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https://issues.apache.org/jira/browse/HIVE-25975?focusedWorklogId=734758&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-734758
]
ASF GitHub Bot logged work on HIVE-25975:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 01/Mar/22 16:41
Start Date: 01/Mar/22 16:41
Worklog Time Spent: 10m
Work Description: marton-bod commented on a change in pull request #3060:
URL: https://github.com/apache/hive/pull/3060#discussion_r816948553
##########
File path: ql/src/java/org/apache/hadoop/hive/ql/plan/DynamicPartitionCtx.java
##########
@@ -50,6 +52,14 @@
private String defaultPartName; // default partition name in case of null or
empty value
private int maxPartsPerNode; // maximum dynamic partitions created per
mapper/reducer
private Pattern whiteListPattern;
+ /**
+ * Expressions describing a custom way of sorting the table before write.
Expressions can reference simple
+ * column descriptions or a tree of expressions containing more columns and
UDFs.
+ * Can be useful for custom bucket/hash sorting.
+ * A custom expression should be a lambda that is given the original column
description expressions as per read
+ * schema and returns a single expression. Example for simply just
referencing column 3: cols -> cols.get(3).clone()
+ */
+ private transient List<Function<List<ExprNodeDesc>, ExprNodeDesc>>
customSortExpressions;
Review comment:
Yeah, I was wondering if it's okay if these expressions don't get to the
executors. But I got my answer - Thanks!
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Issue Time Tracking
-------------------
Worklog Id: (was: 734758)
Time Spent: 4h (was: 3h 50m)
> Optimize ClusteredWriter for bucketed Iceberg tables
> ----------------------------------------------------
>
> Key: HIVE-25975
> URL: https://issues.apache.org/jira/browse/HIVE-25975
> Project: Hive
> Issue Type: Improvement
> Reporter: Ádám Szita
> Assignee: Ádám Szita
> Priority: Major
> Labels: pull-request-available
> Time Spent: 4h
> Remaining Estimate: 0h
>
> The first version of the ClusteredWriter in Hive-Iceberg will be lenient for
> bucketed tables: i.e. the records do not need to be ordered by the bucket
> values, the writer will just close its current file and open a new one for
> out-of-order records.
> This is suboptimal for the long-term due to creating many small files. Spark
> uses a UDF to compute the bucket value for each record and therefore it is
> able to order the records by bucket values, achieving optimal clustering.
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