[SPARK-18429] [SQL] implement a new Aggregate for CountMinSketch #15877
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| /* | ||
| * 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. | ||
| */ | ||
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| package org.apache.spark.sql.catalyst.expressions.aggregate | ||
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| import java.io.{ByteArrayInputStream, ByteArrayOutputStream} | ||
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| 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 | ||
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| /** | ||
| * 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. | ||
|
There was a problem hiding this comment. Maybe something on the return type? A developer should know how to work with these bytes. There was a problem hiding this comment. ok, I wrote this in usage, I'll add it here too, thanks. |
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| * 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] { | ||
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| def this( | ||
| child: Expression, | ||
| epsExpression: Expression, | ||
| confidenceExpression: Expression, | ||
| seedExpression: Expression) = { | ||
| this(child, epsExpression, confidenceExpression, seedExpression, 0, 0) | ||
| } | ||
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| // 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] | ||
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| 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 || | ||
|
There was a problem hiding this comment. Should we also check for negative eps and confidence values? |
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| 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 | ||
| } | ||
| } | ||
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| override def createAggregationBuffer(): CountMinSketch = { | ||
| CountMinSketch.create(eps, confidence, seed) | ||
| } | ||
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| override def update(buffer: CountMinSketch, input: InternalRow): Unit = { | ||
| val value = child.eval(input) | ||
| // Ignore empty rows | ||
| if (value != null) { | ||
| child.dataType match { | ||
|
There was a problem hiding this comment. Lets not do a pattern match for every update. We should use an update function instead, for example: private[this] val doUpdate: (CountMinSketch, Any) => Unit = child.dataType match {
case StringType => (cms, value) => cms.addBinary(value.asInstanceOf[UTF8String].getBytes)
case ByteType => (cms, value) => cms.addLong(value..asInstanceOf[Byte])
...
}
override def update(buffer: CountMinSketch, input: InternalRow): Unit = {
val value = child.eval(input)
if (value != null) {
doUpdate(buffer, value)
}
} |
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| // `Decimal` and `UTF8String` are internal types in spark sql, we need to convert them | ||
| // into acceptable types for `CountMinSketch`. | ||
| case DecimalType() => buffer.add(value.asInstanceOf[Decimal].toJavaBigDecimal) | ||
| // 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 _ => buffer.add(value) | ||
| } | ||
| } | ||
| } | ||
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| override def merge(buffer: CountMinSketch, input: CountMinSketch): Unit = { | ||
| buffer.mergeInPlace(input) | ||
| } | ||
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| override def eval(buffer: CountMinSketch): Any = serialize(buffer) | ||
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| override def serialize(buffer: CountMinSketch): Array[Byte] = { | ||
| val out = new ByteArrayOutputStream() | ||
| buffer.writeTo(out) | ||
| out.toByteArray | ||
| } | ||
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| override def deserialize(storageFormat: Array[Byte]): CountMinSketch = { | ||
| val in = new ByteArrayInputStream(storageFormat) | ||
| CountMinSketch.readFrom(in) | ||
| } | ||
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| override def withNewMutableAggBufferOffset(newMutableAggBufferOffset: Int): CountMinSketchAgg = | ||
| copy(mutableAggBufferOffset = newMutableAggBufferOffset) | ||
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| override def withNewInputAggBufferOffset(newInputAggBufferOffset: Int): CountMinSketchAgg = | ||
| copy(inputAggBufferOffset = newInputAggBufferOffset) | ||
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| override def inputTypes: Seq[AbstractDataType] = { | ||
| Seq(TypeCollection(NumericType, StringType, DateType, TimestampType, BooleanType, BinaryType), | ||
| DoubleType, DoubleType, IntegerType) | ||
| } | ||
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| override def nullable: Boolean = false | ||
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| override def dataType: DataType = BinaryType | ||
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| override def children: Seq[Expression] = | ||
| Seq(child, epsExpression, confidenceExpression, seedExpression) | ||
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| override def prettyName: String = "count_min_sketch" | ||
| } | ||
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Here I use string to represent decimal because there is a one-to-one mapping between BigDecimal and String.
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Is this true?
"1.0" and "1.00" is the same value but not the same string representation.
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Sorry I didn't describe it accurately. It should be "There is a one-to-one mapping between the distinguishable values and the result of this conversion." (from java doc of BigDecimal)