Github user wzhfy commented on a diff in the pull request: https://github.com/apache/spark/pull/15877#discussion_r87928329 --- 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)) --- End diff -- Yes, that's better, thanks!
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