Github user wzhfy commented on a diff in the pull request:
https://github.com/apache/spark/pull/15877#discussion_r87934606
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/CountMinSketchAgg.scala
---
@@ -0,0 +1,131 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You 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.catalyst.expressions.aggregate
+
+import java.io.{ByteArrayInputStream, ByteArrayOutputStream}
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
+import
org.apache.spark.sql.catalyst.analysis.TypeCheckResult.{TypeCheckFailure,
TypeCheckSuccess}
+import org.apache.spark.sql.catalyst.expressions.{Expression,
ExpressionDescription}
+import org.apache.spark.sql.catalyst.util.GenericArrayData
+import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.UTF8String
+import org.apache.spark.util.sketch.CountMinSketch
+
+/**
+ * This function returns a count-min sketch of a column with the given
esp, confidence and seed.
+ * A count-min sketch is a probabilistic data structure used for
summarizing streams of data in
+ * sub-linear space, which is useful for equality predicates and join size
estimation.
+ *
+ * @param child child expression that can produce column value with
`child.eval(inputRow)`
+ * @param epsExpression relative error, must be positive
+ * @param confidenceExpression confidence, must be positive and less than
1.0
+ * @param seedExpression random seed
+ */
+@ExpressionDescription(
+ usage = """
+ _FUNC_(col, eps, confidence, seed) - Returns a count-min sketch of a
column with the given esp,
+ confidence and seed. The result is an array of bytes, which should
be deserialized to a
+ `CountMinSketch` before usage. `CountMinSketch` is useful for
equality predicates and join
+ size estimation.
+ """)
+case class CountMinSketchAgg(
+ child: Expression,
+ epsExpression: Expression,
+ confidenceExpression: Expression,
+ seedExpression: Expression,
+ override val mutableAggBufferOffset: Int,
+ override val inputAggBufferOffset: Int) extends
TypedImperativeAggregate[CountMinSketch] {
+
+ def this(
+ child: Expression,
+ epsExpression: Expression,
+ confidenceExpression: Expression,
+ seedExpression: Expression) = {
+ this(child, epsExpression, confidenceExpression, seedExpression, 0, 0)
+ }
+
+ override def checkInputDataTypes(): TypeCheckResult = {
+ val defaultCheck = super.checkInputDataTypes()
+ if (defaultCheck.isFailure) {
+ defaultCheck
+ } else if (!epsExpression.foldable || !confidenceExpression.foldable ||
+ !seedExpression.foldable) {
+ TypeCheckFailure(
+ "The eps, confidence or seed provided must be a literal or
constant foldable")
+ } else if (epsExpression.eval() == null || confidenceExpression.eval()
== null ||
+ seedExpression.eval() == null) {
+ TypeCheckFailure("The eps, confidence or seed provided should not be
null")
+ } else {
+ // parameter validity will be checked in CountMinSketchImpl
+ TypeCheckSuccess
+ }
+ }
+
+ override def createAggregationBuffer(): CountMinSketch = {
+ val eps: Double = epsExpression.eval().asInstanceOf[Double]
+ val confidence: Double =
confidenceExpression.eval().asInstanceOf[Double]
+ val seed: Int = seedExpression.eval().asInstanceOf[Int]
+ CountMinSketch.create(eps, confidence, seed)
+ }
+
+ override def update(buffer: CountMinSketch, input: InternalRow): Unit = {
+ val value = child.eval(input)
+ // ignore empty rows
+ if (value != null) {
+ // UTF8String is a spark sql type, while CountMinSketch accepts
String type
+ buffer.add(if (value.isInstanceOf[UTF8String]) value.toString else
value)
+ }
+ }
+
+ override def merge(buffer: CountMinSketch, input: CountMinSketch): Unit
= {
+ buffer.mergeInPlace(input)
+ }
+
+ override def eval(buffer: CountMinSketch): Any = new
GenericArrayData(serialize(buffer))
+
+ override def serialize(buffer: CountMinSketch): Array[Byte] = {
+ val out = new ByteArrayOutputStream()
+ buffer.writeTo(out)
+ out.toByteArray
+ }
+
+ override def deserialize(storageFormat: Array[Byte]): CountMinSketch = {
+ val in = new ByteArrayInputStream(storageFormat)
+ CountMinSketch.readFrom(in)
+ }
+
+ override def withNewMutableAggBufferOffset(newMutableAggBufferOffset:
Int): CountMinSketchAgg =
+ copy(mutableAggBufferOffset = newMutableAggBufferOffset)
+
+ override def withNewInputAggBufferOffset(newInputAggBufferOffset: Int):
CountMinSketchAgg =
+ copy(inputAggBufferOffset = newInputAggBufferOffset)
+
+ override def inputTypes: Seq[AbstractDataType] = {
+ // currently `CountMinSketch` supports integral and string types
--- End diff --
@rxin suggested that for unsupported types, we hash it before count min
sketch, i.e. CountMinSketchAgg(hash(col)).
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