Fixed build for 340+

sql-plugin/src/main/spark340/scala/com/nvidia/spark/rapids/shims/PartitionedFileUtilsShimBase.scala

@@ -20,7 +20,6 @@
 {"spark": "342"}
 {"spark": "343"}
 {"spark": "350"}
-{"spark": "350db"}
 {"spark": "351"}
 {"spark": "352"}
 {"spark": "400"}
@@ -42,6 +41,6 @@ trait PartitionedFileUtilsShimBase {
     SparkPath.fromPathString(filePath), start, length)
 
   def withNewLocations(pf: PartitionedFile, locations: Seq[String]): PartitionedFile = {
-    pf.copy(locations = locations.toSeq)
+    pf.copy(locations = locations.toArray)
   }
 }

sql-plugin/src/main/spark350/scala/com/nvidia/spark/rapids/shims/PythonMapInArrowExecShims.scala

@@ -16,7 +16,6 @@
 
 /*** spark-rapids-shim-json-lines
 {"spark": "350"}
-{"spark": "350db"}
 {"spark": "351"}
 {"spark": "352"}
 spark-rapids-shim-json-lines ***/
@@ -25,7 +24,7 @@ package com.nvidia.spark.rapids.shims
 import com.nvidia.spark.rapids._
 
 import org.apache.spark.sql.execution.SparkPlan
-import org.apache.spark.sql.execution.python.MapInArrowExec
+import org.apache.spark.sql.execution.python.PythonMapInArrowExec
 import org.apache.spark.sql.internal.SQLConf
 import org.apache.spark.sql.rapids.execution.TrampolineUtil
 import org.apache.spark.sql.rapids.shims.GpuPythonMapInArrowExecMeta
@@ -34,7 +33,7 @@ import org.apache.spark.sql.types.{BinaryType, StringType}
 object PythonMapInArrowExecShims {
 
   def execs: Map[Class[_ <: SparkPlan], ExecRule[_ <: SparkPlan]] = Seq(
-      GpuOverrides.exec[MapInArrowExec](
+      GpuOverrides.exec[PythonMapInArrowExec](
         "The backend for Map Arrow Iterator UDF. Accelerates the data transfer between the" +
           " Java process and the Python process. It also supports scheduling GPU resources" +
           " for the Python process when enabled.",

sql-plugin/src/main/spark350db/scala/com/nvidia/spark/rapids/shims/PartitionedFileUtilsShimBase.scala

@@ -0,0 +1,39 @@
+/*
+ * Copyright (c) 2024, NVIDIA CORPORATION.
+ *
+ * Licensed 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.
+ */
+
+/*** spark-rapids-shim-json-lines
+{"spark": "350db"}
+spark-rapids-shim-json-lines ***/
+package com.nvidia.spark.rapids.shims
+
+import org.apache.spark.paths.SparkPath
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.execution.datasources.PartitionedFile
+
+trait PartitionedFileUtilsShimBase {
+
+  // Wrapper for case class constructor so Java code can access
+  // the default values across Spark versions.
+  def newPartitionedFile(partitionValues: InternalRow,
+                         filePath: String,
+                         start: Long,
+                         length: Long): PartitionedFile = PartitionedFile(partitionValues,
+    SparkPath.fromPathString(filePath), start, length)
+
+  def withNewLocations(pf: PartitionedFile, locations: Seq[String]): PartitionedFile = {
+    pf.copy(locations = locations)
+  }
+}

sql-plugin/src/main/spark350db/scala/com/nvidia/spark/rapids/shims/PythonMapInArrowExecShims.scala

@@ -0,0 +1,62 @@
+/*
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ *
+ * Licensed 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.
+ */
+
+/*** spark-rapids-shim-json-lines
+{"spark": "350db"}
+spark-rapids-shim-json-lines ***/
+package com.nvidia.spark.rapids.shims
+
+import com.nvidia.spark.rapids._
+
+import org.apache.spark.sql.execution.SparkPlan
+import org.apache.spark.sql.execution.python.MapInArrowExec
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.rapids.execution.TrampolineUtil
+import org.apache.spark.sql.rapids.shims.GpuPythonMapInArrowExecMeta
+import org.apache.spark.sql.types.{BinaryType, StringType}
+
+object PythonMapInArrowExecShims {
+
+  def execs: Map[Class[_ <: SparkPlan], ExecRule[_ <: SparkPlan]] = Seq(
+      GpuOverrides.exec[MapInArrowExec](
+        "The backend for Map Arrow Iterator UDF. Accelerates the data transfer between the" +
+          " Java process and the Python process. It also supports scheduling GPU resources" +
+          " for the Python process when enabled.",
+        ExecChecks((TypeSig.commonCudfTypes + TypeSig.ARRAY + TypeSig.STRUCT).nested(),
+          TypeSig.all),
+        (mapPy, conf, p, r) => new GpuPythonMapInArrowExecMeta(mapPy, conf, p, r) {
+          override def tagPlanForGpu(): Unit = {
+            super.tagPlanForGpu()
+            if (SQLConf.get.getConf(SQLConf.ARROW_EXECUTION_USE_LARGE_VAR_TYPES)) {
+
+              val inputTypes = mapPy.child.schema.fields.map(_.dataType)
+              val outputTypes = mapPy.output.map(_.dataType)
+
+              val hasStringOrBinaryTypes = (inputTypes ++ outputTypes).exists(dataType =>
+                TrampolineUtil.dataTypeExistsRecursively(dataType,
+                  dt => dt == StringType || dt == BinaryType))
+
+              if (hasStringOrBinaryTypes) {
+                willNotWorkOnGpu(s"${SQLConf.ARROW_EXECUTION_USE_LARGE_VAR_TYPES.key} is " +
+                  s"enabled and the schema contains string or binary types. This is not " +
+                  s"supported on the GPU.")
+              }
+            }
+          }
+      })
+    ).map(r => (r.getClassFor.asSubclass(classOf[SparkPlan]), r)).toMap
+
+}