yihua commented on code in PR #8714:
URL: https://github.com/apache/hudi/pull/8714#discussion_r1198414998
##########
.github/workflows/bot.yml:
##########
@@ -63,6 +63,10 @@ jobs:
sparkProfile: "spark3.1"
sparkModules: "hudi-spark-datasource/hudi-spark3.1.x"
+ - scalaProfile: "scala-2.12"
Review Comment:
Would be good to add bundle validation on Spark 3.0.x too in
`validate-bundles` section
##########
hudi-spark-datasource/hudi-spark/src/test/scala/org/apache/spark/sql/hudi/TestMergeIntoTable.scala:
##########
@@ -114,58 +114,65 @@ class TestMergeIntoTable extends HoodieSparkSqlTestBase
with ScalaAssertionSuppo
})
}
- test("Test MergeInto with more than once update actions") {
- withRecordType()(withTempDir {tmp =>
- val targetTable = generateTableName
- spark.sql(
- s"""
- |create table ${targetTable} (
- | id int,
- | name string,
- | data int,
- | country string,
- | ts bigint
- |) using hudi
- |tblproperties (
- | type = 'cow',
- | primaryKey = 'id',
- | preCombineField = 'ts'
- | )
- |partitioned by (country)
- |location '${tmp.getCanonicalPath}/$targetTable'
- |""".stripMargin)
- spark.sql(
- s"""
- |merge into ${targetTable} as target
- |using (
- |select 1 as id, 'lb' as name, 6 as data, 'shu' as country,
1646643193 as ts
- |) source
- |on source.id = target.id
- |when matched then
- |update set *
- |when not matched then
- |insert *
- |""".stripMargin)
- spark.sql(
- s"""
- |merge into ${targetTable} as target
- |using (
- |select 1 as id, 'lb' as name, 5 as data, 'shu' as country,
1646643196 as ts
- |) source
- |on source.id = target.id
- |when matched and source.data > target.data then
- |update set target.data = source.data, target.ts = source.ts
- |when matched and source.data = 5 then
- |update set target.data = source.data, target.ts = source.ts
- |when not matched then
- |insert *
- |""".stripMargin)
-
- checkAnswer(s"select id, name, data, country, ts from $targetTable")(
- Seq(1, "lb", 5, "shu", 1646643196L)
- )
+ /**
+ * For spark3.0.x didn't support 'UPDATE and DELETE can appear at most once
in MATCHED clauses in a MERGE statement'
+ * details:
org.apache.spark.sql.catalyst.parser.AstBuilder#visitMergeIntoTable
Review Comment:
```suggestion
* In Spark 3.0.x, UPDATE and DELETE can appear at most once in MATCHED
clauses in a MERGE INTO statement.
* Refer to:
`org.apache.spark.sql.catalyst.parser.AstBuilder#visitMergeIntoTable`
```
##########
hudi-spark-datasource/hudi-spark/src/test/scala/org/apache/spark/sql/avro/TestAvroSerDe.scala:
##########
@@ -20,11 +20,12 @@ package org.apache.spark.sql.avro
import org.apache.avro.generic.GenericData
import org.apache.hudi.SparkAdapterSupport
import org.apache.hudi.avro.model.{HoodieMetadataColumnStats, IntWrapper}
+import org.apache.spark.internal.Logging
import org.apache.spark.sql.avro.SchemaConverters.SchemaType
import org.junit.jupiter.api.Assertions.assertEquals
import org.junit.jupiter.api.Test
-class TestAvroSerDe extends SparkAdapterSupport {
+class TestAvroSerDe extends SparkAdapterSupport with Logging {
Review Comment:
Is this necessary for testing?
##########
hudi-spark-datasource/hudi-spark3-common/src/main/scala/org/apache/spark/sql/HoodieSpark3CatalystExpressionUtils.scala:
##########
@@ -17,16 +17,9 @@
package org.apache.spark.sql
-import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeSet,
Expression, Predicate, PredicateHelper}
-import org.apache.spark.sql.execution.datasources.DataSourceStrategy
-
-trait HoodieSpark3CatalystExpressionUtils extends HoodieCatalystExpressionUtils
- with PredicateHelper {
-
- override def normalizeExprs(exprs: Seq[Expression], attributes:
Seq[Attribute]): Seq[Expression] =
- DataSourceStrategy.normalizeExprs(exprs, attributes)
-
- override def extractPredicatesWithinOutputSet(condition: Expression,
- outputSet: AttributeSet):
Option[Expression] =
- super[PredicateHelper].extractPredicatesWithinOutputSet(condition,
outputSet)
+import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeSet,
Expression}
+abstract class HoodieSpark3CatalystExpressionUtils extends
HoodieCatalystExpressionUtils {
Review Comment:
could we keep `with PredicateHelper`?
##########
hudi-spark-datasource/hudi-spark3.0.x/src/main/java/org/apache/spark/sql/execution/datasources/parquet/Spark30HoodieVectorizedParquetRecordReader.java:
##########
@@ -0,0 +1,187 @@
+/*
+ * 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.parquet;
+
+import org.apache.hadoop.mapreduce.InputSplit;
+import org.apache.hadoop.mapreduce.TaskAttemptContext;
+import org.apache.hudi.client.utils.SparkInternalSchemaConverter;
+import org.apache.hudi.common.util.collection.Pair;
+import org.apache.spark.memory.MemoryMode;
+import org.apache.spark.sql.catalyst.InternalRow;
+import org.apache.spark.sql.execution.vectorized.OffHeapColumnVector;
+import org.apache.spark.sql.execution.vectorized.OnHeapColumnVector;
+import org.apache.spark.sql.execution.vectorized.WritableColumnVector;
+import org.apache.spark.sql.types.DataType;
+import org.apache.spark.sql.types.StructType;
+import org.apache.spark.sql.vectorized.ColumnarBatch;
+
+import java.io.IOException;
+import java.time.ZoneId;
+import java.util.HashMap;
+import java.util.Map;
+
+public class Spark30HoodieVectorizedParquetRecordReader extends
VectorizedParquetRecordReader {
Review Comment:
I assume these cases are mostly copied from Hudi Spark 3.1.x module.
##########
hudi-spark-datasource/hudi-spark3.0.x/src/main/scala/org/apache/spark/sql/hudi/Spark30ResolveHudiAlterTableCommand.scala:
##########
@@ -0,0 +1,297 @@
+/*
+ * 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.common.config.HoodieCommonConfig
+import org.apache.hudi.internal.schema.action.TableChange.ColumnChangeID
+import org.apache.spark.sql.catalyst.TableIdentifier
+import org.apache.spark.sql.catalyst.catalog.CatalogTable
+import org.apache.spark.sql.catalyst.plans.logical._
+import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.connector.catalog.TableChange._
+import org.apache.spark.sql.connector.catalog.TableChange
+import org.apache.spark.sql.hudi.command.Spark30AlterTableCommand
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.types.{ArrayType, DataType, MapType, NullType,
StructType}
+import org.apache.spark.sql.{AnalysisException, SparkSession}
+
+import java.util.Locale
+import scala.collection.mutable
+
+/**
+ * Rule to mostly resolve, normalize and rewrite column names based on case
sensitivity
+ * for alter table column commands.
+ * TODO: we should remove this file when we support datasourceV2 for hoodie
on spark3.0x
Review Comment:
```suggestion
* TODO: we should remove this file when we support datasourceV2 for hoodie
on spark3.0.x
```
##########
hudi-spark-datasource/hudi-spark3.0.x/src/main/scala/org/apache/spark/sql/avro/AvroDeserializer.scala:
##########
@@ -0,0 +1,492 @@
+/*
+ * 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.avro
+
+import org.apache.avro.Conversions.DecimalConversion
+import org.apache.avro.LogicalTypes.{TimestampMicros, TimestampMillis}
+import org.apache.avro.Schema.Type._
+import org.apache.avro.generic._
+import org.apache.avro.util.Utf8
+import org.apache.avro.{LogicalTypes, Schema, SchemaBuilder}
+import org.apache.spark.sql.avro.AvroDeserializer.{createDateRebaseFuncInRead,
createTimestampRebaseFuncInRead}
+import org.apache.spark.sql.catalyst.expressions.{SpecificInternalRow,
UnsafeArrayData}
+import org.apache.spark.sql.catalyst.util.DateTimeConstants.MILLIS_PER_DAY
+import org.apache.spark.sql.catalyst.util._
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.execution.datasources.DataSourceUtils
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.internal.SQLConf.LegacyBehaviorPolicy
+import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.UTF8String
+
+import java.math.BigDecimal
+import java.nio.ByteBuffer
+import scala.collection.JavaConverters._
+import scala.collection.mutable.ArrayBuffer
+
+/**
+ * A deserializer to deserialize data in avro format to data in catalyst
format.
+ *
+ * NOTE: This code is borrowed from Spark 3.1.2
+ * This code is borrowed, so that we can better control compatibility
w/in Spark minor
+ * branches (3.2.x, 3.1.x, etc)
+ *
+ * PLEASE REFRAIN MAKING ANY CHANGES TO THIS CODE UNLESS ABSOLUTELY
NECESSARY
+ */
+private[sql] class AvroDeserializer(rootAvroType: Schema,
Review Comment:
Should the code of the class from Spark 3.0.x be copied to here, instead of
using Spark 3.1.2 version?
##########
pom.xml:
##########
@@ -2238,6 +2239,44 @@
</activation>
</profile>
+ <profile>
+ <id>spark3.0</id>
+ <properties>
+ <spark3.version>${spark30.version}</spark3.version>
+ <spark.version>${spark3.version}</spark.version>
+ <sparkbundle.version>3.0</sparkbundle.version>
+ <scala.version>${scala12.version}</scala.version>
+ <scala.binary.version>2.12</scala.binary.version>
+ <hudi.spark.module>hudi-spark3.0.x</hudi.spark.module>
+
<hudi.spark.common.modules.1>hudi-spark3-common</hudi.spark.common.modules.1>
+ <kafka.version>${kafka.spark3.version}</kafka.version>
Review Comment:
Do we need
`<scalatest.version>${scalatest.spark3.version}</scalatest.version>`?
##########
hudi-spark-datasource/hudi-spark3.0.x/src/main/scala/org/apache/spark/sql/adapter/Spark3_0Adapter.scala:
##########
@@ -0,0 +1,117 @@
+/*
+ * 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.adapter
+
+import org.apache.avro.Schema
+import org.apache.hudi.Spark30HoodieFileScanRDD
+import org.apache.spark.sql.avro.{HoodieAvroDeserializer,
HoodieAvroSerializer, HoodieSpark3_0AvroDeserializer,
HoodieSpark3_0AvroSerializer}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{AttributeReference,
Expression}
+import org.apache.spark.sql.catalyst.parser.ParserInterface
+import org.apache.spark.sql.catalyst.plans.logical._
+import org.apache.spark.sql.execution.datasources.parquet.{ParquetFileFormat,
Spark30HoodieParquetFileFormat}
+import org.apache.spark.sql.execution.datasources.{FilePartition, FileScanRDD,
PartitionedFile}
+import org.apache.spark.sql.hudi.SparkAdapter
+import org.apache.spark.sql.parser.{HoodieExtendedParserInterface,
HoodieSpark3_0ExtendedSqlParser}
+import org.apache.spark.sql.types.{DataType, Metadata, MetadataBuilder,
StructType}
+import org.apache.spark.sql.vectorized.ColumnarUtils
+import org.apache.spark.sql._
+import org.apache.spark.sql.catalyst.analysis.EliminateSubqueryAliases
+import org.apache.spark.sql.catalyst.catalog.CatalogTable
+import org.apache.spark.sql.catalyst.planning.PhysicalOperation
+import org.apache.spark.sql.execution.datasources.v2.DataSourceV2Relation
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.storage.StorageLevel._
+
+/**
+ * Implementation of [[SparkAdapter]] for Spark 3.0.x
+ */
+class Spark3_0Adapter extends BaseSpark3Adapter {
+
+ override def resolveHoodieTable(plan: LogicalPlan): Option[CatalogTable] = {
+ super.resolveHoodieTable(plan).orElse {
+ EliminateSubqueryAliases(plan) match {
+ // First, we need to weed out unresolved plans
+ case plan if !plan.resolved => None
+ // NOTE: When resolving Hudi table we allow [[Filter]]s and
[[Project]]s be applied
+ // on top of it
+ case PhysicalOperation(_, _, DataSourceV2Relation(table: CatalogTable,
_, _, _, _)) if isHoodieTable(table) =>
+ Some(table)
+ case _ => None
Review Comment:
Is there a way to achieve fallback from V2 to V1 in Spark 3.0? Without the
fallback, the performance is affected.
##########
hudi-spark-datasource/hudi-spark3.0.x/src/main/scala/org/apache/spark/sql/hudi/command/Spark30AlterTableCommand.scala:
##########
@@ -0,0 +1,325 @@
+/*
+ * 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.command
+
+import java.net.URI
+import java.nio.charset.StandardCharsets
+import java.util
+import java.util.concurrent.atomic.AtomicInteger
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.fs.Path
+import org.apache.hudi.DataSourceWriteOptions._
+import org.apache.hudi.avro.AvroSchemaUtils.getAvroRecordQualifiedName
+import org.apache.hudi.client.utils.SparkInternalSchemaConverter
+import org.apache.hudi.common.model.{HoodieCommitMetadata, WriteOperationType}
+import org.apache.hudi.{AvroConversionUtils, DataSourceOptionsHelper,
DataSourceUtils}
+import org.apache.hudi.common.table.timeline.{HoodieActiveTimeline,
HoodieInstant}
+import org.apache.hudi.common.table.timeline.HoodieInstant.State
+import org.apache.hudi.common.table.{HoodieTableMetaClient,
TableSchemaResolver}
+import org.apache.hudi.common.util.{CommitUtils, Option}
+import org.apache.hudi.config.HoodieWriteConfig
+import org.apache.hudi.internal.schema.InternalSchema
+import org.apache.hudi.internal.schema.action.TableChange.ColumnChangeID
+import org.apache.hudi.internal.schema.action.TableChanges
+import org.apache.hudi.internal.schema.convert.AvroInternalSchemaConverter
+import org.apache.hudi.internal.schema.utils.{SchemaChangeUtils, SerDeHelper}
+import org.apache.hudi.internal.schema.io.FileBasedInternalSchemaStorageManager
+import org.apache.hudi.table.HoodieSparkTable
+import org.apache.spark.api.java.JavaSparkContext
+import org.apache.spark.internal.Logging
+import org.apache.spark.sql.{Row, SparkSession}
+import org.apache.spark.sql.catalyst.catalog.{CatalogTable, CatalogTableType}
+import org.apache.spark.sql.connector.catalog.{TableCatalog, TableChange}
+import org.apache.spark.sql.connector.catalog.TableChange.{AddColumn,
DeleteColumn, RemoveProperty, SetProperty}
+import org.apache.spark.sql.execution.command.RunnableCommand
+import org.apache.spark.sql.types.StructType
+
+import scala.collection.JavaConverters._
+import scala.util.control.NonFatal
+
+// TODO: we should remove this file when we support datasourceV2 for hoodie on
spark3.0x
Review Comment:
```suggestion
// TODO: we should remove this file when we support datasourceV2 for hoodie
on spark3.0.x
```
##########
hudi-spark-datasource/hudi-spark3.0.x/src/main/scala/org/apache/spark/sql/HoodieSpark30CatalogUtils.scala:
##########
@@ -0,0 +1,30 @@
+/*
+ * 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.spark.sql.connector.expressions.{BucketTransform,
NamedReference, Transform}
+
+object HoodieSpark30CatalogUtils extends HoodieSpark3CatalogUtils {
+
+ override def unapplyBucketTransform(t: Transform): Option[(Int,
Seq[NamedReference], Seq[NamedReference])] =
+ t match {
+ case BucketTransform(numBuckets, ref) => Some(numBuckets, Seq(ref),
Seq.empty)
+ case _ => None
+ }
+
Review Comment:
nit: remove new line
##########
hudi-spark-datasource/hudi-spark3.0.x/src/main/scala/org/apache/spark/sql/execution/datasources/Spark30NestedSchemaPruning.scala:
##########
@@ -0,0 +1,270 @@
+/*
+ * 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, SparkAdapterSupport}
+import org.apache.spark.sql.HoodieSpark3CatalystPlanUtils
+import org.apache.spark.sql.catalyst.expressions.{Alias, And, Attribute,
AttributeReference, AttributeSet, Expression, NamedExpression,
ProjectionOverSchema}
+import org.apache.spark.sql.catalyst.planning.PhysicalOperation
+import org.apache.spark.sql.catalyst.plans.logical.{Filter, LogicalPlan,
Project}
+import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.sources.BaseRelation
+import org.apache.spark.sql.types.{ArrayType, DataType, MapType, StructField,
StructType}
+
+/**
+ * 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 Spark30NestedSchemaPruning extends Rule[LogicalPlan] {
+ import org.apache.spark.sql.catalyst.expressions.SchemaPruning._
+ override def apply(plan: LogicalPlan): LogicalPlan =
+ if (SQLConf.get.nestedSchemaPruningEnabled) {
+ apply0(plan)
+ } else {
+ plan
+ }
+
+ private def apply0(plan: LogicalPlan): LogicalPlan =
+ plan transformDown {
+ case op @ PhysicalOperation(projects, filters,
+ // NOTE: This is modified to accommodate for Hudi's custom relations,
given that original
+ // [[NestedSchemaPruning]] rule is tightly coupled w/
[[HadoopFsRelation]]
+ // TODO generalize to any file-based relation
+ l @ LogicalRelation(relation: HoodieBaseRelation, _, _, _))
+ if relation.canPruneRelationSchema =>
+
+ prunePhysicalColumns(l.output, projects, filters, relation.dataSchema,
+ prunedDataSchema => {
+ val prunedRelation =
+ relation.updatePrunedDataSchema(prunedSchema = prunedDataSchema)
+ buildPrunedRelation(l, prunedRelation)
+ }).getOrElse(op)
+ }
+
+ /**
+ * This method returns optional logical plan. `None` is returned if no
nested field is required or
+ * all nested fields are required.
+ */
+ private def prunePhysicalColumns(output: Seq[AttributeReference],
+ projects: Seq[NamedExpression],
+ filters: Seq[Expression],
+ dataSchema: StructType,
+ outputRelationBuilder: StructType =>
LogicalRelation): Option[LogicalPlan] = {
+ val (normalizedProjects, normalizedFilters) =
+ normalizeAttributeRefNames(output, projects, filters)
+ val requestedRootFields = identifyRootFields(normalizedProjects,
normalizedFilters)
+
+ // If requestedRootFields includes a nested field, continue. Otherwise,
+ // return op
+ if (requestedRootFields.exists { root: RootField => !root.derivedFromAtt
}) {
+ val prunedDataSchema = pruneDataSchema(dataSchema, requestedRootFields)
+
+ // If the data schema is different from the pruned data schema,
continue. Otherwise,
+ // return op. We effect this comparison by counting the number of "leaf"
fields in
+ // each schemata, assuming the fields in prunedDataSchema are a subset
of the fields
+ // in dataSchema.
+ if (countLeaves(dataSchema) > countLeaves(prunedDataSchema)) {
+ val planUtils =
SparkAdapterSupport.sparkAdapter.getCatalystPlanUtils.asInstanceOf[HoodieSpark3CatalystPlanUtils]
+
+ val prunedRelation = outputRelationBuilder(prunedDataSchema)
+ val projectionOverSchema =
planUtils.projectOverSchema(prunedDataSchema, AttributeSet(output))
+
+ Some(buildNewProjection(projects, normalizedProjects,
normalizedFilters,
+ prunedRelation, projectionOverSchema))
+ } else {
+ None
+ }
+ } else {
+ None
+ }
+ }
+
+ /**
+ * Normalizes the names of the attribute references in the given projects
and filters to reflect
+ * the names in the given logical relation. This makes it possible to
compare attributes and
+ * fields by name. Returns a tuple with the normalized projects and filters,
respectively.
+ */
+ private def normalizeAttributeRefNames(output: Seq[AttributeReference],
+ projects: Seq[NamedExpression],
+ filters: Seq[Expression]):
(Seq[NamedExpression], Seq[Expression]) = {
+ val normalizedAttNameMap = output.map(att => (att.exprId, att.name)).toMap
+ val normalizedProjects = projects.map(_.transform {
+ case att: AttributeReference if
normalizedAttNameMap.contains(att.exprId) =>
+ att.withName(normalizedAttNameMap(att.exprId))
+ }).map { case expr: NamedExpression => expr }
+ val normalizedFilters = filters.map(_.transform {
+ case att: AttributeReference if
normalizedAttNameMap.contains(att.exprId) =>
+ att.withName(normalizedAttNameMap(att.exprId))
+ })
+ (normalizedProjects, normalizedFilters)
+ }
+
+ /**
+ * Builds the new output [[Project]] Spark SQL operator that has the
`leafNode`.
+ */
+ private def buildNewProjection(projects: Seq[NamedExpression],
+ normalizedProjects: Seq[NamedExpression],
+ filters: Seq[Expression],
+ prunedRelation: LogicalRelation,
+ projectionOverSchema: ProjectionOverSchema):
Project = {
+ // Construct a new target for our projection by rewriting and
+ // including the original filters where available
+ val projectionChild =
+ if (filters.nonEmpty) {
+ val projectedFilters = filters.map(_.transformDown {
+ case projectionOverSchema(expr) => expr
+ })
+ val newFilterCondition = projectedFilters.reduce(And)
+ Filter(newFilterCondition, prunedRelation)
+ } else {
+ prunedRelation
+ }
+
+ // Construct the new projections of our Project by
+ // rewriting the original projections
+ val newProjects = normalizedProjects.map(_.transformDown {
+ case projectionOverSchema(expr) => expr
+ }).map { case expr: NamedExpression => expr }
+
+ if (log.isDebugEnabled) {
+ logDebug(s"New
projects:\n${newProjects.map(_.treeString).mkString("\n")}")
+ }
+
+ Project(restoreOriginalOutputNames(newProjects, projects.map(_.name)),
projectionChild)
+ }
+
+ /**
+ * Builds a pruned logical relation from the output of the output relation
and the schema of the
+ * pruned base relation.
+ */
+ private def buildPrunedRelation(outputRelation: LogicalRelation,
+ prunedBaseRelation: BaseRelation):
LogicalRelation = {
+ val prunedOutput = getPrunedOutput(outputRelation.output,
prunedBaseRelation.schema)
+ outputRelation.copy(relation = prunedBaseRelation, output = prunedOutput)
+ }
+
+ // Prune the given output to make it consistent with `requiredSchema`.
+ private def getPrunedOutput(output: Seq[AttributeReference],
+ requiredSchema: StructType):
Seq[AttributeReference] = {
+ // We need to replace the expression ids of the pruned relation output
attributes
+ // with the expression ids of the original relation output attributes so
that
+ // references to the original relation's output are not broken
+ val outputIdMap = output.map(att => (att.name, att.exprId)).toMap
+ requiredSchema
+ .toAttributes
+ .map {
+ case att if outputIdMap.contains(att.name) =>
+ att.withExprId(outputIdMap(att.name))
+ case att => att
+ }
+ }
+
+ /**
+ * Counts the "leaf" fields of the given dataType. Informally, this is the
+ * number of fields of non-complex data type in the tree representation of
+ * [[DataType]].
+ */
+ private def countLeaves(dataType: DataType): Int = {
+ dataType match {
+ case array: ArrayType => countLeaves(array.elementType)
+ case map: MapType => countLeaves(map.keyType) +
countLeaves(map.valueType)
+ case struct: StructType =>
+ struct.map(field => countLeaves(field.dataType)).sum
+ case _ => 1
+ }
+ }
+
+ private def restoreOriginalOutputNames(
+ projectList: Seq[NamedExpression],
+ originalNames: Seq[String]):
Seq[NamedExpression] = {
+ projectList.zip(originalNames).map {
+ case (attr: Attribute, name) => attr.withName(name)
+ case (alias: Alias, name) => if (name == alias.name) {
+ alias
+ } else {
+ AttributeReference(name, alias.dataType, alias.nullable,
alias.metadata)(alias.exprId, alias.qualifier)
+ }
+ case (other, _) => other
+ }
+ }
+
+
Review Comment:
```suggestion
// NOTE: `pruneDataSchema` and `sortLeftFieldsByRight` functions are copied
from Spark 3.1.2,
// as these functions in `SchemaPruning` have bugs in Spark 3.0.2 (see
SPARK-35096,
//
https://github.com/apache/spark/commit/2bbe0a4151f2af00f1105489d5757be28ff278d6)
```
##########
hudi-spark-datasource/hudi-spark3-common/src/main/scala/org/apache/spark/sql/HoodieSpark3CatalystExpressionUtils.scala:
##########
@@ -17,16 +17,9 @@
package org.apache.spark.sql
-import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeSet,
Expression, Predicate, PredicateHelper}
-import org.apache.spark.sql.execution.datasources.DataSourceStrategy
-
-trait HoodieSpark3CatalystExpressionUtils extends HoodieCatalystExpressionUtils
- with PredicateHelper {
-
- override def normalizeExprs(exprs: Seq[Expression], attributes:
Seq[Attribute]): Seq[Expression] =
- DataSourceStrategy.normalizeExprs(exprs, attributes)
-
- override def extractPredicatesWithinOutputSet(condition: Expression,
- outputSet: AttributeSet):
Option[Expression] =
- super[PredicateHelper].extractPredicatesWithinOutputSet(condition,
outputSet)
+import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeSet,
Expression}
+abstract class HoodieSpark3CatalystExpressionUtils extends
HoodieCatalystExpressionUtils {
+ def normalizeExprs(exprs: Seq[Expression], attributes: Seq[Attribute]):
Seq[Expression]
+ def extractPredicatesWithinOutputSet(condition: Expression,
+ outputSet: AttributeSet):
Option[Expression]
Review Comment:
Scala docs?
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