org.apache.spark.sql.rapids.GpuInputFileBlock.scala Maven / Gradle / Ivy
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Creates the distribution package of the RAPIDS plugin for Apache Spark
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/*
* Copyright (c) 2020-2023, 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.
*/
package org.apache.spark.sql.rapids
import ai.rapids.cudf.{ColumnVector, Scalar}
import com.nvidia.spark.rapids.{GpuColumnVector, GpuLeafExpression}
import com.nvidia.spark.rapids.Arm.withResource
import org.apache.spark.rdd.InputFileBlockHolder
import org.apache.spark.sql.types.{DataType, LongType, StringType}
import org.apache.spark.sql.vectorized.ColumnarBatch
/**
* Returns the name of the file being read, or empty string if not available.
* This is extra difficult because we cannot coalesce batches in between when this
* is used and the input file or else we could run into problems with returning the wrong thing.
*/
case class GpuInputFileName() extends GpuLeafExpression {
/**
* We need to recompute this if something fails.
*/
override lazy val deterministic: Boolean = false
override def foldable: Boolean = false
override def nullable: Boolean = false
override def dataType: DataType = StringType
override def prettyName: String = "input_file_name"
/**
* We need our input to be a single block per file.
*/
override def disableCoalesceUntilInput(): Boolean = true
override def columnarEval(batch: ColumnarBatch): GpuColumnVector = {
withResource(Scalar.fromString(InputFileBlockHolder.getInputFilePath.toString)) { scalar =>
GpuColumnVector.from(ColumnVector.fromScalar(scalar, batch.numRows()), dataType)
}
}
}
object InputFileUtils {
def setInputFileBlock(filePath: String, start: Long, length: Long): Unit = {
InputFileBlockHolder.set(filePath, start, length)
}
def getCurInputFilePath(): String = InputFileBlockHolder.getInputFilePath.toString
}
/**
* Returns the start offset of the block being read, or -1 if not available.
* This is extra difficult because we cannot coalesce batches in between when this
* is used and the input file or else we could run into problems with returning the wrong thing.
*/
case class GpuInputFileBlockStart() extends GpuLeafExpression {
/**
* We need to recompute this if something fails.
*/
override lazy val deterministic: Boolean = false
override def foldable: Boolean = false
override def nullable: Boolean = false
override def dataType: DataType = LongType
override def prettyName: String = "input_file_block_start"
/**
* We need our input to be a single block per file.
*/
override def disableCoalesceUntilInput(): Boolean = true
override def columnarEval(batch: ColumnarBatch): GpuColumnVector = {
withResource(Scalar.fromLong(InputFileBlockHolder.getStartOffset)) { scalar =>
GpuColumnVector.from(ColumnVector.fromScalar(scalar, batch.numRows()), dataType)
}
}
}
/**
* Returns the length of the block being read, or -1 if not available.
* This is extra difficult because we cannot coalesce batches in between when this
* is used and the input file or else we could run into problems with returning the wrong thing.
*/
case class GpuInputFileBlockLength() extends GpuLeafExpression {
/**
* We need to recompute this if something fails.
*/
override lazy val deterministic: Boolean = false
override def foldable: Boolean = false
override def nullable: Boolean = false
override def dataType: DataType = LongType
override def prettyName: String = "input_file_block_length"
/**
* We need our input to be a single block per file.
*/
override def disableCoalesceUntilInput(): Boolean = true
override def columnarEval(batch: ColumnarBatch): GpuColumnVector = {
withResource(Scalar.fromLong(InputFileBlockHolder.getLength)) { scalar =>
GpuColumnVector.from(ColumnVector.fromScalar(scalar, batch.numRows()), dataType)
}
}
}
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