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https://issues.apache.org/jira/browse/ASTERIXDB-1892?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Taewoo Kim closed ASTERIXDB-1892.
---------------------------------
Resolution: Fixed
> A similarity join fails with "Failed to insert a new buffer into the
> aggregate operator! [HyracksDataException]" exception.
> ---------------------------------------------------------------------------------------------------------------------------
>
> Key: ASTERIXDB-1892
> URL: https://issues.apache.org/jira/browse/ASTERIXDB-1892
> Project: Apache AsterixDB
> Issue Type: Bug
> Reporter: Taewoo Kim
> Assignee: Taewoo Kim
>
> Note: this currently happens in the similarity join branch. It doesn't happen
> in the master branch. However, this symptom can happen in a situation where
> the input data size to the group-by is large.
> The following query fails with "Failed to insert a new buffer into the
> aggregate operator! [HyracksDataException]" exception. This query fetches
> 50,000 tuples and uses them as the inner side when doing the similarity join.
> The original data set size is about 20GB. And there are 8 nodes (each has two
> partitions).
> {code}
> use dataverse exp;
> count(
> for $o in dataset
> "AmazonReviewNoDup"
> for $p in dataset
> "AmazonReviewProductID"
> for $i in dataset
> "AmazonReviewNoDup"
> where $p.asin /* +indexnl */ = $i.asin and $p.id >=
> int64("3748")
> and $p.id <=
> int64("8747")
> and /* +skip-index */ similarity-jaccard(word-tokens($o.summary),
> word-tokens($i.summary)) >= 0.8 and $i.id < $o.id
> return {"oid":$o.id, "iid":$i.id}
> );
> {code}
> compiler.groupmemory size: 128MB
> The cause of this issue:
> When the optimizer sees the group-by operator during the plan optimization,
> it calls ExternalGroupByPOperator.calculateGroupByTableCardinality() method
> to set the cardinality of the hash table size. This ensures that the hash
> table size is never grown beyond the "compiler.groupmemory" size.
> And in the actual external hash group-by operator descriptor, it has two
> phases - build and merge. If the input data can be fit into the memory, then
> the final result will be generated in the build phase. If not, some of
> partitions will be spilled to the disk and these spilled partitions will be
> gradually merged during the merge phase.
> So, it first builds an intermediate result if the given memory is not big
> compared to the data size (spilled partitions). Then, it tries to aggregate
> them using a different seed for the hash function. At this time during the
> merge phase, the cardinality of hash table is set to the number of tuples in
> each spilled partition. This cardinality is just given without considering
> the fact that the hash table can be grown beyound the budget.
> And for the hash table, we only compact the content frames, not the header
> frames, The header frame can grow up to the certain size. This size can be
> bigger than the allocated memory size. The structure of the hash table can be
> found in the following.
> https://docs.google.com/presentation/d/1AExoTqQlx9va-AaiZ6OSPxBuQ3NJqz-cG5NGrjdk5FU/edit
> So, a possible solution would be:
> During the merge phase, calculate the cardinality of the hash table based on
> the memory budget. And compare this number to the actual cardinality that the
> caller wants to set. Pick the smaller one to be the cardinality of the hash
> table. Then, we will be safe. Compacting the header part of the hash table
> will help in some sense. But, it doesn't solve the issue fundamentally.
> In this observed case,
> the cardinality of the hash table during the merge phase was 51,395,283. The
> header part alone will take 392 MB if all tuples are unique. And the budget
> was 128 MB. And, the number of partitions was 24. A spilled partition size
> was 2.8GB. All of these partitions were spilled to the disk. And at that
> time, the hash table alone takes 126MB, not including the data table part.
> Thus, additional memory couldn't be allocated.
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