In Snowflake, floating-point numbers (floats) are stored using a fixed-point format, rather than the IEEE 754 standard format. Snowflake uses a decimal-based fixed-point data type for precise numeric storage and calculations.
In Snowflake, floats are stored using the following fixed-point data types:
– The FLOAT data type in Snowflake is a fixed-point numeric data type.
– It is used for representing floating-point numbers with variable precision.
– The precision and scale of a FLOAT column are specified during table creation or column definition.
– Precision refers to the total number of digits (both integer and decimal) that a FLOAT column can store.
– Scale represents the number of decimal places that can be stored in the FLOAT column.
– The precision can range from 1 to 38, and the scale can range from -84 to 127.
– The storage size for a FLOAT column depends on the specified precision and scale, and it is variable based on the value being stored.
– The DOUBLE data type in Snowflake is a double-precision fixed-point numeric data type.
– It provides higher precision compared to the FLOAT data type.
– DOUBLE columns have a fixed precision of 38 and a scale of -84 to 127, allowing for a wide range of numeric values with high precision.
– DOUBLE columns occupy a fixed storage size of 16 bytes.
Unlike the IEEE 754 binary-based floating-point format, Snowflake’s fixed-point storage format allows for precise numeric calculations without the inherent limitations and precision issues associated with binary-based floats.
It’s important to note that when working with floating-point numbers in Snowflake, you should consider the appropriate precision and scale for your specific use case to ensure accurate storage and calculations.