dtenedor commented on code in PR #37501:
URL: https://github.com/apache/spark/pull/37501#discussion_r944913477
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRowConverter.scala:
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@@ -282,14 +239,17 @@ private[parquet] class ParquetRowConverter(
// Create a RowUpdater instance for converting Parquet objects to
Catalyst rows. If any fields
// in the Catalyst result schema have associated existence default
values, maintain a boolean
// array to track which fields have been explicitly assigned for each
row.
- val rowUpdater: RowUpdater =
- if (catalystType.hasExistenceDefaultValues) {
- resetExistenceDefaultsBitmask(catalystType)
- new RowUpdaterWithBitmask(
- currentRow, catalystFieldIndex,
catalystType.existenceDefaultsBitmask)
- } else {
- new RowUpdater(currentRow, catalystFieldIndex)
+ val rowUpdater: RowUpdater = new RowUpdater(currentRow,
catalystFieldIndex)
+ if (catalystType.hasExistenceDefaultValues) {
+ for (i <- 0 until catalystType.existenceDefaultValues.size) {
+ catalystType.existenceDefaultsBitmask(i) =
+ if (i < parquetType.getFieldCount) {
Review Comment:
We discussed this offline. I moved this code out of the
`parquetType.getFields.asScala.map { parquetField => ...` loop, and also ported
the explanation into a comment here:
```
// Assume the schema for a Parquet file-based table contains N fields. Then
if we later
// run a command "ALTER TABLE t ADD COLUMN c DEFAULT <value>" on the Parquet
table, this
// adds one field to the Catalyst schema. Then if we query the old files
with the new
// Catalyst schema, we should only apply the existence default value to all
columns > N.
```
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