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Enable kudo serializer by default #12222

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Original file line number Diff line number Diff line change
Expand Up @@ -2052,7 +2052,7 @@ val SHUFFLE_COMPRESSION_LZ4_CHUNK_SIZE = conf("spark.rapids.shuffle.compression.
.internal()
.startupOnly()
.booleanConf
.createWithDefault(false)
.createWithDefault(true)
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For spark.rapids.shuffle.kudo.serializer.measure.buffer.copy.enabled, should we enable that by default?


val SHUFFLE_KUDO_SERIALIZER_MEASURE_BUFFER_COPY_ENABLED =
conf("spark.rapids.shuffle.kudo.serializer.measure.buffer.copy.enabled")
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Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
/*
* Copyright (c) 2020-2024, NVIDIA CORPORATION.
* Copyright (c) 2020-2025, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
Expand Down Expand Up @@ -500,7 +500,7 @@ class AdaptiveQueryExecSuite
val conf = new SparkConf()
.set(SQLConf.ADAPTIVE_EXECUTION_ENABLED.key, "true")
.set(SQLConf.LOCAL_SHUFFLE_READER_ENABLED.key, "true")
.set(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key, "400")
.set(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key, "50")
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This change is required since shuffle size changed.

.set(SQLConf.ADVISORY_PARTITION_SIZE_IN_BYTES.key, "50")
// disable DemoteBroadcastHashJoin rule from removing BHJ due to empty partitions
.set(SQLConf.NON_EMPTY_PARTITION_RATIO_FOR_BROADCAST_JOIN.key, "0")
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Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
/*
* Copyright (c) 2020-2024, NVIDIA CORPORATION.
* Copyright (c) 2020-2025, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
Expand Down Expand Up @@ -99,7 +99,7 @@ class GpuLoreSuite extends SparkQueryCompareTestSuite with FunSuiteWithTempDir w
}

test("AQE broadcast") {
doTestReplay("90[*]") { spark =>
doTestReplay("93[*]") { spark =>
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Same as above, shuffle size change leads to plan change.

spark.conf.set(SQLConf.ADAPTIVE_EXECUTION_ENABLED.key, "true")

val df1 = spark.range(0, 1000, 1, 10)
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