wangyum commented on a change in pull request #31993:
URL: https://github.com/apache/spark/pull/31993#discussion_r613652117
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SchemaPruning.scala
##########
@@ -32,11 +33,10 @@ object SchemaPruning {
// in the resulting schema may differ from their ordering in the logical
relation's
// original schema
val mergedSchema = requestedRootFields
- .map { case root: RootField => StructType(Array(root.field)) }
+ .map { root: RootField => StructType(Array(root.field)) }
.reduceLeft(_ merge _)
- val dataSchemaFieldNames = dataSchema.fieldNames.toSet
val mergedDataSchema =
- StructType(mergedSchema.filter(f =>
dataSchemaFieldNames.contains(f.name)))
+ StructType(dataSchema.map(s =>
mergedSchema.find(_.name.equals(s.name)).getOrElse(s)))
Review comment:
```scala
spark.sql(
"""
|CREATE TABLE t1 (
| _col0 INT,
| _col1 STRING,
| _col2 STRUCT<c1: STRING, c2: STRING, c3: STRING, c4: BIGINT>)
|USING ORC
|""".stripMargin)
spark.sql("SELECT _col0, _col2.c1 FROM t1").show
```
The origin schema is:
```
`_col0` INT,`_col1` STRING,`_col2` STRUCT<`c1`: STRING, `c2`: STRING, `c3`:
STRING, `c4`: BIGINT>
```
Before this PR, the `pruneDataSchema` returns:
```
`_col0` INT,`_col2` STRUCT<`c1`: STRING>
```
After this PR, the `pruneDataSchema` returns:
```
`_col0` INT,`_col1` STRING,`_col2` STRUCT<`c1`: STRING>
```
It only prune nested schemas.
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