Github user mallman commented on a diff in the pull request:
https://github.com/apache/spark/pull/22357#discussion_r216683076
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaPruning.scala
---
@@ -110,7 +110,17 @@ private[sql] object ParquetSchemaPruning extends
Rule[LogicalPlan] {
val projectionRootFields = projects.flatMap(getRootFields)
val filterRootFields = filters.flatMap(getRootFields)
- (projectionRootFields ++ filterRootFields).distinct
+ // Kind of expressions don't need to access any fields of a root
fields, e.g., `IsNotNull`.
+ // For them, if there are any nested fields accessed in the query, we
don't need to add root
+ // field access of above expressions.
+ // For example, for a query `SELECT name.first FROM contacts WHERE
name IS NOT NULL`,
+ // we don't need to read nested fields of `name` struct other than
`first` field.
--- End diff --
@viirya, I see your point about the difference between a complex type being
null and a subfield being null. So to answer the following query
select address from contacts where name is not null
do we need to read any of the fields in `name`? Or perhaps just read one
arbitrary field of simple type, like `name.first`? That's surprising, but I'm
starting to believe it's true.
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