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The following commit(s) were added to refs/heads/branch-3.2 by this push:
new 24818bf4c9d [SPARK-40280][SQL] Add support for parquet push down for
annotated int and long
24818bf4c9d is described below
commit 24818bf4c9d410259fab9ab63c407bedb7a932d1
Author: Robert (Bobby) Evans <bo...@apache.org>
AuthorDate: Thu Sep 8 08:54:28 2022 -0500
[SPARK-40280][SQL] Add support for parquet push down for annotated int and
long
### What changes were proposed in this pull request?
This fixes SPARK-40280 by normalizing a parquet int/long that has optional
metadata with it to look like the expected version that does not have the extra
metadata.
## Why are the changes needed?
This allows predicate push down in parquet to work when reading files that
are complaint with the parquet specification, but different from what Spark
writes.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
I added unit tests that cover this use case. I also did some manual testing
on some queries to verify that less data is actually read after this change.
Closes #37747 from revans2/normalize_int_long_parquet_push.
Authored-by: Robert (Bobby) Evans <bo...@apache.org>
Signed-off-by: Thomas Graves <tgra...@apache.org>
(cherry picked from commit 24b3baf0177fc1446bf59bb34987296aefd4b318)
Signed-off-by: Thomas Graves <tgra...@apache.org>
---
.../datasources/parquet/ParquetFilters.scala | 16 +++-
.../test/resources/test-data/tagged_int.parquet | Bin 0 -> 305 bytes
.../test/resources/test-data/tagged_long.parquet | Bin 0 -> 313 bytes
.../datasources/parquet/ParquetFilterSuite.scala | 82 +++++++++++++++++++++
4 files changed, 96 insertions(+), 2 deletions(-)
diff --git
a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
index d1b4506a0e4..e2cc3698a47 100644
---
a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
+++
b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
@@ -30,7 +30,7 @@ import org.apache.parquet.filter2.predicate._
import org.apache.parquet.filter2.predicate.SparkFilterApi._
import org.apache.parquet.io.api.Binary
import org.apache.parquet.schema.{GroupType, LogicalTypeAnnotation,
MessageType, PrimitiveComparator, PrimitiveType, Type}
-import
org.apache.parquet.schema.LogicalTypeAnnotation.{DecimalLogicalTypeAnnotation,
TimeUnit}
+import
org.apache.parquet.schema.LogicalTypeAnnotation.{DecimalLogicalTypeAnnotation,
IntLogicalTypeAnnotation, TimeUnit}
import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName
import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName._
import org.apache.parquet.schema.Type.Repetition
@@ -59,6 +59,18 @@ class ParquetFilters(
// nested columns. If any part of the names contains `dots`, it is quoted to
avoid confusion.
// See `org.apache.spark.sql.connector.catalog.quote` for implementation
details.
private val nameToParquetField : Map[String, ParquetPrimitiveField] = {
+ def getNormalizedLogicalType(p: PrimitiveType): LogicalTypeAnnotation = {
+ // SPARK-40280: Signed 64 bits on an INT64 and signed 32 bits on an
INT32 are optional, but
+ // the rest of the code here assumes they are not set, so normalize them
to not being set.
+ (p.getPrimitiveTypeName, p.getLogicalTypeAnnotation) match {
+ case (INT32, intType: IntLogicalTypeAnnotation)
+ if intType.getBitWidth() == 32 && intType.isSigned() => null
+ case (INT64, intType: IntLogicalTypeAnnotation)
+ if intType.getBitWidth() == 64 && intType.isSigned() => null
+ case (_, otherType) => otherType
+ }
+ }
+
// Recursively traverse the parquet schema to get primitive fields that
can be pushed-down.
// `parentFieldNames` is used to keep track of the current nested level
when traversing.
def getPrimitiveFields(
@@ -70,7 +82,7 @@ class ParquetFilters(
// repeated columns
(https://issues.apache.org/jira/browse/PARQUET-34)
case p: PrimitiveType if p.getRepetition != Repetition.REPEATED =>
Some(ParquetPrimitiveField(fieldNames = parentFieldNames :+
p.getName,
- fieldType = ParquetSchemaType(p.getLogicalTypeAnnotation,
+ fieldType = ParquetSchemaType(getNormalizedLogicalType(p),
p.getPrimitiveTypeName, p.getTypeLength)))
// Note that when g is a `Struct`, `g.getOriginalType` is `null`.
// When g is a `Map`, `g.getOriginalType` is `MAP`.
diff --git a/sql/core/src/test/resources/test-data/tagged_int.parquet
b/sql/core/src/test/resources/test-data/tagged_int.parquet
new file mode 100644
index 00000000000..840f5dbdb93
Binary files /dev/null and
b/sql/core/src/test/resources/test-data/tagged_int.parquet differ
diff --git a/sql/core/src/test/resources/test-data/tagged_long.parquet
b/sql/core/src/test/resources/test-data/tagged_long.parquet
new file mode 100644
index 00000000000..3ff9450d834
Binary files /dev/null and
b/sql/core/src/test/resources/test-data/tagged_long.parquet differ
diff --git
a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala
b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala
index 307b4e848a8..f7b290da3fd 100644
---
a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala
+++
b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala
@@ -364,6 +364,47 @@ abstract class ParquetFilterSuite extends QueryTest with
ParquetTest with Shared
}
}
+ test("SPARK-40280: filter pushdown - int with annotation") {
+ implicit val df = readResourceParquetFile("test-data/tagged_int.parquet")
+
+ val intAttr = df("_c0").expr
+ assert(intAttr.dataType === IntegerType)
+
+ checkFilterPredicate(intAttr.isNull, classOf[Eq[_]], Seq.empty[Row])
+ checkFilterPredicate(intAttr.isNotNull, classOf[NotEq[_]],
+ (1 to 4).map(i => Row.apply(i)))
+
+ checkFilterPredicate(intAttr === 1, classOf[Eq[_]], 1)
+ checkFilterPredicate(intAttr <=> 1, classOf[Eq[_]], 1)
+ checkFilterPredicate(intAttr =!= 1, classOf[NotEq[_]],
+ (2 to 4).map(i => Row.apply(i)))
+
+ checkFilterPredicate(intAttr < 2, classOf[Lt[_]], 1)
+ checkFilterPredicate(intAttr > 3, classOf[Gt[_]], 4)
+ checkFilterPredicate(intAttr <= 1, classOf[LtEq[_]], 1)
+ checkFilterPredicate(intAttr >= 4, classOf[GtEq[_]], 4)
+
+ checkFilterPredicate(Literal(1) === intAttr, classOf[Eq[_]], 1)
+ checkFilterPredicate(Literal(1) <=> intAttr, classOf[Eq[_]], 1)
+ checkFilterPredicate(Literal(2) > intAttr, classOf[Lt[_]], 1)
+ checkFilterPredicate(Literal(3) < intAttr, classOf[Gt[_]], 4)
+ checkFilterPredicate(Literal(1) >= intAttr, classOf[LtEq[_]], 1)
+ checkFilterPredicate(Literal(4) <= intAttr, classOf[GtEq[_]], 4)
+
+ checkFilterPredicate(!(intAttr < 4), classOf[GtEq[_]], 4)
+ checkFilterPredicate(intAttr < 2 || intAttr > 3, classOf[Operators.Or],
+ Seq(Row(1), Row(4)))
+
+ Seq(3, 20).foreach { threshold =>
+ withSQLConf(SQLConf.PARQUET_FILTER_PUSHDOWN_INFILTERTHRESHOLD.key ->
s"$threshold") {
+ checkFilterPredicate(
+ In(intAttr, Array(2, 3, 4, 5, 6, 7).map(Literal.apply)),
+ if (threshold == 3) classOf[FilterIn[_]] else classOf[Operators.Or],
+ Seq(Row(2), Row(3), Row(4)))
+ }
+ }
+ }
+
test("filter pushdown - long") {
val data = (1 to 4).map(i => Tuple1(Option(i.toLong)))
withNestedParquetDataFrame(data) { case (inputDF, colName, resultFun) =>
@@ -408,6 +449,47 @@ abstract class ParquetFilterSuite extends QueryTest with
ParquetTest with Shared
}
}
+ test("SPARK-40280: filter pushdown - long with annotation") {
+ implicit val df = readResourceParquetFile("test-data/tagged_long.parquet")
+
+ val longAttr = df("_c0").expr
+ assert(longAttr.dataType === LongType)
+
+ checkFilterPredicate(longAttr.isNull, classOf[Eq[_]], Seq.empty[Row])
+ checkFilterPredicate(longAttr.isNotNull, classOf[NotEq[_]],
+ (1 to 4).map(i => Row.apply(i)))
+
+ checkFilterPredicate(longAttr === 1, classOf[Eq[_]], 1)
+ checkFilterPredicate(longAttr <=> 1, classOf[Eq[_]], 1)
+ checkFilterPredicate(longAttr =!= 1, classOf[NotEq[_]],
+ (2 to 4).map(i => Row.apply(i)))
+
+ checkFilterPredicate(longAttr < 2, classOf[Lt[_]], 1)
+ checkFilterPredicate(longAttr > 3, classOf[Gt[_]], 4)
+ checkFilterPredicate(longAttr <= 1, classOf[LtEq[_]], 1)
+ checkFilterPredicate(longAttr >= 4, classOf[GtEq[_]], 4)
+
+ checkFilterPredicate(Literal(1) === longAttr, classOf[Eq[_]], 1)
+ checkFilterPredicate(Literal(1) <=> longAttr, classOf[Eq[_]], 1)
+ checkFilterPredicate(Literal(2) > longAttr, classOf[Lt[_]], 1)
+ checkFilterPredicate(Literal(3) < longAttr, classOf[Gt[_]], 4)
+ checkFilterPredicate(Literal(1) >= longAttr, classOf[LtEq[_]], 1)
+ checkFilterPredicate(Literal(4) <= longAttr, classOf[GtEq[_]], 4)
+
+ checkFilterPredicate(!(longAttr < 4), classOf[GtEq[_]], 4)
+ checkFilterPredicate(longAttr < 2 || longAttr > 3, classOf[Operators.Or],
+ Seq(Row(1), Row(4)))
+
+ Seq(3, 20).foreach { threshold =>
+ withSQLConf(SQLConf.PARQUET_FILTER_PUSHDOWN_INFILTERTHRESHOLD.key ->
s"$threshold") {
+ checkFilterPredicate(
+ In(longAttr, Array(2L, 3L, 4L, 5L, 6L, 7L).map(Literal.apply)),
+ if (threshold == 3) classOf[FilterIn[_]] else classOf[Operators.Or],
+ Seq(Row(2L), Row(3L), Row(4L)))
+ }
+ }
+ }
+
test("filter pushdown - float") {
val data = (1 to 4).map(i => Tuple1(Option(i.toFloat)))
withNestedParquetDataFrame(data) { case (inputDF, colName, resultFun) =>
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