weiting-chen commented on code in PR #12077: URL: https://github.com/apache/gluten/pull/12077#discussion_r3234919575
########## backends-velox/src/test/scala/org/apache/gluten/execution/VeloxRDDScanSuite.scala: ########## @@ -0,0 +1,235 @@ +/* + * 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 + +import org.apache.gluten.execution._ + +import org.apache.spark.SparkConf +import org.apache.spark.sql.{DataFrame, Row} +import org.apache.spark.sql.classic.ClassicDataset +import org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanHelper +import org.apache.spark.sql.types._ + +class VeloxRDDScanSuite extends VeloxWholeStageTransformerSuite with AdaptiveSparkPlanHelper { + + override protected val resourcePath: String = "/tpch-data-parquet" + override protected val fileFormat: String = "parquet" + + override protected def sparkConf: SparkConf = { + super.sparkConf + .set("spark.sql.ansi.enabled", "false") + } + + override def beforeAll(): Unit = { + super.beforeAll() + createTPCHNotNullTables() + } + + /** Creates a DataFrame backed by LogicalRDD/RDDScanExec from an existing DataFrame. */ + private def asRDDScanDF(data: DataFrame): DataFrame = { + val node = LogicalRDD( + data.logicalPlan.output, + data.queryExecution.toRdd)(data.sparkSession) + ClassicDataset.ofRows(spark, node).toDF() + } + + test("basic RDDScanExec is replaced by VeloxRDDScanTransformer") { + val data = spark.sql("SELECT l_orderkey, l_partkey FROM lineitem LIMIT 10") + val expectedAnswer = data.collect() + val df = asRDDScanDF(data) + + checkAnswer(df, expectedAnswer) + val cnt = collect(df.queryExecution.executedPlan) { case _: VeloxRDDScanTransformer => true } + assert(cnt.nonEmpty, "Expected VeloxRDDScanTransformer in plan") + } + + test("RDDScan with string and numeric types") { + val data = spark.sql("""SELECT l_returnflag, l_linestatus, l_quantity, l_extendedprice + |FROM lineitem LIMIT 20""".stripMargin) + val expectedAnswer = data.collect() + val df = asRDDScanDF(data) + + checkAnswer(df, expectedAnswer) + val cnt = collect(df.queryExecution.executedPlan) { case _: VeloxRDDScanTransformer => true } + assert(cnt.nonEmpty, "Expected VeloxRDDScanTransformer in plan") + } + + test("RDDScan with aggregation downstream") { + val query = + """SELECT l_returnflag, sum(l_quantity) AS sum_qty + |FROM lineitem + |WHERE l_shipdate <= date'1998-09-02' + |GROUP BY l_returnflag""".stripMargin + val data = spark.sql(query) + val expectedAnswer = data.collect() + val df = asRDDScanDF(data) + + checkAnswer(df, expectedAnswer) + val cnt = collect(df.queryExecution.executedPlan) { case _: VeloxRDDScanTransformer => true } + assert(cnt.nonEmpty, "Expected VeloxRDDScanTransformer in plan") + } + + test("RDDScan with empty RDD") { + val data = spark.sql("SELECT l_orderkey FROM lineitem WHERE 1 = 0") + val expectedAnswer = data.collect() + val df = asRDDScanDF(data) + + checkAnswer(df, expectedAnswer) + assert(df.count() == 0) + val cnt = collect(df.queryExecution.executedPlan) { case _: VeloxRDDScanTransformer => true } + assert(cnt.nonEmpty, "Expected VeloxRDDScanTransformer in plan") + } + + test("RDDScan preserves data correctness with multiple re-reads") { + val data = spark.sql("SELECT l_orderkey, l_partkey FROM lineitem LIMIT 50") + val expectedAnswer = data.collect() + val df = asRDDScanDF(data) + + // Read twice to verify idempotency + checkAnswer(df, expectedAnswer) + checkAnswer(df, expectedAnswer) + val cnt = collect(df.queryExecution.executedPlan) { case _: VeloxRDDScanTransformer => true } + assert(cnt.nonEmpty, "Expected VeloxRDDScanTransformer in plan") + } + + test("RDDScan with null values") { + val rdd = spark.sparkContext.parallelize( + Seq( + Row(1, "a", null), + Row(null, "b", 2.0), + Row(3, null, 3.0) + )) + val schema = StructType( + Seq( + StructField("id", IntegerType, nullable = true), + StructField("name", StringType, nullable = true), + StructField("value", DoubleType, nullable = true) + )) + val data = spark.createDataFrame(rdd, schema) + val expectedAnswer = data.collect() + val df = asRDDScanDF(data) + + checkAnswer(df, expectedAnswer) + val cnt = collect(df.queryExecution.executedPlan) { case _: VeloxRDDScanTransformer => true } + assert(cnt.nonEmpty, "Expected VeloxRDDScanTransformer in plan") + } + + test("RDDScan with all supported primitive types") { + val rdd = spark.sparkContext.parallelize( + Seq( + Row( + true, + 1.toByte, + 2.toShort, + 3, + 4L, + 5.0f, + 6.0, + "hello", + java.sql.Date.valueOf("2024-01-01"), + java.sql.Timestamp.valueOf("2024-01-01 12:00:00"), + Array[Byte](1, 2, 3), + BigDecimal("123.45").underlying() + ) + )) + val schema = StructType( + Seq( + StructField("bool", BooleanType), + StructField("byte", ByteType), + StructField("short", ShortType), + StructField("int", IntegerType), + StructField("long", LongType), + StructField("float", FloatType), + StructField("double", DoubleType), + StructField("string", StringType), + StructField("date", DateType), + StructField("timestamp", TimestampType), + StructField("binary", BinaryType), + StructField("decimal", DecimalType(10, 2)) + )) + val data = spark.createDataFrame(rdd, schema) + val expectedAnswer = data.collect() + val df = asRDDScanDF(data) + + checkAnswer(df, expectedAnswer) + val cnt = collect(df.queryExecution.executedPlan) { case _: VeloxRDDScanTransformer => true } + assert(cnt.nonEmpty, "Expected VeloxRDDScanTransformer in plan") + } + + test("RDDScan with array type") { + val rdd = spark.sparkContext.parallelize( + Seq( + Row(Seq(1, 2, 3)), + Row(Seq(4, 5)) + )) + val schema = StructType(Seq(StructField("arr", ArrayType(IntegerType)))) + val data = spark.createDataFrame(rdd, schema) + val expectedAnswer = data.collect() + val df = asRDDScanDF(data) + + checkAnswer(df, expectedAnswer) + val cnt = collect(df.queryExecution.executedPlan) { case _: VeloxRDDScanTransformer => true } + assert(cnt.nonEmpty, "Expected VeloxRDDScanTransformer in plan") + } + + test("RDDScan with map type") { + val rdd = spark.sparkContext.parallelize( + Seq( + Row(Map("a" -> 1, "b" -> 2)), + Row(Map("c" -> 3)) + )) + val schema = StructType(Seq(StructField("m", MapType(StringType, IntegerType)))) + val data = spark.createDataFrame(rdd, schema) + val expectedAnswer = data.collect() + val df = asRDDScanDF(data) + + checkAnswer(df, expectedAnswer) + val cnt = collect(df.queryExecution.executedPlan) { case _: VeloxRDDScanTransformer => true } + assert(cnt.nonEmpty, "Expected VeloxRDDScanTransformer in plan") + } + + test("RDDScan with struct type") { + val rdd = spark.sparkContext.parallelize( + Seq( + Row(Row("hello", 1)), + Row(Row("world", 2)) + )) + val innerSchema = StructType( + Seq(StructField("name", StringType), StructField("value", IntegerType))) + val schema = StructType(Seq(StructField("s", innerSchema))) + val data = spark.createDataFrame(rdd, schema) + val expectedAnswer = data.collect() + val df = asRDDScanDF(data) + + checkAnswer(df, expectedAnswer) + val cnt = collect(df.queryExecution.executedPlan) { case _: VeloxRDDScanTransformer => true } + assert(cnt.nonEmpty, "Expected VeloxRDDScanTransformer in plan") + } + + test("RDDScan falls back for unsupported types") { + val data = spark.sql("SELECT INTERVAL '1' DAY AS di") + val expectedAnswer = data.collect() + val result = asRDDScanDF(data) + + // Should still produce correct results via fallback to vanilla Spark + checkAnswer(result, expectedAnswer) + val cnt = collect(result.queryExecution.executedPlan) { + case _: VeloxRDDScanTransformer => true + } + assert(cnt.isEmpty, "Expected fallback - VeloxRDDScanTransformer should NOT be in plan") + } +} Review Comment: **Missing test for BatchCarrierRow unwrap path** **Problem:** The new `BatchCarrierRow` detection logic in `doExecuteColumnar` is production code added to handle checkpointed Gluten DataFrames, but no test exercises this specific branch. If the unwrap logic regresses, the existing tests won't catch it since they all go through the standard InternalRow conversion path. **Suggested Fix:** Add a test that forces the BatchCarrierRow path: ```scala test("RDDScan handles BatchCarrierRow from checkpoint") { spark.sparkContext.setCheckpointDir(tempPath) val df = spark.range(100).selectExpr("id", "id * 2 as value") val checkpointed = df.localCheckpoint() val result = asRDDScanDF(checkpointed) checkAnswer(result, df.collect()) val cnt = collect(result.queryExecution.executedPlan) { case _: VeloxRDDScanTransformer => true } assert(cnt.nonEmpty, "Expected VeloxRDDScanTransformer in plan") } ``` ########## backends-velox/src/main/scala/org/apache/gluten/execution/VeloxRDDScanTransformer.scala: ########## @@ -0,0 +1,111 @@ +/* + * 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.gluten.execution + +import org.apache.gluten.backendsapi.velox.VeloxValidatorApi +import org.apache.gluten.config.{GlutenConfig, VeloxConfig} + +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.expressions.{Attribute, SortOrder} +import org.apache.spark.sql.catalyst.plans.physical.Partitioning +import org.apache.spark.sql.execution.{RDDScanTransformer, SparkPlan} +import org.apache.spark.sql.execution.metric.{SQLMetric, SQLMetrics} +import org.apache.spark.sql.vectorized.ColumnarBatch + +/** + * Velox-backend implementation of RDDScanTransformer. + * + * Converts an RDD[InternalRow] into columnar batches using Velox's native row-to-columnar + * conversion (same JNI path as RowToVeloxColumnarExec). + */ +case class VeloxRDDScanTransformer( + outputAttributes: Seq[Attribute], + rdd: RDD[InternalRow], + name: String, + // Row-to-columnar conversion preserves data distribution, so we carry through + // the original partitioning. This differs from CH which uses UnknownPartitioning(0) + // but is consistent with RowToVeloxColumnarExec's behavior. + override val outputPartitioning: Partitioning, + override val outputOrdering: Seq[SortOrder] +) extends RDDScanTransformer(outputAttributes, outputPartitioning, outputOrdering) { + + @transient override lazy val metrics: Map[String, SQLMetric] = Map( + "numInputRows" -> SQLMetrics.createMetric(sparkContext, "number of input rows"), + "numOutputBatches" -> SQLMetrics.createMetric(sparkContext, "number of output batches"), + "convertTime" -> SQLMetrics.createTimingMetric(sparkContext, "time to convert") + ) + + override protected def doValidateInternal(): ValidationResult = { + for (field <- schema.fields) { + val reason = VeloxValidatorApi.validateSchema(field.dataType) + if (reason.isDefined) { + return ValidationResult.failed(reason.get) + } + } + ValidationResult.succeeded Review Comment: **Metrics gap in BatchCarrierRow unwrap path** **Problem:** When the RDD contains `BatchCarrierRow` instances (e.g., from `df.checkpoint()` on a Gluten plan), the code unwraps columnar batches directly without updating `numInputRows`, `numOutputBatches`, or `convertTime`. Spark UI will show zeros for this operator when processing checkpointed data, making performance debugging difficult. **Evidence:** ```scala case _: BatchCarrierRow => // No metrics updated here (Iterator.single(first) ++ iter).flatMap(row => BatchCarrierRow.unwrap(row)) ``` **Suggested Fix:** ```scala case _: BatchCarrierRow => (Iterator.single(first) ++ iter).flatMap { row => BatchCarrierRow.unwrap(row).map { batch => numOutputBatches += 1 numInputRows += batch.numRows() batch } } ``` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
