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Misha Dmitriev commented on HIVE-17684: --------------------------------------- I've fixed some checkstyle warnings (several others, e.g. about indentation, seem strange, so I'd rather ignore them). I've looked at the test failures and I am not sure how to debug this. Note that 3 previous runs of the same jenkins build have 14..16 test failures each, so I suspect something is already broken here. In my case there are a lot more test failures, but I suspect that most of them are irrelevant given that my change is quite small. I wonder if my update of the hadoop dependency could have such an effect? > 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 > Attachments: HIVE-17684.01.patch, HIVE-17684.02.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. -- This message was sent by Atlassian JIRA (v6.4.14#64029)