HyukjinKwon commented on code in PR #38375:
URL: https://github.com/apache/spark/pull/38375#discussion_r1003168887
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala:
##########
@@ -85,42 +51,142 @@ object FrequentItems extends Logging {
cols: Seq[String],
support: Double): DataFrame = {
require(support >= 1e-4 && support <= 1.0, s"Support must be in [1e-4, 1],
but got $support.")
- val numCols = cols.length
+
// number of max items to keep counts for
val sizeOfMap = (1 / support).toInt
- val countMaps = Seq.tabulate(numCols)(i => new FreqItemCounter(sizeOfMap))
-
- val freqItems = df.select(cols.map(Column(_)) :
_*).rdd.treeAggregate(countMaps)(
- seqOp = (counts, row) => {
- var i = 0
- while (i < numCols) {
- val thisMap = counts(i)
- val key = row.get(i)
- thisMap.add(key, 1L)
- i += 1
- }
- counts
- },
- combOp = (baseCounts, counts) => {
- var i = 0
- while (i < numCols) {
- baseCounts(i).merge(counts(i))
- i += 1
+
+ val frequentItemCols = cols.map { col =>
+ val aggExpr = new CollectFrequentItems(functions.col(col).expr,
sizeOfMap)
+ Column(aggExpr.toAggregateExpression(isDistinct =
false)).as(s"${col}_freqItems")
+ }
+
+ df.select(frequentItemCols: _*)
+ }
+}
+
+case class CollectFrequentItems(
+ child: Expression,
+ size: Int,
+ mutableAggBufferOffset: Int = 0,
+ inputAggBufferOffset: Int = 0) extends
TypedImperativeAggregate[mutable.Map[Any, Long]]
+ with ImplicitCastInputTypes with UnaryLike[Expression] {
+ require(size > 0)
+
+ def this(child: Expression, size: Int) = this(child, size, 0, 0)
+
+ // Returns empty array for empty inputs
+ override def nullable: Boolean = false
+
+ override def dataType: DataType = ArrayType(child.dataType, containsNull =
child.nullable)
+
+ override def inputTypes: Seq[AbstractDataType] = Seq(AnyDataType)
+
+ override def prettyName: String = "collect_frequent_items"
+
+ override def createAggregationBuffer(): mutable.Map[Any, Long] =
+ mutable.Map.empty[Any, Long]
+
+ private def add(map: mutable.Map[Any, Long], key: Any, count: Long):
mutable.Map[Any, Long] = {
+ if (map.contains(key)) {
+ map(key) += count
+ } else {
+ if (map.size < size) {
+ map += key -> count
+ } else {
+ val minCount = if (map.values.isEmpty) 0 else map.values.min
+ val remainder = count - minCount
+ if (remainder >= 0) {
+ map += key -> count // something will get kicked out, so we can add
this
+ map.retain((k, v) => v > minCount)
+ map.transform((k, v) => v - minCount)
+ } else {
+ map.transform((k, v) => v - count)
}
- baseCounts
}
- )
- val justItems = freqItems.map(m => m.baseMap.keys.toArray)
- val resultRow = Row(justItems : _*)
+ }
+ map
+ }
+
+ override def update(
+ buffer: mutable.Map[Any, Long],
+ input: InternalRow): mutable.Map[Any, Long] = {
+ val key = child.eval(input)
+ if (key != null) {
+ this.add(buffer, InternalRow.copyValue(key), 1L)
+ } else {
+ this.add(buffer, key, 1L)
+ }
+ }
+
+ override def merge(
+ buffer: mutable.Map[Any, Long],
+ input: mutable.Map[Any, Long]): mutable.Map[Any, Long] = {
+ input.foreach { case (k, v) =>
+ add(buffer, k, v)
+ }
+ buffer
+ }
- val outputCols = cols.map { name =>
- val originalField = df.resolve(name)
+ override def eval(buffer: mutable.Map[Any, Long]): Any =
+ new GenericArrayData(buffer.keys.toArray)
Review Comment:
Can we reuse one `GenericArrayData` with cleaning up?
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
