Github user tejasapatil commented on a diff in the pull request:
https://github.com/apache/spark/pull/16898#discussion_r100697138
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/FileFormatWriter.scala
---
@@ -134,8 +142,26 @@ object FileFormatWriter extends Logging {
// prepares the job, any exception thrown from here shouldn't cause
abortJob() to be called.
committer.setupJob(job)
+ val bucketIdExpression = bucketSpec.map { spec =>
+ // Use `HashPartitioning.partitionIdExpression` as our bucket id
expression, so that we can
+ // guarantee the data distribution is same between shuffle and
bucketed data source, which
+ // enables us to only shuffle one side when join a bucketed table
and a normal one.
+ HashPartitioning(bucketColumns,
spec.numBuckets).partitionIdExpression
+ }
+ // We should first sort by partition columns, then bucket id, and
finally sorting columns.
+ val requiredOrdering = (partitionColumns ++ bucketIdExpression ++
sortColumns)
--- End diff --
Possible over-optimization : Spark allows sorting over partition columns so
`requiredOrdering` can be changed to do:
`partitionColumns` + `bucketIdExpression` + (`sortColumns` which are not
in` partitionColumns`)
so that any extra column(s) in sort expression can be deduped.
```
scala> df1.write.format("orc").partitionBy("i").bucketBy(8,
"i").sortBy("k").saveAsTable("table70")
org.apache.spark.sql.AnalysisException: bucketBy columns 'i' should not be
part of partitionBy columns 'i';
```
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
