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https://issues.apache.org/jira/browse/DRILL-7675?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17076961#comment-17076961
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ASF GitHub Bot commented on DRILL-7675:
---------------------------------------
paul-rogers commented on issue #2047: DRILL-7675: Work around for partitions
sender memory use
URL: https://github.com/apache/drill/pull/2047#issuecomment-610200359
@arina-ielchiieva you made good points. I copied the PR description into the
file where the option is used. This will at least explain the option for
developers.
I've been impressed with the documentation which @vvysotskyi has provided.
It is first class. My thought is to do a bunch of updates as a sparate project
when we get closer to Drill 1.18. There is this option. There is the confusion
over the various `typeof()` methods. There will be new JSON and CSV options.
I'm hoping there will be better error handling in plugin configs (if I get that
part done in time.) Gathering the growing list of provided schema options (I'm
working on the ability to include partition dirs as first-class columns.)
I've been leaving ever more detailed notes in the PRs so I can refer to them
when doing the doc updates. There is even a JIRA ticket for how we might
restructure the docs just a bit; because at present it can be hard to find a
place for an extended description of some topics.
Will this address your concerns?
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> Very slow performance and Memory exhaustion while querying on very small
> dataset of parquet files
> -------------------------------------------------------------------------------------------------
>
> Key: DRILL-7675
> URL: https://issues.apache.org/jira/browse/DRILL-7675
> Project: Apache Drill
> Issue Type: Bug
> Components: Query Planning & Optimization, Storage - Parquet
> Affects Versions: 1.18.0
> Environment: [^sample-dataset.zip]
> Reporter: Idan Sheinberg
> Assignee: Paul Rogers
> Priority: Critical
> Fix For: 1.18.0
>
> Attachments: sample-dataset.zip
>
>
> Per our discussion in Slack/Dev-list Here are all details and sample data-set
> to recreate problematic query behavior:
> * We are using Drill 1.18.0-SNAPSHOT built on March 6
> * We are joining on two small Parquet datasets residing on S3 using the
> following query:
> {code:java}
> SELECT
> CASE
> WHEN tbl1.`timestamp` IS NULL THEN tbl2.`timestamp`
> ELSE tbl1.`timestamp`
> END AS ts, *
> FROM `s3-store.state.`/164` AS tbl1
> FULL OUTER JOIN `s3-store.result`.`/164` AS tbl2
> ON tbl1.`timestamp`*10 = tbl2.`timestamp`
> ORDER BY ts ASC
> LIMIT 500 OFFSET 0 ROWS
> {code}
> * We are running drill in a single node setup on a 16 core, 64GB ram
> machine. Drill heap size is set to 16GB, while max direct memory is set to
> 32GB.
> * As the dataset consist of really small files, Drill has been tweaked to
> parallelize on small item count by tweaking the following variables:
> {code:java}
> planner.slice_target = 25
> planner.width.max_per_node = 16 (to match the core count){code}
> * Without the above parallelization, query speeds on parquet files are super
> slow (tens of seconds)
> * While queries do work, we are seeing non-proportional direct memory/heap
> utilization. (up 20GB of direct memory used, a min of 12GB heap required)
> * We're still encountering the occasional OOM of memory error (we're also
> seeing heap exhaustion, but I guess that's another indication to same
> problem. Reducing the node parallelization width to say, 8, reduces memory
> contention, though it still reaches 8 gb of direct memory
> {code:java}
> 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{code}
> I've attached a (real!) sample data-set to match the query above. That same
> dataset recreates the aforementioned memory behavior
> Help, please.
> Idan
>
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