lokeshj1703 commented on code in PR #9345:
URL: https://github.com/apache/hudi/pull/9345#discussion_r1285197000
##########
hudi-spark-datasource/hudi-spark-common/src/main/scala/org/apache/hudi/HoodieFileIndex.scala:
##########
@@ -122,86 +141,132 @@ case class HoodieFileIndex(spark: SparkSession,
* @return list of PartitionDirectory containing partition to base files
mapping
*/
override def listFiles(partitionFilters: Seq[Expression], dataFilters:
Seq[Expression]): Seq[PartitionDirectory] = {
- // Look up candidate files names in the col-stats index, if all of the
following conditions are true
- // - Data-skipping is enabled
- // - Col-Stats Index is present
- // - List of predicates (filters) is present
- val candidateFilesNamesOpt: Option[Set[String]] =
+ // Prune the partition path by the partition filters
+ // NOTE: Non-partitioned tables are assumed to consist from a single
partition
+ // encompassing the whole table
+ val partitionsAndFileSlices =
getFileSlicesForPrunedPartitions(partitionFilters)
+ val partitionsAndFilteredFileSlices = filterFileSlices(dataFilters,
partitionsAndFileSlices).map {
+ case (partitionOpt, fileSlices) =>
+ val allCandidateFiles: Seq[FileStatus] = fileSlices.flatMap(fs => {
+ val baseFileStatusOpt =
getBaseFileStatus(Option.apply(fs.getBaseFile.orElse(null)))
+ val logFilesStatus = if (includeLogFiles) {
+
fs.getLogFiles.map[FileStatus](JFunction.toJavaFunction[HoodieLogFile,
FileStatus](lf => lf.getFileStatus))
+ } else {
+ java.util.stream.Stream.empty()
+ }
+ val files =
logFilesStatus.collect(Collectors.toList[FileStatus]).asScala
+ baseFileStatusOpt.foreach(f => files.append(f))
+ files
+ })
+
+ PartitionDirectory(InternalRow.fromSeq(partitionOpt.get.values),
allCandidateFiles)
+ }
+
+ hasPushedDownPartitionPredicates = true
+
+ if (shouldReadAsPartitionedTable()) {
+ partitionsAndFilteredFileSlices
+ } else {
+ Seq(PartitionDirectory(InternalRow.empty,
partitionsAndFilteredFileSlices.flatMap(_.files)))
+ }
+ }
+
+ def filterFileSlices(dataFilters: Seq[Expression], partitionAndFileSlices:
Seq[(Option[BaseHoodieTableFileIndex.PartitionPath], Seq[FileSlice])])
+ : Seq[(Option[BaseHoodieTableFileIndex.PartitionPath], Seq[FileSlice])] = {
+ // If there are no data filters, return all the file slices.
+ // If there are no file slices, return empty list.
+ if (partitionAndFileSlices.isEmpty || dataFilters.isEmpty) {
+ partitionAndFileSlices
+ } else {
+ // Look up candidate files names in the col-stats index, if all of the
following conditions are true
+ // - Data-skipping is enabled
+ // - Col-Stats Index is present
+ // - List of predicates (filters) is present
+ val candidateFilesNamesOpt: Option[Set[String]] =
lookupCandidateFilesInMetadataTable(dataFilters) match {
case Success(opt) => opt
case Failure(e) =>
logError("Failed to lookup candidate files in File Index", e)
spark.sqlContext.getConf(DataSkippingFailureMode.configName,
DataSkippingFailureMode.Fallback.value) match {
case DataSkippingFailureMode.Fallback.value => Option.empty
- case DataSkippingFailureMode.Strict.value => throw new
HoodieException(e);
+ case DataSkippingFailureMode.Strict.value => throw new
HoodieException(e);
}
}
- logDebug(s"Overlapping candidate files from Column Stats Index:
${candidateFilesNamesOpt.getOrElse(Set.empty)}")
-
- var totalFileSize = 0
- var candidateFileSize = 0
+ logDebug(s"Overlapping candidate files from Column Stats Index:
${candidateFilesNamesOpt.getOrElse(Set.empty)}")
+
+ var totalFileSliceSize = 0
+ var candidateFileSliceSize = 0
+
+ val partitionsAndFilteredFileSlices = partitionAndFileSlices.map {
+ case (partitionOpt, fileSlices) =>
+ // Filter in candidate files based on the col-stats index lookup
+ val candidateFileSlices: Seq[FileSlice] = {
+ fileSlices.filter(fs => {
+ val baseFileStatusOpt =
getBaseFileStatus(Option.apply(fs.getBaseFile.orElse(null)))
+ val logFiles =
fs.getLogFiles.collect(Collectors.toSet[HoodieLogFile]).asScala.toSet[HoodieLogFile]
+ // NOTE: This predicate is true when {@code Option} is empty
+ if (candidateFilesNamesOpt.forall(files =>
baseFileStatusOpt.exists(f => files.contains(f.getPath.getName))
+ || files.intersect(logFiles.map(f =>
f.getPath.getName)).nonEmpty)) {
Review Comment:
Simplified.
##########
hudi-spark-datasource/hudi-spark-common/src/main/scala/org/apache/hudi/HoodieFileIndex.scala:
##########
@@ -122,86 +141,132 @@ case class HoodieFileIndex(spark: SparkSession,
* @return list of PartitionDirectory containing partition to base files
mapping
*/
override def listFiles(partitionFilters: Seq[Expression], dataFilters:
Seq[Expression]): Seq[PartitionDirectory] = {
- // Look up candidate files names in the col-stats index, if all of the
following conditions are true
- // - Data-skipping is enabled
- // - Col-Stats Index is present
- // - List of predicates (filters) is present
- val candidateFilesNamesOpt: Option[Set[String]] =
+ // Prune the partition path by the partition filters
+ // NOTE: Non-partitioned tables are assumed to consist from a single
partition
+ // encompassing the whole table
+ val partitionsAndFileSlices =
getFileSlicesForPrunedPartitions(partitionFilters)
+ val partitionsAndFilteredFileSlices = filterFileSlices(dataFilters,
partitionsAndFileSlices).map {
+ case (partitionOpt, fileSlices) =>
+ val allCandidateFiles: Seq[FileStatus] = fileSlices.flatMap(fs => {
+ val baseFileStatusOpt =
getBaseFileStatus(Option.apply(fs.getBaseFile.orElse(null)))
+ val logFilesStatus = if (includeLogFiles) {
+
fs.getLogFiles.map[FileStatus](JFunction.toJavaFunction[HoodieLogFile,
FileStatus](lf => lf.getFileStatus))
+ } else {
+ java.util.stream.Stream.empty()
+ }
+ val files =
logFilesStatus.collect(Collectors.toList[FileStatus]).asScala
+ baseFileStatusOpt.foreach(f => files.append(f))
+ files
+ })
+
+ PartitionDirectory(InternalRow.fromSeq(partitionOpt.get.values),
allCandidateFiles)
+ }
+
+ hasPushedDownPartitionPredicates = true
+
+ if (shouldReadAsPartitionedTable()) {
+ partitionsAndFilteredFileSlices
+ } else {
+ Seq(PartitionDirectory(InternalRow.empty,
partitionsAndFilteredFileSlices.flatMap(_.files)))
+ }
+ }
+
+ def filterFileSlices(dataFilters: Seq[Expression], partitionAndFileSlices:
Seq[(Option[BaseHoodieTableFileIndex.PartitionPath], Seq[FileSlice])])
Review Comment:
Addressed
##########
hudi-spark-datasource/hudi-spark/src/test/scala/org/apache/hudi/functional/TestColumnStatsIndexWithSQL.scala:
##########
@@ -0,0 +1,379 @@
+/*
+ * 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.{DELETE_OPERATION_OPT_VAL,
PRECOMBINE_FIELD, RECORDKEY_FIELD}
+import org.apache.hudi.async.SparkAsyncCompactService
+import org.apache.hudi.client.SparkRDDWriteClient
+import org.apache.hudi.client.common.HoodieSparkEngineContext
+import org.apache.hudi.client.utils.MetadataConversionUtils
+import org.apache.hudi.common.config.HoodieMetadataConfig
+import org.apache.hudi.common.model.{FileSlice, HoodieCommitMetadata,
HoodieTableType, WriteOperationType}
+import org.apache.hudi.common.table.HoodieTableConfig
+import org.apache.hudi.common.table.timeline.HoodieInstant
+import org.apache.hudi.config.{HoodieCompactionConfig, HoodieIndexConfig,
HoodieWriteConfig}
+import org.apache.hudi.functional.ColumnStatIndexTestBase.ColumnStatsTestCase
+import org.apache.hudi.index.HoodieIndex.IndexType.INMEMORY
+import org.apache.hudi.metadata.HoodieMetadataFileSystemView
+import org.apache.hudi.util.{JFunction, JavaConversions}
+import org.apache.hudi.{DataSourceReadOptions, DataSourceWriteOptions,
HoodieFileIndex}
+import org.apache.spark.sql._
+import org.apache.spark.sql.catalyst.expressions.{AttributeReference,
Expression, GreaterThan, Literal}
+import org.apache.spark.sql.types.StringType
+import org.junit.jupiter.api.Assertions.{assertEquals, assertFalse, assertTrue}
+import org.junit.jupiter.api.Disabled
+import org.junit.jupiter.params.ParameterizedTest
+import org.junit.jupiter.params.provider.MethodSource
+
+import java.util.Properties
+import scala.collection.JavaConverters
+
+class TestColumnStatsIndexWithSQL extends ColumnStatIndexTestBase {
+
+ @ParameterizedTest
+ @MethodSource(Array("testMetadataColumnStatsIndexParams"))
+ def testMetadataColumnStatsIndexWithSQL(testCase: ColumnStatsTestCase): Unit
= {
+ val metadataOpts = Map(
+ HoodieMetadataConfig.ENABLE.key -> "true",
+ HoodieMetadataConfig.ENABLE_METADATA_INDEX_COLUMN_STATS.key -> "true"
+ )
+
+ val commonOpts = Map(
+ "hoodie.insert.shuffle.parallelism" -> "4",
+ "hoodie.upsert.shuffle.parallelism" -> "4",
+ HoodieWriteConfig.TBL_NAME.key -> "hoodie_test",
+ DataSourceWriteOptions.TABLE_TYPE.key -> testCase.tableType.toString,
+ RECORDKEY_FIELD.key -> "c1",
+ PRECOMBINE_FIELD.key -> "c1",
+ HoodieTableConfig.POPULATE_META_FIELDS.key -> "true",
+ DataSourceReadOptions.ENABLE_DATA_SKIPPING.key -> "true",
+ DataSourceReadOptions.QUERY_TYPE.key ->
DataSourceReadOptions.QUERY_TYPE_INCREMENTAL_OPT_VAL
+ ) ++ metadataOpts
+ setupTable(testCase, metadataOpts, commonOpts, shouldValidate = true)
+ verifyFileIndexAndSQLQueries(commonOpts)
+ }
+
+ @ParameterizedTest
+ @MethodSource(Array("testMetadataColumnStatsIndexParamsForMOR"))
+ def testMetadataColumnStatsIndexSQLWithInMemoryIndex(testCase:
ColumnStatsTestCase): Unit = {
+ val metadataOpts = Map(
+ HoodieMetadataConfig.ENABLE.key -> "true",
+ HoodieMetadataConfig.ENABLE_METADATA_INDEX_COLUMN_STATS.key -> "true"
+ )
+
+ val commonOpts = Map(
+ "hoodie.insert.shuffle.parallelism" -> "4",
+ "hoodie.upsert.shuffle.parallelism" -> "4",
+ HoodieWriteConfig.TBL_NAME.key -> "hoodie_test",
+ DataSourceWriteOptions.TABLE_TYPE.key -> testCase.tableType.toString,
+ RECORDKEY_FIELD.key -> "c1",
+ PRECOMBINE_FIELD.key -> "c1",
+ HoodieTableConfig.POPULATE_META_FIELDS.key -> "true",
+ DataSourceReadOptions.ENABLE_DATA_SKIPPING.key -> "true",
+ DataSourceReadOptions.QUERY_TYPE.key ->
DataSourceReadOptions.QUERY_TYPE_INCREMENTAL_OPT_VAL,
+ HoodieIndexConfig.INDEX_TYPE.key() -> INMEMORY.name()
+ ) ++ metadataOpts
+
+ doWriteAndValidateColumnStats(testCase, metadataOpts, commonOpts,
+ dataSourcePath = "index/colstats/input-table-json",
+ expectedColStatsSourcePath =
"index/colstats/column-stats-index-table.json",
+ operation = DataSourceWriteOptions.INSERT_OPERATION_OPT_VAL,
+ saveMode = SaveMode.Overwrite,
+ shouldValidate = false)
+
+ assertEquals(4, getLatestDataFilesCount(commonOpts))
+ assertEquals(0, getLatestDataFilesCount(commonOpts, includeLogFiles =
false))
+ var dataFilter = GreaterThan(attribute("c5"), literal("90"))
+ verifyPruningFileCount(commonOpts, dataFilter, 3)
+ dataFilter = GreaterThan(attribute("c5"), literal("95"))
+ verifyPruningFileCount(commonOpts, dataFilter, 1)
+ }
+
+ @ParameterizedTest
+ @MethodSource(Array("testMetadataColumnStatsIndexParams"))
+ def testMetadataColumnStatsIndexDeletionWithSQL(testCase:
ColumnStatsTestCase): Unit = {
+ val metadataOpts = Map(
+ HoodieMetadataConfig.ENABLE.key -> "true",
+ HoodieMetadataConfig.ENABLE_METADATA_INDEX_COLUMN_STATS.key -> "true"
+ )
+
+ val commonOpts = Map(
+ "hoodie.insert.shuffle.parallelism" -> "4",
+ "hoodie.upsert.shuffle.parallelism" -> "4",
+ HoodieWriteConfig.TBL_NAME.key -> "hoodie_test",
+ DataSourceWriteOptions.TABLE_TYPE.key -> testCase.tableType.toString,
+ RECORDKEY_FIELD.key -> "c1",
+ PRECOMBINE_FIELD.key -> "c1",
+ HoodieTableConfig.POPULATE_META_FIELDS.key -> "true",
+ DataSourceReadOptions.ENABLE_DATA_SKIPPING.key -> "true",
+ DataSourceReadOptions.QUERY_TYPE.key ->
DataSourceReadOptions.QUERY_TYPE_INCREMENTAL_OPT_VAL
+ ) ++ metadataOpts
+ setupTable(testCase, metadataOpts, commonOpts, shouldValidate = true)
+ val lastDf = dfList.last
+
+ lastDf.write.format("org.apache.hudi")
+ .options(commonOpts)
+ .option(DataSourceWriteOptions.OPERATION.key, DELETE_OPERATION_OPT_VAL)
+ .mode(SaveMode.Append)
+ .save(basePath)
+ verifyFileIndexAndSQLQueries(commonOpts,
isTableDataSameAsAfterSecondInstant = true)
+
+ // Add the last df back and verify the queries
+ doWriteAndValidateColumnStats(testCase, metadataOpts, commonOpts,
+ dataSourcePath = "index/colstats/update-input-table-json",
+ expectedColStatsSourcePath = "",
+ operation = DataSourceWriteOptions.UPSERT_OPERATION_OPT_VAL,
+ saveMode = SaveMode.Append,
+ shouldValidate = false)
+ verifyFileIndexAndSQLQueries(commonOpts, verifyFileCount = false)
+ }
+
+ @ParameterizedTest
+ @MethodSource(Array("testMetadataColumnStatsIndexParamsForMOR"))
+ def testMetadataColumnStatsIndexCompactionWithSQL(testCase:
ColumnStatsTestCase): Unit = {
+ val metadataOpts = Map(
+ HoodieMetadataConfig.ENABLE.key -> "true",
+ HoodieMetadataConfig.ENABLE_METADATA_INDEX_COLUMN_STATS.key -> "true"
+ )
+
+ val commonOpts = Map(
+ "hoodie.insert.shuffle.parallelism" -> "4",
+ "hoodie.upsert.shuffle.parallelism" -> "4",
+ HoodieWriteConfig.TBL_NAME.key -> "hoodie_test",
+ DataSourceWriteOptions.TABLE_TYPE.key -> testCase.tableType.toString,
+ RECORDKEY_FIELD.key -> "c1",
+ PRECOMBINE_FIELD.key -> "c1",
+ HoodieTableConfig.POPULATE_META_FIELDS.key -> "true",
+ DataSourceReadOptions.ENABLE_DATA_SKIPPING.key -> "true",
+ DataSourceReadOptions.QUERY_TYPE.key ->
DataSourceReadOptions.QUERY_TYPE_INCREMENTAL_OPT_VAL,
+ HoodieCompactionConfig.INLINE_COMPACT.key() -> "true",
+ HoodieCompactionConfig.INLINE_COMPACT_NUM_DELTA_COMMITS.key() -> "1"
+ ) ++ metadataOpts
+ setupTable(testCase, metadataOpts, commonOpts, shouldValidate = false)
+
+ assertFalse(hasLogFiles())
+ verifyFileIndexAndSQLQueries(commonOpts)
+ }
+
+ @Disabled("Needs more work")
+ @ParameterizedTest
+ @MethodSource(Array("testMetadataColumnStatsIndexParamsForMOR"))
+ def testMetadataColumnStatsIndexAsyncCompactionWithSQL(testCase:
ColumnStatsTestCase): Unit = {
+ val metadataOpts = Map(
+ HoodieMetadataConfig.ENABLE.key -> "true",
+ HoodieMetadataConfig.ENABLE_METADATA_INDEX_COLUMN_STATS.key -> "true"
+ )
+
+ val commonOpts = Map(
+ "hoodie.insert.shuffle.parallelism" -> "4",
+ "hoodie.upsert.shuffle.parallelism" -> "4",
+ HoodieWriteConfig.TBL_NAME.key -> "hoodie_test",
+ DataSourceWriteOptions.TABLE_TYPE.key -> testCase.tableType.toString,
+ RECORDKEY_FIELD.key -> "c1",
+ PRECOMBINE_FIELD.key -> "c1",
+ HoodieTableConfig.POPULATE_META_FIELDS.key -> "true",
+ DataSourceReadOptions.ENABLE_DATA_SKIPPING.key -> "true",
+ DataSourceReadOptions.QUERY_TYPE.key ->
DataSourceReadOptions.QUERY_TYPE_INCREMENTAL_OPT_VAL,
+ HoodieCompactionConfig.INLINE_COMPACT_NUM_DELTA_COMMITS.key() -> "1"
+ ) ++ metadataOpts
+ setupTable(testCase, metadataOpts, commonOpts, shouldValidate = false)
+
+ val writeClient = new SparkRDDWriteClient(new
HoodieSparkEngineContext(jsc), getWriteConfig(commonOpts))
+ writeClient.scheduleCompaction(org.apache.hudi.common.util.Option.empty())
+ val compactionService = new SparkAsyncCompactService(new
HoodieSparkEngineContext(jsc), writeClient)
+
compactionService.enqueuePendingAsyncServiceInstant(metaClient.reloadActiveTimeline().lastInstant().get())
+ compactionService.start(JFunction.toJavaFunction(b => true))
+ compactionService.waitTillPendingAsyncServiceInstantsReducesTo(0)
+ assertFalse(hasLogFiles())
+ verifyFileIndexAndSQLQueries(commonOpts)
+ }
+
+ private def setupTable(testCase: ColumnStatsTestCase, metadataOpts:
Map[String, String], commonOpts: Map[String, String], shouldValidate: Boolean):
Unit = {
+ doWriteAndValidateColumnStats(testCase, metadataOpts, commonOpts,
+ dataSourcePath = "index/colstats/input-table-json",
+ expectedColStatsSourcePath =
"index/colstats/column-stats-index-table.json",
+ operation = DataSourceWriteOptions.INSERT_OPERATION_OPT_VAL,
+ saveMode = SaveMode.Overwrite)
+
+ doWriteAndValidateColumnStats(testCase, metadataOpts, commonOpts,
+ dataSourcePath = "index/colstats/another-input-table-json",
+ expectedColStatsSourcePath =
"index/colstats/updated-column-stats-index-table.json",
+ operation = DataSourceWriteOptions.UPSERT_OPERATION_OPT_VAL,
+ saveMode = SaveMode.Append)
+
+ // NOTE: MOR and COW have different fixtures since MOR is bearing
delta-log files (holding
+ // deferred updates), diverging from COW
+ val expectedColStatsSourcePath = if (testCase.tableType ==
HoodieTableType.COPY_ON_WRITE) {
+ "index/colstats/cow-updated2-column-stats-index-table.json"
+ } else {
+ "index/colstats/mor-updated2-column-stats-index-table.json"
+ }
+
+ doWriteAndValidateColumnStats(testCase, metadataOpts, commonOpts,
+ dataSourcePath = "index/colstats/update-input-table-json",
+ expectedColStatsSourcePath = expectedColStatsSourcePath,
+ operation = DataSourceWriteOptions.UPSERT_OPERATION_OPT_VAL,
+ saveMode = SaveMode.Append,
+ shouldValidate)
+ }
+
+ def verifyFileIndexAndSQLQueries(opts: Map[String, String],
isTableDataSameAsAfterSecondInstant: Boolean = false, verifyFileCount: Boolean
= true): Unit = {
+ var commonOpts = opts
+ val inputDF1 = spark.read.format("hudi")
+ .options(commonOpts)
+ .option("as.of.instant",
metaClient.getActiveTimeline.getInstants.get(1).getTimestamp)
+ .option(DataSourceReadOptions.QUERY_TYPE.key,
DataSourceReadOptions.QUERY_TYPE_SNAPSHOT_OPT_VAL)
+ .option(DataSourceReadOptions.ENABLE_DATA_SKIPPING.key, "false")
+ .load(basePath)
+ inputDF1.createOrReplaceTempView("tbl")
+ val numRecordsWithC5ColumnGreaterThan70 = spark.sql("select * from tbl
where c5 > 70").count()
+ // verify snapshot query
+ verifySQLQueries(numRecordsWithC5ColumnGreaterThan70,
DataSourceReadOptions.QUERY_TYPE_SNAPSHOT_OPT_VAL, commonOpts,
isTableDataSameAsAfterSecondInstant)
+
+ // verify read_optimized query
+ verifySQLQueries(numRecordsWithC5ColumnGreaterThan70,
DataSourceReadOptions.QUERY_TYPE_READ_OPTIMIZED_OPT_VAL, commonOpts,
isTableDataSameAsAfterSecondInstant)
+
+ // verify incremental query
+ verifySQLQueries(numRecordsWithC5ColumnGreaterThan70,
DataSourceReadOptions.QUERY_TYPE_INCREMENTAL_OPT_VAL, commonOpts,
isTableDataSameAsAfterSecondInstant)
+ commonOpts = commonOpts +
(DataSourceReadOptions.INCREMENTAL_FALLBACK_TO_FULL_TABLE_SCAN_FOR_NON_EXISTING_FILES.key
-> "true")
+ verifySQLQueries(numRecordsWithC5ColumnGreaterThan70,
DataSourceReadOptions.QUERY_TYPE_INCREMENTAL_OPT_VAL, commonOpts,
isTableDataSameAsAfterSecondInstant)
+
+ if (verifyFileCount) {
+ // First commit creates 4 parquet files
+ // Second commit creates 4 parquet files
+ // Last commit creates one parquet file and 4 log files - total 5 files
(for MOR)
+ // and 4 parquet files and no log file (for COW)
+ // therefore on deletion all those 5 files create a new log file whereas
COW file count remains same
+ var numFiles = if (isTableMOR && isTableDataSameAsAfterSecondInstant) 17
else if (hasLogFiles()) 12 else 8
+ var dataFilter = GreaterThan(attribute("c5"), literal("70"))
+ verifyPruningFileCount(commonOpts, dataFilter, numFiles)
+
+ dataFilter = GreaterThan(attribute("c5"), literal("90"))
+ numFiles = if (isTableMOR && isTableDataSameAsAfterSecondInstant) 11
else if (hasLogFiles()) 7 else 4
+ verifyPruningFileCount(commonOpts, dataFilter, numFiles)
+ }
+ }
+
+ private def verifyPruningFileCount(opts: Map[String, String], dataFilter:
Expression, numFiles: Int): Unit = {
+ val fileIndex = HoodieFileIndex(spark, metaClient, None, opts + ("path" ->
basePath))
+ fileIndex.setIncludeLogFiles(isTableMOR())
+ val filteredPartitionDirectories = fileIndex.listFiles(Seq(),
Seq(dataFilter))
+ val filteredFilesCount = filteredPartitionDirectories.flatMap(s =>
s.files).size
+ assertTrue(filteredFilesCount < getLatestDataFilesCount(opts))
+ assertEquals(filteredFilesCount, numFiles)
+ }
+
+ private def getLatestDataFilesCount(opts: Map[String, String],
includeLogFiles: Boolean = true) = {
+ var totalLatestDataFiles = 0L
+
getTableFileSystenView(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 getTableFileSystenView(opts: Map[String, String]):
HoodieMetadataFileSystemView = {
+ new HoodieMetadataFileSystemView(metaClient, metaClient.getActiveTimeline,
metadataWriter(getWriteConfig(opts)).getTableMetadata)
+ }
+
+ protected def getWriteConfig(hudiOpts: Map[String, String]):
HoodieWriteConfig = {
+ val props = new Properties()
+ props.putAll(JavaConverters.mapAsJavaMapConverter(hudiOpts).asJava)
+ HoodieWriteConfig.newBuilder()
+ .withProps(props)
+ .withPath(basePath)
+ .build()
+ }
+
+ private def attribute(partition: String): AttributeReference = {
+ AttributeReference(partition, StringType, true)()
+ }
+
+ private def literal(value: String): Literal = {
+ Literal.create(value)
+ }
+
+ private def
verifySQLQueries(numRecordsWithC5ColumnGreaterThan70AtPrevInstant: Long,
queryType: String, opts: Map[String, String], isLastOperationDelete: Boolean):
Unit = {
+ // 2 records are updated with c5 greater than 70 and one record is
inserted with c5 value greater than 70
+ var commonOpts: Map[String, String] = opts
+ createSQLTable(commonOpts, queryType)
+ val increment = if
(queryType.equals(DataSourceReadOptions.QUERY_TYPE_READ_OPTIMIZED_OPT_VAL) &&
hasLogFiles()) {
+ 1 // only one insert
+ } else if (isLastOperationDelete) {
+ 0 // no increment
+ } else {
+ 3 // one insert and two upserts
+ }
+ assertEquals(spark.sql("select * from tbl where c5 > 70").count(),
numRecordsWithC5ColumnGreaterThan70AtPrevInstant + increment)
+ val numRecordsWithC5ColumnGreaterThan50WithDataSkipping =
spark.sql("select * from tbl where c5 > 70").count()
+
+ if
(queryType.equals(DataSourceReadOptions.QUERY_TYPE_INCREMENTAL_OPT_VAL)) {
+ createIncrementalSQLTable(commonOpts,
metaClient.reloadActiveTimeline().getInstants.get(1).getTimestamp)
+ assertEquals(spark.sql("select * from tbl where c5 > 70").count(), if
(isLastOperationDelete) 0 else 3)
Review Comment:
Addressed
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