dramaticlly commented on code in PR #11045:
URL: https://github.com/apache/iceberg/pull/11045#discussion_r1739202341
##########
spark/v3.5/spark/src/main/java/org/apache/iceberg/spark/procedures/CreateChangelogViewProcedure.java:
##########
@@ -183,21 +185,26 @@ private boolean shouldComputeUpdateImages(ProcedureInput
input) {
}
private Dataset<Row> removeCarryoverRows(Dataset<Row> df, boolean
netChanges) {
- Predicate<String> columnsToKeep;
- if (netChanges) {
- Set<String> metadataColumn =
- Sets.newHashSet(
- MetadataColumns.CHANGE_TYPE.name(),
- MetadataColumns.CHANGE_ORDINAL.name(),
- MetadataColumns.COMMIT_SNAPSHOT_ID.name());
-
- columnsToKeep = column -> !metadataColumn.contains(column);
- } else {
- columnsToKeep = column ->
!column.equals(MetadataColumns.CHANGE_TYPE.name());
- }
+ Set<String> metadataColumn =
+ netChanges
+ ? Sets.newHashSet(
+ MetadataColumns.CHANGE_TYPE.name(),
+ MetadataColumns.CHANGE_ORDINAL.name(),
+ MetadataColumns.COMMIT_SNAPSHOT_ID.name())
+ : Sets.newHashSet(MetadataColumns.CHANGE_TYPE.name());
+
+ Predicate<StructField> columnsToDiscard =
+ field ->
+ metadataColumn.contains(field.name())
+ // avoid sort on incomparable columns
+ || field.dataType() instanceof MapType
+ || field.dataType() instanceof BinaryType;
Column[] repartitionSpec =
-
Arrays.stream(df.columns()).filter(columnsToKeep).map(df::col).toArray(Column[]::new);
+ Arrays.stream(df.schema().fields())
+ .filter(Predicate.not(columnsToDiscard))
Review Comment:
I think spark does not seem to complain about have those columns in
repartitioned spec, if you have more spark background can you share a bit more
what's the best practise and whether we shall remove?
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