Overview

Numeric Types

Doris supports the following numeric data types:

BOOLEAN

There are two possible values: 0 represents false, and 1 represents true.

For more info, please refer BOOLEAN。

Integer

All are signed integers. The differences among the INT types are the number of bytes occupied and the range of values they can represent:

• TINYINT: 1 byte, [-128, 127]

• SMALLINT: 2 bytes, [-32768, 32767]

• INT: 4 bytes, [-2147483648, 2147483647]

• BIGINT: 8 bytes, [-9223372036854775808, 9223372036854775807]

• LARGEINT: 16 bytes, [-2^127, 2^127 - 1]

Floating-point

Including imprecise floating-point types FLOAT and DOUBLE, corresponding to the float and double in common programming languages

Fixed-point

The precise fixed-point type DECIMAL, used in financial and other cases that require strict accuracy.

Date Types

Date types include DATE, TIME and DATETIME, DATE type only stores the date accurate to the day, DATETIME type stores the date and time, which can be accurate to microseconds. TIME type only stores the time, and does not support the construction of the table storage for the time being, can only be used in the query process.

Do calculation for datetime types or converting them to numeric types, please use functions like TIME_TO_SEC, DATE_DIFF, UNIX_TIMESTAMP . The result of directly converting them as numeric types as not guaranteed.

For more information refer to DATE, TIME and DATETIME documents.

String Types

Doris supports both fixed-length and variable-length strings, including:

• CHAR(M): A fixed-length string, where M is the byte length. The range for M is [1, 255].

• VARCHAR(M): A variable-length string, where M is the maximum length. The range for M is [1, 65533].

• STRING: A variable-length string with a default maximum length of 1,048,576 bytes (1 MB). This maximum length can be increased up to 2,147,483,643 bytes (2 GB) by configuring the string_type_length_soft_limit_bytessetting.

Semi-Structured Types

Doris supports different semi-structured data types for JSON data processing, each tailored to different use cases.

• ARRAY / MAP / STRUCT: They support nested data and fixed schema, making them well-suited for analytical workloads such as user behavior and profile analysis, as well as querying data lake formats like Parquet. Due to the fixed schema, there is no overhead for dynamic schema inference, resulting in high write and analysis performance.

• VARIANT: It supports nested data and flexible schema. It is well-suited for analytical workloads such as log, trace, and IoT data analysis. It can accommodate any legal JSON data, which will be automatically expanded into sub-columns in a columnar storage format. This approach enables high compression rate in storage and high performance in data aggregation, filtering, and sorting.

• JSON: It supports nested data and flexible schema. It is optimized for high-concurrency point query use cases. The flexible schema allows for ingesting any legal JSON data, which will be stored in a binary format. Extracting fields from this binary JSON format is more than 2X faster than using regular JSON strings.

Aggregation Types

The aggregation data types store aggregation results or intermediate results during aggregation. They are used for accelerating aggregation-heavy queries.

• BITMAP: It is used for exact deduplication, such as in (UV) statistics and audience segmentation. It works in conjunction with BITMAP functions like bitmap_union, bitmap_union_count, bitmap_hash, and bitmap_hash64.

• HLL: It is used for approximate deduplication and provides better performance than COUNT DISTINCT. It works in conjunction with HLL functions like hll_union_agg, hll_raw_agg, hll_cardinality, and hll_hash.

• QUANTILE_STATE: It is used for approximate percentile calculations and offers better performance than the PERCENTILE function. It works with functions like QUANTILE_PERCENT, QUANTILE_UNION, and TO_QUANTILE_STATE.

• AGG_STATE: It is used to accelerate aggregations, utilized in combination with aggregation function combinators like state/merge/union.

IP Types

IP data types store IP addresses in a binary format, which is faster and more space-efficient for querying compared to storing them as strings. There are two supported IP data types:

• IPv4: It stores IPv4 addresses as a 4-byte binary value. It is used in conjunction with the ipv4_* family of functions.
• IPv6: It stores IPv6 addresses as a 16-byte binary value. It is used in conjunction with the ipv6_* family of functions.