danny0405 commented on code in PR #11043: URL: https://github.com/apache/hudi/pull/11043#discussion_r1623048159
########## hudi-spark-datasource/hudi-spark/src/test/scala/org/apache/hudi/functional/TestBloomFiltersIndexSupport.scala: ########## @@ -0,0 +1,260 @@ +/* + * 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.config.{HoodieMetadataConfig, TypedProperties} +import org.apache.hudi.common.model.{FileSlice, HoodieTableType} +import org.apache.hudi.common.table.{HoodieTableConfig, HoodieTableMetaClient} +import org.apache.hudi.common.testutils.RawTripTestPayload.recordsToStrings +import org.apache.hudi.config.HoodieWriteConfig +import org.apache.hudi.metadata.HoodieMetadataFileSystemView +import org.apache.hudi.testutils.HoodieSparkClientTestBase +import org.apache.hudi.util.{JFunction, JavaConversions} +import org.apache.hudi.{DataSourceReadOptions, DataSourceWriteOptions, HoodieFileIndex} +import org.apache.spark.sql.catalyst.expressions.{AttributeReference, EqualTo, Expression, Literal} +import org.apache.spark.sql.functions.{col, not} +import org.apache.spark.sql.types.StringType +import org.apache.spark.sql.{DataFrame, Row, SaveMode, SparkSession} +import org.junit.jupiter.api.Assertions.assertTrue +import org.junit.jupiter.api.{AfterEach, BeforeEach, Test} +import org.junit.jupiter.params.ParameterizedTest +import org.junit.jupiter.params.provider.EnumSource + +import java.util.concurrent.atomic.AtomicInteger +import java.util.stream.Collectors +import scala.collection.JavaConverters._ +import scala.collection.{JavaConverters, mutable} + +class TestBloomFiltersIndexSupport extends HoodieSparkClientTestBase { + + val sqlTempTable = "hudi_tbl_bloom" + var spark: SparkSession = _ + var instantTime: AtomicInteger = _ + val metadataOpts: Map[String, String] = Map( + HoodieMetadataConfig.ENABLE.key -> "true", + HoodieMetadataConfig.ENABLE_METADATA_INDEX_BLOOM_FILTER.key -> "true", + HoodieMetadataConfig.BLOOM_FILTER_INDEX_FOR_COLUMNS.key -> "_row_key" + ) + val commonOpts: Map[String, String] = Map( + "hoodie.insert.shuffle.parallelism" -> "4", + "hoodie.upsert.shuffle.parallelism" -> "4", + HoodieWriteConfig.TBL_NAME.key -> "hoodie_test", + RECORDKEY_FIELD.key -> "_row_key", + PARTITIONPATH_FIELD.key -> "partition", + PRECOMBINE_FIELD.key -> "timestamp", + HoodieTableConfig.POPULATE_META_FIELDS.key -> "true" + ) ++ metadataOpts + var mergedDfList: List[DataFrame] = List.empty + + @BeforeEach + override def setUp(): Unit = { + initPath() + initSparkContexts() + initHoodieStorage() + initTestDataGenerator() + + setTableName("hoodie_test") + initMetaClient() + + instantTime = new AtomicInteger(1) + + spark = sqlContext.sparkSession + } + + @AfterEach + override def tearDown(): Unit = { + cleanupFileSystem() + cleanupSparkContexts() + } + + @ParameterizedTest + @EnumSource(classOf[HoodieTableType]) + def testIndexInitialization(tableType: HoodieTableType): Unit = { + val hudiOpts = commonOpts + (DataSourceWriteOptions.TABLE_TYPE.key -> tableType.name()) + doWriteAndValidateBloomFilters( + hudiOpts, + operation = DataSourceWriteOptions.INSERT_OPERATION_OPT_VAL, + saveMode = SaveMode.Overwrite) + } + + /** + * Test case to do a write with updates and then validate file pruning using bloom filters. + */ + @Test + def testBloomFiltersIndexFilePruning(): Unit = { + var hudiOpts = commonOpts + hudiOpts = hudiOpts + ( + DataSourceReadOptions.ENABLE_DATA_SKIPPING.key -> "true") + + doWriteAndValidateBloomFilters( + hudiOpts, + operation = DataSourceWriteOptions.INSERT_OPERATION_OPT_VAL, + saveMode = SaveMode.Overwrite, + shouldValidate = false) + doWriteAndValidateBloomFilters( + hudiOpts, + operation = DataSourceWriteOptions.UPSERT_OPERATION_OPT_VAL, + saveMode = SaveMode.Append) + + createTempTable(hudiOpts) + verifyQueryPredicate(hudiOpts, "_row_key") + } + + private def createTempTable(hudiOpts: Map[String, String]): Unit = { + val readDf = spark.read.format("hudi").options(hudiOpts).load(basePath) + readDf.createOrReplaceTempView(sqlTempTable) + } + + def verifyQueryPredicate(hudiOpts: Map[String, String], columnName: String): Unit = { + val reckey = mergedDfList.last.limit(1).collect().map(row => row.getAs(columnName).toString) + val dataFilter = EqualTo(attribute(columnName), Literal(reckey(0))) + verifyFilePruning(hudiOpts, dataFilter) + } + + private def attribute(partition: String): AttributeReference = { + AttributeReference(partition, StringType, true)() + } + + + private def verifyFilePruning(opts: Map[String, String], dataFilter: Expression): Unit = { + // with data skipping + val commonOpts = opts + ("path" -> basePath) + metaClient = HoodieTableMetaClient.reload(metaClient) + var fileIndex = HoodieFileIndex(spark, metaClient, None, commonOpts, includeLogFiles = true) + val filteredPartitionDirectories = fileIndex.listFiles(Seq(), Seq(dataFilter)) + val filteredFilesCount = filteredPartitionDirectories.flatMap(s => s.files).size + assertTrue(filteredFilesCount < getLatestDataFilesCount(opts)) + + // with no data skipping + fileIndex = HoodieFileIndex(spark, metaClient, None, commonOpts + (DataSourceReadOptions.ENABLE_DATA_SKIPPING.key -> "false"), includeLogFiles = true) + val filesCountWithNoSkipping = fileIndex.listFiles(Seq(), Seq(dataFilter)).flatMap(s => s.files).size + assertTrue(filesCountWithNoSkipping == getLatestDataFilesCount(opts)) + } + + private def getLatestDataFilesCount(opts: Map[String, String], includeLogFiles: Boolean = true) = { + var totalLatestDataFiles = 0L + getTableFileSystemView(opts).getAllLatestFileSlicesBeforeOrOn(metaClient.getActiveTimeline.lastInstant().get().getTimestamp) + .values() + .forEach(JFunction.toJavaConsumer[java.util.stream.Stream[FileSlice]] + (slices => slices.forEach(JFunction.toJavaConsumer[FileSlice]( + slice => totalLatestDataFiles += (if (includeLogFiles) slice.getLogFiles.count() else 0) + + (if (slice.getBaseFile.isPresent) 1 else 0))))) + totalLatestDataFiles + } + + private def getTableFileSystemView(opts: Map[String, String]): HoodieMetadataFileSystemView = { + new HoodieMetadataFileSystemView(metaClient, metaClient.getActiveTimeline, metadataWriter(getWriteConfig(opts)).getTableMetadata) + } + + private def doWriteAndValidateBloomFilters(hudiOpts: Map[String, String], + operation: String, + saveMode: SaveMode, + shouldValidate: Boolean = true): Unit = { + var latestBatch: mutable.Buffer[String] = null + if (operation == UPSERT_OPERATION_OPT_VAL) { + val instantTime = getInstantTime() + val records = recordsToStrings(dataGen.generateUniqueUpdates(instantTime, 1)) + records.addAll(recordsToStrings(dataGen.generateInserts(instantTime, 1))) + latestBatch = records.asScala + } else if (operation == INSERT_OVERWRITE_OPERATION_OPT_VAL) { + latestBatch = recordsToStrings(dataGen.generateInsertsForPartition( + getInstantTime(), 5, dataGen.getPartitionPaths.last)).asScala + } else { + latestBatch = recordsToStrings(dataGen.generateInserts(getInstantTime(), 5)).asScala + } + val latestBatchDf = spark.read.json(spark.sparkContext.parallelize(latestBatch.toSeq, 2)) + latestBatchDf.cache() + latestBatchDf.write.format("org.apache.hudi") + .options(hudiOpts) + .option(DataSourceWriteOptions.OPERATION.key, operation) + .mode(saveMode) + .save(basePath) + + metaClient = HoodieTableMetaClient.reload(metaClient) + calculateMergedDf(latestBatchDf, operation) + if (shouldValidate) { + validateBloomFiltersIndex(hudiOpts) + } + } + + private def getInstantTime(): String = { + String.format("%03d", new Integer(instantTime.incrementAndGet())) + } + + private def validateBloomFiltersIndex(hudiOpts: Map[String, String]): Unit = { + metaClient = HoodieTableMetaClient.reload(metaClient) + val writeConfig = getWriteConfig(hudiOpts) + val metadata = metadataWriter(writeConfig).getTableMetadata + val readDf = spark.read.format("hudi").load(basePath) + val rowArr = readDf.collect() + + assertTrue(rowArr.length > 0) + + for (row <- rowArr) { + val recordKey: String = row.getAs("_hoodie_record_key") + val partitionPath: String = row.getAs("_hoodie_partition_path") + val fileName: String = row.getAs("_hoodie_file_name") + val bloomFilter = metadata.getBloomFilter(partitionPath, fileName) + assertTrue(bloomFilter.isPresent, "BloomFilter should be present for " + fileName) + assertTrue(bloomFilter.get().mightContain(recordKey)) + } + } + + private def getWriteConfig(hudiOpts: Map[String, String]): HoodieWriteConfig = { + val props = TypedProperties.fromMap(JavaConverters.mapAsJavaMapConverter(hudiOpts).asJava) + HoodieWriteConfig.newBuilder() + .withProps(props) + .withPath(basePath) + .build() + } + + private def calculateMergedDf(latestBatchDf: DataFrame, operation: String): DataFrame = { + val prevDfOpt = mergedDfList.lastOption + if (prevDfOpt.isEmpty) { + mergedDfList = mergedDfList :+ latestBatchDf + sparkSession.emptyDataFrame + } else { + if (operation == INSERT_OVERWRITE_TABLE_OPERATION_OPT_VAL) { + mergedDfList = mergedDfList :+ latestBatchDf + // after insert_overwrite_table, all previous snapshot's records should be deleted from RLI Review Comment: Not sure why the bloom_filter index is related with RLI? -- This is an automated message from the Apache Git Service. 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