akshaytayal commented on code in PR #13163:
URL: https://github.com/apache/gluten/pull/13163#discussion_r4163209118
##########
gluten-ut/pom.xml:
##########
@@ -238,5 +238,11 @@
<module>spark41</module>
</modules>
</profile>
+ <profile>
+ <id>spark-4.2</id>
+ <modules>
+ <module>spark42</module>
Review Comment:
Resolved — `gluten-ut/pom.xml` now declares `jackson-annotations` with
`${fasterxml.annotations.version}` (2.21) as suggested. Dependency resolution
passes and all spark42 UT jobs are green.
##########
gluten-ut/spark42/src/test/scala/org/apache/spark/sql/connector/GlutenKeyGroupedPartitioningSuite.scala:
##########
@@ -0,0 +1,2088 @@
+/*
+ * 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.connector
+
+import org.apache.gluten.config.GlutenConfig
+import org.apache.gluten.execution.SortMergeJoinExecTransformer
+
+import org.apache.spark.SparkConf
+import org.apache.spark.sql.{DataFrame, GlutenSQLTestsBaseTrait, Row}
+import org.apache.spark.sql.catalyst.plans.physical.KeyedPartitioning
+import org.apache.spark.sql.connector.catalog.{Column, Identifier,
InMemoryTableCatalog}
+import org.apache.spark.sql.connector.distributions.Distributions
+import org.apache.spark.sql.connector.expressions.Expressions.{bucket, days,
identity, years}
+import org.apache.spark.sql.connector.expressions.Transform
+import org.apache.spark.sql.execution.{ColumnarShuffleExchangeExec, SparkPlan}
+import org.apache.spark.sql.execution.datasources.v2.BatchScanExec
+import org.apache.spark.sql.execution.exchange.{ShuffleExchangeExec,
ShuffleExchangeLike}
+import org.apache.spark.sql.execution.joins.SortMergeJoinExec
+import org.apache.spark.sql.functions.{col, max}
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.types._
+
+import java.util.Collections
+
+class GlutenKeyGroupedPartitioningSuite
+ extends KeyGroupedPartitioningSuite
+ with GlutenSQLTestsBaseTrait {
+ override def sparkConf: SparkConf = {
+ // Native SQL configs
+ super.sparkConf
+ .set(GlutenConfig.COLUMNAR_FORCE_SHUFFLED_HASH_JOIN_ENABLED.key, "false")
+ .set("spark.sql.adaptive.enabled", "false")
+ .set("spark.sql.shuffle.partitions", "5")
+ }
+
+ private val emptyProps: java.util.Map[String, String] = {
+ Collections.emptyMap[String, String]
+ }
+
+ private val columns: Array[Column] = Array(
+ Column.create("id", IntegerType),
+ Column.create("data", StringType),
+ Column.create("ts", TimestampType))
+
+ private val columns2: Array[Column] = Array(
+ Column.create("store_id", IntegerType),
+ Column.create("dept_id", IntegerType),
+ Column.create("data", StringType))
+
+ private def createTable(
+ table: String,
+ columns: Array[Column],
+ partitions: Array[Transform],
+ catalog: InMemoryTableCatalog = catalog): Unit = {
+ catalog.createTable(
+ Identifier.of(Array("ns"), table),
+ columns,
+ partitions,
+ emptyProps,
+ Distributions.unspecified(),
+ Array.empty,
+ None,
+ None,
+ numRowsPerSplit = 1)
+ }
+
+ private def collectColumnarShuffleExchangeExec(
+ plan: SparkPlan): Seq[ColumnarShuffleExchangeExec] = {
+ // here we skip collecting shuffle operators that are not associated with
SMJ
+ collect(plan) {
+ case s: SortMergeJoinExecTransformer => s
+ case s: SortMergeJoinExec => s
+ }.flatMap(smj => collect(smj) { case s: ColumnarShuffleExchangeExec => s })
+ }
+
+ override protected def collectShuffles(plan: SparkPlan):
Seq[ShuffleExchangeLike] = {
+ // here we skip collecting shuffle operators that are not associated with
SMJ
+ collect(plan) {
+ case s: SortMergeJoinExec => s
+ case s: SortMergeJoinExecTransformer => s
+ }.flatMap(
+ smj =>
+ collect(smj) {
+ case s: ShuffleExchangeExec => s
+ case s: ColumnarShuffleExchangeExec => s
+ })
+ }
+
+ override protected def collectAllShuffles(plan: SparkPlan):
Seq[ColumnarShuffleExchangeExec] = {
+ collect(plan) { case s: ColumnarShuffleExchangeExec => s }
+ }
+
+ private def collectVanillaShuffles(plan: SparkPlan):
Seq[ShuffleExchangeExec] = {
+ collect(plan) { case s: ShuffleExchangeExec => s }
+ }
+
+ private def collectScans(plan: SparkPlan): Seq[BatchScanExec] = {
+ collect(plan) { case s: BatchScanExec => s }
+ }
+
+ private def selectWithMergeJoinHint(t1: String, t2: String): String = {
+ s"SELECT /*+ MERGE($t1, $t2) */ "
+ }
+
+ private def createJoinTestDF(
+ keys: Seq[(String, String)],
+ extraColumns: Seq[String] = Nil,
+ joinType: String = ""): DataFrame = {
+ val extraColList = if (extraColumns.isEmpty) "" else
extraColumns.mkString(", ", ", ", "")
+ sql(s"""
+ |${selectWithMergeJoinHint("i", "p")}
+ |id, name, i.price as purchase_price, p.price as sale_price
$extraColList
+ |FROM testcat.ns.$items i $joinType JOIN testcat.ns.$purchases p
+ |ON ${keys.map(k => s"i.${k._1} = p.${k._2}").mkString(" AND ")}
+ |ORDER BY id, purchase_price, sale_price $extraColList
+ |""".stripMargin)
+ }
+
+ private val customers: String = "customers"
+ private val customersColumns: Array[Column] = Array(
+ Column.create("customer_name", StringType),
+ Column.create("customer_age", IntegerType),
+ Column.create("customer_id", LongType))
+
+ private val orders: String = "orders"
+ private val ordersColumns: Array[Column] =
+ Array(Column.create("order_amount", DoubleType),
Column.create("customer_id", LongType))
+
+ private def testWithCustomersAndOrders(
+ customers_partitions: Array[Transform],
+ orders_partitions: Array[Transform],
+ expectedNumOfShuffleExecs: Int): Unit = {
+ createTable(customers, customersColumns, customers_partitions)
+ sql(
+ s"INSERT INTO testcat.ns.$customers VALUES " +
+ s"('aaa', 10, 1), ('bbb', 20, 2), ('ccc', 30, 3)")
+
+ createTable(orders, ordersColumns, orders_partitions)
+ sql(
+ s"INSERT INTO testcat.ns.$orders VALUES " +
+ s"(100.0, 1), (200.0, 1), (150.0, 2), (250.0, 2), (350.0, 2), (400.50,
3)")
+
+ val df = sql(
+ "SELECT customer_name, customer_age, order_amount " +
+ s"FROM testcat.ns.$customers c JOIN testcat.ns.$orders o " +
+ "ON c.customer_id = o.customer_id ORDER BY c.customer_id,
order_amount")
+
+ val shuffles =
collectColumnarShuffleExchangeExec(df.queryExecution.executedPlan)
+ assert(shuffles.length == expectedNumOfShuffleExecs)
+
+ checkAnswer(
+ df,
+ Seq(
+ Row("aaa", 10, 100.0),
+ Row("aaa", 10, 200.0),
+ Row("bbb", 20, 150.0),
+ Row("bbb", 20, 250.0),
+ Row("bbb", 20, 350.0),
+ Row("ccc", 30, 400.50)))
+ }
+
+ testGluten("partitioned join: only one side reports partitioning") {
+ val customers_partitions = Array(bucket(4, "customer_id"))
+ val orders_partitions = Array(bucket(2, "customer_id"))
+
+ testWithCustomersAndOrders(customers_partitions, orders_partitions, 2)
+ }
+ testGluten("partitioned join: exact distribution (same number of buckets)
from both sides") {
+ val customers_partitions = Array(bucket(4, "customer_id"))
+ val orders_partitions = Array(bucket(4, "customer_id"))
+
+ testWithCustomersAndOrders(customers_partitions, orders_partitions, 0)
+ }
+
+ private val items: String = "items"
+ private val itemsColumns: Array[Column] = Array(
+ Column.create("id", LongType),
+ Column.create("name", StringType),
+ Column.create("price", FloatType),
+ Column.create("arrive_time", TimestampType))
+ private val purchases: String = "purchases"
+ private val purchasesColumns: Array[Column] = Array(
+ Column.create("item_id", LongType),
+ Column.create("price", FloatType),
+ Column.create("time", TimestampType))
+
+ testGluten(
+ "SPARK-41413: partitioned join: partition values" +
+ " from one side are subset of those from the other side") {
+ val items_partitions = Array(bucket(4, "id"))
+ createTable(items, itemsColumns, items_partitions)
+
+ sql(
+ s"INSERT INTO testcat.ns.$items VALUES " +
+ "(1, 'aa', 40.0, cast('2020-01-01' as timestamp)), " +
+ "(3, 'bb', 10.0, cast('2020-01-01' as timestamp)), " +
+ "(4, 'cc', 15.5, cast('2020-02-01' as timestamp))")
+
+ val purchases_partitions = Array(bucket(4, "item_id"))
+ createTable(purchases, purchasesColumns, purchases_partitions)
+
+ sql(
+ s"INSERT INTO testcat.ns.$purchases VALUES " +
+ "(1, 42.0, cast('2020-01-01' as timestamp)), " +
+ "(3, 19.5, cast('2020-02-01' as timestamp))")
+
+ Seq(true, false).foreach {
+ pushDownValues =>
+ withSQLConf(SQLConf.V2_BUCKETING_PUSH_PART_VALUES_ENABLED.key ->
pushDownValues.toString) {
+ val df = sql(
+ "SELECT id, name, i.price as purchase_price, p.price as sale_price
" +
+ s"FROM testcat.ns.$items i JOIN testcat.ns.$purchases p " +
+ "ON i.id = p.item_id ORDER BY id, purchase_price, sale_price")
+
+ val shuffles =
collectColumnarShuffleExchangeExec(df.queryExecution.executedPlan)
+ if (pushDownValues) {
+ assert(shuffles.isEmpty, "should not add shuffle when partition
values mismatch")
+ } else {
+ assert(
+ shuffles.nonEmpty,
+ "should add shuffle when partition values mismatch, and " +
+ "pushing down partition values is not enabled")
+ }
+
+ checkAnswer(df, Seq(Row(1, "aa", 40.0, 42.0), Row(3, "bb", 10.0,
19.5)))
+ }
+ }
+ }
+
+ testGluten("SPARK-41413: partitioned join: partition values from both sides
overlaps") {
+ val items_partitions = Array(identity("id"))
+ createTable(items, itemsColumns, items_partitions)
+
+ sql(
+ s"INSERT INTO testcat.ns.$items VALUES " +
+ "(1, 'aa', 40.0, cast('2020-01-01' as timestamp)), " +
+ "(2, 'bb', 10.0, cast('2020-01-01' as timestamp)), " +
+ "(3, 'cc', 15.5, cast('2020-02-01' as timestamp))")
+
+ val purchases_partitions = Array(identity("item_id"))
+ createTable(purchases, purchasesColumns, purchases_partitions)
+ sql(
+ s"INSERT INTO testcat.ns.$purchases VALUES " +
+ "(1, 42.0, cast('2020-01-01' as timestamp)), " +
+ "(2, 19.5, cast('2020-02-01' as timestamp)), " +
+ "(4, 30.0, cast('2020-02-01' as timestamp))")
+
+ Seq(true, false).foreach {
+ pushDownValues =>
+ withSQLConf(SQLConf.V2_BUCKETING_PUSH_PART_VALUES_ENABLED.key ->
pushDownValues.toString) {
+ val df = sql(
+ "SELECT id, name, i.price as purchase_price, p.price as sale_price
" +
+ s"FROM testcat.ns.$items i JOIN testcat.ns.$purchases p " +
+ "ON i.id = p.item_id ORDER BY id, purchase_price, sale_price")
+
+ val shuffles =
collectColumnarShuffleExchangeExec(df.queryExecution.executedPlan)
+ if (pushDownValues) {
+ assert(shuffles.isEmpty, "should not add shuffle when partition
values mismatch")
+ } else {
+ assert(
+ shuffles.nonEmpty,
+ "should add shuffle when partition values mismatch, and " +
+ "pushing down partition values is not enabled")
+ }
+
+ checkAnswer(df, Seq(Row(1, "aa", 40.0, 42.0), Row(2, "bb", 10.0,
19.5)))
+ }
+ }
+ }
+
+ testGluten("SPARK-41413: partitioned join: non-overlapping partition values
from both sides") {
+ val items_partitions = Array(identity("id"))
+ createTable(items, itemsColumns, items_partitions)
+ sql(
+ s"INSERT INTO testcat.ns.$items VALUES " +
+ "(1, 'aa', 40.0, cast('2020-01-01' as timestamp)), " +
+ "(2, 'bb', 10.0, cast('2020-01-01' as timestamp)), " +
+ "(3, 'cc', 15.5, cast('2020-02-01' as timestamp))")
+
+ val purchases_partitions = Array(identity("item_id"))
+ createTable(purchases, purchasesColumns, purchases_partitions)
+ sql(
+ s"INSERT INTO testcat.ns.$purchases VALUES " +
+ "(4, 42.0, cast('2020-01-01' as timestamp)), " +
+ "(5, 19.5, cast('2020-02-01' as timestamp)), " +
+ "(6, 30.0, cast('2020-02-01' as timestamp))")
+
+ Seq(true, false).foreach {
+ pushDownValues =>
+ withSQLConf(SQLConf.V2_BUCKETING_PUSH_PART_VALUES_ENABLED.key ->
pushDownValues.toString) {
+ val df = sql(
+ "SELECT id, name, i.price as purchase_price, p.price as sale_price
" +
+ s"FROM testcat.ns.$items i JOIN testcat.ns.$purchases p " +
+ "ON i.id = p.item_id ORDER BY id, purchase_price, sale_price")
+
+ val shuffles =
collectColumnarShuffleExchangeExec(df.queryExecution.executedPlan)
+ if (pushDownValues) {
+ assert(shuffles.isEmpty, "should not add shuffle when partition
values mismatch")
+ } else {
+ assert(
+ shuffles.nonEmpty,
+ "should add shuffle when partition values mismatch, and " +
+ "pushing down partition values is not enabled")
+ }
+
+ checkAnswer(df, Seq.empty)
+ }
+ }
+ }
+
+ testGluten(
+ "SPARK-42038: partially clustered:" +
+ " with same partition keys and one side fully clustered") {
+ val items_partitions = Array(identity("id"))
+ createTable(items, itemsColumns, items_partitions)
+ sql(
+ s"INSERT INTO testcat.ns.$items VALUES " +
+ s"(1, 'aa', 40.0, cast('2020-01-01' as timestamp)), " +
+ s"(2, 'bb', 10.0, cast('2020-01-01' as timestamp)), " +
+ s"(3, 'cc', 15.5, cast('2020-02-01' as timestamp))")
+
+ val purchases_partitions = Array(identity("item_id"))
+ createTable(purchases, purchasesColumns, purchases_partitions)
+ sql(
+ s"INSERT INTO testcat.ns.$purchases VALUES " +
+ s"(1, 45.0, cast('2020-01-01' as timestamp)), " +
+ s"(1, 50.0, cast('2020-01-02' as timestamp)), " +
+ s"(2, 15.0, cast('2020-01-02' as timestamp)), " +
+ s"(2, 20.0, cast('2020-01-03' as timestamp)), " +
+ s"(3, 20.0, cast('2020-02-01' as timestamp))")
+
+ Seq(true, false).foreach {
+ pushDownValues =>
+ Seq(("true", 5), ("false", 3)).foreach {
+ case (enable, expected) =>
+ withSQLConf(
+ SQLConf.V2_BUCKETING_PUSH_PART_VALUES_ENABLED.key ->
pushDownValues.toString,
+
SQLConf.V2_BUCKETING_PARTIALLY_CLUSTERED_DISTRIBUTION_ENABLED.key -> enable
+ ) {
+ val df = sql(
+ "SELECT id, name, i.price as purchase_price, p.price as
sale_price " +
+ s"FROM testcat.ns.$items i JOIN testcat.ns.$purchases p " +
+ "ON i.id = p.item_id ORDER BY id, purchase_price,
sale_price")
+
+ val shuffles =
collectColumnarShuffleExchangeExec(df.queryExecution.executedPlan)
+ assert(shuffles.isEmpty, "should not contain any shuffle")
+ if (pushDownValues) {
+ val scans = collectScans(df.queryExecution.executedPlan)
+ assert(scans.forall(_.inputRDD.partitions.length == expected))
+ }
Review Comment:
Agreed on the RCA — Spark 4.2 moved SPJ grouping to `GroupPartitionsExec`,
so the raw scan-count assertions can't hold. For this enablement PR these SPJ
tests are disabled (CI-confirmed) and the proper fix (asserting grouping output
via `collectAllGroupPartitions`, and the SPARK-47094 oracle) is tracked in
#13174.
##########
gluten-ut/spark42/src/test/scala/org/apache/spark/sql/connector/GlutenKeyGroupedPartitioningSuite.scala:
##########
@@ -0,0 +1,2088 @@
+/*
+ * 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.connector
+
+import org.apache.gluten.config.GlutenConfig
+import org.apache.gluten.execution.SortMergeJoinExecTransformer
+
+import org.apache.spark.SparkConf
+import org.apache.spark.sql.{DataFrame, GlutenSQLTestsBaseTrait, Row}
+import org.apache.spark.sql.catalyst.plans.physical.KeyedPartitioning
+import org.apache.spark.sql.connector.catalog.{Column, Identifier,
InMemoryTableCatalog}
+import org.apache.spark.sql.connector.distributions.Distributions
+import org.apache.spark.sql.connector.expressions.Expressions.{bucket, days,
identity, years}
+import org.apache.spark.sql.connector.expressions.Transform
+import org.apache.spark.sql.execution.{ColumnarShuffleExchangeExec, SparkPlan}
+import org.apache.spark.sql.execution.datasources.v2.BatchScanExec
+import org.apache.spark.sql.execution.exchange.{ShuffleExchangeExec,
ShuffleExchangeLike}
+import org.apache.spark.sql.execution.joins.SortMergeJoinExec
+import org.apache.spark.sql.functions.{col, max}
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.types._
+
+import java.util.Collections
+
+class GlutenKeyGroupedPartitioningSuite
+ extends KeyGroupedPartitioningSuite
+ with GlutenSQLTestsBaseTrait {
+ override def sparkConf: SparkConf = {
+ // Native SQL configs
+ super.sparkConf
+ .set(GlutenConfig.COLUMNAR_FORCE_SHUFFLED_HASH_JOIN_ENABLED.key, "false")
+ .set("spark.sql.adaptive.enabled", "false")
+ .set("spark.sql.shuffle.partitions", "5")
+ }
+
+ private val emptyProps: java.util.Map[String, String] = {
+ Collections.emptyMap[String, String]
+ }
+
+ private val columns: Array[Column] = Array(
+ Column.create("id", IntegerType),
+ Column.create("data", StringType),
+ Column.create("ts", TimestampType))
+
+ private val columns2: Array[Column] = Array(
+ Column.create("store_id", IntegerType),
+ Column.create("dept_id", IntegerType),
+ Column.create("data", StringType))
+
+ private def createTable(
+ table: String,
+ columns: Array[Column],
+ partitions: Array[Transform],
+ catalog: InMemoryTableCatalog = catalog): Unit = {
+ catalog.createTable(
+ Identifier.of(Array("ns"), table),
+ columns,
+ partitions,
+ emptyProps,
+ Distributions.unspecified(),
+ Array.empty,
+ None,
+ None,
+ numRowsPerSplit = 1)
+ }
+
+ private def collectColumnarShuffleExchangeExec(
+ plan: SparkPlan): Seq[ColumnarShuffleExchangeExec] = {
+ // here we skip collecting shuffle operators that are not associated with
SMJ
+ collect(plan) {
+ case s: SortMergeJoinExecTransformer => s
+ case s: SortMergeJoinExec => s
+ }.flatMap(smj => collect(smj) { case s: ColumnarShuffleExchangeExec => s })
+ }
+
+ override protected def collectShuffles(plan: SparkPlan):
Seq[ShuffleExchangeLike] = {
+ // here we skip collecting shuffle operators that are not associated with
SMJ
+ collect(plan) {
+ case s: SortMergeJoinExec => s
+ case s: SortMergeJoinExecTransformer => s
+ }.flatMap(
+ smj =>
+ collect(smj) {
+ case s: ShuffleExchangeExec => s
+ case s: ColumnarShuffleExchangeExec => s
+ })
+ }
+
+ override protected def collectAllShuffles(plan: SparkPlan):
Seq[ColumnarShuffleExchangeExec] = {
+ collect(plan) { case s: ColumnarShuffleExchangeExec => s }
Review Comment:
Acknowledged (nonblocking) — the columnar-only `collectAllShuffles` override
can hide vanilla fallback shuffles from inherited 'no shuffle remains'
assertions. Captured as a coverage follow-up in #13174; will adopt the
`ShuffleExchangeLike` (vanilla + columnar) return plus a separate
`collectAllColumnarShuffles` for the native-only checks when re-enabling this
suite.
##########
.github/workflows/velox_backend_x86.yml:
##########
@@ -1508,3 +1508,132 @@ jobs:
**/target/*.log
**/gluten-ut/**/hs_err_*.log
**/gluten-ut/**/core.*
+
+ spark-test-spark42:
+ needs: [detect-changes, build-native-lib-centos-8]
+ if: >-
+ needs.detect-changes.outputs.java == 'true' ||
+ needs.detect-changes.outputs.shims42 == 'true' ||
+ needs.detect-changes.outputs.cpp == 'true'
+ runs-on: ubuntu-22.04
+ strategy:
+ fail-fast: false
+ matrix:
+ # Split tests into 3 groups to run in parallel and cut the ~2h
wall-clock time.
+ # group1 – streaming tests
+ # group2 – execution / catalyst / errors / extension tests
+ # group3 – top-level sql, connector, sources, hive and remaining
tests
+ group: [1, 2, 3]
+ env:
+ SPARK_TESTING: true
+ container: apache/gluten:centos-9-jdk17
+ steps:
+ - uses: actions/checkout@v7
+ - name: Download All Artifacts
+ uses: actions/download-artifact@v8
+ with:
+ name: velox-native-lib-centos-8-${{github.sha}}
+ path: ./cpp/build/releases/
+ - name: Prepare
+ run: |
+ dnf install -y python3.11 python3.11-pip python3.11-devel && \
+ ls -la /usr/bin/python3.11 && \
+ alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11
1 && \
+ alternatives --set python3 /usr/bin/python3.11 && \
+ pip3 install setuptools==77.0.3 && \
+ pip3 install pyspark==3.5.5 cython && \
+ pip3 install pandas==2.2.3 pyarrow==20.0.0
+ - name: Build and Run unit test for Spark 4.2.0 with scala-2.13 (other
tests, group ${{ matrix.group }})
+ run: |
+ cd $GITHUB_WORKSPACE/
+ export SPARK_SCALA_VERSION=2.13
+ yum install -y java-17-openjdk-devel
+ export JAVA_HOME=/usr/lib/jvm/java-17-openjdk
+ export PATH=$JAVA_HOME/bin:$PATH
+ java -version
+
TAGS_EXCLUDE="org.apache.spark.tags.ExtendedSQLTest,org.apache.spark.tags.SlowHiveTest,org.apache.gluten.tags.UDFTest,org.apache.gluten.tags.EnhancedFeaturesTest,org.apache.gluten.tags.CudfTest,org.apache.gluten.tags.SkipTest"
+ if [ "${{ matrix.group }}" = "1" ]; then
+ # Group 1: streaming + gluten utils + top-level spark tests (~55
classes)
+ $MVN_CMD clean test -Pspark-4.2 -Pscala-2.13 -Pjava-17
-Pbackends-velox -Pspark-ut \
+ -DargLine="-Dspark.test.home=/opt/shims/spark42/spark_home/"
-DtagsToExclude="$TAGS_EXCLUDE" \
+
-DwildcardSuites="org.apache.spark.sql.streaming,org.apache.spark.GlutenSortShuffleSuite,org.apache.gluten"
Review Comment:
Acknowledged (nonblocking) —
`org.apache.spark.rpc.GlutenDriverEndpointSuite` isn't selected by any wildcard
group (same omission as spark41). Tracked in #13180 to add
`org.apache.spark.rpc` to the group1 wildcard.
##########
.github/workflows/velox_backend_x86.yml:
##########
@@ -1508,3 +1508,132 @@ jobs:
**/target/*.log
**/gluten-ut/**/hs_err_*.log
**/gluten-ut/**/core.*
+
+ spark-test-spark42:
+ needs: [detect-changes, build-native-lib-centos-8]
+ if: >-
+ needs.detect-changes.outputs.java == 'true' ||
+ needs.detect-changes.outputs.shims42 == 'true' ||
+ needs.detect-changes.outputs.cpp == 'true'
+ runs-on: ubuntu-22.04
+ strategy:
+ fail-fast: false
+ matrix:
+ # Split tests into 3 groups to run in parallel and cut the ~2h
wall-clock time.
+ # group1 – streaming tests
+ # group2 – execution / catalyst / errors / extension tests
+ # group3 – top-level sql, connector, sources, hive and remaining
tests
+ group: [1, 2, 3]
+ env:
+ SPARK_TESTING: true
+ container: apache/gluten:centos-9-jdk17
+ steps:
+ - uses: actions/checkout@v7
+ - name: Download All Artifacts
+ uses: actions/download-artifact@v8
+ with:
+ name: velox-native-lib-centos-8-${{github.sha}}
+ path: ./cpp/build/releases/
+ - name: Prepare
+ run: |
+ dnf install -y python3.11 python3.11-pip python3.11-devel && \
+ ls -la /usr/bin/python3.11 && \
+ alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11
1 && \
+ alternatives --set python3 /usr/bin/python3.11 && \
+ pip3 install setuptools==77.0.3 && \
+ pip3 install pyspark==3.5.5 cython && \
+ pip3 install pandas==2.2.3 pyarrow==20.0.0
+ - name: Build and Run unit test for Spark 4.2.0 with scala-2.13 (other
tests, group ${{ matrix.group }})
+ run: |
+ cd $GITHUB_WORKSPACE/
+ export SPARK_SCALA_VERSION=2.13
+ yum install -y java-17-openjdk-devel
+ export JAVA_HOME=/usr/lib/jvm/java-17-openjdk
+ export PATH=$JAVA_HOME/bin:$PATH
+ java -version
+
TAGS_EXCLUDE="org.apache.spark.tags.ExtendedSQLTest,org.apache.spark.tags.SlowHiveTest,org.apache.gluten.tags.UDFTest,org.apache.gluten.tags.EnhancedFeaturesTest,org.apache.gluten.tags.CudfTest,org.apache.gluten.tags.SkipTest"
+ if [ "${{ matrix.group }}" = "1" ]; then
+ # Group 1: streaming + gluten utils + top-level spark tests (~55
classes)
+ $MVN_CMD clean test -Pspark-4.2 -Pscala-2.13 -Pjava-17
-Pbackends-velox -Pspark-ut \
+ -DargLine="-Dspark.test.home=/opt/shims/spark42/spark_home/"
-DtagsToExclude="$TAGS_EXCLUDE" \
+
-DwildcardSuites="org.apache.spark.sql.streaming,org.apache.spark.GlutenSortShuffleSuite,org.apache.gluten"
+ elif [ "${{ matrix.group }}" = "2" ]; then
+ # Group 2: execution + catalyst + errors + extension tests (~140
classes)
+ $MVN_CMD clean test -Pspark-4.2 -Pscala-2.13 -Pjava-17
-Pbackends-velox -Pspark-ut \
+ -DargLine="-Dspark.test.home=/opt/shims/spark42/spark_home/"
-DtagsToExclude="$TAGS_EXCLUDE" \
+
-DwildcardSuites="org.apache.spark.sql.execution,org.apache.spark.sql.catalyst,org.apache.spark.sql.errors,org.apache.spark.sql.extension"
Review Comment:
Adopted exactly — re-enabled the test (`ignore` -> `test`) and replaced the
message assertion with the key/requirement split, keeping the
`IllegalArgumentException` interception:
`assert(e.getMessage.contains("spark.task.cpus"))` +
`assert(e.getMessage.contains("positive"))`. Validated locally on Spark 4.2
(GlutenAutoAdjustStageResourceProfileSuite: succeeded 5, failed 0). Fixed in
91b58f0ec.
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