yihua commented on code in PR #5428:
URL: https://github.com/apache/hudi/pull/5428#discussion_r923975439
##########
hudi-client/hudi-spark-client/src/main/scala/org/apache/hudi/HoodieSparkUtils.scala:
##########
@@ -44,25 +44,24 @@ import org.apache.hudi.avro.HoodieAvroUtils
import scala.collection.JavaConverters._
-object HoodieSparkUtils extends SparkAdapterSupport {
-
- def isSpark2: Boolean = SPARK_VERSION.startsWith("2.")
-
- def isSpark3: Boolean = SPARK_VERSION.startsWith("3.")
-
- def isSpark3_0: Boolean = SPARK_VERSION.startsWith("3.0")
-
- def isSpark3_1: Boolean = SPARK_VERSION.startsWith("3.1")
-
- def gteqSpark3_1: Boolean = SPARK_VERSION > "3.1"
-
- def gteqSpark3_1_3: Boolean = SPARK_VERSION >= "3.1.3"
-
- def isSpark3_2: Boolean = SPARK_VERSION.startsWith("3.2")
+private[hudi] trait SparkVersionsSupport {
+ def getSparkVersion: String
+
+ def isSpark2: Boolean = getSparkVersion.startsWith("2.")
+ def isSpark3: Boolean = getSparkVersion.startsWith("3.")
+ def isSpark3_0: Boolean = getSparkVersion.startsWith("3.0")
+ def isSpark3_1: Boolean = getSparkVersion.startsWith("3.1")
+ def isSpark3_2: Boolean = getSparkVersion.startsWith("3.2")
+
+ def gteqSpark3_1: Boolean = getSparkVersion >= "3.1"
Review Comment:
I see the actual matching condition has changed. Is that intended, and is
the flag used before, which causes behavior change?
##########
hudi-spark-datasource/hudi-spark-common/src/main/scala/org/apache/hudi/DefaultSource.scala:
##########
@@ -109,6 +110,11 @@ class DefaultSource extends RelationProvider
Option(schema)
}
+ // NOTE: We have to handle explicitly case of the Metadata Table (MT)
since by default all of Hudi
+ // relations will try to apply schema pruning techniques (like
nested schema pruning) which couldn't
+ // be applied to MT
+ val canPruneRelationSchema = !isMetadataTable(tablePath)
Review Comment:
Is this because MT uses HFile as the file format?
##########
hudi-spark-datasource/hudi-spark3-common/src/main/scala/org/apache/spark/sql/HoodieSpark3CatalystPlanUtils.scala:
##########
@@ -0,0 +1,77 @@
+/*
+ * 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
+
+import org.apache.hudi.spark3.internal.ReflectUtil
+import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation
+import org.apache.spark.sql.catalyst.expressions.{Expression, Like}
+import org.apache.spark.sql.catalyst.plans.JoinType
+import org.apache.spark.sql.catalyst.plans.logical.{InsertIntoStatement, Join,
JoinHint, LogicalPlan}
+import org.apache.spark.sql.catalyst.{AliasIdentifier, TableIdentifier}
+import org.apache.spark.sql.connector.catalog.CatalogV2Implicits._
+import org.apache.spark.sql.execution.command.ExplainCommand
+import org.apache.spark.sql.execution.{ExtendedMode, SimpleMode}
+
+abstract class HoodieSpark3CatalystPlanUtils extends HoodieCatalystPlansUtils {
Review Comment:
Are these refactoring changes related to HoodieCatalystPlansUtils only
moving code around?
##########
hudi-client/hudi-spark-client/src/main/scala/org/apache/hudi/HoodieSparkUtils.scala:
##########
@@ -44,25 +44,24 @@ import org.apache.hudi.avro.HoodieAvroUtils
import scala.collection.JavaConverters._
-object HoodieSparkUtils extends SparkAdapterSupport {
-
- def isSpark2: Boolean = SPARK_VERSION.startsWith("2.")
-
- def isSpark3: Boolean = SPARK_VERSION.startsWith("3.")
-
- def isSpark3_0: Boolean = SPARK_VERSION.startsWith("3.0")
-
- def isSpark3_1: Boolean = SPARK_VERSION.startsWith("3.1")
-
- def gteqSpark3_1: Boolean = SPARK_VERSION > "3.1"
-
- def gteqSpark3_1_3: Boolean = SPARK_VERSION >= "3.1.3"
-
- def isSpark3_2: Boolean = SPARK_VERSION.startsWith("3.2")
+private[hudi] trait SparkVersionsSupport {
+ def getSparkVersion: String
+
+ def isSpark2: Boolean = getSparkVersion.startsWith("2.")
+ def isSpark3: Boolean = getSparkVersion.startsWith("3.")
+ def isSpark3_0: Boolean = getSparkVersion.startsWith("3.0")
+ def isSpark3_1: Boolean = getSparkVersion.startsWith("3.1")
+ def isSpark3_2: Boolean = getSparkVersion.startsWith("3.2")
+
+ def gteqSpark3_1: Boolean = getSparkVersion >= "3.1"
Review Comment:
Or it does not matter, because the spark version always has the patch
version (`x.y.z`).
##########
hudi-spark-datasource/hudi-spark-common/src/main/scala/org/apache/hudi/HoodieBaseRelation.scala:
##########
@@ -127,36 +133,46 @@ abstract class HoodieBaseRelation(val sqlContext:
SQLContext,
* NOTE: Initialization of teh following members is coupled on purpose to
minimize amount of I/O
* required to fetch table's Avro and Internal schemas
*/
- protected lazy val (tableAvroSchema: Schema, internalSchema: InternalSchema)
= {
+ protected lazy val (tableAvroSchema: Schema, internalSchemaOpt:
Option[InternalSchema]) = {
val schemaResolver = new TableSchemaResolver(metaClient)
- val avroSchema: Schema = schemaSpec.map(convertToAvroSchema).getOrElse {
- Try(schemaResolver.getTableAvroSchema) match {
- case Success(schema) => schema
+ val internalSchemaOpt = if (!isSchemaEvolutionEnabled) {
+ None
+ } else {
+ Try(schemaResolver.getTableInternalSchemaFromCommitMetadata) match {
+ case Success(internalSchemaOpt) => toScalaOption(internalSchemaOpt)
case Failure(e) =>
- logError("Failed to fetch schema from the table", e)
- throw new HoodieSchemaException("Failed to fetch schema from the
table")
+ logWarning("Failed to fetch internal-schema from the table", e)
+ None
}
}
- val internalSchema: InternalSchema = if (!isSchemaEvolutionEnabled) {
- InternalSchema.getEmptyInternalSchema
- } else {
- Try(schemaResolver.getTableInternalSchemaFromCommitMetadata) match {
- case Success(internalSchemaOpt) =>
-
toScalaOption(internalSchemaOpt).getOrElse(InternalSchema.getEmptyInternalSchema)
+ val avroSchema = internalSchemaOpt.map { is =>
+ AvroInternalSchemaConverter.convert(is, "schema")
+ } orElse {
+ schemaSpec.map(convertToAvroSchema)
+ } getOrElse {
+ Try(schemaResolver.getTableAvroSchema) match {
+ case Success(schema) => schema
case Failure(e) =>
- logWarning("Failed to fetch internal-schema from the table", e)
- InternalSchema.getEmptyInternalSchema
+ logError("Failed to fetch schema from the table", e)
+ throw new HoodieSchemaException("Failed to fetch schema from the
table")
}
}
- (avroSchema, internalSchema)
+ (avroSchema, internalSchemaOpt)
}
- protected lazy val tableStructSchema: StructType =
AvroConversionUtils.convertAvroSchemaToStructType(tableAvroSchema)
+ protected val tableStructSchema: StructType =
AvroConversionUtils.convertAvroSchemaToStructType(tableAvroSchema)
Review Comment:
Should this be lazy?
##########
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)) {
Review Comment:
Are these from Spark? How do we track any compatibility issues?
##########
hudi-spark-datasource/hudi-spark-common/src/main/scala/org/apache/hudi/HoodieBaseRelation.scala:
##########
@@ -227,9 +251,38 @@ abstract class HoodieBaseRelation(val sqlContext:
SQLContext,
/**
* Returns true in case table supports Schema on Read (Schema Evolution)
*/
- def hasSchemaOnRead: Boolean = !internalSchema.isEmptySchema
+ def hasSchemaOnRead: Boolean = internalSchemaOpt.isDefined
- override def schema: StructType = tableStructSchema
+ /**
+ * Data schema is determined as the actual schema of the Table's Data Files
(for ex, parquet/orc/etc);
+ *
+ * In cases when partition values are not persisted w/in the data files,
data-schema is defined as
+ * <pre>table's schema - partition columns</pre>
+ *
+ * Check scala-doc for [[shouldExtractPartitionValuesFromPartitionPath]] for
more details
+ */
+ def dataSchema: StructType =
+ if (shouldExtractPartitionValuesFromPartitionPath) {
+ prunePartitionColumns(tableStructSchema)
+ } else {
+ tableStructSchema
+ }
+
+ /**
+ * Determines whether relation's schema could be pruned by Spark's Optimizer
+ */
+ def canPruneRelationSchema: Boolean =
+ (fileFormat.isInstanceOf[ParquetFileFormat] ||
fileFormat.isInstanceOf[OrcFileFormat]) &&
Review Comment:
Should file format check automatically disable schema pruning for metadata
table?
##########
hudi-spark-datasource/hudi-spark-common/src/main/scala/org/apache/hudi/HoodieBaseRelation.scala:
##########
@@ -378,11 +433,12 @@ abstract class HoodieBaseRelation(val sqlContext:
SQLContext,
!SubqueryExpression.hasSubquery(condition)
}
- protected final def appendMandatoryRootFields(requestedColumns:
Array[String]): Array[String] = {
+ protected final def appendMandatoryColumns(requestedColumns: Array[String]):
Array[String] = {
// For a nested field in mandatory columns, we should first get the
root-level field, and then
// check for any missing column, as the requestedColumns should only
contain root-level fields
// We should only append root-level field as well
- val missing = mandatoryRootFields.filter(rootField =>
!requestedColumns.contains(rootField))
+ val missing = mandatoryFields.map(col =>
HoodieAvroUtils.getRootLevelFieldName(col))
+ .filter(rootField => !requestedColumns.contains(rootField))
Review Comment:
To clarify, here the top-level field (`x`) with all nested fields (`x.*.*`)
is kept even if only one nested field (`x.y.z`)under the top is needed because
of how Spark prunes the nested schema, right?
##########
hudi-spark-datasource/hudi-spark-common/src/main/scala/org/apache/hudi/HoodieBaseRelation.scala:
##########
@@ -514,32 +594,66 @@ abstract class HoodieBaseRelation(val sqlContext:
SQLContext,
// t/h Spark Session configuration (for ex, for Spark SQL)
optParams.getOrElse(DataSourceReadOptions.SCHEMA_EVOLUTION_ENABLED.key,
DataSourceReadOptions.SCHEMA_EVOLUTION_ENABLED.defaultValue.toString).toBoolean
||
- sparkSession.conf.get(DataSourceReadOptions.SCHEMA_EVOLUTION_ENABLED.key,
-
DataSourceReadOptions.SCHEMA_EVOLUTION_ENABLED.defaultValue.toString).toBoolean
+ sparkSession.conf.get(DataSourceReadOptions.SCHEMA_EVOLUTION_ENABLED.key,
+
DataSourceReadOptions.SCHEMA_EVOLUTION_ENABLED.defaultValue.toString).toBoolean
}
}
object HoodieBaseRelation extends SparkAdapterSupport {
private def generateUnsafeProjection(from: StructType, to: StructType) =
-
sparkAdapter.createCatalystExpressionUtils().generateUnsafeProjection(from, to)
+ sparkAdapter.getCatalystExpressionUtils().generateUnsafeProjection(from,
to)
def convertToAvroSchema(structSchema: StructType): Schema =
sparkAdapter.getAvroSchemaConverters.toAvroType(structSchema, nullable =
false, "Record")
def getPartitionPath(fileStatus: FileStatus): Path =
fileStatus.getPath.getParent
+ /**
+ * Projects provided schema by picking only required (projected) top-level
columns from it
+ *
+ * @param tableSchema schema to project (either of [[InternalSchema]] or
Avro's [[Schema]])
+ * @param requiredColumns required top-level columns to be projected
+ */
+ def projectSchema(tableSchema: Either[Schema, InternalSchema],
requiredColumns: Array[String]): (Schema, StructType, InternalSchema) = {
Review Comment:
So this is our own projection by keeping top-level required columns. The
nested schema pruning is based on
`org.apache.spark.sql.execution.datasources.NestedSchemaPruning`.
##########
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:
nit: add docs in this class to explain that?
##########
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:
Do we expect the implementation to vary across Spark versions?
##########
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:
It does support both Spark 3.1 and 3.2 after your changes, right?
##########
hudi-spark-datasource/hudi-spark/src/test/scala/org/apache/spark/sql/hudi/TestNestedSchemaPruningOptimization.scala:
##########
@@ -0,0 +1,115 @@
+/*
+ * 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.hudi
+
+import org.apache.hudi.{HoodieSparkUtils, SparkAdapterSupport}
+import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.execution.{FileSourceScanExec, ProjectExec,
RowDataSourceScanExec, SparkPlan}
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.types.{IntegerType, StringType, StructField,
StructType}
+import org.junit.jupiter.api.Assertions.assertEquals
+
+class TestNestedSchemaPruningOptimization extends HoodieSparkSqlTestBase with
SparkAdapterSupport {
Review Comment:
Have you run this manually with selective patch versions of Spark 2.4, 3.1,
and 3.2?
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