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[SPARK-25579][SQL] Use quoted attribute names if needed in pushed ORC predicates #22597

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[SPARK-25579][SQL] Use quoted attribute names if needed in pushed ORC predicates #22597

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[SPARK-25579][SQL] Use quoted attribute names if needed in pushed ORC…
dongjoon-hyun Oct 1, 2018
335a39f
Add a test case.
dongjoon-hyun Oct 13, 2018
849c7fa
Add withSQLConf with ORC_FILTER_PUSHDOWN_ENABLED=true
dongjoon-hyun Oct 14, 2018
7686179
Address comments
dongjoon-hyun Oct 16, 2018
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37 changes: 28 additions & 9 deletions sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcFilters.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -67,6 +67,16 @@ private[sql] object OrcFilters {
}
}

// Since ORC 1.5.0 (ORC-323), we need to quote for column names with `.` characters
// in order to distinguish predicate pushdown for nested columns.
private def quoteAttributeNameIfNeeded(name: String) : String = {
if (!name.contains("`") && name.contains(".")) {
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@HyukjinKwon HyukjinKwon Oct 1, 2018

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Does this condition take the backtick in column name into account? For instance,

>>> spark.range(1).toDF("abc`.abc").show()
+--------+
|abc`.abc|
+--------+
|       0|
+--------+

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@dongjoon-hyun dongjoon-hyun Oct 1, 2018

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Thank you for review. I'll consider that, too.

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@dongjoon-hyun dongjoon-hyun Oct 13, 2018

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@HyukjinKwon . Actually, Spark 2.3.2 ORC (native/hive) doesn't support a backtick character in column names. It fails on writing operation. And, although Spark 2.4.0 broadens the supported special characters like . and " in column names, the backtick character is not handled yet.

So, for that one, I'll proceed in another PR since it's an improvement instead of a regression.

Also, cc @gatorsmile and @dbtsai .

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@dongjoon-hyun dongjoon-hyun Oct 13, 2018

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For ORC and AVRO improvement, SPARK-25722 is created.

s"`$name`"
} else {
name
}
}

/**
* Create ORC filter as a SearchArgument instance.
*/
Expand Down Expand Up @@ -178,38 +188,47 @@ private[sql] object OrcFilters {
// wrapped by a "parent" predicate (`And`, `Or`, or `Not`).

case EqualTo(attribute, value) if isSearchableType(dataTypeMap(attribute)) =>
val quotedName = quoteAttributeNameIfNeeded(attribute)
val castedValue = castLiteralValue(value, dataTypeMap(attribute))
Some(builder.startAnd().equals(attribute, getType(attribute), castedValue).end())
Some(builder.startAnd().equals(quotedName, getType(attribute), castedValue).end())

case EqualNullSafe(attribute, value) if isSearchableType(dataTypeMap(attribute)) =>
val quotedName = quoteAttributeNameIfNeeded(attribute)
val castedValue = castLiteralValue(value, dataTypeMap(attribute))
Some(builder.startAnd().nullSafeEquals(attribute, getType(attribute), castedValue).end())
Some(builder.startAnd().nullSafeEquals(quotedName, getType(attribute), castedValue).end())

case LessThan(attribute, value) if isSearchableType(dataTypeMap(attribute)) =>
val quotedName = quoteAttributeNameIfNeeded(attribute)
val castedValue = castLiteralValue(value, dataTypeMap(attribute))
Some(builder.startAnd().lessThan(attribute, getType(attribute), castedValue).end())
Some(builder.startAnd().lessThan(quotedName, getType(attribute), castedValue).end())

case LessThanOrEqual(attribute, value) if isSearchableType(dataTypeMap(attribute)) =>
val quotedName = quoteAttributeNameIfNeeded(attribute)
val castedValue = castLiteralValue(value, dataTypeMap(attribute))
Some(builder.startAnd().lessThanEquals(attribute, getType(attribute), castedValue).end())
Some(builder.startAnd().lessThanEquals(quotedName, getType(attribute), castedValue).end())

case GreaterThan(attribute, value) if isSearchableType(dataTypeMap(attribute)) =>
val quotedName = quoteAttributeNameIfNeeded(attribute)
val castedValue = castLiteralValue(value, dataTypeMap(attribute))
Some(builder.startNot().lessThanEquals(attribute, getType(attribute), castedValue).end())
Some(builder.startNot().lessThanEquals(quotedName, getType(attribute), castedValue).end())

case GreaterThanOrEqual(attribute, value) if isSearchableType(dataTypeMap(attribute)) =>
val quotedName = quoteAttributeNameIfNeeded(attribute)
val castedValue = castLiteralValue(value, dataTypeMap(attribute))
Some(builder.startNot().lessThan(attribute, getType(attribute), castedValue).end())
Some(builder.startNot().lessThan(quotedName, getType(attribute), castedValue).end())

case IsNull(attribute) if isSearchableType(dataTypeMap(attribute)) =>
Some(builder.startAnd().isNull(attribute, getType(attribute)).end())
val quotedName = quoteAttributeNameIfNeeded(attribute)
Some(builder.startAnd().isNull(quotedName, getType(attribute)).end())

case IsNotNull(attribute) if isSearchableType(dataTypeMap(attribute)) =>
Some(builder.startNot().isNull(attribute, getType(attribute)).end())
val quotedName = quoteAttributeNameIfNeeded(attribute)
Some(builder.startNot().isNull(quotedName, getType(attribute)).end())

case In(attribute, values) if isSearchableType(dataTypeMap(attribute)) =>
val quotedName = quoteAttributeNameIfNeeded(attribute)
val castedValues = values.map(v => castLiteralValue(v, dataTypeMap(attribute)))
Some(builder.startAnd().in(attribute, getType(attribute),
Some(builder.startAnd().in(quotedName, getType(attribute),
castedValues.map(_.asInstanceOf[AnyRef]): _*).end())

case _ => None
Expand Down
17 changes: 16 additions & 1 deletion sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/orc/OrcFilterSuite.scala
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Expand Up @@ -17,18 +17,20 @@

package org.apache.spark.sql.execution.datasources.orc

import java.io.File
import java.nio.charset.StandardCharsets
import java.sql.{Date, Timestamp}

import scala.collection.JavaConverters._

import org.apache.orc.storage.ql.io.sarg.{PredicateLeaf, SearchArgument}

import org.apache.spark.sql.{Column, DataFrame}
import org.apache.spark.sql.{Column, DataFrame, Row}
import org.apache.spark.sql.catalyst.dsl.expressions._
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.planning.PhysicalOperation
import org.apache.spark.sql.execution.datasources.{DataSourceStrategy, HadoopFsRelation, LogicalRelation}
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.test.SharedSQLContext
import org.apache.spark.sql.types._

Expand Down Expand Up @@ -383,4 +385,17 @@ class OrcFilterSuite extends OrcTest with SharedSQLContext {
)).get.toString
}
}

test("SPARK-25579 ORC PPD should support column names with dot") {
import testImplicits._
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@HyukjinKwon HyukjinKwon Oct 14, 2018

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Can we add a test at OrcFilterSuite too?

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@dongjoon-hyun dongjoon-hyun Oct 14, 2018

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Ur, this is OrcFilterSuite.

Can we add a test at OrcFilterSuite too?

For HiveOrcFilterSuite, hive ORC implementation doesn't support dot.

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@HyukjinKwon HyukjinKwon Oct 14, 2018

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Okay. One end to end test should be enough


withSQLConf(SQLConf.ORC_FILTER_PUSHDOWN_ENABLED.key -> "true") {
withTempDir { dir =>
val path = new File(dir, "orc").getCanonicalPath
Seq((1, 2), (3, 4)).toDF("col.dot.1", "col.dot.2").write.orc(path)
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@HyukjinKwon HyukjinKwon Oct 15, 2018

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How about explicitly repartition to make separate output files?

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@dongjoon-hyun dongjoon-hyun Oct 15, 2018 •
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We are using the default parallelism from TestSparkSession on two rows and it generates separate output files already.

If you are concerning some possibility of flakiness, we are able to increase the number of rows to 10 and call repartition(10) and check assert(actual < 10) as you did before. Do you want that?

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@cloud-fan cloud-fan Oct 16, 2018

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Do not rely on implicit environment values, let's make the test as explicit as possible.

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@dongjoon-hyun dongjoon-hyun Oct 16, 2018

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Sure. Thank you for confirmation, @cloud-fan and @HyukjinKwon .

val df = spark.read.orc(path).where("`col.dot.1` = 1 and `col.dot.2` = 2")
checkAnswer(stripSparkFilter(df), Row(1, 2))
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@cloud-fan cloud-fan Oct 15, 2018 •
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to confirm, this only works when (1, 2) and (3, 4) are in different row groups? (not sure what's the terminology in ORC)

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@HyukjinKwon HyukjinKwon Oct 15, 2018

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Yup.

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@dongjoon-hyun dongjoon-hyun Oct 15, 2018

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Yep. It works when they are in different stripes.

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@dbtsai dbtsai Oct 15, 2018

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How do we generalize this into nested cases? The parent struct can contain dot as well.

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@cloud-fan cloud-fan Oct 16, 2018

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ORC data source doesn't support nested column pruning yet.

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@dongjoon-hyun dongjoon-hyun Oct 16, 2018 •
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Thank you for review, @dbtsai ! I ignored PPDs with nested columns here because Spark doesn't pushdown in Spark 2.4 and until now without your PR (#22573). With your PR, Spark 3.0 will support that and we can update this to handle that cases, too.

@cloud-fan . Actually, ORC 1.5.0 starts to support PPD with nested columns ORC-323. So, @dbtsai and I discussed about supporting that before. We are going to support ORC PPDs with nested columns in Spark 3.0 without regression.

}
}
}
}