sql-data-types.md

id	title	sidebar_label
sql-data-types	SQL data types	SQL data types

Apache Druid supports two query languages: Druid SQL and native queries. This document describes the SQL language.

Columns in Druid are associated with a specific data type. This topic describes supported data types in Druid SQL.

Standard types

Druid natively supports five basic column types: "long" (64 bit signed int), "float" (32 bit float), "double" (64 bit float) "string" (UTF-8 encoded strings and string arrays), and "complex" (catch-all for more exotic data types like json, hyperUnique, and approxHistogram columns).

Timestamps (including the __time column) are treated by Druid as longs, with the value being the number of milliseconds since 1970-01-01 00:00:00 UTC, not counting leap seconds. Therefore, timestamps in Druid do not carry any timezone information, but only carry information about the exact moment in time they represent. See the Time functions section for more information about timestamp handling.

The following table describes how Druid maps SQL types onto native types at query runtime. Casts between two SQL types that have the same Druid runtime type will have no effect, other than exceptions noted in the table. Casts between two SQL types that have different Druid runtime types will generate a runtime cast in Druid. If a value cannot be properly cast to another value, as in CAST('foo' AS BIGINT), the runtime will substitute a default value. NULL values cast to non-nullable types will also be substituted with a default value (for example, nulls cast to numbers will be converted to zeroes).

SQL type	Druid runtime type	Default value	Notes
CHAR	STRING	''
VARCHAR	STRING	''	Druid STRING columns are reported as VARCHAR. Can include multi-value strings as well.
DECIMAL	DOUBLE	0.0	DECIMAL uses floating point, not fixed point math
FLOAT	FLOAT	0.0	Druid FLOAT columns are reported as FLOAT
REAL	DOUBLE	0.0
DOUBLE	DOUBLE	0.0	Druid DOUBLE columns are reported as DOUBLE
BOOLEAN	LONG	false
TINYINT	LONG	0
SMALLINT	LONG	0
INTEGER	LONG	0
BIGINT	LONG	0	Druid LONG columns (except __time) are reported as BIGINT
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, e.g. 2000-01-02 03:04:05, not ISO8601 formatting. For handling other formats, use one of the 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, e.g. 2000-01-02. For handling other formats, use one of the time functions.
OTHER	COMPLEX	none	May represent various Druid column types such as hyperUnique, approxHistogram, etc.

Multi-value strings

Druid's native type system allows strings to potentially have multiple values. These multi-value string dimensions will be reported in SQL as VARCHAR typed, and can be syntactically used like any other VARCHAR. Regular string functions that refer to multi-value string dimensions will be applied to all values for each row individually. Multi-value string dimensions can also be treated as arrays via special multi-value string functions, which can perform powerful array-aware operations.

Grouping by a multi-value expression will observe the native Druid multi-value aggregation behavior, which is similar to the UNNEST functionality available in some other SQL dialects. Refer to the documentation on multi-value string dimensions for additional details.

Because multi-value dimensions are treated by the SQL planner as VARCHAR, there are some inconsistencies between how they are handled in Druid SQL and in native queries. For example, expressions involving multi-value dimensions may be incorrectly optimized by the Druid SQL planner: multi_val_dim = 'a' AND multi_val_dim = 'b' will be 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 will change in a future release to more closely align with their behavior in native queries.

NULL values

The druid.generic.useDefaultValueForNull runtime property controls Druid's NULL handling mode. For the most SQL compliant behavior, set this to false.

When druid.generic.useDefaultValueForNull = true (the default mode), Druid treats NULLs and empty strings interchangeably, rather than according to the SQL standard. In this mode Druid SQL only has partial support for NULLs. For example, the expressions col IS NULL and col = '' are equivalent, and both will evaluate to true if col contains an empty string. Similarly, the expression COALESCE(col1, col2) will return col2 if col1 is an empty string. While the COUNT(*) aggregator counts all rows, the COUNT(expr) aggregator will count the number of rows where expr is neither null nor the empty string. Numeric columns in this mode are not nullable; any null or missing values will be treated as zeroes.

When druid.generic.useDefaultValueForNull = false, NULLs are treated more closely to the SQL standard. In this mode, numeric NULL is permitted, and NULLs and empty strings are no longer treated as interchangeable. This property affects both storage and querying, and must be set on all Druid service types to be available at both ingestion time and query time. There is some overhead associated with the ability to handle NULLs; see the segment internals documentation for more details.

Boolean logic

The druid.expressions.useStrictBooleans runtime property controls Druid's boolean logic mode. For the most SQL compliant behavior, set this to true.

When druid.expressions.useStrictBooleans = false (the default mode), Druid uses two-valued logic.

When druid.expressions.useStrictBooleans = true, Druid uses three-valued logic for expressions evaluation, such as expression virtual columns or expression filters. However, even in this mode, Druid uses two-valued logic for filter types other than expression.

Nested columns

Druid supports storing nested data structures in segments using the native COMPLEX<json> type. See Nested columns for more information.

You can interact with nested data using JSON functions, which can extract nested values, parse from string, serialize to string, and create new COMPLEX<json> structures.

COMPLEX types have limited functionality outside the specialized functions that use them, so their behavior is undefined when:

• Grouping on complex values.
• Filtering directly on complex values, such as WHERE json is NULL.
• Used as inputs to aggregators without specialized handling for a specific complex type.

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.