelImpl.java:869)
at org.apache.spark.storage.DiskStore$$anonfun$getBytes.apply(DiskStore.scala:103)
at org.apache.spark.storage.DiskStore$$anonfun$getBytes.apply(DiskStore.scala:91)
at org.apache.spark.util.Utils$.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)

Note that the same query works for smaller amounts of data. What's the problem here?

请注意，相同的查询适用于更少量的数据。这里有什么问题？

回答by Traian

No Spark shuffle block can be larger than 2GB (Integer.MAX_VALUE bytes) so you need more / smaller partitions.

没有 Spark shuffle 块可以大于 2GB（Integer.MAX_VALUE 字节），因此您需要更多/更小的分区。

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.

您应该调整 spark.default.parallelism 和 spark.sql.shuffle.partitions（默认为 200），以便分区数量可以容纳您的数据而不会达到 2GB 的限制（您可以尝试瞄准 256MB/分区，因此对于 200GB，您将获得 800分区）。数千个分区很常见，所以不要害怕按照建议重新分区到 1000 个。

FYI, you may check the number of partitions for an RDD with something like rdd.getNumPartitions (i.e. d2.rdd.getNumPartitions)

仅供参考，您可以使用 rdd.getNumPartitions（即 d2.rdd.getNumPartitions）之类的东西检查 RDD 的分区数

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

有一个故事来跟踪解决各种 2GB 限制的努力（现在已经开放了一段时间）：https: //issues.apache.org/jira/browse/SPARK-6235

See http://www.slideshare.net/cloudera/top-5-mistakes-to-avoid-when-writing-apache-spark-applications/25for more info on this error.

有关此错误的更多信息，请参阅http://www.slideshare.net/cloudera/top-5-mistakes-to-avoid-when-writing-apache-spark-applications/25。