iemejia commented on code in PR #12400: URL: https://github.com/apache/gluten/pull/12400#discussion_r3498403568
########## backends-velox/src/test/scala/org/apache/spark/sql/execution/VeloxFileHandleCacheSuite.scala: ########## @@ -0,0 +1,244 @@ +/* + * 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.execution + +import org.apache.gluten.config.VeloxConfig +import org.apache.gluten.execution.{BasicScanExecTransformer, VeloxWholeStageTransformerSuite} + +import org.apache.spark.SparkConf + +/** + * Test suite for Velox file handle cache behavior. + * + * Tests correctness, config propagation, and edge cases for the file handle cache which caches open + * file handles (descriptors) to avoid repeated open/close overhead. + */ +class VeloxFileHandleCacheSuite extends VeloxWholeStageTransformerSuite { + override protected val resourcePath: String = "/parquet-for-read" + override protected val fileFormat: String = "parquet" + + override protected def sparkConf: SparkConf = { + super.sparkConf + .set(VeloxConfig.COLUMNAR_VELOX_FILE_HANDLE_CACHE_ENABLED.key, "true") + .set(VeloxConfig.COLUMNAR_VELOX_FILE_HANDLE_EXPIRATION_DURATION_MS.key, "600000") + .set(VeloxConfig.COLUMNAR_VELOX_NUM_CACHE_FILE_HANDLES.key, "20000") + } + + testWithSpecifiedSparkVersion( + "basic scan correctness with file handle cache enabled", + "3.5", + "3.5") { + // Verify that enabling file handle cache produces correct scan results + withTempPath { + dir => + spark + .range(10000) + .selectExpr("id", "cast(id % 7 as int) as category", "id * 1.5 as value") + .repartition(10) + .write + .parquet(dir.getCanonicalPath) + + val df = spark.read.parquet(dir.getCanonicalPath) + df.createOrReplaceTempView("t") + + runQueryAndCompare("SELECT count(*) FROM t") { + checkGlutenPlan[BasicScanExecTransformer] + } + runQueryAndCompare("SELECT sum(value) FROM t WHERE category = 3") { + checkGlutenPlan[BasicScanExecTransformer] + } + runQueryAndCompare("SELECT category, count(*) FROM t GROUP BY category") { + checkGlutenPlan[BasicScanExecTransformer] + } + } + } + + testWithSpecifiedSparkVersion( + "repeated scans produce consistent results (cache hit path)", + "3.5", + "3.5") { + // When file handles are cached, repeated scans of the same files must produce + // identical results. This exercises the cache hit path. + withTempPath { + dir => + spark + .range(5000) + .selectExpr("id", "cast(id as string) as name") + .repartition(50) // 50 files to exercise many cache entries + .write + .parquet(dir.getCanonicalPath) + + val path = dir.getCanonicalPath + val expected = spark.read.parquet(path).count() + assert(expected == 5000) + + // Scan the same files multiple times - each should hit the cache + for (i <- 1 to 5) { + val count = spark.read.parquet(path).count() + assert( + count == expected, + s"Iteration $i: expected $expected rows but got $count") + } + + // Verify aggregation consistency across repeated scans + val firstSum = spark.read.parquet(path).selectExpr("sum(id)").collect()(0).getLong(0) + for (i <- 1 to 3) { + val sum = spark.read.parquet(path).selectExpr("sum(id)").collect()(0).getLong(0) + assert( + sum == firstSum, + s"Iteration $i: sum mismatch, expected $firstSum but got $sum") + } + } + } + + testWithSpecifiedSparkVersion( + "many small files do not cause errors with file handle cache", + "3.5", + "3.5") { + // Verify that scanning many small files with caching enabled does not cause + // file descriptor exhaustion or other resource-related errors. + withTempPath { + dir => + // Create 200 small parquet files + spark + .range(20000) + .selectExpr("id", "uuid() as payload") + .repartition(200) + .write + .parquet(dir.getCanonicalPath) + + val fileCount = dir.listFiles().count(_.getName.endsWith(".parquet")) + assert(fileCount >= 100, s"Expected at least 100 files, got $fileCount") + + // Scan all files - should work without resource errors + val count = spark.read.parquet(dir.getCanonicalPath).count() + assert(count == 20000) + + // Scan again (cache hit path) - should also work + val count2 = spark.read.parquet(dir.getCanonicalPath).count() + assert(count2 == 20000) + } + } + + testWithSpecifiedSparkVersion( + "filtered scan correctness with file handle cache", + "3.5", + "3.5") { + // Verify that predicate pushdown works correctly with cached file handles. + // This exercises the row group skipping path through cached handles. + withTempPath { + dir => + spark + .range(100000) + .selectExpr( + "id", + "cast(id % 10 as int) as partition_key", + "cast(id * 0.01 as double) as metric") + .repartition(20) + .write + .parquet(dir.getCanonicalPath) + + val path = dir.getCanonicalPath + + // Filter that matches ~10% of rows + val filtered = spark.read.parquet(path).where("partition_key = 5").count() + assert(filtered == 10000, s"Expected 10000 filtered rows, got $filtered") + + // Range filter + val rangeFiltered = spark.read.parquet(path).where("id >= 50000").count() + assert(rangeFiltered == 50000, s"Expected 50000 range-filtered rows, got $rangeFiltered") + + // Re-run same filters (cache hit path) + val filtered2 = spark.read.parquet(path).where("partition_key = 5").count() + assert(filtered2 == filtered, "Filtered count mismatch on repeated scan") + } + } + + testWithSpecifiedSparkVersion( + "scan after file deletion produces appropriate error or empty result", + "3.5", + "3.5") { + // If a file is deleted between scans, the next scan should either: + // - Succeed (if the cached FD still works on Linux with unlinked inodes) + // - Produce an error (not silently return wrong data) + withTempPath { + dir => + spark + .range(1000) + .selectExpr("id") + .repartition(5) + .write + .parquet(dir.getCanonicalPath) + + val path = dir.getCanonicalPath + // First scan populates the cache + val count1 = spark.read.parquet(path).count() + assert(count1 == 1000) + + // Delete one parquet file + val parquetFiles = dir.listFiles().filter(_.getName.endsWith(".parquet")) + assert(parquetFiles.nonEmpty) + val deletedFile = parquetFiles.head + val deletedRows = spark.read.parquet(deletedFile.getCanonicalPath).count() + deletedFile.delete() + + // On Linux, the cached FD to the deleted file may still work (unlinked inode). + // Either way, the remaining files should be readable. + // We don't assert on exact count because the deleted file's FD might still be valid. + val count2 = spark.read.parquet(path).count() + // The count should be either (count1 - deletedRows) or count1 + // depending on whether the OS kept the inode accessible + assert( + count2 == count1 || count2 == count1 - deletedRows, + s"Unexpected count after deletion: $count2 (original: $count1, deleted: $deletedRows)") Review Comment: test ########## backends-velox/src/test/scala/org/apache/spark/sql/execution/VeloxFileHandleCacheSuite.scala: ########## @@ -0,0 +1,244 @@ +/* + * 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.execution + +import org.apache.gluten.config.VeloxConfig +import org.apache.gluten.execution.{BasicScanExecTransformer, VeloxWholeStageTransformerSuite} + +import org.apache.spark.SparkConf + +/** + * Test suite for Velox file handle cache behavior. + * + * Tests correctness, config propagation, and edge cases for the file handle cache which caches open + * file handles (descriptors) to avoid repeated open/close overhead. + */ +class VeloxFileHandleCacheSuite extends VeloxWholeStageTransformerSuite { + override protected val resourcePath: String = "/parquet-for-read" + override protected val fileFormat: String = "parquet" + + override protected def sparkConf: SparkConf = { + super.sparkConf + .set(VeloxConfig.COLUMNAR_VELOX_FILE_HANDLE_CACHE_ENABLED.key, "true") + .set(VeloxConfig.COLUMNAR_VELOX_FILE_HANDLE_EXPIRATION_DURATION_MS.key, "600000") + .set(VeloxConfig.COLUMNAR_VELOX_NUM_CACHE_FILE_HANDLES.key, "20000") + } + + testWithSpecifiedSparkVersion( + "basic scan correctness with file handle cache enabled", + "3.5", + "3.5") { + // Verify that enabling file handle cache produces correct scan results + withTempPath { + dir => + spark + .range(10000) + .selectExpr("id", "cast(id % 7 as int) as category", "id * 1.5 as value") + .repartition(10) + .write + .parquet(dir.getCanonicalPath) + + val df = spark.read.parquet(dir.getCanonicalPath) + df.createOrReplaceTempView("t") + + runQueryAndCompare("SELECT count(*) FROM t") { + checkGlutenPlan[BasicScanExecTransformer] + } + runQueryAndCompare("SELECT sum(value) FROM t WHERE category = 3") { + checkGlutenPlan[BasicScanExecTransformer] + } + runQueryAndCompare("SELECT category, count(*) FROM t GROUP BY category") { + checkGlutenPlan[BasicScanExecTransformer] + } + } + } + + testWithSpecifiedSparkVersion( + "repeated scans produce consistent results (cache hit path)", + "3.5", + "3.5") { + // When file handles are cached, repeated scans of the same files must produce + // identical results. This exercises the cache hit path. + withTempPath { + dir => + spark + .range(5000) + .selectExpr("id", "cast(id as string) as name") + .repartition(50) // 50 files to exercise many cache entries + .write + .parquet(dir.getCanonicalPath) + + val path = dir.getCanonicalPath + val expected = spark.read.parquet(path).count() + assert(expected == 5000) + + // Scan the same files multiple times - each should hit the cache + for (i <- 1 to 5) { + val count = spark.read.parquet(path).count() + assert( + count == expected, + s"Iteration $i: expected $expected rows but got $count") + } + + // Verify aggregation consistency across repeated scans + val firstSum = spark.read.parquet(path).selectExpr("sum(id)").collect()(0).getLong(0) + for (i <- 1 to 3) { + val sum = spark.read.parquet(path).selectExpr("sum(id)").collect()(0).getLong(0) + assert( + sum == firstSum, + s"Iteration $i: sum mismatch, expected $firstSum but got $sum") + } + } + } + + testWithSpecifiedSparkVersion( + "many small files do not cause errors with file handle cache", + "3.5", + "3.5") { + // Verify that scanning many small files with caching enabled does not cause + // file descriptor exhaustion or other resource-related errors. + withTempPath { + dir => + // Create 200 small parquet files + spark + .range(20000) + .selectExpr("id", "uuid() as payload") + .repartition(200) + .write + .parquet(dir.getCanonicalPath) + + val fileCount = dir.listFiles().count(_.getName.endsWith(".parquet")) + assert(fileCount >= 100, s"Expected at least 100 files, got $fileCount") + + // Scan all files - should work without resource errors + val count = spark.read.parquet(dir.getCanonicalPath).count() + assert(count == 20000) + + // Scan again (cache hit path) - should also work + val count2 = spark.read.parquet(dir.getCanonicalPath).count() + assert(count2 == 20000) + } + } + + testWithSpecifiedSparkVersion( + "filtered scan correctness with file handle cache", + "3.5", + "3.5") { + // Verify that predicate pushdown works correctly with cached file handles. + // This exercises the row group skipping path through cached handles. + withTempPath { + dir => + spark + .range(100000) + .selectExpr( + "id", + "cast(id % 10 as int) as partition_key", + "cast(id * 0.01 as double) as metric") + .repartition(20) + .write + .parquet(dir.getCanonicalPath) + + val path = dir.getCanonicalPath + + // Filter that matches ~10% of rows + val filtered = spark.read.parquet(path).where("partition_key = 5").count() + assert(filtered == 10000, s"Expected 10000 filtered rows, got $filtered") + + // Range filter + val rangeFiltered = spark.read.parquet(path).where("id >= 50000").count() + assert(rangeFiltered == 50000, s"Expected 50000 range-filtered rows, got $rangeFiltered") + + // Re-run same filters (cache hit path) + val filtered2 = spark.read.parquet(path).where("partition_key = 5").count() + assert(filtered2 == filtered, "Filtered count mismatch on repeated scan") + } + } + + testWithSpecifiedSparkVersion( + "scan after file deletion produces appropriate error or empty result", + "3.5", + "3.5") { + // If a file is deleted between scans, the next scan should either: + // - Succeed (if the cached FD still works on Linux with unlinked inodes) + // - Produce an error (not silently return wrong data) + withTempPath { + dir => + spark + .range(1000) + .selectExpr("id") + .repartition(5) + .write + .parquet(dir.getCanonicalPath) + + val path = dir.getCanonicalPath + // First scan populates the cache + val count1 = spark.read.parquet(path).count() + assert(count1 == 1000) + + // Delete one parquet file + val parquetFiles = dir.listFiles().filter(_.getName.endsWith(".parquet")) + assert(parquetFiles.nonEmpty) + val deletedFile = parquetFiles.head + val deletedRows = spark.read.parquet(deletedFile.getCanonicalPath).count() + deletedFile.delete() + + // On Linux, the cached FD to the deleted file may still work (unlinked inode). + // Either way, the remaining files should be readable. + // We don't assert on exact count because the deleted file's FD might still be valid. + val count2 = spark.read.parquet(path).count() + // The count should be either (count1 - deletedRows) or count1 + // depending on whether the OS kept the inode accessible + assert( + count2 == count1 || count2 == count1 - deletedRows, + s"Unexpected count after deletion: $count2 (original: $count1, deleted: $deletedRows)") Review Comment: test -- This is an automated message from the Apache Git Service. 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