Idan Sheinberg 8:21 AM
Hi there
I'm trying run a simple offset query (ORDER BY timestamp LIMIT 500 OFFSET 1000)
against rather complex parquet files (say 4 columns, once being an array
currently consisting of a single element comprised of 15 columns)
All files share the same Schema, of course.
User Error Occurred: One or more nodes ran out of memory while executing the
query. (null)
org.apache.drill.common.exceptions.UserException: RESOURCE ERROR: One or more
nodes ran out of memory while executing the query.
null
[Error Id: 67b61fc9-320f-47a1-8718-813843a10ecc ]
at
org.apache.drill.common.exceptions.UserException$Builder.build(UserException.java:657)
at
org.apache.drill.exec.work.fragment.FragmentExecutor.run(FragmentExecutor.java:338)
at
org.apache.drill.common.SelfCleaningRunnable.run(SelfCleaningRunnable.java:38)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.drill.exec.exception.OutOfMemoryException: null
at
org.apache.drill.exec.vector.complex.AbstractContainerVector.allocateNew(AbstractContainerVector.java:59)
at
org.apache.drill.exec.test.generated.PartitionerGen5$OutgoingRecordBatch.allocateOutgoingRecordBatch(PartitionerTemplate.java:380)
at
org.apache.drill.exec.test.generated.PartitionerGen5$OutgoingRecordBatch.initializeBatch(PartitionerTemplate.java:400)
at
org.apache.drill.exec.test.generated.PartitionerGen5.setup(PartitionerTemplate.java:126)
at
org.apache.drill.exec.physical.impl.partitionsender.PartitionSenderRootExec.createClassInstances(PartitionSenderRootExec.java:263)
at
org.apache.drill.exec.physical.impl.partitionsender.PartitionSenderRootExec.createPartitioner(PartitionSenderRootExec.java:218)
at
org.apache.drill.exec.physical.impl.partitionsender.PartitionSenderRootExec.innerNext(PartitionSenderRootExec.java:188)
at
org.apache.drill.exec.physical.impl.BaseRootExec.next(BaseRootExec.java:93)
at
org.apache.drill.exec.work.fragment.FragmentExecutor$1.run(FragmentExecutor.java:323)
at
org.apache.drill.exec.work.fragment.FragmentExecutor$1.run(FragmentExecutor.java:310)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:422)
at
org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1730)
at
org.apache.drill.exec.work.fragment.FragmentExecutor.run(FragmentExecutor.java:310)
... 4 common frames omitted
Now, I'm running this query from a 16 core, 32GB Ram machine, with Heap sized
at 20GB, Eden sized at 16GB (added manually to JAVA_OPTS) and Direct Sized at 8
GB.
By querying sys.memory I can confirm all limits apply. At no point throughout
the query Am I nearing memory limit of the HEAP/DIRECT or the OS itself
8:25
However, due to the way
org.apache.drill.exec.vector.complex.AbstractContainerVector.allocateNew is
impelmented
8:27
@Override
public void allocateNew() throws OutOfMemoryException {
if (!allocateNewSafe()) {
throw new OutOfMemoryException();
}
}
8:27
The actual exception/error is swallowed, and I have no idea what's the cause of
the failure
8:28
The data-set itself consists of say 15 parquet files, each one weighing at
about 100kb
8:30
but as mentioned earlier, the parquet files are a bit more complex than the
usual.
8:32
@cgivre @Vova Vysotskyi is there anything I can do or tweak to make this error
go away?
cgivre 8:40 AM
Hmm...
8:40
This may be a bug. Can you create an issue on our JIRA board?
Idan Sheinberg 8:43 AM
Sure
8:43
I'll get to it
cgivre 8:44 AM
I'd like for Paul Rogers to see this as I think he was the author of some of
this.
Idan Sheinberg 8:44 AM
Hmm. I'll keep that in mind
cgivre 8:47 AM
We've been refactoring some of the complex readers as well, so its possible
that is caused this, but I'm not really sure.
8:47
What version of Drill?
cgivre 9:11 AM
This kind of info is super helpful as we're trying to work out all these
details.
9:11
Reading schemas on the fly is not trivial, so when we find issues, we do like
to resolve them
Idan Sheinberg 9:16 AM
This is drill 0.18 -SNAPSHOT as of last month
9:16
Ummmm
9:16
I do think I managed to resolve the issue however
9:16
I'm going to run some additional tests and let you know
cgivre 9:16 AM
What did you do?
9:17
You might want to rebase with today's build as well
Idan Sheinberg 9:21 AM
I'll come back with the details in a few moments
cgivre 9:38 AM
Thx
new messages
Idan Sheinberg 9:50 AM
Ok. See it seems as though it's a combination of a few things.
The data-set in question is still small (as mentioned before), but we are
setting planner.slice_target to an extremely low value in order to trigger
parallelism and speed up parquet parsing by using multiple fragments.
We have 16 cores, 32 GB (C5.4xlarge on AWS) but we set
planner.width.max_per_node to further increase parallelism. it seems as
though each fragment is handling parquet parsing on it's own, and somehow
incurs a great burden on
the direct memory buffer pool, as I do see 16GB peaks of direct memory usage
after lowering the planner.width.max_per_node to 16 (our available core).
The query planner itself reports the HASH_PARTITION_SENDER as the largest phase
with 1-2 GB of memory utilization
Seeing such an impact (16 GB of direct memory) for 1K items spread across 15
files, even with a very complex parquet schema, seems unreasonable to me
