dawidwys commented on a change in pull request #12725:
URL: https://github.com/apache/flink/pull/12725#discussion_r443371169
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File path:
flink-table/flink-table-api-java/src/main/java/org/apache/flink/table/api/internal/CatalogTableSchemaResolver.java
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@@ -76,16 +78,16 @@ public TableSchema resolve(TableSchema tableSchema) {
for (int i = 0; i < tableSchema.getFieldCount(); ++i) {
TableColumn tableColumn =
tableSchema.getTableColumns().get(i);
DataType fieldType = fieldTypes[i];
- if (tableColumn.isGenerated() &&
isProctimeType(tableColumn.getExpr().get(), tableSchema)) {
+ DataType exprType = null;
+ if (tableColumn.isGenerated()) {
+ exprType =
resolveExpressionDataType(tableColumn.getExpr().get(), tableSchema);
+ }
+
+ if (exprType != null && isProctime(exprType)) {
if (fieldNames[i].equals(rowtime)) {
throw new TableException("Watermark can
not be defined for a processing time attribute column.");
}
- TimestampType originalType = (TimestampType)
fieldType.getLogicalType();
- LogicalType proctimeType = new TimestampType(
- originalType.isNullable(),
- TimestampKind.PROCTIME,
- originalType.getPrecision());
- fieldType =
TypeConversions.fromLogicalToDataType(proctimeType);
+ fieldType = exprType;
Review comment:
I was also thinking about it and I can do that. Nevertheless in my
opinion this requires careful consideration.
What is the point of data type of a computed column, if we completely ignore
it?
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