nsivabalan commented on code in PR #5244:
URL: https://github.com/apache/hudi/pull/5244#discussion_r846676319
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hudi-spark-datasource/hudi-spark-common/src/main/scala/org/apache/hudi/HoodieFileIndex.scala:
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@@ -196,12 +191,20 @@ case class HoodieFileIndex(spark: SparkSession,
* @return list of pruned (data-skipped) candidate base-files' names
*/
private def lookupCandidateFilesInMetadataTable(queryFilters:
Seq[Expression]): Try[Option[Set[String]]] = Try {
- if (!isDataSkippingEnabled || queryFilters.isEmpty ||
!HoodieTableMetadataUtil.getCompletedMetadataPartitions(metaClient.getTableConfig)
- .contains(HoodieTableMetadataUtil.PARTITION_NAME_COLUMN_STATS)) {
+ // NOTE: Data Skipping is only effective when it references columns that
are indexed w/in
+ // the Column Stats Index (CSI). Following cases could not be
effectively handled by Data Skipping:
+ // - Expressions on top-level column's fields (ie, for ex filters
like "struct.field > 0", since
+ // CSI only contains stats for top-level columns, in this case
for "struct")
+ // - Any expression not directly referencing top-level column
(for ex, sub-queries, since there's
+ // nothing CSI in particular could be applied for)
+ lazy val queryReferencedColumns = collectReferencedColumns(spark,
queryFilters, schema)
+
+ if (!isMetadataTableEnabled || !isColumnStatsIndexAvailable ||
!isDataSkippingEnabled) {
Review Comment:
given that CSI does not have stats for top level columns, if predicate
references both top level and non-top level columns, we gonna skip leveraging
CSI is it? since anyways, for non top level column, we have to visit all data
files?
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