pan3793 commented on code in PR #36995:
URL: https://github.com/apache/spark/pull/36995#discussion_r935925857
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DistributionAndOrderingUtils.scala:
##########
@@ -17,22 +17,33 @@
package org.apache.spark.sql.execution.datasources.v2
-import org.apache.spark.sql.catalyst.expressions.Expression
+import org.apache.spark.sql.catalyst.analysis.{AnsiTypeCoercion, TypeCoercion}
+import org.apache.spark.sql.catalyst.expressions.{Expression, Literal,
SortOrder, TransformExpression, V2ExpressionUtils}
import org.apache.spark.sql.catalyst.expressions.V2ExpressionUtils._
import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan,
RebalancePartitions, RepartitionByExpression, Sort}
+import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.connector.catalog.FunctionCatalog
+import org.apache.spark.sql.connector.catalog.functions.ScalarFunction
import org.apache.spark.sql.connector.distributions._
import org.apache.spark.sql.connector.write.{RequiresDistributionAndOrdering,
Write}
import org.apache.spark.sql.errors.QueryCompilationErrors
object DistributionAndOrderingUtils {
- def prepareQuery(write: Write, query: LogicalPlan): LogicalPlan = write
match {
+ def prepareQuery(
Review Comment:
Thanks for explaining, educated. After reading the code, I think you are
right that "the value of `col` is essentially hashed twice", but I don't think
it will bring correctness issues, because it still guarantees that the same
bucket values will be clustered into the same partition.
One example is Hive bucket. In `V1WritesUtils#getWriterBucketSpec`, both
`HiveHash` and `HashPartitioning#partitionIdExpression` can be used to
construct `bucketIdExpression`.
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DistributionAndOrderingUtils.scala:
##########
@@ -17,22 +17,33 @@
package org.apache.spark.sql.execution.datasources.v2
-import org.apache.spark.sql.catalyst.expressions.Expression
+import org.apache.spark.sql.catalyst.analysis.{AnsiTypeCoercion, TypeCoercion}
+import org.apache.spark.sql.catalyst.expressions.{Expression, Literal,
SortOrder, TransformExpression, V2ExpressionUtils}
import org.apache.spark.sql.catalyst.expressions.V2ExpressionUtils._
import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan,
RebalancePartitions, RepartitionByExpression, Sort}
+import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.connector.catalog.FunctionCatalog
+import org.apache.spark.sql.connector.catalog.functions.ScalarFunction
import org.apache.spark.sql.connector.distributions._
import org.apache.spark.sql.connector.write.{RequiresDistributionAndOrdering,
Write}
import org.apache.spark.sql.errors.QueryCompilationErrors
object DistributionAndOrderingUtils {
- def prepareQuery(write: Write, query: LogicalPlan): LogicalPlan = write
match {
+ def prepareQuery(
Review Comment:
Thanks for explaining, educated. After reading the code, I think you are
right that "the value of `col` is essentially hashed twice", but I don't think
it will bring correctness issues, because it still guarantees that the same
values will be clustered into the same partition.
One example is Hive bucket. In `V1WritesUtils#getWriterBucketSpec`, both
`HiveHash` and `HashPartitioning#partitionIdExpression` can be used to
construct `bucketIdExpression`.
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