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[SPARK-36647][SQL][TESTS] Push down Aggregate (Min/Max/Count) for Parquet if filter is on partition col #34248

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[SPARK-36647][SQL][TESTS] Push down Aggregate (Min/Max/Count) for Parquet if filter is on partition col #34248

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[SPARK-36647][SQL] Push down Aggregate (Min/Max/Count) for Parquet if…
huaxingao Oct 11, 2021
6cc6f7b
address comments
huaxingao Oct 13, 2021
d7ecec4
address comments
huaxingao Oct 13, 2021
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fix
huaxingao Oct 13, 2021
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huaxingao Oct 18, 2021
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8 changes: 4 additions & 4 deletions ...main/scala/org/apache/spark/sql/execution/datasources/v2/parquet/ParquetScanBuilder.scala
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Expand Up @@ -130,10 +130,10 @@ case class ParquetScanBuilder(
// are combined with filter or group by
// e.g. SELECT COUNT(col1) FROM t WHERE col2 = 8
// SELECT COUNT(col1) FROM t GROUP BY col2
// Todo: 1. add support if groupby column is partition col
// (https://issues.apache.org/jira/browse/SPARK-36646)
// 2. add support if filter col is partition col
// (https://issues.apache.org/jira/browse/SPARK-36647)
// However, if the filter or group by is on partition column,
// max/min/count can still be pushed down

@viirya viirya Oct 13, 2021

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So group by on partition column is not supported yet. Then this comment is not correct.

// Todo: add support if groupby column is partition col
// (https://issues.apache.org/jira/browse/SPARK-36646)
return false
}

Expand Down
40 changes: 30 additions & 10 deletions ...la/org/apache/spark/sql/execution/datasources/parquet/ParquetAggregatePushDownSuite.scala
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Expand Up @@ -129,10 +129,9 @@ abstract class ParquetAggregatePushDownSuite
.write.partitionBy("p").parquet(dir.getCanonicalPath)
withTempView("tmp") {
spark.read.parquet(dir.getCanonicalPath).createOrReplaceTempView("tmp");
val enableVectorizedReader = Seq("false", "true")
for (testVectorizedReader <- enableVectorizedReader) {
Seq("false", "true").foreach { enableVectorizedReader =>
withSQLConf(SQLConf.PARQUET_AGGREGATE_PUSHDOWN_ENABLED.key -> "true",
vectorizedReaderEnabledKey -> testVectorizedReader) {
vectorizedReaderEnabledKey -> enableVectorizedReader) {
val count = sql("SELECT COUNT(p) FROM tmp")
count.queryExecution.optimizedPlan.collect {
case _: DataSourceV2ScanRelation =>
Expand Down Expand Up @@ -221,7 +220,7 @@ abstract class ParquetAggregatePushDownSuite
}
}

test("aggregate push down - query with filter not push down") {
test("aggregate push down - aggregate with data filter cannot be pushed down") {
val data = Seq((-2, "abc", 2), (3, "def", 4), (6, "ghi", 2), (0, null, 19),
(9, "mno", 7), (2, null, 7))
withParquetTable(data, "t") {
Expand All @@ -240,6 +239,29 @@ abstract class ParquetAggregatePushDownSuite
}
}

test("aggregate push down - aggregate with partition filter can be pushed down") {
withTempPath { dir =>
spark.range(10).selectExpr("id", "id % 3 as p")
.write.partitionBy("p").parquet(dir.getCanonicalPath)
withTempView("tmp") {
spark.read.parquet(dir.getCanonicalPath).createOrReplaceTempView("tmp");
Seq("false", "true").foreach { enableVectorizedReader =>
withSQLConf(SQLConf.PARQUET_AGGREGATE_PUSHDOWN_ENABLED.key -> "true",
vectorizedReaderEnabledKey -> enableVectorizedReader) {
val max = sql("SELECT max(id) FROM tmp WHERE p = 0")

@viirya viirya Oct 13, 2021

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Can you add other two supported aggregate functions? And how about group by on partition column case?

@huaxingao huaxingao Oct 13, 2021

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added.
Group by on partition column is a little more complicated and needs some code changes: currently, we only have the aggregate values in the returned row. For group by on partition column, we will need to pass down the partition col value and prepend that value to the aggregation row. I will have a separate PR for that work.

max.queryExecution.optimizedPlan.collect {
case _: DataSourceV2ScanRelation =>
val expected_plan_fragment =
"PushedAggregation: [MAX(id)]"
checkKeywordsExistsInExplain(max, expected_plan_fragment)
}
checkAnswer(max, Seq(Row(9)))
}
}
}
}
}

test("aggregate push down - push down only if all the aggregates can be pushed down") {
val data = Seq((-2, "abc", 2), (3, "def", 4), (6, "ghi", 2), (0, null, 19),
(9, "mno", 7), (2, null, 7))
Expand Down Expand Up @@ -356,10 +378,9 @@ abstract class ParquetAggregatePushDownSuite
spark.createDataFrame(rdd, schema).write.parquet(file.getCanonicalPath)
withTempView("test") {
spark.read.parquet(file.getCanonicalPath).createOrReplaceTempView("test")
val enableVectorizedReader = Seq("false", "true")
for (testVectorizedReader <- enableVectorizedReader) {
Seq("false", "true").foreach { enableVectorizedReader =>
withSQLConf(SQLConf.PARQUET_AGGREGATE_PUSHDOWN_ENABLED.key -> "true",
vectorizedReaderEnabledKey -> testVectorizedReader) {
vectorizedReaderEnabledKey -> enableVectorizedReader) {

val testMinWithTS = sql("SELECT min(StringCol), min(BooleanCol), min(ByteCol), " +
"min(BinaryCol), min(ShortCol), min(IntegerCol), min(LongCol), min(FloatCol), " +
Expand Down Expand Up @@ -477,10 +498,9 @@ abstract class ParquetAggregatePushDownSuite
}

test("aggregate push down - column name case sensitivity") {
val enableVectorizedReader = Seq("false", "true")
for (testVectorizedReader <- enableVectorizedReader) {
Seq("false", "true").foreach { enableVectorizedReader =>
withSQLConf(SQLConf.PARQUET_AGGREGATE_PUSHDOWN_ENABLED.key -> "true",
vectorizedReaderEnabledKey -> testVectorizedReader) {
vectorizedReaderEnabledKey -> enableVectorizedReader) {
withTempPath { dir =>
spark.range(10).selectExpr("id", "id % 3 as p")
.write.partitionBy("p").parquet(dir.getCanonicalPath)
Expand Down