Github user hvanhovell commented on a diff in the pull request:
https://github.com/apache/spark/pull/15877#discussion_r88014627
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/CountMinSketchAgg.scala
---
@@ -0,0 +1,146 @@
+/*
+ * 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.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.
+ * The result returned by the function is an array of bytes, which should
be deserialized to a
+ * `CountMinSketch` before usage.
+ *
+ * @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)
+ }
+
+ // Mark as lazy so that they are not evaluated during tree
transformation.
+ private lazy val eps: Double = epsExpression.eval().asInstanceOf[Double]
+ private lazy val confidence: Double =
confidenceExpression.eval().asInstanceOf[Double]
+ private lazy val seed: Int = seedExpression.eval().asInstanceOf[Int]
+
+ 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 if (eps <= 0D) {
+ TypeCheckFailure(s"Relative error must be positive (current value =
$eps)")
+ } else if (confidence <= 0D || confidence >= 1D) {
+ TypeCheckFailure(s"Confidence must be within range (0.0, 1.0)
(current value = $confidence)")
+ } else {
+ TypeCheckSuccess
+ }
+ }
+
+ override def createAggregationBuffer(): CountMinSketch = {
+ CountMinSketch.create(eps, confidence, seed)
+ }
+
+ override def update(buffer: CountMinSketch, input: InternalRow): Unit = {
+ val value = child.eval(input)
+ // ignore empty rows
+ if (value != null) {
+ child.dataType match {
+ // For string type, we can get bytes of our `UTF8String` directly,
and call the `addBinary`
+ // instead of `addString` to avoid unnecessary conversion.
+ case StringType =>
buffer.addBinary(value.asInstanceOf[UTF8String].getBytes)
+ case ByteType => buffer.addLong(value.asInstanceOf[Byte])
+ case ShortType => buffer.addLong(value.asInstanceOf[Short])
+ case IntegerType => buffer.addLong(value.asInstanceOf[Int])
--- End diff --
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