Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/21889#discussion_r209527485
--- Diff:
sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaPruningSuite.scala
---
@@ -0,0 +1,245 @@
+/*
+ * 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 java.io.File
+
+import org.scalactic.Equality
+
+import org.apache.spark.sql.{DataFrame, QueryTest, Row}
+import org.apache.spark.sql.catalyst.parser.CatalystSqlParser
+import org.apache.spark.sql.execution.FileSourceScanExec
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.test.SharedSQLContext
+import org.apache.spark.sql.types.StructType
+
+class ParquetSchemaPruningSuite
+ extends QueryTest
+ with ParquetTest
+ with SharedSQLContext {
+ case class FullName(first: String, middle: String, last: String)
+ case class Contact(
+ id: Int,
+ name: FullName,
+ address: String,
+ pets: Int,
+ friends: Array[FullName] = Array(),
+ relatives: Map[String, FullName] = Map())
+
+ val janeDoe = FullName("Jane", "X.", "Doe")
+ val johnDoe = FullName("John", "Y.", "Doe")
+ val susanSmith = FullName("Susan", "Z.", "Smith")
+
+ private val contacts =
+ Contact(0, janeDoe, "123 Main Street", 1, friends = Array(susanSmith),
+ relatives = Map("brother" -> johnDoe)) ::
+ Contact(1, johnDoe, "321 Wall Street", 3, relatives = Map("sister" ->
janeDoe)) :: Nil
+
+ case class Name(first: String, last: String)
+ case class BriefContact(id: Int, name: Name, address: String)
+
+ private val briefContacts =
+ BriefContact(2, Name("Janet", "Jones"), "567 Maple Drive") ::
+ BriefContact(3, Name("Jim", "Jones"), "6242 Ash Street") :: Nil
+
+ case class ContactWithDataPartitionColumn(
+ id: Int,
+ name: FullName,
+ address: String,
+ pets: Int,
+ friends: Array[FullName] = Array(),
+ relatives: Map[String, FullName] = Map(),
+ p: Int)
+
+ case class BriefContactWithDataPartitionColumn(id: Int, name: Name,
address: String, p: Int)
+
+ private val contactsWithDataPartitionColumn =
+ contacts.map { case Contact(id, name, address, pets, friends,
relatives) =>
+ ContactWithDataPartitionColumn(id, name, address, pets, friends,
relatives, 1) }
+ private val briefContactsWithDataPartitionColumn =
+ briefContacts.map { case BriefContact(id, name, address) =>
+ BriefContactWithDataPartitionColumn(id, name, address, 2) }
+
+ override def beforeEach(): Unit = {
+ super.beforeAll()
+ conf.setConf(SQLConf.NESTED_SCHEMA_PRUNING_ENABLED, true)
+ }
+
+ override def afterEach(): Unit = {
+ try {
+ conf.unsetConf(SQLConf.NESTED_SCHEMA_PRUNING_ENABLED)
+ } finally {
+ super.afterEach()
+ }
+ }
+
+ testSchemaPruning("select a single complex field") {
+ val query = sql("select name.middle from contacts")
+ checkScan(query, "struct<name:struct<middle:string>>")
+ checkAnswer(query.orderBy("id"), Row("X.") :: Row("Y.") :: Row(null)
:: Row(null) :: Nil)
+ }
+
+ testSchemaPruning("select a single complex field and its parent struct")
{
+ val query = sql("select name.middle, name from contacts")
+ checkScan(query,
"struct<name:struct<first:string,middle:string,last:string>>")
+ checkAnswer(query.orderBy("id"),
+ Row("X.", Row("Jane", "X.", "Doe")) ::
+ Row("Y.", Row("John", "Y.", "Doe")) ::
+ Row(null, Row("Janet", null, "Jones")) ::
+ Row(null, Row("Jim", null, "Jones")) ::
+ Nil)
+ }
+
+ testSchemaPruning("select a single complex field array and its parent
struct array") {
+ val query = sql("select friends.middle, friends from contacts where
p=1")
+ checkScan(query,
+
"struct<friends:array<struct<first:string,middle:string,last:string>>>")
+ checkAnswer(query.orderBy("id"),
+ Row(Array("Z."), Array(Row("Susan", "Z.", "Smith"))) ::
+ Row(Array.empty[String], Array.empty[Row]) ::
+ Nil)
+ }
+
+ testSchemaPruning("select a single complex field from a map entry and
its parent map entry") {
+ val query =
+ sql("select relatives[\"brother\"].middle, relatives[\"brother\"]
from contacts where p=1")
+ checkScan(query,
+
"struct<relatives:map<string,struct<first:string,middle:string,last:string>>>")
+ checkAnswer(query.orderBy("id"),
+ Row("Y.", Row("John", "Y.", "Doe")) ::
+ Row(null, null) ::
+ Nil)
+ }
+
+ testSchemaPruning("select a single complex field and the partition
column") {
+ val query = sql("select name.middle, p from contacts")
+ checkScan(query, "struct<name:struct<middle:string>>")
+ checkAnswer(query.orderBy("id"),
+ Row("X.", 1) :: Row("Y.", 1) :: Row(null, 2) :: Row(null, 2) :: Nil)
+ }
+
+ ignore("partial schema intersection - select missing subfield") {
+ val query = sql("select name.middle, address from contacts where p=2")
+ checkScan(query, "struct<name:struct<middle:string>,address:string>")
+ checkAnswer(query.orderBy("id"),
+ Row(null, "567 Maple Drive") ::
+ Row(null, "6242 Ash Street") :: Nil)
+ }
+
+ testSchemaPruning("no unnecessary schema pruning") {
+ val query =
+ sql("select id, name.last, name.middle, name.first,
relatives[''].last, " +
+ "relatives[''].middle, relatives[''].first, friends[0].last,
friends[0].middle, " +
+ "friends[0].first, pets, address from contacts where p=2")
+ // We've selected every field in the schema. Therefore, no schema
pruning should be performed.
+ // We check this by asserting that the scanned schema of the query is
identical to the schema
+ // of the contacts relation, even though the fields are selected in
different orders.
+ checkScan(query,
+
"struct<id:int,name:struct<first:string,middle:string,last:string>,address:string,pets:int,"
+
+ "friends:array<struct<first:string,middle:string,last:string>>," +
+
"relatives:map<string,struct<first:string,middle:string,last:string>>>")
+ checkAnswer(query.orderBy("id"),
+ Row(2, "Jones", null, "Janet", null, null, null, null, null, null,
null, "567 Maple Drive") ::
+ Row(3, "Jones", null, "Jim", null, null, null, null, null, null,
null, "6242 Ash Street") ::
+ Nil)
+ }
+
+ testSchemaPruning("empty schema intersection") {
+ val query = sql("select name.middle from contacts where p=2")
+ checkScan(query, "struct<name:struct<middle:string>>")
+ checkAnswer(query.orderBy("id"),
+ Row(null) :: Row(null) :: Nil)
+ }
+
+ private def testSchemaPruning(testName: String)(testThunk: => Unit) {
+ withSQLConf(SQLConf.PARQUET_VECTORIZED_READER_ENABLED.key -> "true") {
+ test(s"Spark vectorized reader - without partition data column -
$testName") {
+ withContacts(testThunk)
+ }
+ test(s"Spark vectorized reader - with partition data column -
$testName") {
+ withContactsWithDataPartitionColumn(testThunk)
+ }
+ }
+
+ withSQLConf(SQLConf.PARQUET_VECTORIZED_READER_ENABLED.key -> "false") {
+ test(s"Native Parquet reader - without partition data column -
$testName") {
+ withContacts(testThunk)
+ }
+ test(s"Native Parquet reader - with partition data column -
$testName") {
+ withContactsWithDataPartitionColumn(testThunk)
+ }
+ }
+ }
+
+ private def withContacts(testThunk: => Unit) {
+ withTempPath { dir =>
+ val path = dir.getCanonicalPath
+
+ makeParquetFile(contacts, new File(path + "/contacts/p=1"))
+ makeParquetFile(briefContacts, new File(path + "/contacts/p=2"))
+
+ spark.read.parquet(path +
"/contacts").createOrReplaceTempView("contacts")
+
+ testThunk
+ }
+ }
+
+ private def withContactsWithDataPartitionColumn(testThunk: => Unit) {
+ withTempPath { dir =>
+ val path = dir.getCanonicalPath
+
+ makeParquetFile(contactsWithDataPartitionColumn, new File(path +
"/contacts/p=1"))
+ makeParquetFile(briefContactsWithDataPartitionColumn, new File(path
+ "/contacts/p=2"))
+
+ spark.read.parquet(path +
"/contacts").createOrReplaceTempView("contacts")
+
+ testThunk
+ }
+ }
+
+ private val schemaEquality = new Equality[StructType] {
+ override def areEqual(a: StructType, b: Any): Boolean =
+ b match {
+ case otherType: StructType => a sameType otherType
--- End diff --
nit: avoid infix notation per
https://github.com/databricks/scala-style-guide#infix
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