rahil-c commented on code in PR #18432: URL: https://github.com/apache/hudi/pull/18432#discussion_r3033751662
########## hudi-spark-datasource/hudi-spark-common/src/main/scala/org/apache/spark/sql/hudi/analysis/VectorDistanceUtils.scala: ########## @@ -0,0 +1,161 @@ +/* + * 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.hudi.analysis + +import org.apache.spark.ml.linalg.{DenseVector, Vectors} +import org.apache.spark.sql.catalyst.plans.logical.HoodieVectorSearchTableValuedFunction.DistanceMetric +import org.apache.spark.sql.expressions.UserDefinedFunction +import org.apache.spark.sql.functions.udf +import org.apache.spark.sql.hudi.command.exception.HoodieAnalysisException +import org.apache.spark.sql.types.{ByteType, DataType, DoubleType, FloatType} + +/** + * Vector distance utilities: raw distance functions and Spark UDF factories. + * + * UDF factories produce typed Spark UDFs for Float, Double, and Byte corpus columns. + */ +object VectorDistanceUtils { + + /** + * Cosine distance: 1 - (a . b) / (||a|| * ||b||). + * Returns 0.0 for identical vectors, 1.0 for orthogonal, 2.0 for opposite. + * Returns 1.0 if either vector is zero (convention: maximal distance). + */ + def cosineDistance(a: Array[Double], b: Array[Double]): Double = { + require(a.length == b.length, s"Vector dimension mismatch: ${a.length} vs ${b.length}") + val va = new DenseVector(a) + val vb = new DenseVector(b) + val denom = Vectors.norm(va, 2.0) * Vectors.norm(vb, 2.0) + if (denom == 0.0) 1.0 else math.min(2.0, math.max(0.0, 1.0 - (va.dot(vb) / denom))) + } + + /** + * L2 (Euclidean) distance: sqrt(sum((a[i] - b[i])^2)). + * Returns 0.0 for identical vectors. + */ + def l2Distance(a: Array[Double], b: Array[Double]): Double = { + require(a.length == b.length, s"Vector dimension mismatch: ${a.length} vs ${b.length}") + math.sqrt(Vectors.sqdist(new DenseVector(a), new DenseVector(b))) + } + + /** + * Negated dot product distance: -(a . b). + * Lower values indicate higher similarity (for ascending sort compatibility). + */ + def dotProductDistance(a: Array[Double], b: Array[Double]): Double = { + require(a.length == b.length, s"Vector dimension mismatch: ${a.length} vs ${b.length}") + -(new DenseVector(a)).dot(new DenseVector(b)) + } + + /** + * Creates a Spark UDF for the given distance metric and corpus element type. + * Both arguments (corpus vector and query vector) are evaluated per row. + * Prefer [[createSingleQueryDistanceUdf]] when the query vector is constant. + * Supports Float, Double, and Byte element types. + */ + def createDistanceUdf(metric: DistanceMetric.Value, elementType: DataType): UserDefinedFunction = + elementType match { + case FloatType => createFloatDistanceUdf(metric) + case DoubleType => createDoubleDistanceUdf(metric) + case ByteType => createByteDistanceUdf(metric) + case _ => throw new HoodieAnalysisException( + s"Unsupported vector element type for distance computation: $elementType") + } + + /** + * Creates a Spark UDF optimized for single-query mode: the query vector's + * [[DenseVector]] is pre-computed once and closed over, so only the corpus + * vector is converted per row. + */ + def createSingleQueryDistanceUdf( + metric: DistanceMetric.Value, + elementType: DataType, + queryVector: Array[Double]): UserDefinedFunction = { + val queryDv = new DenseVector(queryVector) + val queryNorm = Vectors.norm(queryDv, 2.0) + val distFn = resolveDistanceFn(metric) + + elementType match { + case FloatType => udf((corpus: Seq[Float]) => { + requireSameLength(corpus.length, queryVector.length) + distFn(new DenseVector(corpus.iterator.map(_.toDouble).toArray), queryDv, queryNorm) + }) + case DoubleType => udf((corpus: Seq[Double]) => { + requireSameLength(corpus.length, queryVector.length) + distFn(new DenseVector(corpus.toArray), queryDv, queryNorm) + }) + case ByteType => udf((corpus: Seq[Byte]) => { + requireSameLength(corpus.length, queryVector.length) + distFn(new DenseVector(corpus.iterator.map(_.toDouble).toArray), queryDv, queryNorm) + }) + case _ => throw new HoodieAnalysisException( + s"Unsupported vector element type for distance computation: $elementType") + } + } + + /** + * Returns a distance function that takes (corpusDenseVector, queryDenseVector, queryNorm) + * and returns a Double distance. The queryNorm parameter avoids recomputing + * the query vector's norm on every row for cosine distance. + */ + private def resolveDistanceFn( + metric: DistanceMetric.Value): (DenseVector, DenseVector, Double) => Double = + metric match { + case DistanceMetric.COSINE => (a, b, bNorm) => + val aNorm = Vectors.norm(a, 2.0) + val denom = aNorm * bNorm + if (denom == 0.0) 1.0 else math.min(2.0, math.max(0.0, 1.0 - (a.dot(b) / denom))) + case DistanceMetric.L2 => (a, b, _) => + math.sqrt(Vectors.sqdist(a, b)) + case DistanceMetric.DOT_PRODUCT => (a, b, _) => + -(a.dot(b)) + } + + private def computeRawDistance(metric: DistanceMetric.Value, a: Array[Double], b: Array[Double]): Double = + metric match { + case DistanceMetric.COSINE => cosineDistance(a, b) + case DistanceMetric.L2 => l2Distance(a, b) + case DistanceMetric.DOT_PRODUCT => dotProductDistance(a, b) + } + + private def createFloatDistanceUdf(metric: DistanceMetric.Value): UserDefinedFunction = + udf((a: Seq[Float], b: Seq[Float]) => { + requireSameLength(a.length, b.length) + computeRawDistance(metric, a.iterator.map(_.toDouble).toArray, b.iterator.map(_.toDouble).toArray) + }) Review Comment: Yea i think thats a good catch let me see if I can fix this. -- This is an automated message from the Apache Git Service. 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