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Support
raise_error() on [databricks] 14.3, Spark 4. (#11969)
Fixes #10969. This commit adds support for `raise_error()` on Databricks 14.3 and Spark 4.0. On these new Spark versions, the `RaiseError` expression (that powers the `raise_error()` API function) was changed from a Unary expression to a Binary one. This was done without modifying the arity of `raise_error()`. The ostensible reason seems to have been to eventually allow user-code to raise custom errors via `raise_error()`. This commit allows `raise_error()` to work on the GPU as it currently does on the CPU: as a unary function powered by a binary expression in the background. The tests have been modified to verify both the new behaviour and the legacy one on new platforms, while continuing to run as before on legacy platforms. --------- Signed-off-by: MithunR <[email protected]>
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141 changes: 141 additions & 0 deletions
141
sql-plugin/src/main/spark350db143/scala/org/apache/spark/sql/rapids/shims/misc.scala
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| @@ -0,0 +1,141 @@ | ||
| /* | ||
| * Copyright (c) 2025, 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": "350db143"} | ||
| {"spark": "400"} | ||
| spark-rapids-shim-json-lines ***/ | ||
| package org.apache.spark.sql.rapids.shims | ||
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| import ai.rapids.cudf.{ColumnVector, ColumnView, Scalar} | ||
| import com.nvidia.spark.rapids.{BinaryExprMeta, DataFromReplacementRule, GpuBinaryExpression, GpuColumnVector, GpuExpression, GpuMapUtils, GpuScalar, RapidsConf, RapidsMeta} | ||
| import com.nvidia.spark.rapids.Arm.withResource | ||
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| import org.apache.spark.sql.catalyst.expressions.{ExpectsInputTypes, Expression, RaiseError} | ||
| import org.apache.spark.sql.catalyst.util.{ArrayBasedMapData, MapData} | ||
| import org.apache.spark.sql.errors.QueryExecutionErrors.raiseError | ||
| import org.apache.spark.sql.types.{AbstractDataType, DataType, NullType, StringType} | ||
| import org.apache.spark.unsafe.types.UTF8String | ||
|
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| /** | ||
| * Implements `raise_error()` for Databricks 14.3 and Spark 4.0. | ||
| * Note that while the arity `raise_error()` remains 1 for all user-facing APIs | ||
| * (SQL, Scala, Python). But internally, the implementation uses a binary expression, | ||
| * where the first argument indicates the "error-class" for the error being raised. | ||
| */ | ||
| case class GpuRaiseError(left: Expression, right: Expression, dataType: DataType) | ||
| extends GpuBinaryExpression with ExpectsInputTypes { | ||
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| val errorClass: Expression = left | ||
| val errorParams: Expression = right | ||
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| override def inputTypes: Seq[AbstractDataType] = Seq(StringType) | ||
| override def toString: String = s"raise_error($errorClass, $errorParams)" | ||
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| /** Could evaluating this expression cause side-effects, such as throwing an exception? */ | ||
| override def hasSideEffects: Boolean = true | ||
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| override def doColumnar(lhs: GpuColumnVector, rhs: GpuColumnVector): ColumnVector = | ||
| throw new UnsupportedOperationException("Expected errorClass (lhs) to be a String literal") | ||
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| override def doColumnar(lhs: GpuColumnVector, rhs: GpuScalar): ColumnVector = | ||
| throw new UnsupportedOperationException("Expected errorClass (lhs) to be a String literal") | ||
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| private def extractScalaUTF8String(stringScalar: Scalar): UTF8String = { | ||
| // This is guaranteed to be a string scalar. | ||
| GpuScalar.extract(stringScalar).asInstanceOf[UTF8String] | ||
| } | ||
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| private def extractStrings(stringsColumn: ColumnView): Array[UTF8String] = { | ||
| val size = stringsColumn.getRowCount.asInstanceOf[Int] // Already checked if exceeds threshold. | ||
| val output: Array[UTF8String] = new Array[UTF8String](size) | ||
| for (i <- 0 until size) { | ||
| output(i) = withResource(stringsColumn.getScalarElement(i)) { | ||
| extractScalaUTF8String(_) | ||
| } | ||
| } | ||
| output | ||
| } | ||
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| private def makeMapData(listOfStructs: ColumnView): MapData = { | ||
| val THRESHOLD: Int = 10 // Avoiding surprises with large maps. | ||
| // All testing indicates a map with 1 entry. | ||
| val mapSize = listOfStructs.getRowCount | ||
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| if (mapSize > THRESHOLD) { | ||
| throw new UnsupportedOperationException("Unexpectedly large error-parameter map") | ||
| } | ||
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| val outputKeys: Array[UTF8String] = | ||
| withResource(GpuMapUtils.getKeysAsListView(listOfStructs)) { listOfKeys => | ||
| withResource(listOfKeys.getChildColumnView(0)) { // Strings child of LIST column. | ||
| extractStrings(_) | ||
| } | ||
| } | ||
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| val outputVals: Array[UTF8String] = | ||
| withResource(GpuMapUtils.getValuesAsListView(listOfStructs)) { listOfVals => | ||
| withResource(listOfVals.getChildColumnView(0)) { // Strings child of LIST column. | ||
| extractStrings(_) | ||
| } | ||
| } | ||
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| ArrayBasedMapData(outputKeys, outputVals) | ||
| } | ||
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| override def doColumnar(lhs: GpuScalar, rhs: GpuColumnVector): ColumnVector = { | ||
| if (rhs.getRowCount <= 0) { | ||
| // For the case: when(condition, raise_error(col("a")) | ||
| // When `condition` selects no rows, a vector of nulls should be returned, | ||
| // instead of throwing. | ||
| return GpuColumnVector.columnVectorFromNull(0, NullType) | ||
| } | ||
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| val lhsErrorClass = lhs.getValue.asInstanceOf[UTF8String] | ||
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| val rhsMapData = withResource(rhs.getBase.slice(0,1)) { slices => | ||
| val firstRhsRow = slices(0) | ||
| makeMapData(firstRhsRow) | ||
| } | ||
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| throw raiseError(lhsErrorClass, rhsMapData) | ||
| } | ||
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| override def doColumnar(numRows: Int, lhs: GpuScalar, rhs: GpuScalar): ColumnVector = { | ||
| if (numRows <= 0) { | ||
| // For the case: when(condition, raise_error(col("a")) | ||
| // When `condition` selects no rows, a vector of nulls should be returned, | ||
| // instead of throwing. | ||
| return GpuColumnVector.columnVectorFromNull(0, NullType) | ||
| } | ||
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| val errorClass = lhs.getValue.asInstanceOf[UTF8String] | ||
| // TODO (future): Check if the map-data needs to be extracted differently. | ||
| // All testing indicates that the host value of the map literal is set always pre-set. | ||
| // But if it isn't, then GpuScalar.getValue might extract it incorrectly. | ||
| // https://github.com/NVIDIA/spark-rapids/issues/11974 | ||
| val errorParams = rhs.getValue.asInstanceOf[MapData] | ||
| throw raiseError(errorClass, errorParams) | ||
| } | ||
| } | ||
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| class RaiseErrorMeta(r: RaiseError, | ||
| conf: RapidsConf, | ||
| parent: Option[RapidsMeta[_, _, _]], | ||
| rule: DataFromReplacementRule ) | ||
| extends BinaryExprMeta[RaiseError](r, conf, parent, rule) { | ||
| override def convertToGpu(lhsErrorClass: Expression, rhsErrorParams: Expression): GpuExpression | ||
| = GpuRaiseError(lhsErrorClass, rhsErrorParams, r.dataType) | ||
| } |
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| Original file line number | Diff line number | Diff line change |
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@@ -218,6 +218,7 @@ PythonUDAF,4 | |
| PythonUDF,4 | ||
| Quarter,4 | ||
| RLike,4 | ||
| RaiseError,4 | ||
| Rand,4 | ||
| Rank,4 | ||
| RegExpExtract,4 | ||
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