revans2 commented on a change in pull request #29067:
URL: https://github.com/apache/spark/pull/29067#discussion_r458988318
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/columnar/InMemoryRelation.scala
##########
@@ -85,77 +232,81 @@ case class CachedRDDBuilder(
}
private def buildBuffers(): RDD[CachedBatch] = {
- val output = cachedPlan.output
- val cached = cachedPlan.execute().mapPartitionsInternal { rowIterator =>
- new Iterator[CachedBatch] {
- def next(): CachedBatch = {
- val columnBuilders = output.map { attribute =>
- ColumnBuilder(attribute.dataType, batchSize, attribute.name,
useCompression)
- }.toArray
-
- var rowCount = 0
- var totalSize = 0L
- while (rowIterator.hasNext && rowCount < batchSize
- && totalSize < ColumnBuilder.MAX_BATCH_SIZE_IN_BYTE) {
- val row = rowIterator.next()
-
- // Added for SPARK-6082. This assertion can be useful for
scenarios when something
- // like Hive TRANSFORM is used. The external data generation
script used in TRANSFORM
- // may result malformed rows, causing
ArrayIndexOutOfBoundsException, which is somewhat
- // hard to decipher.
- assert(
- row.numFields == columnBuilders.length,
- s"Row column number mismatch, expected ${output.size} columns, "
+
- s"but got ${row.numFields}." +
- s"\nRow content: $row")
-
- var i = 0
- totalSize = 0
- while (i < row.numFields) {
- columnBuilders(i).appendFrom(row, i)
- totalSize += columnBuilders(i).columnStats.sizeInBytes
- i += 1
- }
- rowCount += 1
- }
-
- sizeInBytesStats.add(totalSize)
- rowCountStats.add(rowCount)
-
- val stats = InternalRow.fromSeq(
- columnBuilders.flatMap(_.columnStats.collectedStatistics).toSeq)
- CachedBatch(rowCount, columnBuilders.map { builder =>
- JavaUtils.bufferToArray(builder.build())
- }, stats)
- }
-
- def hasNext: Boolean = rowIterator.hasNext
- }
+ val cb = if (cachedPlan.supportsColumnar) {
+ serializer.convertForCacheColumnar(cachedPlan.executeColumnar(),
+ cachedPlan.output,
+ storageLevel,
+ cachedPlan.conf)
+ } else {
+ serializer.convertForCache(cachedPlan.execute(),
+ cachedPlan.output,
+ storageLevel,
+ cachedPlan.conf)
+ }
+ val cached = cb.map { batch =>
+ sizeInBytesStats.add(batch.sizeInBytes)
+ rowCountStats.add(batch.numRows)
+ batch
}.persist(storageLevel)
-
cached.setName(cachedName)
cached
}
}
object InMemoryRelation {
+ private[this] var ser: Option[CachedBatchSerializer] = None
+ private[this] def getSerializer(sqlConf: SQLConf): CachedBatchSerializer =
synchronized {
+ if (ser.isEmpty) {
+ val serName = sqlConf.getConf(StaticSQLConf.SPARK_CACHE_SERIALIZER)
+ val serClass = Utils.classForName(serName)
+ val instance =
serClass.getConstructor().newInstance().asInstanceOf[CachedBatchSerializer]
+ ser = Some(instance)
+ }
+ ser.get
+ }
+
def apply(
- useCompression: Boolean,
- batchSize: Int,
+ storageLevel: StorageLevel,
+ qe: QueryExecution,
+ tableName: Option[String]): InMemoryRelation = {
+ val optimizedPlan = qe.optimizedPlan
+ val serializer = getSerializer(optimizedPlan.conf)
+ val child = if (serializer.supportsColumnarInput(optimizedPlan.output)) {
Review comment:
I'll switch over to doing `isInstanceOf[ColumnarToRow]` just to get this
unblocked.
I don't consider the change I made a hack. It is a relatively small and
focused change. It also will result in less implicit coupling between the cache
code and the columnar planning code.
I like SPARK-32334 too, but for what I have proposed so far it really only
covers transformations to arrow. If you have ideas on how you want it to impact
these changes please add some comments there so we can discuss it and get a
better idea of how it should all fit together.
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