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https://issues.apache.org/jira/browse/HIVE-17684?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16627012#comment-16627012
]
KaiXu commented on HIVE-17684:
------------------------------
Hi [~stakiar] and [~mi...@cloudera.com], I recently encountered a similar issue
as this Jira with Hive2.1 on Spark2.2, the issue seems randomly occurred when
under high concurrency and pressure. Below is the stack trace, I am not sure if
it's the same issue, and do you have any suggestions for the workaround?
18/09/24 14:30:42 ERROR spark.SparkMapRecordHandler: Error processing row:
org.apache.hadoop.hive.ql.metadata.HiveException: Hive Runtime Error while
processing row \{"i_item_sk":118975}
org.apache.hadoop.hive.ql.metadata.HiveException: Hive Runtime Error while
processing row \{"i_item_sk":118975}
at org.apache.hadoop.hive.ql.exec.MapOperator.process(MapOperator.java:497)
at
org.apache.hadoop.hive.ql.exec.spark.SparkMapRecordHandler.processRow(SparkMapRecordHandler.java:136)
at
org.apache.hadoop.hive.ql.exec.spark.HiveMapFunctionResultList.processNextRecord(HiveMapFunctionResultList.java:48)
at
org.apache.hadoop.hive.ql.exec.spark.HiveMapFunctionResultList.processNextRecord(HiveMapFunctionResultList.java:27)
at
org.apache.hadoop.hive.ql.exec.spark.HiveBaseFunctionResultList.hasNext(HiveBaseFunctionResultList.java:85)
at
scala.collection.convert.Wrappers$JIteratorWrapper.hasNext(Wrappers.scala:42)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at
org.apache.spark.rdd.AsyncRDDActions$$anonfun$foreachAsync$1$$anonfun$apply$12.apply(AsyncRDDActions.scala:127)
at
org.apache.spark.rdd.AsyncRDDActions$$anonfun$foreachAsync$1$$anonfun$apply$12.apply(AsyncRDDActions.scala:127)
at org.apache.spark.SparkContext$$anonfun$34.apply(SparkContext.scala:2182)
at org.apache.spark.SparkContext$$anonfun$34.apply(SparkContext.scala:2182)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:109)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:338)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Caused by:
org.apache.hadoop.hive.ql.exec.mapjoin.MapJoinMemoryExhaustionException:
2018-09-24 14:30:42 Processing rows: 200000 Hashtable size: 199999 Memory
usage: 5920999680 percentage: 0.551
at
org.apache.hadoop.hive.ql.exec.mapjoin.MapJoinMemoryExhaustionHandler.checkMemoryStatus(MapJoinMemoryExhaustionHandler.java:99)
at
org.apache.hadoop.hive.ql.exec.HashTableSinkOperator.process(HashTableSinkOperator.java:259)
at
org.apache.hadoop.hive.ql.exec.SparkHashTableSinkOperator.process(SparkHashTableSinkOperator.java:85)
at org.apache.hadoop.hive.ql.exec.Operator.forward(Operator.java:879)
at
org.apache.hadoop.hive.ql.exec.FilterOperator.process(FilterOperator.java:126)
at org.apache.hadoop.hive.ql.exec.Operator.forward(Operator.java:879)
at
org.apache.hadoop.hive.ql.exec.TableScanOperator.process(TableScanOperator.java:130)
at
org.apache.hadoop.hive.ql.exec.MapOperator$MapOpCtx.forward(MapOperator.java:147)
at org.apache.hadoop.hive.ql.exec.MapOperator.process(MapOperator.java:487)
... 17 more
> HoS memory issues with MapJoinMemoryExhaustionHandler
> -----------------------------------------------------
>
> Key: HIVE-17684
> URL: https://issues.apache.org/jira/browse/HIVE-17684
> Project: Hive
> Issue Type: Bug
> Components: Spark
> Reporter: Sahil Takiar
> Assignee: Misha Dmitriev
> Priority: Major
> Attachments: HIVE-17684.01.patch, HIVE-17684.02.patch,
> HIVE-17684.03.patch, HIVE-17684.04.patch, HIVE-17684.05.patch,
> HIVE-17684.06.patch, HIVE-17684.07.patch, HIVE-17684.08.patch,
> HIVE-17684.09.patch, HIVE-17684.10.patch, HIVE-17684.11.patch
>
>
> We have seen a number of memory issues due the {{HashSinkOperator}} use of
> the {{MapJoinMemoryExhaustionHandler}}. This handler is meant to detect
> scenarios where the small table is taking too much space in memory, in which
> case a {{MapJoinMemoryExhaustionError}} is thrown.
> The configs to control this logic are:
> {{hive.mapjoin.localtask.max.memory.usage}} (default 0.90)
> {{hive.mapjoin.followby.gby.localtask.max.memory.usage}} (default 0.55)
> The handler works by using the {{MemoryMXBean}} and uses the following logic
> to estimate how much memory the {{HashMap}} is consuming:
> {{MemoryMXBean#getHeapMemoryUsage().getUsed() /
> MemoryMXBean#getHeapMemoryUsage().getMax()}}
> The issue is that {{MemoryMXBean#getHeapMemoryUsage().getUsed()}} can be
> inaccurate. The value returned by this method returns all reachable and
> unreachable memory on the heap, so there may be a bunch of garbage data, and
> the JVM just hasn't taken the time to reclaim it all. This can lead to
> intermittent failures of this check even though a simple GC would have
> reclaimed enough space for the process to continue working.
> We should re-think the usage of {{MapJoinMemoryExhaustionHandler}} for HoS.
> In Hive-on-MR this probably made sense to use because every Hive task was run
> in a dedicated container, so a Hive Task could assume it created most of the
> data on the heap. However, in Hive-on-Spark there can be multiple Hive Tasks
> running in a single executor, each doing different things.
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