zhengruifeng commented on code in PR #38375:
URL: https://github.com/apache/spark/pull/38375#discussion_r1003976467
##########
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)
- // append frequent Items to the column name for easy debugging
- StructField(name + "_freqItems", ArrayType(originalField.dataType,
originalField.nullable))
- }.toArray
+ private lazy val projection =
+ UnsafeProjection.create(Array[DataType](child.dataType, LongType))
- val schema = StructType(outputCols).toAttributes
- Dataset.ofRows(df.sparkSession, LocalRelation.fromExternalRows(schema,
Seq(resultRow)))
+ override def serialize(map: mutable.Map[Any, Long]): Array[Byte] = {
+ val buffer = new Array[Byte](4 << 10) // 4K
+ val bos = new ByteArrayOutputStream()
+ val out = new DataOutputStream(bos)
+ try {
Review Comment:
good point, will update
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