[GitHub] [hudi] nsivabalan commented on a diff in pull request #5428: [HUDI-3896] Porting Nested Schema Pruning optimization for Hudi's custom Relations

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

nsivabalan commented on code in PR #5428:
URL: https://github.com/apache/hudi/pull/5428#discussion_r886193694


##########
hudi-spark-datasource/hudi-spark-common/src/main/scala/org/apache/hudi/BaseFileOnlyRelation.scala:
##########
@@ -59,10 +59,8 @@ class BaseFileOnlyRelation(sqlContext: SQLContext,
   //                 For more details please check HUDI-4161
   // NOTE: This override has to mirror semantic of whenever this Relation is converted into [[HadoopFsRelation]],
   //       which is currently done for all cases, except when Schema Evolution is enabled
-  override protected val shouldExtractPartitionValuesFromPartitionPath: Boolean = {
-    val enableSchemaOnRead = !internalSchema.isEmptySchema
-    !enableSchemaOnRead
-  }
+  override protected val shouldExtractPartitionValuesFromPartitionPath: Boolean =
+    internalSchemaOpt.isEmpty

Review Comment:
   is this patch fixed after the partition pruning fix that got landed last friday ? 



##########
hudi-spark-datasource/hudi-spark/src/main/scala/org/apache/spark/sql/hudi/analysis/HoodieAnalysis.scala:
##########
@@ -41,25 +41,38 @@ import java.util
 import scala.collection.JavaConverters._
 
 object HoodieAnalysis {
-  def customResolutionRules(): Seq[SparkSession => Rule[LogicalPlan]] =
+
+  type RuleBuilder = SparkSession => Rule[LogicalPlan]
+
+  def customOptimizerRules(): Seq[RuleBuilder] =
+    if (HoodieSparkUtils.gteqSpark3_1) {
+      val nestedSchemaPruningClass = "org.apache.spark.sql.execution.datasources.NestedSchemaPruning"

Review Comment:
   why only 3_1? who not 3.2 or greater than 3_1 ? 



##########
hudi-spark-datasource/hudi-spark-common/src/main/scala/org/apache/spark/sql/avro/SchemaConverters.scala:
##########
@@ -187,23 +189,46 @@ private[sql] object SchemaConverters {
           .values(toAvroType(vt, valueContainsNull, recordName, nameSpace))
       case st: StructType =>
         val childNameSpace = if (nameSpace != "") s"$nameSpace.$recordName" else recordName
-        val fieldsAssembler = builder.record(recordName).namespace(nameSpace).fields()
-        st.foreach { f =>
-          val fieldAvroType =
-            toAvroType(f.dataType, f.nullable, f.name, childNameSpace)
-          fieldsAssembler.name(f.name).`type`(fieldAvroType).noDefault()
+        if (canBeUnion(st)) {
+          val nonNullUnionFieldTypes = st.map(f => toAvroType(f.dataType, nullable = false, f.name, childNameSpace))
+          val unionFieldTypes = if (nullable) {
+            nullSchema +: nonNullUnionFieldTypes
+          } else {
+            nonNullUnionFieldTypes
+          }
+          Schema.createUnion(unionFieldTypes:_*)
+        } else {
+          val fieldsAssembler = builder.record(recordName).namespace(nameSpace).fields()
+          st.foreach { f =>
+            val fieldAvroType =
+              toAvroType(f.dataType, f.nullable, f.name, childNameSpace)
+            fieldsAssembler.name(f.name).`type`(fieldAvroType).noDefault()
+          }
+          fieldsAssembler.endRecord()
         }
-        fieldsAssembler.endRecord()
 
       // This should never happen.
       case other => throw new IncompatibleSchemaException(s"Unexpected type $other.")
     }
-    if (nullable && catalystType != NullType) {
+
+    if (nullable && catalystType != NullType && schema.getType != Schema.Type.UNION) {
       Schema.createUnion(schema, nullSchema)
     } else {
       schema
     }
   }
+
+  private def canBeUnion(st: StructType): Boolean = {

Review Comment:
   may I know from where did we deduce this logic. 



##########
hudi-spark-datasource/hudi-spark3-common/src/main/scala/org/apache/spark/sql/execution/datasources/NestedSchemaPruning.scala:
##########
@@ -0,0 +1,195 @@
+/*
+ * 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.execution.datasources
+
+import org.apache.hudi.HoodieBaseRelation
+import org.apache.spark.sql.catalyst.expressions.{And, AttributeReference, Expression, NamedExpression, ProjectionOverSchema}
+import org.apache.spark.sql.catalyst.planning.PhysicalOperation
+import org.apache.spark.sql.catalyst.plans.logical.{Filter, LeafNode, LogicalPlan, Project}
+import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.execution.datasources.orc.OrcFileFormat
+import org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat
+import org.apache.spark.sql.sources.BaseRelation
+import org.apache.spark.sql.types.{ArrayType, DataType, MapType, StructType}
+import org.apache.spark.sql.util.SchemaUtils.restoreOriginalOutputNames
+
+/**
+ * Prunes unnecessary physical columns given a [[PhysicalOperation]] over a data source relation.
+ * By "physical column", we mean a column as defined in the data source format like Parquet format
+ * or ORC format. For example, in Spark SQL, a root-level Parquet column corresponds to a SQL
+ * column, and a nested Parquet column corresponds to a [[StructField]].
+ *
+ * NOTE: This class is borrowed from Spark 3.2.1, with modifications adapting it to handle [[HoodieBaseRelation]],
+ *       instead of [[HadoopFsRelation]]
+ */
+class NestedSchemaPruning extends Rule[LogicalPlan] {

Review Comment:
   would you mind pointing me to the changes we had to do comapred to HadoopFSRelation, would be easier for me to review just the required changes. 



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