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From "Paul Rogers (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (DRILL-6166) RecordBatchSizer does not handle hyper vectors
Date Mon, 19 Feb 2018 01:19:00 GMT

    [ https://issues.apache.org/jira/browse/DRILL-6166?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16368734#comment-16368734
] 

Paul Rogers commented on DRILL-6166:
------------------------------------

The behavior described is by design: the `RecordBatchSizer` was intended to use only for single
batches (and of those, we assume no SV2).

The no-SV2 assumption arises because the only operator that accepts an SV2 is the selection-vector
remover (SVR). But, since the SVR has one batch out for every batch in, if the input batch
is sized properly, the output batch will be no larger than the (properly sized) input batch.

(At present, the SV2 does not attempt to coalesce mostly-empty batches; it will happily produce
non-performant batches with just a few records. But, that is a separate issue.)

Extend this discussion to SV4. Few operators produce an SV4. (I believe only sort does, but
perhaps there are others.) The planner should insert an SVR after any operator that produces
an SV4. Since we already explained why the SVR does not need to use the "sizer", then this
reasoning explains why the "sizer" need not handle an SV4.

Under what situation are you trying to use the "sizer" in which you receive an SV4? The stack
shows the operators above (downstream) from the sizer use, but not those below (upstream)
that supplied the batches.

The simplest reading is that the planner somehow omitted an SV4. If, however, the streaming
aggregate created its on SV4, then the sizer is not needed: batch sizing should have been
done on the (SV-less) input batches.

Now, let's set aside all the above and assume that we really do want to apply the sizer to
an SV4 (hyper-batch). Doing so is really quite difficult. The SV4 provides a "view" into a
much larger array of single batches. Each input batch sees the same memory, but a different
view. Since the "sizer" works by summing memory sizes, it will be "fooled" by a hyper-batch:
it will think the entire hyper-batch memory belongs to the current view (assuming that the
sizer were modified to get the ledgers for all the vectors that make up the hyper batch.)

But, there is no way to decide which memory goes with view 1 (the first batch) and which go
with view 2 (the second batch): they all share the same memory pool.

It might be possible to create a new sizer that:

* Sums all ledgers (by iterating over the vectors within a `VectorWrapper`.)
* Sum all rows in the hyper vector (not just those in the current view.)
* Use the existing calcs to work out average sizes.
* Apply the resulting numbers to all views of the same hyper-batch.
* Repeat the above if/when we get a view on a new hyper batch.

The above is possible, but complex. So, better to figure out if it is really needed before
we go down this path. (As noted above, such a solution probably is *not* needed.)

> RecordBatchSizer does not handle hyper vectors
> ----------------------------------------------
>
>                 Key: DRILL-6166
>                 URL: https://issues.apache.org/jira/browse/DRILL-6166
>             Project: Apache Drill
>          Issue Type: Bug
>          Components: Execution - Flow
>    Affects Versions: 1.12.0
>            Reporter: Padma Penumarthy
>            Assignee: Padma Penumarthy
>            Priority: Critical
>             Fix For: 1.13.0
>
>
> RecordBatchSizer throws an exception when incoming batch has hyper vector.
> (java.lang.UnsupportedOperationException) null
>  org.apache.drill.exec.record.HyperVectorWrapper.getValueVector():61
>  org.apache.drill.exec.record.RecordBatchSizer.<init>():346
>  org.apache.drill.exec.record.RecordBatchSizer.<init>():311
>  org.apache.drill.exec.physical.impl.aggregate.StreamingAggBatch$StreamingAggregateMemoryManager.update():198
>  org.apache.drill.exec.physical.impl.aggregate.StreamingAggBatch.innerNext():328
>  org.apache.drill.exec.record.AbstractRecordBatch.next():164
>  org.apache.drill.exec.physical.impl.validate.IteratorValidatorBatchIterator.next():228
>  org.apache.drill.exec.physical.impl.BaseRootExec.next():105
>  org.apache.drill.exec.physical.impl.partitionsender.PartitionSenderRootExec.innerNext():155
>  org.apache.drill.exec.physical.impl.BaseRootExec.next():95
>  org.apache.drill.exec.work.fragment.FragmentExecutor$1.run():233
>  org.apache.drill.exec.work.fragment.FragmentExecutor$1.run():226
>  java.security.AccessController.doPrivileged():-2
>  javax.security.auth.Subject.doAs():422
>  org.apache.hadoop.security.UserGroupInformation.doAs():1657
>  org.apache.drill.exec.work.fragment.FragmentExecutor.run():226
>  org.apache.drill.common.SelfCleaningRunnable.run():38
>  java.util.concurrent.ThreadPoolExecutor.runWorker():1142
>  java.util.concurrent.ThreadPoolExecutor$Worker.run():617
>  java.lang.Thread.run():745



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