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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