support GpuConcat on ArrayType

docs/configs.md

@@ -155,7 +155,7 @@ Name | SQL Function(s) | Description | Default Value | Notes
 <a name="sql.expression.Ceil"></a>spark.rapids.sql.expression.Ceil|`ceiling`, `ceil`|Ceiling of a number|true|None|
 <a name="sql.expression.CheckOverflow"></a>spark.rapids.sql.expression.CheckOverflow| |CheckOverflow after arithmetic operations between DecimalType data|true|None|
 <a name="sql.expression.Coalesce"></a>spark.rapids.sql.expression.Coalesce|`coalesce`|Returns the first non-null argument if exists. Otherwise, null|true|None|
-<a name="sql.expression.Concat"></a>spark.rapids.sql.expression.Concat|`concat`|String concatenate NO separator|true|None|
+<a name="sql.expression.Concat"></a>spark.rapids.sql.expression.Concat|`concat`|List/String concatenate|true|None|
 <a name="sql.expression.Contains"></a>spark.rapids.sql.expression.Contains| |Contains|true|None|
 <a name="sql.expression.Cos"></a>spark.rapids.sql.expression.Cos|`cos`|Cosine|true|None|
 <a name="sql.expression.Cosh"></a>spark.rapids.sql.expression.Cosh|`cosh`|Hyperbolic cosine|true|None|

docs/supported_ops.md

@@ -3463,7 +3463,7 @@ Accelerator support is described below.
 <tr>
 <td rowSpan="4">Concat</td>
 <td rowSpan="4">`concat`</td>
-<td rowSpan="4">String concatenate NO separator</td>
+<td rowSpan="4">List/String concatenate</td>
 <td rowSpan="4">None</td>
 <td rowSpan="2">project</td>
 <td>input</td>
@@ -3481,7 +3481,7 @@ Accelerator support is described below.
 <td> </td>
 <td><b>NS</b></td>
 <td> </td>
-<td><b>NS</b></td>
+<td><em>PS* (missing nested BINARY, CALENDAR, ARRAY, MAP, STRUCT, UDT)</em></td>
 <td> </td>
 <td> </td>
 <td> </td>
@@ -3502,7 +3502,7 @@ Accelerator support is described below.
 <td> </td>
 <td><b>NS</b></td>
 <td> </td>
-<td><b>NS</b></td>
+<td><em>PS* (missing nested BINARY, CALENDAR, ARRAY, MAP, STRUCT, UDT)</em></td>
 <td> </td>
 <td> </td>
 <td> </td>

integration_tests/src/main/python/collection_ops_test.py

@@ -17,10 +17,69 @@
 from asserts import assert_gpu_and_cpu_are_equal_collect
 from data_gen import *
 from pyspark.sql.types import *
+from string_test import mk_str_gen
+import pyspark.sql.functions as f
 
 nested_gens = [ArrayGen(LongGen()),
                StructGen([("a", LongGen())]),
                MapGen(StringGen(pattern='key_[0-9]', nullable=False), StringGen())]
+# additional test for NonNull Array because of https://github.com/rapidsai/cudf/pull/8181
+non_nested_array_gens = [ArrayGen(sub_gen, nullable=nullable)
+                         for nullable in [True, False] for sub_gen in all_gen + [null_gen]]
+
+@pytest.mark.parametrize('data_gen', non_nested_array_gens, ids=idfn)
+def test_concat_list(data_gen):
+    assert_gpu_and_cpu_are_equal_collect(
+        lambda spark: binary_op_df(spark, data_gen).selectExpr('concat(a)'))
+
+    assert_gpu_and_cpu_are_equal_collect(
+        lambda spark: binary_op_df(spark, data_gen).selectExpr('concat(a, b)'))
+
+    assert_gpu_and_cpu_are_equal_collect(
+        lambda spark: three_col_df(spark, data_gen, data_gen, data_gen
+                                   ).selectExpr('concat(a, b, c)'))
+
+def test_empty_concat_list():
+    assert_gpu_and_cpu_are_equal_collect(
+        lambda spark: binary_op_df(spark, ArrayGen(LongGen())).selectExpr('concat()'))
+
+@pytest.mark.parametrize('data_gen', non_nested_array_gens, ids=idfn)
+def test_concat_list_with_lit(data_gen):
+    array_lit = gen_scalar(data_gen)
+    array_lit2 = gen_scalar(data_gen)
+    assert_gpu_and_cpu_are_equal_collect(
+        lambda spark: binary_op_df(spark, data_gen).select(
+            f.concat(f.col('a'),
+                     f.col('b'),
+                     f.lit(array_lit).cast(data_gen.data_type))))
+
+    assert_gpu_and_cpu_are_equal_collect(
+        lambda spark: binary_op_df(spark, data_gen).select(
+            f.concat(f.lit(array_lit).cast(data_gen.data_type),
+                     f.col('a'),
+                     f.lit(array_lit2).cast(data_gen.data_type))))
+
+    assert_gpu_and_cpu_are_equal_collect(
+        lambda spark: binary_op_df(spark, data_gen).select(
+            f.concat(f.lit(array_lit).cast(data_gen.data_type),
+                     f.lit(array_lit2).cast(data_gen.data_type))))
+
+def test_concat_string():
+    gen = mk_str_gen('.{0,5}')
+    (s1, s2) = gen_scalars(gen, 2, force_no_nulls=True)
+    assert_gpu_and_cpu_are_equal_collect(
+            lambda spark: binary_op_df(spark, gen).select(
+                f.concat(),
+                f.concat(f.col('a')),
+                f.concat(s1),
+                f.concat(f.col('a'), f.col('b')),
+                f.concat(f.col('a'), f.col('b'), f.col('a')),
+                f.concat(s1, f.col('b')),
+                f.concat(f.col('a'), s2),
+                f.concat(f.lit(None).cast('string'), f.col('b')),
+                f.concat(f.col('a'), f.lit(None).cast('string')),
+                f.concat(f.lit(''), f.col('b')),
+                f.concat(f.col('a'), f.lit(''))))
 
 @pytest.mark.parametrize('data_gen', all_gen + nested_gens, ids=idfn)
 @pytest.mark.parametrize('size_of_null', ['true', 'false'], ids=idfn)

integration_tests/src/main/python/string_test.py

@@ -145,22 +145,6 @@ def test_endswith():
                 f.col('a').endswith(None),
                 f.col('a').endswith('A\ud720')))
 
-# We currently only support strings, but this should be extended to other types
-# later on
-def test_concat():
-    gen = mk_str_gen('.{0,5}')
-    (s1, s2) = gen_scalars(gen, 2, force_no_nulls=True)
-    assert_gpu_and_cpu_are_equal_collect(
-            lambda spark: binary_op_df(spark, gen).select(
-                f.concat(f.col('a'), f.col('b')),
-                f.concat(f.col('a'), f.col('b'), f.col('a')),
-                f.concat(s1, f.col('b')),
-                f.concat(f.col('a'), s2),
-                f.concat(f.lit(None).cast('string'), f.col('b')),
-                f.concat(f.col('a'), f.lit(None).cast('string')),
-                f.concat(f.lit(''), f.col('b')),
-                f.concat(f.col('a'), f.lit(''))))
-
 def test_substring():
     gen = mk_str_gen('.{0,30}')
     assert_gpu_and_cpu_are_equal_collect(

sql-plugin/src/main/scala/com/nvidia/spark/rapids/GpuOverrides.scala

@@ -2473,10 +2473,13 @@ object GpuOverrides {
           GpuEndsWith(lhs, rhs)
       }),
     expr[Concat](
-      "String concatenate NO separator",
-      ExprChecks.projectNotLambda(TypeSig.STRING,
+      "List/String concatenate",
+      ExprChecks.projectNotLambda((TypeSig.STRING + TypeSig.ARRAY).nested(
+        TypeSig.commonCudfTypes + TypeSig.NULL + TypeSig.DECIMAL),
         (TypeSig.STRING + TypeSig.BINARY + TypeSig.ARRAY).nested(TypeSig.all),
-        repeatingParamCheck = Some(RepeatingParamCheck("input", TypeSig.STRING,
+        repeatingParamCheck = Some(RepeatingParamCheck("input",
+          (TypeSig.STRING + TypeSig.ARRAY).nested(
+            TypeSig.commonCudfTypes + TypeSig.NULL + TypeSig.DECIMAL),
           (TypeSig.STRING + TypeSig.BINARY + TypeSig.ARRAY).nested(TypeSig.all)))),
       (a, conf, p, r) => new ComplexTypeMergingExprMeta[Concat](a, conf, p, r) {
         override def convertToGpu(child: Seq[Expression]): GpuExpression = GpuConcat(child)

sql-plugin/src/main/scala/org/apache/spark/sql/rapids/collectionOperations.scala

@@ -16,37 +16,61 @@
 
 package org.apache.spark.sql.rapids
 
-import ai.rapids.cudf.{ColumnVector, Scalar}
-import com.nvidia.spark.rapids.{GpuBinaryExpression, GpuColumnVector, GpuScalar, GpuUnaryExpression}
+import scala.collection.mutable.ArrayBuffer
+
+import ai.rapids.cudf.{ColumnVector, ColumnView, Scalar}
+import com.nvidia.spark.rapids.{GpuBinaryExpression, GpuColumnVector, GpuComplexTypeMergingExpression, GpuExpressionsUtils, GpuScalar, GpuUnaryExpression}
+import com.nvidia.spark.rapids.GpuExpressionsUtils.columnarEvalToColumn
+import com.nvidia.spark.rapids.RapidsPluginImplicits._
 
 import org.apache.spark.sql.catalyst.analysis.{TypeCheckResult, TypeCoercion}
 import org.apache.spark.sql.catalyst.expressions.{ExpectsInputTypes, Expression}
 import org.apache.spark.sql.types._
+import org.apache.spark.sql.vectorized.ColumnarBatch
 import org.apache.spark.unsafe.types.UTF8String
 
-case class GpuSize(child: Expression, legacySizeOfNull: Boolean)
-  extends GpuUnaryExpression {
+case class GpuConcat(children: Seq[Expression]) extends GpuComplexTypeMergingExpression {
 
-  require(child.dataType.isInstanceOf[ArrayType] || child.dataType.isInstanceOf[MapType],
-    s"The size function doesn't support the operand type ${child.dataType}")
+  @transient override lazy val dataType: DataType = {
+    if (children.isEmpty) {
+      StringType
+    } else {
+      super.dataType
+    }
+  }
 
-  override def dataType: DataType = IntegerType
-  override def nullable: Boolean = if (legacySizeOfNull) false else super.nullable
+  override def nullable: Boolean = children.exists(_.nullable)
 
-  override protected def doColumnar(input: GpuColumnVector): ColumnVector = {
+  override def columnarEval(batch: ColumnarBatch): Any = dataType match {
+    // Explicitly return null for empty concat as Spark, since cuDF doesn't support empty concat.
+    case dt if children.isEmpty => GpuScalar.from(null, dt)
+    // For single column concat, we pass the result of child node to avoid extra cuDF call.
+    case _ if children.length == 1 => children.head.columnarEval(batch)
+    case StringType => stringConcat(batch)
+    case ArrayType(_, _) => listConcat(batch)
+    case _ => throw new IllegalArgumentException(s"unsupported dataType $dataType")
+  }
 
-    // Compute sizes of cuDF.ListType to get sizes of each ArrayData or MapData, considering
-    // MapData is represented as List of Struct in terms of cuDF.
-    withResource(input.getBase.countElements()) { collectionSize =>
-      if (legacySizeOfNull) {
-        withResource(Scalar.fromInt(-1)) { nullScalar =>
-          withResource(input.getBase.isNull) { inputIsNull =>
-            inputIsNull.ifElse(nullScalar, collectionSize)
-          }
-        }
-      } else {
-        collectionSize.incRefCount()
+  private def stringConcat(batch: ColumnarBatch): GpuColumnVector = {
+    withResource(ArrayBuffer.empty[ColumnVector]) { buffer =>
+      // build input buffer
+      children.foreach {
+        buffer += columnarEvalToColumn(_, batch).getBase
+      }
+      // run string concatenate
+      GpuColumnVector.from(
+        ColumnVector.stringConcatenate(buffer.toArray[ColumnView]), StringType)
+    }
+  }
+
+  private def listConcat(batch: ColumnarBatch): GpuColumnVector = {
+    withResource(ArrayBuffer[ColumnVector]()) { buffer =>
+      // build input buffer
+      children.foreach {
+        buffer += columnarEvalToColumn(_, batch).getBase
       }
+      // run list concatenate
+      GpuColumnVector.from(ColumnVector.listConcatenateByRow(buffer: _*), dataType)
     }
   }
 }
@@ -164,3 +188,30 @@ case class GpuElementAt(left: Expression, right: Expression, failOnError: Boolea
 
   override def prettyName: String = "element_at"
 }
+
+case class GpuSize(child: Expression, legacySizeOfNull: Boolean)
+    extends GpuUnaryExpression {
+
+  require(child.dataType.isInstanceOf[ArrayType] || child.dataType.isInstanceOf[MapType],
+    s"The size function doesn't support the operand type ${child.dataType}")
+
+  override def dataType: DataType = IntegerType
+  override def nullable: Boolean = if (legacySizeOfNull) false else super.nullable
+
+  override protected def doColumnar(input: GpuColumnVector): ColumnVector = {
+
+    // Compute sizes of cuDF.ListType to get sizes of each ArrayData or MapData, considering
+    // MapData is represented as List of Struct in terms of cuDF.
+    withResource(input.getBase.countElements()) { collectionSize =>
+      if (legacySizeOfNull) {
+        withResource(Scalar.fromInt(-1)) { nullScalar =>
+          withResource(input.getBase.isNull) { inputIsNull =>
+            inputIsNull.ifElse(nullScalar, collectionSize)
+          }
+        }
+      } else {
+        collectionSize.incRefCount()
+      }
+    }
+  }
+}

sql-plugin/src/main/scala/org/apache/spark/sql/rapids/stringFunctions.scala

@@ -16,15 +16,11 @@
 
 package org.apache.spark.sql.rapids
 
-import scala.collection.mutable.ArrayBuffer
-
-import ai.rapids.cudf.{ColumnVector, ColumnView, DType, PadSide, Scalar, Table}
+import ai.rapids.cudf.{ColumnVector, DType, PadSide, Scalar, Table}
 import com.nvidia.spark.rapids._
-import com.nvidia.spark.rapids.RapidsPluginImplicits._
 
 import org.apache.spark.sql.catalyst.expressions.{ExpectsInputTypes, Expression, ImplicitCastInputTypes, NullIntolerant, Predicate, StringSplit, SubstringIndex}
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.vectorized.ColumnarBatch
 import org.apache.spark.unsafe.types.UTF8String
 
 abstract class GpuUnaryString2StringExpression extends GpuUnaryExpression with ExpectsInputTypes {
@@ -267,36 +263,6 @@ case class GpuStringTrimRight(column: Expression, trimParameters: Option[Express
     GpuColumnVector.from(column.getBase.rstrip(t), dataType)
 }
 
-case class GpuConcat(children: Seq[Expression]) extends GpuComplexTypeMergingExpression {
-  override def dataType: DataType = StringType
-  override def nullable: Boolean = children.exists(_.nullable)
-
-  override def columnarEval(batch: ColumnarBatch): Any = {
-    var nullStrScalar: Scalar = null
-    var emptyStrScalar: Scalar = null
-    val columns: ArrayBuffer[ColumnVector] = new ArrayBuffer[ColumnVector](children.size)
-    try {
-      children.foreach { childExpr =>
-        withResource(GpuExpressionsUtils.columnarEvalToColumn(childExpr, batch)) {
-          gcv => columns += gcv.getBase.incRefCount()
-        }
-      }
-      emptyStrScalar = Scalar.fromString("")
-      nullStrScalar = Scalar.fromNull(DType.STRING)
-      GpuColumnVector.from(ColumnVector.stringConcatenate(emptyStrScalar, nullStrScalar,
-        columns.toArray[ColumnView]), dataType)
-    } finally {
-      columns.safeClose()
-      if (emptyStrScalar != null) {
-        emptyStrScalar.close()
-      }
-      if (nullStrScalar != null) {
-        nullStrScalar.close()
-      }
-    }
-  }
-}
-
 case class GpuContains(left: Expression, right: Expression) extends GpuBinaryExpression
   with Predicate with ImplicitCastInputTypes with NullIntolerant {