Categories:

Aggregate Functions (Similarity Estimation) , Window Functions

# APPROXIMATE_JACCARD_INDEX¶

Returns an estimation of the similarity (Jaccard index) of inputs based on their MinHash states. For more information about Jaccard indexes and the related function MINHASH, see Estimating Similarity of Two or More Sets.

Alias for APPROXIMATE_SIMILARITY

## Syntax¶

APPROXIMATE_JACCARD_INDEX( [ DISTINCT ] <expr> [ , ... ] )

APPROXIMATE_JACCARD_INDEX(*)

## Arguments¶

expr

The expression(s) should be one or more MinHash states returned by calls to the MINHASH function. In other words, the expressions must be MinHash state information, not the column or expression for which you want the approximate similarity. (The example below helps make this clear.)

## Returns¶

A floating point number between 0.0 and 1.0 (inclusive), where 1.0 indicates that the sets are identical, and 0.0 indicates that the sets have no overlap.

## Usage Notes¶

• DISTINCT can be included as an argument, but has no effect.

• The input MinHash states must have MinHash arrays of equal length.

• The array length of the input MinHash states is an indicator of the quality of approximation.

The larger the value of k used in function MINHASH, the better the approximation. However, this value has a linear impact on the computation time for estimating similarity.

## Examples¶

USE SCHEMA snowflake_sample_data.tpch_sf1;

SELECT APPROXIMATE_JACCARD_INDEX(mh) FROM
(
(SELECT MINHASH(100, C5) mh FROM orders WHERE c2 <= 50000)
UNION
(SELECT MINHASH(100, C5) mh FROM orders WHERE C2 > 50000)
);

+-------------------------------+
| APPROXIMATE_JACCARD_INDEX(MH) |
|-------------------------------|
|                          0.97 |
+-------------------------------+