codope commented on code in PR #11043: URL: https://github.com/apache/hudi/pull/11043#discussion_r1621821717
########## hudi-spark-datasource/hudi-spark/src/test/scala/org/apache/hudi/functional/TestBloomFiltersIndexSupport.scala: ########## @@ -0,0 +1,261 @@ +/* + * 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.{assertEquals, 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) + } + + 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]): Unit = { + val reckey = mergedDfList.last.limit(1).collect().map(row => row.getAs("_row_key").toString) + val dataFilter = EqualTo(attribute("_row_key"), Literal(reckey(0))) + assertEquals(1, spark.sql("select * from " + sqlTempTable + " where " + dataFilter.sql).count()) + 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)) Review Comment: > can we make a case avoid this problem? before hash, will judge min/max first. For this we need to have column stats enabled as well right. We don't want bloom to be dependent on availability of colstats. However, what we can do is to expose a config and if column stats is also enabled, then we can prune based on min/max. It is going to be a fairly involved change. HUDI-7820 to do this change. -- 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]
