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https://issues.apache.org/jira/browse/HIVE-26184?focusedWorklogId=763422&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-763422
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ASF GitHub Bot logged work on HIVE-26184:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 28/Apr/22 10:52
Start Date: 28/Apr/22 10:52
Worklog Time Spent: 10m
Work Description: kgyrtkirk commented on code in PR #3253:
URL: https://github.com/apache/hive/pull/3253#discussion_r860749534
##########
ql/src/java/org/apache/hadoop/hive/ql/udf/generic/GenericUDAFMkCollectionEvaluator.java:
##########
@@ -95,11 +95,27 @@ public MkArrayAggregationBuffer() {
throw new RuntimeException("Buffer type unknown");
}
}
+
+ private void reset() {
+ if (bufferType == BufferType.LIST) {
+ container.clear();
+ } else if (bufferType == BufferType.SET) {
+ // Don't reuse a container because HashSet#clear can be very slow. The
operation takes O(N)
Review Comment:
why did the entries got skewed in the firstplace? don't we miss or have
incorrect implementation of some `hashCode()` method?
could you please add a testcase which reproduces the issue?
maybe you could probably write a test against the UDF itself..
Issue Time Tracking
-------------------
Worklog Id: (was: 763422)
Time Spent: 20m (was: 10m)
> COLLECT_SET with GROUP BY is very slow when some keys are highly skewed
> -----------------------------------------------------------------------
>
> Key: HIVE-26184
> URL: https://issues.apache.org/jira/browse/HIVE-26184
> Project: Hive
> Issue Type: Bug
> Components: Hive
> Affects Versions: 2.3.8, 3.1.3
> Reporter: okumin
> Assignee: okumin
> Priority: Major
> Labels: pull-request-available
> Time Spent: 20m
> Remaining Estimate: 0h
>
> I observed some reducers spend 98% of CPU time in invoking
> `java.util.HashMap#clear`.
> Looking the detail, I found COLLECT_SET reuses a LinkedHashSet and its
> `clear` can be quite heavy when a relation has a small number of highly
> skewed keys.
>
> To reproduce the issue, first, we will create rows with a skewed key.
> {code:java}
> INSERT INTO test_collect_set
> SELECT '00000000-0000-0000-0000-000000000000' AS key, CAST(UUID() AS VARCHAR)
> AS value
> FROM table_with_many_rows
> LIMIT 100000;{code}
> Then, we will create many non-skewed rows.
> {code:java}
> INSERT INTO test_collect_set
> SELECT UUID() AS key, UUID() AS value
> FROM sample_datasets.nasdaq
> LIMIT 5000000;{code}
> We can observe the issue when we aggregate values by `key`.
> {code:java}
> SELECT key, COLLECT_SET(value) FROM group_by_skew GROUP BY key{code}
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