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';
```
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