rahil-c commented on code in PR #18432: URL: https://github.com/apache/hudi/pull/18432#discussion_r3019157691
########## hudi-spark-datasource/hudi-spark/src/test/scala/org/apache/hudi/functional/TestHoodieVectorSearchFunction.scala: ########## @@ -0,0 +1,1441 @@ +/* + * 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.hudi.functional + +import org.apache.hudi.DataSourceWriteOptions._ +import org.apache.hudi.common.schema.HoodieSchema +import org.apache.hudi.testutils.HoodieSparkClientTestBase + +import org.apache.spark.sql.{Row, SaveMode, SparkSession} +import org.apache.spark.sql.types._ +import org.junit.jupiter.api.{AfterEach, BeforeEach, Test} +import org.junit.jupiter.api.Assertions._ + +/** + * End-to-end tests for the hudi_vector_search table-valued function. + * Tests both single-query and batch-query modes with Spark SQL and DataFrame API. + */ +class TestHoodieVectorSearchFunction extends HoodieSparkClientTestBase { + + var spark: SparkSession = null + private val corpusPath = "corpus" + private val corpusViewName = "corpus_view" + + // Test corpus: 5 unit-ish vectors in 3D for easy manual verification + // doc_1: [1, 0, 0] - x-axis + // doc_2: [0, 1, 0] - y-axis + // doc_3: [0, 0, 1] - z-axis + // doc_4: [0.707, 0.707, 0] - 45 degrees in xy-plane (normalized) + // doc_5: [0.577, 0.577, 0.577] - equal in all 3 dims (normalized) + private val corpusData = Seq( + ("doc_1", Seq(1.0f, 0.0f, 0.0f), "x-axis"), + ("doc_2", Seq(0.0f, 1.0f, 0.0f), "y-axis"), + ("doc_3", Seq(0.0f, 0.0f, 1.0f), "z-axis"), + ("doc_4", Seq(0.70710678f, 0.70710678f, 0.0f), "xy-diagonal"), + ("doc_5", Seq(0.57735027f, 0.57735027f, 0.57735027f), "xyz-diagonal") + ) + + @BeforeEach override def setUp(): Unit = { + initPath() + initSparkContexts() + spark = sqlContext.sparkSession + initTestDataGenerator() + initHoodieStorage() + createCorpusTable() + } + + @AfterEach override def tearDown(): Unit = { + spark.catalog.dropTempView(corpusViewName) + cleanupSparkContexts() + cleanupTestDataGenerator() + cleanupFileSystem() + } + + private def createCorpusTable(): Unit = { + val metadata = new MetadataBuilder() + .putString(HoodieSchema.TYPE_METADATA_FIELD, "VECTOR(3)") + .build() + + val schema = StructType(Seq( + StructField("id", StringType, nullable = false), + StructField("embedding", ArrayType(FloatType, containsNull = false), + nullable = false, metadata), + StructField("label", StringType, nullable = true) + )) + + val rows = corpusData.map { case (id, emb, label) => + Row(id, emb, label) + } + + val df = spark.createDataFrame( + spark.sparkContext.parallelize(rows), + schema + ) + + df.write.format("hudi") + .option(RECORDKEY_FIELD.key, "id") + .option(PRECOMBINE_FIELD.key, "id") + .option(TABLE_NAME.key, "vector_search_corpus") + .option(TABLE_TYPE.key, "COPY_ON_WRITE") + .mode(SaveMode.Overwrite) + .save(basePath + "/" + corpusPath) + + spark.read.format("hudi").load(basePath + "/" + corpusPath) + .createOrReplaceTempView(corpusViewName) + } + + // ==================== Single Query Mode: Spark SQL ==================== + + @Test + def testSingleQueryCosineDistance(): Unit = { + // Query vector [1, 0, 0] should be closest to doc_1, then doc_4, then doc_5 + val result = spark.sql( + s""" + |SELECT id, label, _distance + |FROM hudi_vector_search( + | '$corpusViewName', + | 'embedding', + | ARRAY(1.0, 0.0, 0.0), + | 3, + | 'cosine' + |) + |ORDER BY _distance + |""".stripMargin + ).collect() + + assertEquals(3, result.length) + + // doc_1 [1,0,0]: cosine distance to [1,0,0] = 1 - 1.0 = 0.0 + assertEquals("doc_1", result(0).getAs[String]("id")) + assertEquals(0.0, result(0).getAs[Double]("_distance"), 1e-5) + + // doc_4 [0.707,0.707,0]: cosine distance to [1,0,0] = 1 - 0.707 ~= 0.293 + assertEquals("doc_4", result(1).getAs[String]("id")) + assertEquals(1.0 - 0.70710678, result(1).getAs[Double]("_distance"), 1e-4) + + // doc_5 [0.577,0.577,0.577]: cosine distance to [1,0,0] = 1 - 0.577 ~= 0.423 + assertEquals("doc_5", result(2).getAs[String]("id")) + assertEquals(1.0 - 0.57735027, result(2).getAs[Double]("_distance"), 1e-4) + } + + @Test + def testSingleQueryL2Distance(): Unit = { + // Query [1, 0, 0] with L2 + val result = spark.sql( + s""" + |SELECT id, _distance + |FROM hudi_vector_search( + | '$corpusViewName', + | 'embedding', + | ARRAY(1.0, 0.0, 0.0), + | 3, + | 'l2' + |) + |ORDER BY _distance + |""".stripMargin + ).collect() + + assertEquals(3, result.length) + + // doc_1: L2 = 0.0 + assertEquals("doc_1", result(0).getAs[String]("id")) + assertEquals(0.0, result(0).getAs[Double]("_distance"), 1e-5) + + // doc_4: L2 = sqrt((1-0.707)^2 + (0-0.707)^2 + 0) = sqrt(0.086 + 0.5) ~= 0.765 + assertEquals("doc_4", result(1).getAs[String]("id")) + val expectedL2Doc4 = math.sqrt( + math.pow(1.0 - 0.70710678, 2) + math.pow(0.70710678, 2)) + assertEquals(expectedL2Doc4, result(1).getAs[Double]("_distance"), 1e-4) + } + + @Test + def testSingleQueryDotProduct(): Unit = { + // Query [1, 0, 0] with dot_product (negated: lower = more similar) + val result = spark.sql( + s""" + |SELECT id, _distance + |FROM hudi_vector_search( + | '$corpusViewName', + | 'embedding', + | ARRAY(1.0, 0.0, 0.0), + | 3, + | 'dot_product' + |) + |ORDER BY _distance + |""".stripMargin + ).collect() + + assertEquals(3, result.length) + + // doc_1: -dot = -1.0 (most similar) + assertEquals("doc_1", result(0).getAs[String]("id")) + assertEquals(-1.0, result(0).getAs[Double]("_distance"), 1e-5) + + // doc_4: -dot = -0.707 + assertEquals("doc_4", result(1).getAs[String]("id")) + assertEquals(-0.70710678, result(1).getAs[Double]("_distance"), 1e-4) + } + + @Test + def testSingleQueryDefaultMetric(): Unit = { + // Omit metric arg, should default to cosine + val result = spark.sql( + s""" + |SELECT id, _distance + |FROM hudi_vector_search( + | '$corpusViewName', + | 'embedding', + | ARRAY(1.0, 0.0, 0.0), + | 3 + |) + |ORDER BY _distance + |""".stripMargin + ).collect() + + assertEquals(3, result.length) + // Should match cosine: doc_1 first with distance ~0 + assertEquals("doc_1", result(0).getAs[String]("id")) + assertEquals(0.0, result(0).getAs[Double]("_distance"), 1e-5) + } + + @Test + def testSingleQueryReturnsAllCorpusColumns(): Unit = { + val result = spark.sql( + s""" + |SELECT * + |FROM hudi_vector_search( + | '$corpusViewName', + | 'embedding', + | ARRAY(1.0, 0.0, 0.0), + | 2 + |) + |""".stripMargin + ) + + // Should have the _distance column plus original corpus columns (embedding is dropped) + assertTrue(result.columns.contains("_distance")) + assertTrue(result.columns.contains("id")) + assertTrue(result.columns.contains("label")) + assertFalse(result.columns.contains("embedding")) + assertEquals(2, result.count()) + } + + @Test + def testKGreaterThanCorpus(): Unit = { + // k=100, corpus has 5 rows -> should return all 5 + val result = spark.sql( + s""" + |SELECT id, _distance + |FROM hudi_vector_search( + | '$corpusViewName', + | 'embedding', + | ARRAY(1.0, 0.0, 0.0), + | 100 + |) + |""".stripMargin + ).collect() + + assertEquals(5, result.length) + } + + // ==================== Single Query Mode: Composability ==================== Review Comment: remove comment. -- This is an automated message from the Apache Git Service. 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