|TIMESTAMP|LONG|`0`, meaning 1970-01-01 00:00:00 UTC|Druid's `__time` column is reported as TIMESTAMP. Casts between string and timestamp types assume standard SQL formatting, such as `2000-01-02 03:04:05`, not ISO 8601 formatting. For handling other formats, use one of the [time functions](sql-scalar.md#date-and-time-functions).|
|DATE|LONG|`0`, meaning 1970-01-01|Casting TIMESTAMP to DATE rounds down the timestamp to the nearest day. Casts between string and date types assume standard SQL formatting—for example, `2000-01-02`. For handling other formats, use one of the [time functions](sql-scalar.md#date-and-time-functions).|
|ARRAY|ARRAY|`NULL`|Druid native array types work as SQL arrays, and multi-value strings can be converted to arrays. See [Arrays](#arrays) for more information.|
<sup>*</sup> Default value applies if `druid.generic.useDefaultValueForNull = true` (the default mode). Otherwise, the default value is `NULL` for all types.
Casts between two SQL types with the same Druid runtime type have no effect other than the exceptions noted in the table.
Casts between two SQL types that have different Druid runtime types generate a runtime cast in Druid.
If a value cannot be cast to the target type, as in `CAST('foo' AS BIGINT)`, Druid either substitutes a default
value (when `druid.generic.useDefaultValueForNull = true`, the default mode), or substitutes [NULL](#null-values) (when
`druid.generic.useDefaultValueForNull = false`). NULL values cast to non-nullable types are also substituted with a default value. For example, if `druid.generic.useDefaultValueForNull = true`, a null VARCHAR cast to BIGINT is converted to a zero.
Druid's native type system allows strings to have multiple values. These [multi-value string dimensions](multi-value-dimensions.md) are reported in SQL as type VARCHAR and can be
syntactically used like any other VARCHAR. Regular string functions that refer to multi-value string dimensions are applied to all values for each row individually.
You can treat multi-value string dimensions as arrays using special
[multi-value string functions](sql-multivalue-string-functions.md), which perform powerful array-aware operations, but retain their VARCHAR type and behavior.
Grouping by multi-value dimensions observes the native Druid multi-value aggregation behavior, which is similar to an implicit SQL UNNEST. See [Grouping](multi-value-dimensions.md#grouping) for more information.
> Because the SQL planner treats multi-value dimensions as VARCHAR, there are some inconsistencies between how they are handled in Druid SQL and in native queries. For instance, expressions involving multi-value dimensions may be incorrectly optimized by the Druid SQL planner. For example, `multi_val_dim = 'a' AND multi_val_dim = 'b'` is optimized to
`false`, even though it is possible for a single row to have both `'a'` and `'b'` as values for `multi_val_dim`.
>
> The SQL behavior of multi-value dimensions may change in a future release to more closely align with their behavior in native queries, but the [multi-value string functions](./sql-multivalue-string-functions.md) should be able to provide nearly all possible native functionality.
Druid supports ARRAY types constructed at query time. ARRAY types behave as standard SQL arrays, where results are grouped by matching entire arrays. This is in contrast to the implicit UNNEST that occurs when grouping on multi-value dimensions directly or when used with multi-value functions.
You can convert multi-value dimensions to standard SQL arrays explicitly with `MV_TO_ARRAY` or implicitly using [array functions](./sql-array-functions.md). You can also use the array functions to construct arrays from multiple columns.
You can use [schema auto-discovery](../ingestion/schema-design.md#schema-auto-discovery-for-dimensions) to detect and ingest arrays as ARRAY typed columns.
Druid supports storing nested data structures in segments using the native `COMPLEX<json>` type. See [Nested columns](./nested-columns.md) for more information.
You can interact with nested data using [JSON functions](./sql-json-functions.md), which can extract nested values, parse from string, serialize to string, and create new `COMPLEX<json>` structures.
In many cases, functions are provided to translate COMPLEX value types to STRING, which serves as a workaround solution until COMPLEX type functionality can be improved.
