[GitHub] [iceberg] izchen commented on a change in pull request #2141: Spark: Add DistributionAndOrderingUtils

[GitBox <gi...@apache.org>]

izchen commented on a change in pull request #2141:
URL: https://github.com/apache/iceberg/pull/2141#discussion_r668627749



##########
File path: spark3-extensions/src/main/scala/org/apache/spark/sql/catalyst/utils/DistributionAndOrderingUtils.scala
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@@ -0,0 +1,162 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+package org.apache.spark.sql.catalyst.utils
+
+import org.apache.spark.sql.AnalysisException
+import org.apache.spark.sql.catalyst
+import org.apache.spark.sql.catalyst.analysis.Resolver
+import org.apache.spark.sql.catalyst.expressions.IcebergBucketTransform
+import org.apache.spark.sql.catalyst.expressions.IcebergDayTransform
+import org.apache.spark.sql.catalyst.expressions.IcebergHourTransform
+import org.apache.spark.sql.catalyst.expressions.IcebergMonthTransform
+import org.apache.spark.sql.catalyst.expressions.IcebergYearTransform
+import org.apache.spark.sql.catalyst.expressions.NamedExpression
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.catalyst.plans.logical.RepartitionByExpression
+import org.apache.spark.sql.catalyst.plans.logical.Sort
+import org.apache.spark.sql.connector.catalog.CatalogV2Implicits
+import org.apache.spark.sql.connector.expressions.BucketTransform
+import org.apache.spark.sql.connector.expressions.DaysTransform
+import org.apache.spark.sql.connector.expressions.Expression
+import org.apache.spark.sql.connector.expressions.FieldReference
+import org.apache.spark.sql.connector.expressions.HoursTransform
+import org.apache.spark.sql.connector.expressions.IdentityTransform
+import org.apache.spark.sql.connector.expressions.MonthsTransform
+import org.apache.spark.sql.connector.expressions.NamedReference
+import org.apache.spark.sql.connector.expressions.Transform
+import org.apache.spark.sql.connector.expressions.YearsTransform
+import org.apache.spark.sql.connector.iceberg.distributions.ClusteredDistribution
+import org.apache.spark.sql.connector.iceberg.distributions.Distribution
+import org.apache.spark.sql.connector.iceberg.distributions.OrderedDistribution
+import org.apache.spark.sql.connector.iceberg.distributions.UnspecifiedDistribution
+import org.apache.spark.sql.connector.iceberg.expressions.NullOrdering
+import org.apache.spark.sql.connector.iceberg.expressions.SortDirection
+import org.apache.spark.sql.connector.iceberg.expressions.SortOrder
+import org.apache.spark.sql.internal.SQLConf
+
+object DistributionAndOrderingUtils {
+
+  def prepareQuery(
+      requiredDistribution: Distribution,
+      requiredOrdering: Seq[SortOrder],
+      query: LogicalPlan,
+      conf: SQLConf): LogicalPlan = {
+
+    val resolver = conf.resolver
+
+    val distribution = requiredDistribution match {
+      case d: OrderedDistribution =>
+        d.ordering.map(e => toCatalyst(e, query, resolver))
+      case d: ClusteredDistribution =>
+        d.clustering.map(e => toCatalyst(e, query, resolver))
+      case _: UnspecifiedDistribution =>
+        Array.empty[catalyst.expressions.Expression]
+    }
+
+    val queryWithDistribution = if (distribution.nonEmpty) {
+      val numShufflePartitions = conf.numShufflePartitions
+      // the conversion to catalyst expressions above produces SortOrder expressions
+      // for OrderedDistribution and generic expressions for ClusteredDistribution
+      // this allows RepartitionByExpression to pick either range or hash partitioning
+      RepartitionByExpression(distribution, query, numShufflePartitions)

Review comment:
       When `WRITE_DISTRIBUTION_MODE = range`, before this PR, Logical Plan is
   
   ```
   Sort [dt#8 ASC NULLS FIRST, v#7 ASC NULLS FIRST], true
   +- Repartition 2000, true
      +- MergeInto 
   ```
   After this PR, Logical Plan is
   
   ```
   Sort [dt#8 ASC NULLS FIRST, v#7 ASC NULLS FIRST], false
   +- RepartitionByExpression [dt#8], 2000
      +- Repartition 2000, true
         +- MergeInto
   ```
   
   In my opinion, the conversion of `global sort` to `local sort + range partitioning` is correct, but here we need to consider the `CollapseRepartition` rule in Spark `Optimizer`. In this case, this rule will eliminate the `Repartition 2000, true` node.
   
   Please take a look here, thanks @aokolnychyi @rdblue 




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