SQL data types

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

    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 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.

    Casts between two SQL types with the same Druid runtime type (see below table) have no effect, other than 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 (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.

    The following table describes how Druid maps SQL types onto native types when running queries.

    Druid’s native type system allows strings to potentially have multiple values. These are 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 are 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 observes 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 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' 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 will change in a future release to more closely align with their behavior in native queries.

    The 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 evaluate to true if col contains an empty string. Similarly, the expression COALESCE(col1, col2) returns if col1 is an empty string. While the COUNT(*) aggregator counts all rows, the COUNT(expr) aggregator counts 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 are treated as zeroes.

    The 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.

    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 , which can extract nested values, parse from string, serialize to string, and create new COMPLEX<json> structures.

    • 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 type functionality can be improved.