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refactor: Split read benchmarks and add addParquetScanCases helper #3407

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refactor: Split read benchmarks and add addParquetScanCases helper #3407

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andygrove Feb 5, 2026
02e8ca4
refactor: Split read benchmarks and add addParquetScanCases helper
andygrove Feb 5, 2026
75750af
feat: Native batch passthrough for native_iceberg_compat V1 scans
andygrove Feb 5, 2026
fef0576
Revert "feat: Native batch passthrough for native_iceberg_compat V1 s…
andygrove Feb 5, 2026
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27 changes: 27 additions & 0 deletions spark/src/test/scala/org/apache/spark/sql/benchmark/CometBenchmarkBase.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -38,6 +38,7 @@ import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.types.DecimalType

import org.apache.comet.CometConf
import org.apache.comet.CometConf.{SCAN_NATIVE_DATAFUSION, SCAN_NATIVE_ICEBERG_COMPAT}
import org.apache.comet.CometSparkSessionExtensions

trait CometBenchmarkBase
Expand Down Expand Up @@ -164,6 +165,32 @@ trait CometBenchmarkBase
benchmark.run()
}

protected def addParquetScanCases(
benchmark: Benchmark,
query: String,
caseSuffix: String = "",
extraConf: Map[String, String] = Map.empty): Unit = {
val suffix = if (caseSuffix.nonEmpty) s" ($caseSuffix)" else ""

benchmark.addCase(s"SQL Parquet - Spark$suffix") { _ =>
withSQLConf(extraConf.toSeq: _*) {
spark.sql(query).noop()
}
}

for (scanImpl <- Seq(SCAN_NATIVE_DATAFUSION, SCAN_NATIVE_ICEBERG_COMPAT)) {
benchmark.addCase(s"SQL Parquet - Comet ($scanImpl)$suffix") { _ =>
withSQLConf(
(extraConf ++ Map(
CometConf.COMET_ENABLED.key -> "true",
CometConf.COMET_EXEC_ENABLED.key -> "true",
CometConf.COMET_NATIVE_SCAN_IMPL.key -> scanImpl)).toSeq: _*) {
spark.sql(query).noop()
}
}
}
}

protected def prepareTable(dir: File, df: DataFrame, partition: Option[String] = None): Unit = {
val testDf = if (partition.isDefined) {
df.write.partitionBy(partition.get)
Expand Down
81 changes: 81 additions & 0 deletions spark/src/test/scala/org/apache/spark/sql/benchmark/CometIcebergReadBenchmark.scala
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@@ -0,0 +1,81 @@
/*
* 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.benchmark

import org.apache.spark.benchmark.Benchmark
import org.apache.spark.sql.types._

import org.apache.comet.CometConf

/**
* Benchmark to measure Comet Iceberg read performance. To run this benchmark:
* `SPARK_GENERATE_BENCHMARK_FILES=1 make
* benchmark-org.apache.spark.sql.benchmark.CometIcebergReadBenchmark` Results will be written to
* "spark/benchmarks/CometIcebergReadBenchmark-**results.txt".
*/
object CometIcebergReadBenchmark extends CometBenchmarkBase {

def icebergScanBenchmark(values: Int, dataType: DataType): Unit = {
val sqlBenchmark =
new Benchmark(s"SQL Single ${dataType.sql} Iceberg Column Scan", values, output = output)

withTempPath { dir =>
withTempTable("icebergTable") {
prepareIcebergTable(
dir,
spark.sql(s"SELECT CAST(value as ${dataType.sql}) id FROM $tbl"),
"icebergTable")

val query = dataType match {
case BooleanType => "sum(cast(id as bigint))"
case _ => "sum(id)"
}

sqlBenchmark.addCase("SQL Iceberg - Spark") { _ =>
withSQLConf(
"spark.memory.offHeap.enabled" -> "true",
"spark.memory.offHeap.size" -> "10g") {
spark.sql(s"select $query from icebergTable").noop()
}
}

sqlBenchmark.addCase("SQL Iceberg - Comet Iceberg-Rust") { _ =>
withSQLConf(
CometConf.COMET_ENABLED.key -> "true",
CometConf.COMET_EXEC_ENABLED.key -> "true",
"spark.memory.offHeap.enabled" -> "true",
"spark.memory.offHeap.size" -> "10g",
CometConf.COMET_ICEBERG_NATIVE_ENABLED.key -> "true") {
spark.sql(s"select $query from icebergTable").noop()
}
}

sqlBenchmark.run()
}
}
}

override def runCometBenchmark(mainArgs: Array[String]): Unit = {
runBenchmarkWithTable("SQL Single Numeric Iceberg Column Scan", 1024 * 1024 * 128) { v =>
Seq(BooleanType, ByteType, ShortType, IntegerType, LongType, FloatType, DoubleType)
.foreach(icebergScanBenchmark(v, _))
}
}
}
82 changes: 82 additions & 0 deletions spark/src/test/scala/org/apache/spark/sql/benchmark/CometPartitionColumnBenchmark.scala
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@@ -0,0 +1,82 @@
/*
* 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.benchmark

import org.apache.spark.benchmark.Benchmark

/**
* Benchmark to measure partition column scan performance. This exercises the CometConstantVector
* path where constant columns are exported as 1-element Arrow arrays and expanded on the native
* side.
*
* To run this benchmark:
* {{{
* SPARK_GENERATE_BENCHMARK_FILES=1 make \
* benchmark-org.apache.spark.sql.benchmark.CometPartitionColumnBenchmark
* }}}
*
* Results will be written to "spark/benchmarks/CometPartitionColumnBenchmark-**results.txt".
*/
object CometPartitionColumnBenchmark extends CometBenchmarkBase {

def partitionColumnScanBenchmark(values: Int, numPartitionCols: Int): Unit = {
val sqlBenchmark = new Benchmark(
s"Partitioned Scan with $numPartitionCols partition column(s)",
values,
output = output)

withTempPath { dir =>
withTempTable("parquetV1Table") {
val partCols =
(1 to numPartitionCols).map(i => s"'part$i' as p$i").mkString(", ")
val partNames = (1 to numPartitionCols).map(i => s"p$i")
val df = spark.sql(s"SELECT value as id, $partCols FROM $tbl")
val parquetDir = dir.getCanonicalPath + "/parquetV1"
df.write
.partitionBy(partNames: _*)
.mode("overwrite")
.option("compression", "snappy")
.parquet(parquetDir)
spark.read.parquet(parquetDir).createOrReplaceTempView("parquetV1Table")

addParquetScanCases(sqlBenchmark, "select sum(id) from parquetV1Table")

// Also benchmark reading partition columns themselves
val partSumExpr =
(1 to numPartitionCols).map(i => s"sum(length(p$i))").mkString(", ")

addParquetScanCases(
sqlBenchmark,
s"select $partSumExpr from parquetV1Table",
caseSuffix = "partition cols")

sqlBenchmark.run()
}
}
}

override def runCometBenchmark(mainArgs: Array[String]): Unit = {
runBenchmarkWithTable("Partitioned Column Scan", 1024 * 1024 * 15) { v =>
for (numPartCols <- List(1, 5)) {
partitionColumnScanBenchmark(v, numPartCols)
}
}
}
}
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