iemejia commented on code in PR #12400: URL: https://github.com/apache/gluten/pull/12400#discussion_r3498549924
########## backends-velox/src/test/scala/org/apache/spark/sql/execution/VeloxFileHandleCacheSuite.scala: ########## @@ -0,0 +1,283 @@ +/* + * 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, "2000") + .set(VeloxConfig.COLUMNAR_VELOX_NUM_CACHE_FILE_HANDLES.key, "10000") + } + + 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 >= 200, s"Expected at least 200 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() + assert(deletedFile.delete(), s"Failed to delete ${deletedFile.getCanonicalPath}") + + // On Linux, the cached FD to the deleted file may still work (unlinked inode). + // Either way, the remaining files should be readable. + // The scan may also throw if the FS detects the missing file. + try { + 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)") + } catch { + case _: Exception => + // Acceptable: the scan failed because the deleted file is no longer accessible. + // The important thing is that it does not silently return wrong data. + } Review Comment: Fixed. Narrowed the catch to only accept exceptions whose message contains file-not-found indicators (`FileNotFoundException`, `No such file`, `Path does not exist`, `does not exist`). Unrelated failures will now propagate and fail the test. ########## backends-velox/src/test/scala/org/apache/spark/sql/execution/VeloxFileHandleCacheSuite.scala: ########## @@ -0,0 +1,283 @@ +/* + * 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, "2000") + .set(VeloxConfig.COLUMNAR_VELOX_NUM_CACHE_FILE_HANDLES.key, "10000") + } + + 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 >= 200, s"Expected at least 200 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() + assert(deletedFile.delete(), s"Failed to delete ${deletedFile.getCanonicalPath}") + + // On Linux, the cached FD to the deleted file may still work (unlinked inode). + // Either way, the remaining files should be readable. + // The scan may also throw if the FS detects the missing file. + try { + 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)") + } catch { + case _: Exception => + // Acceptable: the scan failed because the deleted file is no longer accessible. + // The important thing is that it does not silently return wrong data. + } + } + } + + testWithSpecifiedSparkVersion( + "TTL-based eviction: scans succeed after cached handles expire", + "3.5", + "3.5") { + // Verify that after the TTL expires (2s, set in sparkConf), cached handles + // are evicted and subsequent scans re-open files correctly. + withTempPath { + dir => + spark + .range(5000) + .selectExpr("id", "id * 2 as doubled") + .repartition(20) + .write + .parquet(dir.getCanonicalPath) + + val path = dir.getCanonicalPath + + // First scan populates the cache + val count1 = spark.read.parquet(path).count() + assert(count1 == 5000) + val sum1 = spark.read.parquet(path).selectExpr("sum(id)").collect()(0).getLong(0) + + // Wait for TTL to expire (configured to 2s in sparkConf) + Thread.sleep(3000) + + // Scan after expiration: handles should be evicted and re-opened + val count2 = spark.read.parquet(path).count() + assert(count2 == 5000, s"Count mismatch after TTL expiration: expected 5000, got $count2") + val sum2 = spark.read.parquet(path).selectExpr("sum(id)").collect()(0).getLong(0) + assert(sum2 == sum1, s"Sum mismatch after TTL expiration: expected $sum1, got $sum2") + } + } + + testWithSpecifiedSparkVersion( + "column pruning with cached file handles", + "3.5", + "3.5") { + // Verify that column pruning works correctly when file handles are cached. + // The cache key includes the file path but not the projected columns, so + // different projections on the same file must still work correctly. + withTempPath { + dir => + spark + .range(5000) + .selectExpr("id", "id * 2 as doubled", "id * 3 as tripled", "uuid() as text") + .repartition(10) + .write + .parquet(dir.getCanonicalPath) + + val path = dir.getCanonicalPath + + // Read all columns + val allCols = spark.read.parquet(path).select("id", "doubled", "tripled", "text").count() + assert(allCols == 5000) + + // Read subset of columns (same file handles, different projection) + val subset1Df = spark.read.parquet(path).select("id") + assert(subset1Df.schema.fieldNames.sameElements(Array("id"))) + assert(subset1Df.collect().length == 5000) + Review Comment: Fixed. Replaced `subset1Df.collect().length` with `subset1Df.count()` — validates the same scan path without materializing 5000 rows on the driver. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
