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https://issues.apache.org/jira/browse/HIVE-25616?focusedWorklogId=666559&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-666559
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ASF GitHub Bot logged work on HIVE-25616:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 18/Oct/21 21:23
Start Date: 18/Oct/21 21:23
Worklog Time Spent: 10m
Work Description: vihangk1 commented on a change in pull request #2730:
URL: https://github.com/apache/hive/pull/2730#discussion_r731326469
##########
File path: metastore/src/java/org/apache/hadoop/hive/metastore/ObjectStore.java
##########
@@ -2571,8 +2571,8 @@ private Collection getPartitionPsQueryResults(String
dbName, String tableName,
* all the partitions and hence we can attempt to use a directSQL equivalent
API which
* is considerably faster.
* @param partVals The partitions values used to filter out the partitions.
- * @return true if partVals is empty or if all the values in partVals is
empty strings.
- * other wise false. If user or groups is valid then returns false since the
directSQL
+ * @return false if partVals is empty, true if all the values in partVals
are empty strings and
Review comment:
the comment should be "...and otherwise false for all the other cases"
Or more simply, the method returns true only when partVals is non-empty and
contains only empty strings, otherwise false.
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Issue Time Tracking
-------------------
Worklog Id: (was: 666559)
Time Spent: 40m (was: 0.5h)
> Backport HIVE-24741 to Hive 2, 3
> ---------------------------------
>
> Key: HIVE-25616
> URL: https://issues.apache.org/jira/browse/HIVE-25616
> Project: Hive
> Issue Type: Improvement
> Reporter: Neelesh Srinivas Salian
> Priority: Major
> Labels: pull-request-available
> Time Spent: 40m
> Remaining Estimate: 0h
>
> HIVE-24741 adds a major improvement to the `{{get_partitions_ps_with_auth}}`
> API that is used by Spark to retrieve all partitions of the table.
> This has caused problems in Spark 3 - running on a Hive 2.3.x metastore.
> Patched this in my org and it helped get past large partitioned tables having
> problems reading metadata through Spark.
> [~vihangk1], thoughts?
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