fqaiser94 commented on a change in pull request #29795:
URL: https://github.com/apache/spark/pull/29795#discussion_r494700714
##########
File path: sql/core/src/main/scala/org/apache/spark/sql/Column.scala
##########
@@ -901,39 +901,125 @@ class Column(val expr: Expression) extends Logging {
* // result: org.apache.spark.sql.AnalysisException: Ambiguous reference
to fields
* }}}
*
+ * This method supports adding/replacing nested fields directly e.g.
+ *
+ * {{{
+ * val df = sql("SELECT named_struct('a', named_struct('a', 1, 'b', 2))
struct_col")
+ * df.select($"struct_col".withField("a.c", lit(3)).withField("a.d",
lit(4)))
+ * // result: {"a":{"a":1,"b":2,"c":3,"d":4}}
+ * }}}
+ *
+ * However, if you are going to add/replace multiple nested fields, it is
more optimal to extract
+ * out the nested struct before adding/replacing multiple fields e.g.
+ *
+ * {{{
+ * val df = sql("SELECT named_struct('a', named_struct('a', 1, 'b', 2))
struct_col")
+ * df.select($"struct_col".withField("a", $"struct_col.a".withField("c",
lit(3)).withField("d", lit(4))))
+ * // result: {"a":{"a":1,"b":2,"c":3,"d":4}}
+ * }}}
+ *
* @group expr_ops
* @since 3.1.0
*/
// scalastyle:on line.size.limit
def withField(fieldName: String, col: Column): Column = withExpr {
require(fieldName != null, "fieldName cannot be null")
require(col != null, "col cannot be null")
+ updateFieldsHelper(expr, nameParts(fieldName), name => WithField(name,
col.expr))
+ }
- val nameParts = if (fieldName.isEmpty) {
+ // scalastyle:off line.size.limit
+ /**
+ * An expression that drops fields in `StructType` by name.
+ *
+ * {{{
+ * val df = sql("SELECT named_struct('a', 1, 'b', 2) struct_col")
+ * df.select($"struct_col".dropFields("b"))
+ * // result: {"a":1}
+ *
+ * val df = sql("SELECT named_struct('a', 1, 'b', 2) struct_col")
+ * df.select($"struct_col".dropFields("c"))
+ * // result: {"a":1,"b":2}
+ *
+ * val df = sql("SELECT named_struct('a', 1, 'b', 2, 'c', 3) struct_col")
+ * df.select($"struct_col".dropFields("b", "c"))
+ * // result: {"a":1}
+ *
+ * val df = sql("SELECT named_struct('a', 1, 'b', 2) struct_col")
+ * df.select($"struct_col".dropFields("a", "b"))
+ * // result: org.apache.spark.sql.AnalysisException: cannot resolve
'update_fields(update_fields(`struct_col`))' due to data type mismatch: cannot
drop all fields in struct
+ *
+ * val df = sql("SELECT CAST(NULL AS struct<a:int,b:int>) struct_col")
+ * df.select($"struct_col".dropFields("b"))
+ * // result: null of type struct<a:int>
+ *
+ * val df = sql("SELECT named_struct('a', 1, 'b', 2, 'b', 3) struct_col")
+ * df.select($"struct_col".dropFields("b"))
+ * // result: {"a":1}
+ *
+ * val df = sql("SELECT named_struct('a', named_struct('a', 1, 'b', 2))
struct_col")
+ * df.select($"struct_col".dropFields("a.b"))
+ * // result: {"a":{"a":1}}
+ *
+ * val df = sql("SELECT named_struct('a', named_struct('b', 1), 'a',
named_struct('c', 2)) struct_col")
+ * df.select($"struct_col".dropFields("a.c"))
+ * // result: org.apache.spark.sql.AnalysisException: Ambiguous reference
to fields
+ * }}}
+ *
+ * This method supports dropping multiple nested fields directly e.g.
+ *
+ * {{{
+ * val df = sql("SELECT named_struct('a', named_struct('a', 1, 'b', 2))
struct_col")
+ * df.select($"struct_col".dropFields("a.b", "a.c"))
+ * // result: {"a":{"a":1}}
+ * }}}
+ *
+ * However, if you are going to drop multiple nested fields, it is more
optimal to extract
+ * out the nested struct before dropping multiple fields from it e.g.
+ *
+ * {{{
+ * val df = sql("SELECT named_struct('a', named_struct('a', 1, 'b', 2))
struct_col")
+ * df.select($"struct_col".withField("a", $"struct_col.a".dropFields("b",
"c")))
+ * // result: {"a":{"a":1}}
+ * }}}
+ *
+ * @group expr_ops
+ * @since 3.1.0
+ */
+ // scalastyle:on line.size.limit
+ def dropFields(fieldNames: String*): Column = withExpr {
+ def dropField(structExpr: Expression, fieldName: String): UpdateFields =
+ updateFieldsHelper(structExpr, nameParts(fieldName), name =>
DropField(name))
+
+ fieldNames.tail.foldLeft(dropField(expr, fieldNames.head)) {
+ (resExpr, fieldName) => dropField(resExpr, fieldName)
+ }
+ }
+
+ private def nameParts(fieldName: String): Seq[String] = {
+ require(fieldName != null, "fieldName cannot be null")
+
+ if (fieldName.isEmpty) {
fieldName :: Nil
Review comment:
we've discussed this before
[here](https://github.com/apache/spark/pull/27066#discussion_r448416127) :)
Its needed for `withField` and I think we should support it in `dropFields`
as well because `Dataset.drop` supports it:
```
scala> Seq((1, 2)).toDF("a", "").drop("").printSchema
root
|-- a: integer (nullable = false)
```
I've added a test case to demonstrate this works on the `dropFields` side
but otherwise left the code unchanged.
##########
File path: sql/core/src/main/scala/org/apache/spark/sql/Column.scala
##########
@@ -901,39 +901,125 @@ class Column(val expr: Expression) extends Logging {
* // result: org.apache.spark.sql.AnalysisException: Ambiguous reference
to fields
* }}}
*
+ * This method supports adding/replacing nested fields directly e.g.
+ *
+ * {{{
+ * val df = sql("SELECT named_struct('a', named_struct('a', 1, 'b', 2))
struct_col")
+ * df.select($"struct_col".withField("a.c", lit(3)).withField("a.d",
lit(4)))
+ * // result: {"a":{"a":1,"b":2,"c":3,"d":4}}
+ * }}}
+ *
+ * However, if you are going to add/replace multiple nested fields, it is
more optimal to extract
+ * out the nested struct before adding/replacing multiple fields e.g.
+ *
+ * {{{
+ * val df = sql("SELECT named_struct('a', named_struct('a', 1, 'b', 2))
struct_col")
+ * df.select($"struct_col".withField("a", $"struct_col.a".withField("c",
lit(3)).withField("d", lit(4))))
+ * // result: {"a":{"a":1,"b":2,"c":3,"d":4}}
+ * }}}
+ *
* @group expr_ops
* @since 3.1.0
*/
// scalastyle:on line.size.limit
def withField(fieldName: String, col: Column): Column = withExpr {
require(fieldName != null, "fieldName cannot be null")
require(col != null, "col cannot be null")
+ updateFieldsHelper(expr, nameParts(fieldName), name => WithField(name,
col.expr))
+ }
- val nameParts = if (fieldName.isEmpty) {
+ // scalastyle:off line.size.limit
+ /**
+ * An expression that drops fields in `StructType` by name.
+ *
+ * {{{
+ * val df = sql("SELECT named_struct('a', 1, 'b', 2) struct_col")
+ * df.select($"struct_col".dropFields("b"))
+ * // result: {"a":1}
+ *
+ * val df = sql("SELECT named_struct('a', 1, 'b', 2) struct_col")
+ * df.select($"struct_col".dropFields("c"))
+ * // result: {"a":1,"b":2}
+ *
+ * val df = sql("SELECT named_struct('a', 1, 'b', 2, 'c', 3) struct_col")
+ * df.select($"struct_col".dropFields("b", "c"))
+ * // result: {"a":1}
+ *
+ * val df = sql("SELECT named_struct('a', 1, 'b', 2) struct_col")
+ * df.select($"struct_col".dropFields("a", "b"))
+ * // result: org.apache.spark.sql.AnalysisException: cannot resolve
'update_fields(update_fields(`struct_col`))' due to data type mismatch: cannot
drop all fields in struct
+ *
+ * val df = sql("SELECT CAST(NULL AS struct<a:int,b:int>) struct_col")
+ * df.select($"struct_col".dropFields("b"))
+ * // result: null of type struct<a:int>
+ *
+ * val df = sql("SELECT named_struct('a', 1, 'b', 2, 'b', 3) struct_col")
+ * df.select($"struct_col".dropFields("b"))
+ * // result: {"a":1}
+ *
+ * val df = sql("SELECT named_struct('a', named_struct('a', 1, 'b', 2))
struct_col")
+ * df.select($"struct_col".dropFields("a.b"))
+ * // result: {"a":{"a":1}}
+ *
+ * val df = sql("SELECT named_struct('a', named_struct('b', 1), 'a',
named_struct('c', 2)) struct_col")
+ * df.select($"struct_col".dropFields("a.c"))
+ * // result: org.apache.spark.sql.AnalysisException: Ambiguous reference
to fields
+ * }}}
+ *
+ * This method supports dropping multiple nested fields directly e.g.
+ *
+ * {{{
+ * val df = sql("SELECT named_struct('a', named_struct('a', 1, 'b', 2))
struct_col")
+ * df.select($"struct_col".dropFields("a.b", "a.c"))
+ * // result: {"a":{"a":1}}
+ * }}}
+ *
+ * However, if you are going to drop multiple nested fields, it is more
optimal to extract
+ * out the nested struct before dropping multiple fields from it e.g.
+ *
+ * {{{
+ * val df = sql("SELECT named_struct('a', named_struct('a', 1, 'b', 2))
struct_col")
+ * df.select($"struct_col".withField("a", $"struct_col.a".dropFields("b",
"c")))
+ * // result: {"a":{"a":1}}
+ * }}}
+ *
+ * @group expr_ops
+ * @since 3.1.0
+ */
+ // scalastyle:on line.size.limit
+ def dropFields(fieldNames: String*): Column = withExpr {
+ def dropField(structExpr: Expression, fieldName: String): UpdateFields =
+ updateFieldsHelper(structExpr, nameParts(fieldName), name =>
DropField(name))
+
+ fieldNames.tail.foldLeft(dropField(expr, fieldNames.head)) {
+ (resExpr, fieldName) => dropField(resExpr, fieldName)
+ }
+ }
+
+ private def nameParts(fieldName: String): Seq[String] = {
+ require(fieldName != null, "fieldName cannot be null")
+
+ if (fieldName.isEmpty) {
fieldName :: Nil
} else {
CatalystSqlParser.parseMultipartIdentifier(fieldName)
}
- withFieldHelper(expr, nameParts, Nil, col.expr)
}
- private def withFieldHelper(
- struct: Expression,
- namePartsRemaining: Seq[String],
- namePartsDone: Seq[String],
- value: Expression) : WithFields = {
- val name = namePartsRemaining.head
+ private def updateFieldsHelper(
+ structExpr: Expression,
Review comment:
done
##########
File path: sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
##########
@@ -159,6 +160,14 @@ abstract class QueryTest extends PlanTest {
checkAnswer(df, expectedAnswer.collect())
}
+ protected def checkAnswer(
+ df: => DataFrame,
+ expectedAnswer: Seq[Row],
Review comment:
done
##########
File path:
sql/core/src/test/scala/org/apache/spark/sql/UpdateFieldsPerformanceSuite.scala
##########
@@ -0,0 +1,229 @@
+/*
+ * 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
+
+import org.apache.spark.sql.functions.{col, lit}
+import org.apache.spark.sql.test.SharedSparkSession
+import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
+
+class UpdateFieldsPerformanceSuite extends QueryTest with SharedSparkSession {
+
+ private def nestedColName(d: Int, colNum: Int): String =
s"nested${d}Col$colNum"
+
+ private def nestedStructType(
+ depths: Seq[Int], colNums: Seq[Int], nullable: Boolean): StructType = {
Review comment:
done
##########
File path:
sql/core/src/test/scala/org/apache/spark/sql/UpdateFieldsPerformanceSuite.scala
##########
@@ -0,0 +1,229 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql
+
+import org.apache.spark.sql.functions.{col, lit}
+import org.apache.spark.sql.test.SharedSparkSession
+import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
+
+class UpdateFieldsPerformanceSuite extends QueryTest with SharedSparkSession {
+
+ private def nestedColName(d: Int, colNum: Int): String =
s"nested${d}Col$colNum"
+
+ private def nestedStructType(
+ depths: Seq[Int], colNums: Seq[Int], nullable: Boolean): StructType = {
+ if (depths.length == 1) {
+ StructType(colNums.map { colNum =>
+ StructField(nestedColName(depths.head, colNum), IntegerType, nullable
= false)
+ })
+ } else {
+ val depth = depths.head
+ val fields = colNums.foldLeft(Seq.empty[StructField]) {
+ case (structFields, colNum) if colNum == 0 =>
+ val nested = nestedStructType(depths.tail, colNums, nullable)
+ structFields :+ StructField(nestedColName(depth, colNum), nested,
nullable)
+ case (structFields, colNum) =>
+ structFields :+ StructField(nestedColName(depth, colNum),
IntegerType, nullable = false)
+ }
+ StructType(fields)
+ }
+ }
+
+ private def nestedRow(depths: Seq[Int], colNums: Seq[Int]): Row = {
+ if (depths.length == 1) {
+ Row.fromSeq(colNums)
+ } else {
+ val values = colNums.foldLeft(Seq.empty[Any]) {
+ case (values, colNum) if colNum == 0 => values :+
nestedRow(depths.tail, colNums)
+ case (values, colNum) => values :+ colNum
+ }
+ Row.fromSeq(values)
+ }
+ }
+
+ /**
+ * Utility function for generating a DataFrame with nested columns.
+ *
+ * @param depth: The depth to which to create nested columns.
+ * @param numColsAtEachDepth: The number of columns to create at each depth.
The value of each
+ * column will be the same as its index
(IntegerType) at that depth
+ * unless the index = 0, in which case it may be a
StructType which
+ * represents the next depth.
+ * @param nullable: This value is used to set the nullability of StructType
columns.
+ */
+ private def nestedDf(
+ depth: Int, numColsAtEachDepth: Int, nullable: Boolean = false): DataFrame
= {
Review comment:
done
##########
File path:
sql/core/src/test/scala/org/apache/spark/sql/UpdateFieldsPerformanceSuite.scala
##########
@@ -0,0 +1,229 @@
+/*
+ * 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
+
+import org.apache.spark.sql.functions.{col, lit}
+import org.apache.spark.sql.test.SharedSparkSession
+import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
+
+class UpdateFieldsPerformanceSuite extends QueryTest with SharedSparkSession {
+
+ private def nestedColName(d: Int, colNum: Int): String =
s"nested${d}Col$colNum"
+
+ private def nestedStructType(
+ depths: Seq[Int], colNums: Seq[Int], nullable: Boolean): StructType = {
+ if (depths.length == 1) {
+ StructType(colNums.map { colNum =>
+ StructField(nestedColName(depths.head, colNum), IntegerType, nullable
= false)
+ })
+ } else {
+ val depth = depths.head
+ val fields = colNums.foldLeft(Seq.empty[StructField]) {
+ case (structFields, colNum) if colNum == 0 =>
+ val nested = nestedStructType(depths.tail, colNums, nullable)
+ structFields :+ StructField(nestedColName(depth, colNum), nested,
nullable)
+ case (structFields, colNum) =>
+ structFields :+ StructField(nestedColName(depth, colNum),
IntegerType, nullable = false)
+ }
+ StructType(fields)
+ }
+ }
+
+ private def nestedRow(depths: Seq[Int], colNums: Seq[Int]): Row = {
+ if (depths.length == 1) {
+ Row.fromSeq(colNums)
+ } else {
+ val values = colNums.foldLeft(Seq.empty[Any]) {
+ case (values, colNum) if colNum == 0 => values :+
nestedRow(depths.tail, colNums)
+ case (values, colNum) => values :+ colNum
+ }
+ Row.fromSeq(values)
+ }
+ }
+
+ /**
+ * Utility function for generating a DataFrame with nested columns.
+ *
+ * @param depth: The depth to which to create nested columns.
+ * @param numColsAtEachDepth: The number of columns to create at each depth.
The value of each
+ * column will be the same as its index
(IntegerType) at that depth
+ * unless the index = 0, in which case it may be a
StructType which
+ * represents the next depth.
+ * @param nullable: This value is used to set the nullability of StructType
columns.
+ */
+ private def nestedDf(
+ depth: Int, numColsAtEachDepth: Int, nullable: Boolean = false): DataFrame
= {
+ require(depth > 0)
+ require(numColsAtEachDepth > 0)
+
+ val depths = 1 to depth
+ val colNums = 0 until numColsAtEachDepth
+ val nestedColumn = nestedRow(depths, colNums)
+ val nestedColumnDataType = nestedStructType(depths, colNums, nullable)
+
+ spark.createDataFrame(
+ sparkContext.parallelize(Row(nestedColumn) :: Nil),
+ StructType(Seq(StructField(nestedColName(0, 0), nestedColumnDataType,
nullable))))
+ }
+
+ test("nestedDf should generate nested DataFrames") {
+ checkAnswer(
+ nestedDf(1, 1),
+ Row(Row(0)) :: Nil,
+ StructType(Seq(StructField("nested0Col0", StructType(Seq(
+ StructField("nested1Col0", IntegerType, nullable = false))),
+ nullable = false))))
+
+ checkAnswer(
+ nestedDf(1, 2),
+ Row(Row(0, 1)) :: Nil,
+ StructType(Seq(StructField("nested0Col0", StructType(Seq(
+ StructField("nested1Col0", IntegerType, nullable = false),
+ StructField("nested1Col1", IntegerType, nullable = false))),
+ nullable = false))))
+
+ checkAnswer(
+ nestedDf(2, 1),
+ Row(Row(Row(0))) :: Nil,
+ StructType(Seq(StructField("nested0Col0", StructType(Seq(
+ StructField("nested1Col0", StructType(Seq(
+ StructField("nested2Col0", IntegerType, nullable = false))),
+ nullable = false))),
+ nullable = false))))
+
+ checkAnswer(
+ nestedDf(2, 2),
+ Row(Row(Row(0, 1), 1)) :: Nil,
+ StructType(Seq(StructField("nested0Col0", StructType(Seq(
+ StructField("nested1Col0", StructType(Seq(
+ StructField("nested2Col0", IntegerType, nullable = false),
+ StructField("nested2Col1", IntegerType, nullable = false))),
+ nullable = false),
+ StructField("nested1Col1", IntegerType, nullable = false))),
+ nullable = false))))
+
+ checkAnswer(
+ nestedDf(2, 2, nullable = true),
+ Row(Row(Row(0, 1), 1)) :: Nil,
+ StructType(Seq(StructField("nested0Col0", StructType(Seq(
+ StructField("nested1Col0", StructType(Seq(
+ StructField("nested2Col0", IntegerType, nullable = false),
+ StructField("nested2Col1", IntegerType, nullable = false))),
+ nullable = true),
+ StructField("nested1Col1", IntegerType, nullable = false))),
+ nullable = true))))
+ }
+
+ // simulates how a user would add/drop nested fields in a performant manner
+ private def addDropNestedColumns(
+ column: Column,
Review comment:
done
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