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Creates the distribution package of the RAPIDS plugin for Apache Spark
The newest version!
/*
* Copyright (c) 2019-2023, 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 com.nvidia.spark.rapids
import scala.collection.mutable.ArrayBuffer
import ai.rapids.cudf.{Cuda, NvtxColor, Table}
import com.nvidia.spark.rapids.Arm.{closeOnExcept, withResource}
import com.nvidia.spark.rapids.RapidsPluginImplicits._
import com.nvidia.spark.rapids.RmmRapidsRetryIterator.{withRetry, withRetryNoSplit}
import com.nvidia.spark.rapids.ScalableTaskCompletion.onTaskCompletion
import com.nvidia.spark.rapids.jni.GpuSplitAndRetryOOM
import com.nvidia.spark.rapids.shims.{ShimExpression, ShimUnaryExecNode}
import org.apache.spark.TaskContext
import org.apache.spark.internal.Logging
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression, SortOrder}
import org.apache.spark.sql.catalyst.plans.physical.Partitioning
import org.apache.spark.sql.execution.SparkPlan
import org.apache.spark.sql.types.{DataType, NullType, StructType}
import org.apache.spark.sql.vectorized.ColumnarBatch
/**
* Consumes an Iterator of ColumnarBatches and concatenates them into a single ColumnarBatch.
* The batches will be closed when this operation is done.
*/
object ConcatAndConsumeAll {
/**
* Build a single batch from the batches collected so far. If array is empty this will likely
* blow up.
* @param arrayOfBatches the batches to concat. This will be consumed and you do not need to
* close any of the batches after this is called.
* @param schema the schema of the output types.
* @return a single batch with all of them concated together.
*/
def buildNonEmptyBatch(arrayOfBatches: Array[ColumnarBatch],
schema: StructType): ColumnarBatch =
buildNonEmptyBatchFromTypes(
arrayOfBatches, GpuColumnVector.extractTypes(schema))
/**
* Build a single batch from the batches collected so far. If array is empty this will likely
* blow up.
* @param arrayOfBatches the batches to concat. This will be consumed and you do not need to
* close any of the batches after this is called.
* @param dataTypes the output types.
* @return a single batch with all of them concated together.
*/
def buildNonEmptyBatchFromTypes(arrayOfBatches: Array[ColumnarBatch],
dataTypes: Array[DataType]): ColumnarBatch = {
if (arrayOfBatches.length == 1) {
arrayOfBatches(0)
} else {
val tables = arrayOfBatches.map(GpuColumnVector.from)
try {
val combined = Table.concatenate(tables: _*)
try {
GpuColumnVector.from(combined, dataTypes)
} finally {
combined.close()
}
} finally {
tables.foreach(_.close())
arrayOfBatches.foreach(_.close())
}
}
}
/**
* Verify that a single batch was returned from the iterator, or if it is empty return an empty
* batch.
* @param batches batches to be consumed.
* @param format the format of the batches in case we need to return an empty batch. Typically
* this is the output of your exec.
* @return the single batch or an empty batch if needed. Please be careful that your exec
* does not return empty batches as part of an RDD.
*/
def getSingleBatchWithVerification(batches: Iterator[ColumnarBatch],
format: Seq[Attribute]): ColumnarBatch = {
import scala.collection.JavaConverters._
if (!batches.hasNext) {
GpuColumnVector.emptyBatch(format.asJava)
} else {
val batch = batches.next()
if (batches.hasNext) {
batch.close()
throw new IllegalStateException("Expected to only receive a single batch")
}
batch
}
}
}
object CoalesceGoal {
def maxRequirement(a: CoalesceGoal, b: CoalesceGoal): CoalesceGoal = (a, b) match {
case (_: RequireSingleBatchLike, _) => a
case (_, _: RequireSingleBatchLike) => b
case (_: BatchedByKey, _: TargetSize) => a
case (_: TargetSize, _: BatchedByKey) => b
case (a: BatchedByKey, b: BatchedByKey) =>
if (satisfies(a, b)) {
a // They are equal so it does not matter
} else {
// Nothing is the same so there is no guarantee
BatchedByKey(Seq.empty)(Seq.empty)
}
case (TargetSize(aSize), TargetSize(bSize)) if aSize > bSize => a
case _ => b
}
def minProvided(a: CoalesceGoal, b:CoalesceGoal): CoalesceGoal = (a, b) match {
case (_: RequireSingleBatchLike, _) => b
case (_, _: RequireSingleBatchLike) => a
case (_: BatchedByKey, _: TargetSize) => b
case (_: TargetSize, _: BatchedByKey) => a
case (a: BatchedByKey, b: BatchedByKey) =>
if (satisfies(a, b)) {
a // They are equal so it does not matter
} else {
null
}
case (TargetSize(aSize), TargetSize(bSize)) if aSize < bSize => a
case _ => b
}
def satisfies(found: CoalesceGoal, required: CoalesceGoal): Boolean = (found, required) match {
case (_: RequireSingleBatchLike, _) => true
case (_, _: RequireSingleBatchLike) => false
case (_: BatchedByKey, _: TargetSize) => true
case (_: TargetSize, _: BatchedByKey) => false
case (BatchedByKey(aOrder), BatchedByKey(bOrder)) =>
aOrder.length == bOrder.length &&
aOrder.zip(bOrder).forall {
case (a, b) => a.satisfies(b)
}
case (TargetSize(foundSize), TargetSize(requiredSize)) => foundSize >= requiredSize
case _ => false // found is null so it is not satisfied
}
}
/**
* Provides a goal for batching of data.
*/
sealed abstract class CoalesceGoal extends GpuUnevaluable with ShimExpression {
override def nullable: Boolean = false
override def dataType: DataType = NullType
override def children: Seq[Expression] = Seq.empty
}
sealed abstract class CoalesceSizeGoal extends CoalesceGoal {
val targetSizeBytes: Long = Integer.MAX_VALUE
}
/**
* Trait used for pattern matching for single batch coalesce goals.
*/
trait RequireSingleBatchLike
/**
* Trait used for pattern matching for goals that could be split, as they
* only specify that batches won't be too much bigger than a maximum target
* size in bytes.
*/
trait SplittableGoal
/**
* A single batch is required as the input to a node in the SparkPlan. This means
* all of the data for a given task is in a single batch. This should be avoided
* as much as possible because it can result in running out of memory or run into
* limitations of the batch size by both Spark and cudf.
*/
case object RequireSingleBatch extends CoalesceSizeGoal with RequireSingleBatchLike {
override val targetSizeBytes: Long = Long.MaxValue
/** Override toString to improve readability of Spark explain output */
override def toString: String = "RequireSingleBatch"
}
/**
* This is exactly the same as `RequireSingleBatch` except that if the
* batch would fail to coalesce because it reaches cuDF row-count limits, the
* coalesce code is free to null filter given the filter expression in `filterExpression`.
* @note This is an ugly hack because ideally these rows are never read from the input source
* given that we normally push down IsNotNull in Spark. This should be removed when
* we can handle this in a proper way, likely at the logical plan optimization level.
* More details here: https://issues.apache.org/jira/browse/SPARK-39131
*/
case class RequireSingleBatchWithFilter(filterExpression: GpuExpression)
extends CoalesceSizeGoal with RequireSingleBatchLike {
override val targetSizeBytes: Long = Long.MaxValue
/** Override toString to improve readability of Spark explain output */
override def toString: String = "RequireSingleBatchWithFilter"
}
/**
* Produce a stream of batches that are at most the given size in bytes. The size
* is estimated in some cases so it may go over a little, but it should generally be
* very close to the target size. Generally you should not go over 2 GiB to avoid
* limitations in cudf for nested type columns.
* @param targetSizeBytes the size of each batch in bytes.
*/
case class TargetSize(override val targetSizeBytes: Long)
extends CoalesceSizeGoal
with SplittableGoal {
require(targetSizeBytes <= Integer.MAX_VALUE,
"Target cannot exceed 2GB without checks for cudf row count limit")
}
/**
* Split the data into batches where a set of keys are all within a single batch. This is
* generally used for things like a window operation or a sort based aggregation where you
* want all of the keys for a given operation to be available so the GPU can produce a
* correct answer. There is no limit on the target size so if there is a lot of data skew
* for a key, the batch may still run into limits on set by Spark or cudf. It should be noted
* that it is required that a node in the Spark plan that requires this should also require
* an input ordering that satisfies this ordering as well.
* @param gpuOrder the GPU keys that should be used for batching.
* @param cpuOrder the CPU keys that should be used for batching.
*/
case class BatchedByKey(gpuOrder: Seq[SortOrder])(val cpuOrder: Seq[SortOrder])
extends CoalesceGoal {
require(gpuOrder.size == cpuOrder.size)
override def otherCopyArgs: Seq[AnyRef] = cpuOrder :: Nil
override def children: Seq[Expression] = gpuOrder
}
abstract class AbstractGpuCoalesceIterator(
inputIter: Iterator[ColumnarBatch],
goal: CoalesceSizeGoal,
numInputRows: GpuMetric,
numInputBatches: GpuMetric,
numOutputRows: GpuMetric,
numOutputBatches: GpuMetric,
streamTime: GpuMetric,
concatTime: GpuMetric,
opTime: GpuMetric,
opName: String) extends Iterator[ColumnarBatch] with Logging {
private val iter = new CollectTimeIterator(s"$opName: collect", inputIter, streamTime)
private var batchInitialized: Boolean = false
/**
* This iterator is redefined if this coalesce iterator is under a `SplittableGoal`
* and so might retry and split given OOMs
*/
private var coalesceBatchIterator: Iterator[ColumnarBatch] = Iterator.empty
/**
* This is defined iff `goal` is `RequireSingleBatchWithFilter` and we have
* reached the cuDF row-count limit.
*/
private var inputFilterTier: Option[GpuTieredProject] = None
/**
* Return true if there is something saved on deck for later processing.
*/
protected def hasOnDeck: Boolean
/**
* Save a batch for later processing. In case of an exception raised while
* saving the batch, saveOnDeck guarantees it closes batch.
*/
protected def saveOnDeck(batch: ColumnarBatch): Unit
/**
* If there is anything saved on deck close it.
*/
protected def clearOnDeck(): Unit
/**
* Remove whatever is on deck and return it.
*/
protected def popOnDeck(): ColumnarBatch
/** Perform the necessary cleanup for an input batch */
protected def cleanupInputBatch(batch: ColumnarBatch): Unit = batch.close()
/** Optional row limit */
var batchRowLimit: Int = 0
// Don't install the callback if in a unit test
Option(TaskContext.get()).foreach { tc =>
onTaskCompletion(tc) {
clearOnDeck()
}
}
private def getHasOnDeck: Boolean = {
while (!hasOnDeck && iter.hasNext) {
val cb = iter.next()
withResource(new MetricRange(opTime)) { _ =>
val numRows = cb.numRows()
numInputBatches += 1
numInputRows += numRows
if (numRows > 0) {
saveOnDeck(cb)
} else {
cleanupInputBatch(cb)
}
}
}
hasOnDeck
}
override def hasNext: Boolean = {
coalesceBatchIterator.hasNext || getHasOnDeck
}
/**
* Called first to initialize any state needed for a new batch to be created.
*/
def initNewBatch(batch: ColumnarBatch): Unit
/**
* Called to add a new batch to the final output batch. The batch passed in will
* not be closed. If it needs to be closed it is the responsibility of the child class
* to do it.
*
* @param batch the batch to add in.
*/
def addBatchToConcat(batch: ColumnarBatch): Unit
/**
* True if there are some batches to be concatenated, otherwise false.
*/
def hasAnyToConcat: Boolean
/**
* Called after all of the batches have been added in.
*
* @return the concated batches on the GPU.
*/
def concatAllAndPutOnGPU(): ColumnarBatch
/**
* True for coalesce iterators that support an iterator that can retry
* and produce smaller batches on OOMs.
*/
protected val supportsRetryIterator: Boolean = true
/**
* Function that returns a retry iterator that returns coalesced batches, as much
* as possible.
*
* Note this throws if the subclass does not support splitting its input.
* (supportsRetryIterator = false)
*
* @return an iterator that should be used to obtain coalesced batches
*/
def getCoalesceRetryIterator: Iterator[ColumnarBatch]
/**
* Called to cleanup any state when a batch is done (even if there was a failure)
*/
def cleanupConcatIsDone(): Unit
/**
* For tests only.
* Int.MaxValue is quite big for unit tests, then override this in tests
* to change to a smaller value.
*/
protected val filteringModeRowsThreshold = Int.MaxValue
/** For tests only */
def isInFilteringMode: Boolean = inputFilterTier.isDefined
/**
* Gets the size in bytes of the data buffer for a given column
*/
def getBatchDataSize(cb: ColumnarBatch): Long = {
if (cb.numCols() > 0) {
cb.column(0) match {
case g: GpuColumnVectorFromBuffer =>
g.getBuffer.getLength
case _: GpuColumnVector =>
(0 until cb.numCols()).map {
i => cb.column(i).asInstanceOf[GpuColumnVector].getBase.getDeviceMemorySize
}.sum
case g: GpuCompressedColumnVector =>
g.getTableBuffer.getLength
case g =>
throw new IllegalStateException(s"Unexpected column type: $g")
}
} else {
0
}
}
/**
* A Simple wrapper around a ColumnarBatch to let us avoid closing it in some cases.
*/
private class BatchWrapper(var cb: ColumnarBatch) extends AutoCloseable {
def get: ColumnarBatch = cb
def release: ColumnarBatch = {
val tmp = cb
cb = null
tmp
}
override def close(): Unit = {
if (cb != null) {
cb.close()
cb = null
}
}
}
/**
* Add input batches to the `batches` collection up to the limit specified
* by the goal. Note: for a size goal, if any incoming batch is greater than this size
* it will be passed through unmodified.
*
* If the coalesce goal is `RequireSingleBatch` then an exception will be thrown if there
* is remaining data after the first batch is added.
*
* @note protected for testing
* @return boolean that is true if this call reached the last input batch.
*/
protected def populateCandidateBatches(): Boolean = {
var numRows: Long = 0 // to avoid overflows
var numBytes: Long = 0
// check if there is a batch "on deck" from a previous call to next()
if (hasOnDeck) {
val batch = popOnDeck()
numRows += batch.numRows()
numBytes += getBatchDataSize(batch)
addBatch(batch)
}
// there is a hard limit of 2^31 rows
while (numRows < filteringModeRowsThreshold && !hasOnDeck && iter.hasNext) {
val cbFromIter = iter.next()
numInputBatches += 1
val maybeFilteredIter = if (inputFilterTier.isDefined) {
// If we have reached the cuDF limit once, proactively filter batches
// after that first limit is reached.
GpuFilter.filterAndClose(cbFromIter, inputFilterTier.get,
NoopMetric, NoopMetric, NoopMetric)
} else {
Iterator(cbFromIter)
}
while(maybeFilteredIter.hasNext) {
val cb = new BatchWrapper(maybeFilteredIter.next())
closeOnExcept(cb) { _ =>
val nextRows = cb.get.numRows()
// filter out empty batches
if (nextRows > 0) {
numInputRows += nextRows
val nextBytes = getBatchDataSize(cb.get)
// calculate the new sizes based on this input batch being added to the current
// output batch
val wouldBeRows = numRows + nextRows
val wouldBeBytes = numBytes + nextBytes
if (wouldBeRows > filteringModeRowsThreshold) {
goal match {
case RequireSingleBatch =>
throw new IllegalStateException("A single batch is required for this " +
s"operation, but cuDF only supports $filteringModeRowsThreshold rows. " +
s"At least $wouldBeRows are in this partition. Please try increasing " +
"your partition count.")
case RequireSingleBatchWithFilter(filterExpression) =>
if (inputFilterTier.isEmpty) {
// We are going to enter the null-filtering mode
val filterTier = GpuTieredProject(Seq(Seq(filterExpression)))
// 1) Filter what we had already stored, and the rows number should
// be within the limit.
// Re-calculate the filtered rows number and size.
var filteredNumRows = 0L
var filteredBytes = 0L
if (hasAnyToConcat) {
val filteredDowIter = GpuFilter.filterAndClose(concatAllAndPutOnGPU(),
filterTier, NoopMetric, NoopMetric, NoopMetric)
while (filteredDowIter.hasNext) {
closeOnExcept(filteredDowIter.next()) { filteredDownCb =>
filteredNumRows += filteredDownCb.numRows()
filteredBytes += getBatchDataSize(filteredDownCb)
addBatch(filteredDownCb)
}
}
}
// 2) Filter the incoming batch.
// filterAndClose takes ownership of CB so we should not close it on a failure
// anymore...
val filteredCbIter = GpuFilter.filterAndClose(cb.release, filterTier,
NoopMetric, NoopMetric, NoopMetric)
while (filteredCbIter.hasNext) {
closeOnExcept(filteredCbIter.next()) { filteredCb =>
val filteredWouldBeRows = filteredNumRows + filteredCb.numRows()
if (filteredWouldBeRows > filteringModeRowsThreshold) {
throw new IllegalStateException("A single batch is required for " +
"this operation, but cuDF only supports " +
s"$filteringModeRowsThreshold rows. At least $filteredWouldBeRows" +
" are in this partition, even after filtering nulls. " +
"Please try increasing your partition count.")
}
filteredNumRows = filteredWouldBeRows
filteredBytes += getBatchDataSize(filteredCb)
addBatch(filteredCb)
}
} // end of "while(filteredCbIter.hasNext)"
// 3) Setup the filter
inputFilterTier = Some(filterTier)
logWarning("Switched to null-filtering mode. This coalesce iterator " +
"succeeded to fit rows under the cuDF limit only after null " +
"filtering. Please try increasing your partition count.")
numRows = filteredNumRows
numBytes = filteredBytes
} else {
// More filtered batches after we enter the null-filtering mode but
// the rows number is still too big.
throw new IllegalStateException("A single batch is required for this " +
s"operation, but cuDF only supports $filteringModeRowsThreshold rows. " +
s"At least $wouldBeRows are in this partition, even after filtering " +
s"nulls. Please try increasing your partition count.")
}
case _ => saveOnDeck(cb.get) // not a single batch requirement
}
} else if (batchRowLimit > 0 && wouldBeRows > batchRowLimit) {
saveOnDeck(cb.get)
} else if (wouldBeBytes > goal.targetSizeBytes && numBytes > 0) {
// There are no explicit checks for the concatenate result exceeding the cudf 2^31
// row count limit for any column. We are relying on cudf's concatenate to throw
// an exception if this occurs and limiting performance-oriented goals to under
// 2GB data total to avoid hitting that error.
saveOnDeck(cb.get)
} else {
addBatch(cb.get)
numRows = wouldBeRows
numBytes = wouldBeBytes
}
} else {
cleanupInputBatch(cb.get)
}
} // end of closeOnExcept(cb)
} // end of while(maybeFilteredIter.hasNext)
}
val isLastBatch = !(hasOnDeck || iter.hasNext)
// enforce single batch limit when appropriate
if (!isLastBatch) {
goal match {
case _: RequireSingleBatchLike =>
throw new IllegalStateException("A single batch is required for this operation," +
" Please try increasing your partition count.")
case _ =>
}
}
isLastBatch
}
var wasLastBatch: Boolean = false
/**
* Each call to next() will combine batches according to the goal specified.
* However, if any incoming batch is greater than this size it will be passed
* through unmodified.
*
* If the coalesce goal is `RequireSingleBatch` then an exception will be thrown if there
* is remaining data after the first batch is produced.
*
* If OOMs occur while coalescing (which may include decompression depending on the
* instance), this may be retried, and as a result `ColumnarBatch` may be smaller than
* desired, since we follow a "coalesce half of the batches" strategy, which should
* half the number of batches that are candidates for coalesce at each OOM, leaving the rest
* for a subsequent call to `next`.
*
* @return The coalesced batch
*/
override def next(): ColumnarBatch = withResource(new MetricRange(opTime)) { _ =>
if (coalesceBatchIterator.hasNext) {
val batch = coalesceBatchIterator.next()
if (wasLastBatch) {
// if the coalesce iterator is empty, and nothing is left on deck
if (!hasNext) {
GpuColumnVector.tagAsFinalBatch(batch)
} // else, we already marked `wasLastBatch`, will check it again
// next time.
}
numOutputRows += batch.numRows()
numOutputBatches += 1
batch
} else {
// reset batch state
batchInitialized = false
batchRowLimit = 0
try {
val isLastBatch = if (!coalesceBatchIterator.hasNext) {
populateCandidateBatches()
} else {
wasLastBatch
}
withResource(new NvtxWithMetrics(s"$opName concat", NvtxColor.CYAN, concatTime)) { _ =>
goal match {
case _: SplittableGoal if supportsRetryIterator =>
coalesceBatchIterator = getCoalesceRetryIterator
val batch = coalesceBatchIterator.next()
if (isLastBatch) {
if (!hasNext) {
GpuColumnVector.tagAsFinalBatch(batch)
} else {
wasLastBatch = true // but couldn't mark this one because there are leftovers
}
}
numOutputRows += batch.numRows()
numOutputBatches += 1
batch
case _ =>
val singleBatch = concatAllAndPutOnGPU()
if (isLastBatch) {
GpuColumnVector.tagAsFinalBatch(singleBatch)
}
numOutputRows += singleBatch.numRows()
numOutputBatches += 1
singleBatch
}
}
} finally {
cleanupConcatIsDone()
}
}
}
private def addBatch(batch: ColumnarBatch): Unit = {
if (!batchInitialized) {
initNewBatch(batch)
batchInitialized = true
}
addBatchToConcat(batch)
}
/**
* Splits a `BatchesToCoalesce` instance into two.
* @return Seq[BatchesToCoalesce] with 2 items.
*/
protected def splitBatchesToCoalesceFn: BatchesToCoalesce => Seq[BatchesToCoalesce] = {
(batchesToCoalesce: BatchesToCoalesce) => {
closeOnExcept(batchesToCoalesce) { _ =>
val it = batchesToCoalesce.batches
val numBatches = it.length
if (numBatches <= 1) {
throw new GpuSplitAndRetryOOM(s"Cannot split a sequence of $numBatches batches")
}
val res = it.splitAt(numBatches / 2)
Seq(BatchesToCoalesce(res._1), BatchesToCoalesce(res._2))
}
}
}
}
/**
* A helper class that contains a sequence of SpillableColumnarBatch and that
* can be used to split the sequence into two. This class is auto closeable,
* as it is sent to code that will close it, and in turn close the SpillableColumnarBatch
* instances in `batches`
* @param batches a sequence of `SpillableColumnarBatch` to manage.
*/
case class BatchesToCoalesce(batches: Array[SpillableColumnarBatch])
extends AutoCloseable {
override def close(): Unit = {
batches.safeClose()
}
}
class GpuCoalesceIterator(iter: Iterator[ColumnarBatch],
sparkTypes: Array[DataType],
goal: CoalesceSizeGoal,
numInputRows: GpuMetric,
numInputBatches: GpuMetric,
numOutputRows: GpuMetric,
numOutputBatches: GpuMetric,
collectTime: GpuMetric,
concatTime: GpuMetric,
opTime: GpuMetric,
opName: String)
extends AbstractGpuCoalesceIterator(iter,
goal,
numInputRows,
numInputBatches,
numOutputRows,
numOutputBatches,
collectTime,
concatTime,
opTime,
opName) {
protected val batches: ArrayBuffer[SpillableColumnarBatch] = ArrayBuffer.empty
override def initNewBatch(batch: ColumnarBatch): Unit = {
batches.safeClose()
batches.clear()
}
override def addBatchToConcat(batch: ColumnarBatch): Unit =
batches.append(SpillableColumnarBatch(batch, SpillPriorities.ACTIVE_BATCHING_PRIORITY))
private def concatBatches(batches: Array[SpillableColumnarBatch]): ColumnarBatch = {
val wip = batches.safeMap(_.getColumnarBatch())
ConcatAndConsumeAll.buildNonEmptyBatchFromTypes(wip, sparkTypes)
}
override def hasAnyToConcat: Boolean = batches.nonEmpty
override def concatAllAndPutOnGPU(): ColumnarBatch = {
val candidates = batches.toIndexedSeq
batches.clear()
withRetryNoSplit(candidates) { attempt =>
concatBatches(attempt.toArray)
}
}
override def getCoalesceRetryIterator: Iterator[ColumnarBatch] = {
val candidates = BatchesToCoalesce(batches.clone().toArray)
batches.clear()
withRetry(candidates, splitBatchesToCoalesceFn) { attempt: BatchesToCoalesce =>
concatBatches(attempt.batches)
}
}
override def cleanupConcatIsDone(): Unit = {
batches.clear()
}
private var onDeck: Option[SpillableColumnarBatch] = None
override protected def hasOnDeck: Boolean = onDeck.isDefined
override protected def saveOnDeck(batch: ColumnarBatch): Unit = {
// wrap batch on a closeOnExcept, in case assert throws
closeOnExcept(batch) { _ =>
assert(onDeck.isEmpty)
}
onDeck = Some(SpillableColumnarBatch(batch, SpillPriorities.ACTIVE_ON_DECK_PRIORITY))
}
override protected def clearOnDeck(): Unit = {
onDeck.foreach(_.close())
onDeck = None
}
override protected def popOnDeck(): ColumnarBatch = {
val ret = withRetryNoSplit[ColumnarBatch] {
onDeck.get.getColumnarBatch()
}
clearOnDeck()
ret
}
}
/**
* Compression codec-aware `GpuCoalesceIterator` subclass which should be used in cases
* where the RAPIDS Shuffle Manager could be configured, as batches to be coalesced
* may be compressed.
*/
class GpuCompressionAwareCoalesceIterator(
iter: Iterator[ColumnarBatch],
sparkTypes: Array[DataType],
goal: CoalesceSizeGoal,
maxDecompressBatchMemory: Long,
numInputRows: GpuMetric,
numInputBatches: GpuMetric,
numOutputRows: GpuMetric,
numOutputBatches: GpuMetric,
collectTime: GpuMetric,
concatTime: GpuMetric,
opTime: GpuMetric,
opName: String,
codecConfigs: TableCompressionCodecConfig)
extends GpuCoalesceIterator(
iter, sparkTypes, goal,
numInputRows = numInputRows,
numInputBatches = numInputBatches,
numOutputRows = numOutputRows,
numOutputBatches = numOutputBatches,
collectTime = collectTime,
concatTime = concatTime,
opTime = opTime,
opName) {
private[this] var codec: TableCompressionCodec = _
private def concatBatches(batches: Array[SpillableColumnarBatch]): ColumnarBatch = {
val toConcat = closeOnExcept(batches.safeMap(_.getColumnarBatch())) { wip =>
val compressedBatchIndices = wip.zipWithIndex.filter { pair =>
GpuCompressedColumnVector.isBatchCompressed(pair._1)
}.map(_._2)
if (compressedBatchIndices.nonEmpty) {
val compressedVecs = compressedBatchIndices.map { batchIndex =>
wip(batchIndex).column(0).asInstanceOf[GpuCompressedColumnVector]
}
if (codec == null) {
val descr = compressedVecs.head.getTableMeta.bufferMeta.codecBufferDescrs(0)
codec = TableCompressionCodec.getCodec(descr.codec, codecConfigs)
}
withResource(codec.createBatchDecompressor(maxDecompressBatchMemory,
Cuda.DEFAULT_STREAM)) { decompressor =>
compressedVecs.foreach { cv =>
val buffer = cv.getTableBuffer
val bufferMeta = cv.getTableMeta.bufferMeta
// don't currently support switching codecs when partitioning
buffer.incRefCount()
decompressor.addBufferToDecompress(buffer, bufferMeta)
}
withResource(decompressor.finishAsync()) { outputBuffers =>
outputBuffers.zipWithIndex.foreach { case (outputBuffer, outputIndex) =>
val cv = compressedVecs(outputIndex)
val batchIndex = compressedBatchIndices(outputIndex)
val compressedBatch = wip(batchIndex)
withResource(compressedBatch) { _ =>
// the decompressed batch should get a new meta without codec information
// so that future materializations of the batch don't get confused and attempt
// to use GpuCompressedColumnVector instead of GpuPackedTableColumn
wip(batchIndex) =
MetaUtils.getBatchFromMeta(
outputBuffer, MetaUtils.dropCodecs(cv.getTableMeta), sparkTypes)
}
}
}
}
}
wip
}
ConcatAndConsumeAll.buildNonEmptyBatchFromTypes(toConcat, sparkTypes)
}
override def concatAllAndPutOnGPU(): ColumnarBatch = {
val candidates = batches.toIndexedSeq
batches.clear()
withRetryNoSplit(candidates) { attempt =>
concatBatches(attempt.toArray)
}
}
override def getCoalesceRetryIterator(): Iterator[ColumnarBatch] = {
val candidates = BatchesToCoalesce(batches.clone().toArray)
batches.clear()
withRetry(candidates, splitBatchesToCoalesceFn) { attempt: BatchesToCoalesce =>
concatBatches(attempt.batches)
}
}
}
case class GpuCoalesceBatches(child: SparkPlan, goal: CoalesceGoal)
extends ShimUnaryExecNode with GpuExec {
import GpuMetric._
private[this] val (codecConfigs, maxDecompressBatchMemory) = {
val rapidsConf = new RapidsConf(child.conf)
(TableCompressionCodec.makeCodecConfig(rapidsConf),
rapidsConf.shuffleCompressionMaxBatchMemory)
}
protected override val outputBatchesLevel: MetricsLevel = MODERATE_LEVEL
override lazy val additionalMetrics: Map[String, GpuMetric] = Map(
OP_TIME -> createNanoTimingMetric(MODERATE_LEVEL, DESCRIPTION_OP_TIME),
NUM_INPUT_ROWS -> createMetric(DEBUG_LEVEL, DESCRIPTION_NUM_INPUT_ROWS),
NUM_INPUT_BATCHES -> createMetric(DEBUG_LEVEL, DESCRIPTION_NUM_INPUT_BATCHES),
CONCAT_TIME -> createNanoTimingMetric(DEBUG_LEVEL, DESCRIPTION_CONCAT_TIME)
)
override protected def doExecute(): RDD[InternalRow] = {
throw new IllegalStateException("ROW BASED PROCESSING IS NOT SUPPORTED")
}
override def output: Seq[Attribute] = child.output
override def outputPartitioning: Partitioning = child.outputPartitioning
override def outputBatching: CoalesceGoal = goal
override def requiredChildOrdering: Seq[Seq[SortOrder]] = goal match {
case batchingGoal: BatchedByKey =>
Seq(batchingGoal.cpuOrder)
case _ =>
super.requiredChildOrdering
}
override def outputOrdering: Seq[SortOrder] = goal match {
case batchingGoal: BatchedByKey =>
batchingGoal.cpuOrder
case _ =>
child.outputOrdering
}
override def internalDoExecuteColumnar(): RDD[ColumnarBatch] = {
val numInputRows = gpuLongMetric(NUM_INPUT_ROWS)
val numInputBatches = gpuLongMetric(NUM_INPUT_BATCHES)
val numOutputRows = gpuLongMetric(NUM_OUTPUT_ROWS)
val numOutputBatches = gpuLongMetric(NUM_OUTPUT_BATCHES)
val concatTime = gpuLongMetric(CONCAT_TIME)
val opTime = gpuLongMetric(OP_TIME)
// cache in local vars to avoid serializing the plan
val outputSchema = schema
val dataTypes = GpuColumnVector.extractTypes(outputSchema)
val decompressMemoryTarget = maxDecompressBatchMemory
val batches = child.executeColumnar()
val localCodecConfigs = codecConfigs
if (outputSchema.isEmpty) {
batches.mapPartitions { iter =>
val numRows = iter.map(_.numRows).sum
val combinedCb = new ColumnarBatch(Array.empty, numRows)
Iterator.single(combinedCb)
}
} else {
goal match {
case sizeGoal: CoalesceSizeGoal =>
batches.mapPartitions { iter =>
new GpuCompressionAwareCoalesceIterator(
iter, dataTypes, sizeGoal, decompressMemoryTarget,
numInputRows, numInputBatches, numOutputRows, numOutputBatches, NoopMetric,
concatTime, opTime, "GpuCoalesceBatches",
localCodecConfigs)
}
case batchingGoal: BatchedByKey =>
val targetSize = RapidsConf.GPU_BATCH_SIZE_BYTES.get(conf)
val f = GpuKeyBatchingIterator.makeFunc(batchingGoal.gpuOrder, output.toArray, targetSize,
numInputRows, numInputBatches, numOutputRows, numOutputBatches,
concatTime, opTime)
batches.mapPartitions { iter =>
f(iter)
}
}
}
}
}
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