stevenzwu commented on code in PR #11775:
URL: https://github.com/apache/iceberg/pull/11775#discussion_r2113243416
##########
api/src/main/java/org/apache/iceberg/expressions/Literals.java:
##########
@@ -300,8 +300,7 @@ public <T> Literal<T> to(Type type) {
case TIMESTAMP:
return (Literal<T>) new TimestampLiteral(value());
case TIMESTAMP_NANO:
- // assume micros and convert to nanos to match the behavior in the
timestamp case above
- return new TimestampLiteral(value()).to(type);
+ return (Literal<T>) new TimestampNanoLiteral(value());
Review Comment:
long literals is implicit and some precision interpretation rule has to be
applied. The good practice is probably using the timestamp functions to
explicit describe the precision, e.g. Spark SQL
```
TIMESTAMP '2021-06-30T02:00:00.000Z'
timestamp_micros(1230219000123123)
```
Until Spark adds timestamp_nano data type and SQL function, maybe Iceberg
timestamp_nano just doesn't need to work for Spark? Also Spark can't create an
Iceberg table with timestamp_nano field. probably also reasonable for read to
fail too.
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