hudi-agent commented on code in PR #19165: URL: https://github.com/apache/hudi/pull/19165#discussion_r3521543954
########## hudi-spark-datasource/hudi-spark/src/test/scala/org/apache/hudi/functional/TestFileGroupReaderReadPath.scala: ########## @@ -0,0 +1,309 @@ +/* + * 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.{DataSourceReadOptions, DataSourceWriteOptions, HoodieFileIndex, ScalaAssertionSupport} +import org.apache.hudi.HoodieConversionUtils.toJavaOption +import org.apache.hudi.common.model.HoodieTableType +import org.apache.hudi.common.table.HoodieTableConfig +import org.apache.hudi.config.HoodieWriteConfig +import org.apache.hudi.testutils.{DataSourceTestUtils, HoodieSparkClientTestBase} +import org.apache.hudi.util.JFunction + +import org.apache.spark.sql.{SaveMode, SparkSession, SparkSessionExtensions} +import org.apache.spark.sql.catalyst.expressions.{And, AttributeReference, EqualTo, Literal} +import org.apache.spark.sql.execution.datasources.PartitionDirectory +import org.apache.spark.sql.hudi.HoodieSparkSessionExtension +import org.apache.spark.sql.types.{DataType, DoubleType, IntegerType, LongType, StringType} +import org.junit.jupiter.api.{AfterEach, BeforeEach} +import org.junit.jupiter.api.Assertions.{assertEquals, assertFalse, assertTrue} +import org.junit.jupiter.params.ParameterizedTest +import org.junit.jupiter.params.provider.EnumSource + +import java.util.function.Consumer + +/** + * Functional coverage for the default (file-group reader) Spark read path. + * + * These tests keep [[org.apache.hudi.common.config.HoodieReaderConfig.FILE_GROUP_READER_ENABLED]] + * at its default (enabled) and drive read-time schema evolution, MOR base+log merge, partition + * pruning and typed partition-value projection through public DataFrame reads. They intentionally + * target branches left uncovered by the legacy-reader suite (PR #19133) and the existing + * COW/MOR/CDC functional suites: schema-on-read filter rebuilding, add-column / type-promotion + * evolution branches, HoodieFileIndex/SparkHoodieTableFileIndex partition pruning decisions, and + * the per-version HoodiePartitionValues getters used when partition columns are reconstructed. + */ +class TestFileGroupReaderReadPath extends HoodieSparkClientTestBase with ScalaAssertionSupport { + + var spark: SparkSession = _ + + private val baseOpts = Map( + "hoodie.insert.shuffle.parallelism" -> "2", + "hoodie.upsert.shuffle.parallelism" -> "2", + "hoodie.bulkinsert.shuffle.parallelism" -> "2", + "hoodie.delete.shuffle.parallelism" -> "1", + DataSourceWriteOptions.RECORDKEY_FIELD.key -> "id", + HoodieTableConfig.ORDERING_FIELDS.key -> "ts", + HoodieWriteConfig.TBL_NAME.key -> "hoodie_fg_reader_test", + // keep log files around on MOR so snapshot reads exercise base+log merge + "hoodie.compact.inline" -> "false" + ) + + override def getSparkSessionExtensionsInjector: org.apache.hudi.common.util.Option[Consumer[SparkSessionExtensions]] = + toJavaOption( + Some( + JFunction.toJavaConsumer((receiver: SparkSessionExtensions) => + new HoodieSparkSessionExtension().apply(receiver)))) + + @BeforeEach + override def setUp(): Unit = { + initPath() + initSparkContexts() + spark = sqlContext.sparkSession + initTestDataGenerator() + initHoodieStorage() + } + + @AfterEach + override def tearDown(): Unit = { + cleanupSparkContexts() + cleanupTestDataGenerator() + cleanupFileSystem() + } + + /** + * Add-column schema evolution read: a column is introduced by a later commit while + * schema-on-read is enabled. The snapshot read must expose the new column (null for the + * older, un-touched rows), and pushed-down filters over both the pre-existing and the newly + * added columns must produce correct results. On MOR the update batch lands in log files, so + * the snapshot read additionally exercises the base+log merge iteration. + */ + @ParameterizedTest + @EnumSource(classOf[HoodieTableType]) + def testAddColumnEvolutionSnapshotAndIncrementalRead(tableType: HoodieTableType): Unit = { + val _spark = spark + import _spark.implicits._ + + val writeOpts = baseOpts ++ Map( + DataSourceWriteOptions.TABLE_TYPE.key -> tableType.name, + DataSourceWriteOptions.PARTITIONPATH_FIELD.key -> "part", + "hoodie.schema.on.read.enable" -> "true" + ) + + // V1: (id, name, age, ts, part) with ages 10..17 across two partitions + val v1 = (0 until 8).map(i => (s"id$i", s"n$i", 10 + i, 1L, if (i % 2 == 0) "p1" else "p2")) + .toDF("id", "name", "age", "ts", "part") + v1.write.format("hudi") + .options(writeOpts) + .option(DataSourceWriteOptions.OPERATION.key, DataSourceWriteOptions.INSERT_OPERATION_OPT_VAL) + .mode(SaveMode.Overwrite) + .save(basePath) + + val firstCompletion = DataSourceTestUtils.latestCommitCompletionTime(storage, basePath) + + // V2: introduce column `bonus`; update id2/id5 and insert id8/id9 + val v2 = Seq( + ("id2", "n2u", 12, 2L, "p1", 100.0d), + ("id5", "n5u", 15, 2L, "p2", 200.0d), + ("id8", "n8", 20, 2L, "p1", 300.0d), + ("id9", "n9", 21, 2L, "p2", 400.0d) + ).toDF("id", "name", "age", "ts", "part", "bonus") + v2.write.format("hudi") + .options(writeOpts) + .option(DataSourceWriteOptions.OPERATION.key, DataSourceWriteOptions.UPSERT_OPERATION_OPT_VAL) + .mode(SaveMode.Append) + .save(basePath) + + val snapshot = spark.read.format("hudi") + .option("hoodie.schema.on.read.enable", "true") + .load(basePath) + + // new column is surfaced with the expected type + assertEquals(DoubleType, snapshot.schema("bonus").dataType) + assertEquals(10, snapshot.count()) + // bonus is only populated for the rows written by V2 + assertEquals(4, snapshot.filter("bonus is not null").count()) + // filter pushdown over a pre-existing column across the evolved schema + assertEquals(8, snapshot.filter("age >= 12").count()) + // combined filter that rebuilds over both the old and the added column + assertEquals(4, snapshot.filter("age >= 12 AND bonus is not null").count()) + + val byId = snapshot.select("id", "name", "age", "bonus").collect().map(r => r.getString(0) -> r).toMap + // updated row reflects the V2 value + assertEquals("n2u", byId("id2").getString(1)) + assertEquals(100.0d, byId("id2").getDouble(3)) + // old, untouched row keeps its value and has a null bonus + assertEquals(10, byId("id0").getInt(2)) + assertTrue(byId("id0").isNullAt(3)) + + // Incremental read of everything written after V1 must return exactly the V2 records. + val incremental = spark.read.format("hudi") + .option("hoodie.schema.on.read.enable", "true") + .option(DataSourceReadOptions.QUERY_TYPE.key, DataSourceReadOptions.QUERY_TYPE_INCREMENTAL_OPT_VAL) + .option(DataSourceReadOptions.START_COMMIT.key, firstCompletion) + .load(basePath) + val incIds = incremental.select("id").collect().map(_.getString(0)).toSet + assertEquals(Set("id2", "id5", "id8", "id9"), incIds) + assertTrue(incremental.schema.fieldNames.contains("bonus")) + } + + /** + * Type-promotion schema evolution read: with schema-on-read enabled, a later commit widens an + * integer column to long. The snapshot read must return the promoted type and correctly read + * the older files (written as int) through the promotion path, including pushed-down filters + * over the promoted column. + */ + @ParameterizedTest + @EnumSource(classOf[HoodieTableType]) + def testTypePromotionEvolutionRead(tableType: HoodieTableType): Unit = { + val _spark = spark + import _spark.implicits._ + + val writeOpts = baseOpts ++ Map( + DataSourceWriteOptions.TABLE_TYPE.key -> tableType.name, + DataSourceWriteOptions.PARTITIONPATH_FIELD.key -> "part", + "hoodie.schema.on.read.enable" -> "true" + ) + + // V1: age is int + val v1 = (0 until 6).map(i => (s"id$i", 10 + i, 1L, if (i % 2 == 0) "p1" else "p2")) + .toDF("id", "age", "ts", "part") + v1.write.format("hudi") + .options(writeOpts) + .option(DataSourceWriteOptions.OPERATION.key, DataSourceWriteOptions.INSERT_OPERATION_OPT_VAL) + .mode(SaveMode.Overwrite) + .save(basePath) + + // V2: same column, now long -> promotes age int => long via schema-on-read + val v2 = Seq( + ("id2", 12L, 2L, "p1"), + ("id3", 9999999999L, 2L, "p2"), + ("id6", 42L, 2L, "p1") + ).toDF("id", "age", "ts", "part") + v2.write.format("hudi") + .options(writeOpts) + .option(DataSourceWriteOptions.OPERATION.key, DataSourceWriteOptions.UPSERT_OPERATION_OPT_VAL) + .mode(SaveMode.Append) + .save(basePath) + + val snapshot = spark.read.format("hudi") + .option("hoodie.schema.on.read.enable", "true") + .load(basePath) + + // promoted column type is now long + assertEquals(LongType, snapshot.schema("age").dataType) + assertEquals(7, snapshot.count()) + + val byId = snapshot.select("id", "age").collect().map(r => r.getString(0) -> r.getLong(1)).toMap + // value that only fits in a long is read back correctly (id3) + assertEquals(9999999999L, byId("id3")) + // an older (int-written) row is read through the promotion path + assertEquals(10L, byId("id0")) + // an updated row carries its promoted value (id6 == 42) + assertEquals(42L, byId("id6")) + + // filter pushdown over the promoted column rebuilds correctly across file schemas: + // ages after evolution are {10, 11, 12, 9999999999, 14, 15, 42} => two rows exceed 40 (id3, id6) + assertEquals(2, snapshot.filter("age > 40").count()) + // and a single row exceeds the int range + assertEquals(1, snapshot.filter("age > 1000000000").count()) + } + + /** + * Partition pruning + typed partition-value projection over the file-group reader. + * + * Partition columns are dropped from the data files, forcing the reader to reconstruct them + * from the partition path through the per-version HoodiePartitionValues getters (string + int). + * We assert both the pruned partition/file list produced by HoodieFileIndex and the rows / + * typed values returned by the corresponding DataFrame read. + */ + @ParameterizedTest + @EnumSource(classOf[HoodieTableType]) + def testPartitionPruningAndTypedPartitionValues(tableType: HoodieTableType): Unit = { + val _spark = spark + import _spark.implicits._ + + val writeOpts = baseOpts ++ Map( + DataSourceWriteOptions.TABLE_TYPE.key -> tableType.name, + DataSourceWriteOptions.PARTITIONPATH_FIELD.key -> "dt,region", + DataSourceWriteOptions.KEYGENERATOR_CLASS_NAME.key -> "org.apache.hudi.keygen.ComplexKeyGenerator", + DataSourceWriteOptions.URL_ENCODE_PARTITIONING.key -> "false", + DataSourceWriteOptions.HIVE_STYLE_PARTITIONING.key -> "false", + "hoodie.datasource.write.drop.partition.columns" -> "true" + ) + + // 4 partitions: dt in {2024-01-01, 2024-01-02} x region in {1, 2}, 3 rows each + val rows = for { + dt <- Seq("2024-01-01", "2024-01-02") + region <- Seq(1, 2) + i <- 0 until 3 + } yield (s"$dt-$region-$i", s"name$i", 100 + i, 1L, dt, region) + val df = rows.toDF("id", "name", "value", "ts", "dt", "region") + df.write.format("hudi") + .options(writeOpts) + .option(DataSourceWriteOptions.OPERATION.key, DataSourceWriteOptions.INSERT_OPERATION_OPT_VAL) + .mode(SaveMode.Overwrite) + .save(basePath) + + val metaClient = createMetaClient(spark, basePath) + val readOpts = Map( + DataSourceReadOptions.QUERY_TYPE.key -> DataSourceReadOptions.QUERY_TYPE_SNAPSHOT_OPT_VAL, + "path" -> basePath + ) + val fileIndex = HoodieFileIndex(spark, metaClient, None, readOpts) + + // Prune to a single (dt, region) partition; the int predicate exercises typed value parsing. + val singlePartitionFilter = And( + EqualTo(attr("dt", StringType), lit("2024-01-01")), + EqualTo(attr("region", IntegerType), lit(1)) + ) + val prunedSingle = fileIndex.listFiles(Seq(singlePartitionFilter), Seq.empty) + assertEquals(1, prunedSingle.size) + val PartitionDirectory(values, files) = prunedSingle.head + assertTrue(files.nonEmpty) + assertEquals("2024-01-01,1", values.toSeq(Seq(StringType, IntegerType)).mkString(",")) + + // Prune on the string column alone -> both region partitions under that date survive. + val dateOnlyFilter = EqualTo(attr("dt", StringType), lit("2024-01-02")) + val prunedDate = fileIndex.listFiles(Seq(dateOnlyFilter), Seq.empty) + assertEquals(2, prunedDate.size) + assertTrue(prunedDate.forall(_.files.nonEmpty)) + + // No filter -> all four partitions are listed. + assertEquals(4, fileIndex.listFiles(Seq.empty, Seq.empty).size) + + // DataFrame read with the same predicate reconstructs the typed partition columns. + val readDf = spark.read.format("hudi").load(basePath) + assertEquals(12, readDf.count()) + assertEquals(IntegerType, readDf.schema("region").dataType) + + val onePartition = readDf.filter("dt = '2024-01-01' AND region = 1") + assertEquals(3, onePartition.count()) + // region is served from the reconstructed partition values (columns were dropped from data) + val regions = onePartition.select("region").collect().map(_.getInt(0)).toSet + assertEquals(Set(1), regions) + val dates = onePartition.select("dt").collect().map(_.getString(0)).toSet + assertEquals(Set("2024-01-01"), dates) + assertFalse(onePartition.select("value").collect().isEmpty) + } + + private def attr(name: String, dataType: DataType): AttributeReference = + AttributeReference(name, dataType, nullable = true)() + + private def lit(value: Any): Literal = Literal(value) Review Comment: 🤖 nit: `lit` is the canonical name for `org.apache.spark.sql.functions.lit`, which returns a `Column`, but this private helper returns an internal Catalyst `Literal`. Could you rename it to something like `planLit` or `literalExpr` (or just inline `Literal(value)` at the two call sites) so a future reader adding a `functions._` import doesn't get confused by the type mismatch? <sub><i>⚠️ AI-generated; verify before applying. React 👍/👎 to flag quality.</i></sub> -- 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]
