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Version: nightly

Vector Functions

This page lists all vector-related functions supported in GreptimeDB. Vector functions are primarily used for operations such as distance calculation, similarity measurement, and more.

Distance Calculations​

  • l2sq_distance(vec1, vec2): Computes the squared L2 distance between two vectors.
  • cos_distance(vec1, vec2): Computes the cosine distance between two vectors.
  • dot_product(vec1, vec2): Computes the dot product of two vectors.

The parameters support both vector literals and column names. Vector literals must be enclosed in square brackets [] and contain Float32 elements separated by commas, e.g., [1.0, 2.0, 3.0]. The dimensions of the vectors involved in the operations must be consistent.

l2sq_distance​

Calculates the squared Euclidean distance (squared L2 distance) between two vectors. L2 distance is the straight-line distance between two points in geometric space. This function returns the squared value to improve computational efficiency.

Example:

SELECT l2sq_distance('[1.0, 2.0, 3.0]', '[2.0, 1.0, 4.0]');

Details:

  • Input: Two vectors with consistent dimensions (as literals or column names).
  • Output: A scalar value of type Float64.

cos_distance​

Calculates the cosine distance between two vectors. Cosine distance measures the cosine of the angle between two vectors and is used to quantify similarity.

Example:

SELECT cos_distance('[1.0, 2.0, 3.0]', '[2.0, 1.0, 4.0]');

Details:

  • Input: Two vectors with consistent dimensions (as literals or column names).
  • Output: A scalar value of type Float64.

dot_product​

Computes the dot product of two vectors. The dot product is the sum of the element-wise multiplications of two vectors. It is commonly used to measure similarity or for linear transformations in machine learning.

Example:

SELECT dot_product('[1.0, 2.0, 3.0]', '[2.0, 1.0, 4.0]');

Details:

  • Input: Two vectors with consistent dimensions (as literals or column names).
  • Output: A scalar value of type Float64.