raminqaf commented on code in PR #28758:
URL: https://github.com/apache/flink/pull/28758#discussion_r3621070903
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docs/content/docs/sql/reference/data-types.md:
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@@ -1515,19 +1515,62 @@ close to the semantics of JSON. Compared to `ROW` and
`STRUCTURED` type, `VARIAN
flexibility to support highly nested and evolving schema.
`VARIANT` allows for deeply nested data structures, such as arrays within
arrays, maps within maps,
-or combinations of both.This capability makes `VARIANT` ideal for scenarios
where data complexity
+or combinations of both. This capability makes `VARIANT` ideal for scenarios
where data complexity
and nesting are significant.
`VARIANT` allows schema evolution, enabling the storage of data with changing
or unknown schemas
without requiring upfront schema definition. For example, if a new field is
added to the data, it
can be directly incorporated into the `VARIANT` data without modifying the
table schema. This is
particularly useful in dynamic environments where schemas may evolve over time.
-A primitive-valued `VARIANT` can be converted to a scalar type with `CAST` or
`TRY_CAST`. Numeric
-targets are lenient: a variant holding any numeric value casts to any numeric
type, so a JSON integer
-such as `PARSE_JSON('42')` casts to `INT` or `BIGINT`. Other targets require
the stored value to be of
-the matching kind. When the value cannot be converted, `CAST` fails the job
and `TRY_CAST` returns
-`NULL`. Use the `JSON_STRING` function to obtain the JSON string
representation of a `VARIANT`.
+A `VARIANT` stores a single value of one of the following kinds: `NULL`,
`BOOLEAN`, `TINYINT`,
+`SMALLINT`, `INT`, `BIGINT`, `FLOAT`, `DOUBLE`, `DECIMAL` (up to precision
38), `STRING`, `DATE`,
+`TIMESTAMP`, `TIMESTAMP_LTZ`, `BYTES`, or a nested array or object.
`TIMESTAMP` and `TIMESTAMP_LTZ`
+are stored with microsecond precision and `DATE` as a day count. There is no
`TIME` kind.
Review Comment:
@davidradl This is how the implementation of the `BinaryVariant` defines it.
```
@Override
public Instant getInstant() throws VariantTypeException {
checkType(Type.TIMESTAMP_LTZ, getType());
return microsToInstant(BinaryVariantUtil.getLong(value, pos));
}
```
The implementation is done based on the [Encoding types defined in
Parquet](https://parquet.apache.org/docs/file-format/types/variantencoding/).
Flink implements the types `0` (NULL)-`16` (STRING). 17 (Time Macro),18
Timestamp (Nano),19 TimestampNTZ (Nano), 20 (UUID) are not implemented in Flink
yet.
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