Categories:: Aggregate functions (Similarity Estimation) , Window functions (Similarity Estimation)

MINHASH_COMBINE¶

Combines input MinHash states into a single MinHash output state. This Minhash state can then be input to the APPROXIMATE_SIMILARITY function to estimate the similarity with other MinHash states.

This allows use cases in which MINHASH is run over horizontal rowsets of the same table, producing a MinHash state for each rowset. These states can then be combined using MINHASH_COMBINE, producing the same output state as a single run of MINHASH over the entire table.

For more information about MinHash states, see Estimating Similarity of Two or More Sets.

See also:: MINHASH

Syntax¶

Aggregate function

MINHASH_COMBINE( [ DISTINCT ] <state> )

Window function

MINHASH_COMBINE( [ DISTINCT ] <state> ) OVER ( [ PARTITION BY <expr> ] )

For details about the OVER clause, see Window function syntax and usage.

Arguments¶

state: An expression that contains MinHash state information generated by a call to MINHASH. Input MinHash states must have arrays of equal length.

Usage notes¶

This function can be used as an aggregate function or a window function.
DISTINCT can be included as an argument, but has no effect.

Examples¶

USE SCHEMA snowflake_sample_data.tpch_sf1;

SELECT MINHASH_COMBINE(mh) FROM
    (
      (SELECT MINHASH(5, c2) mh FROM orders WHERE c2 <= 10000)
        UNION
      (SELECT MINHASH(5, c2) mh FROM orders WHERE c2 > 10000 AND c2 <= 20000)
        UNION
      (SELECT MINHASH(5, C2) mh FROM orders WHERE c2 > 20000)
    );

+-----------------------+
| MINHASH_COMBINE(MH)   |
|-----------------------|
| {                     |
|   "state": [          |
|     628914288006793,  |
|     1071764954434168, |
|     991489123966035,  |
|     2395105834644106, |
|     680224867834949   |
|   ],                  |
|   "type": "minhash",  |
|   "version": 1        |
| }                     |
+-----------------------+

Here is a more extensive example, showing the three related functions MINHASH, MINHASH_COMBINE and APPROXIMATE_SIMILARITY. This example creates 3 tables (ta, tb, and tc), two of which (ta and tb) are similar, and two of which (ta and tc) are completely dissimilar.

Create and populate tables with values:

CREATE TABLE ta (i INTEGER);
CREATE TABLE tb (i INTEGER);
CREATE TABLE tc (i INTEGER);

INSERT INTO ta (i) VALUES (1), (2), (3), (4), (5), (6), (7), (8), (9), (10);
INSERT INTO tb (i) VALUES (1), (2), (3), (4), (5), (6), (7), (8), (9), (11);
INSERT INTO tc (i) VALUES (-1), (-20), (-300), (-4000);

Calculate minhash info for the initial set of data:

CREATE TABLE minhash_a_1 (mh) AS SELECT MINHASH(100, i) FROM ta;
CREATE TABLE minhash_b (mh) AS SELECT MINHASH(100, i) FROM tb;
CREATE TABLE minhash_c (mh) AS SELECT MINHASH(100, i) FROM tc;

Add more data to one of the tables:

INSERT INTO ta (i) VALUES (12);

Demonstrate the MINHASH_COMBINE function:

CREATE TABLE minhash_a_2 (mh) AS SELECT MINHASH(100, i) FROM ta WHERE i > 10;

CREATE TABLE minhash_a (mh) AS
  SELECT MINHASH_COMBINE(mh)
    FROM (
      (SELECT mh FROM minhash_a_1)
      UNION ALL
      (SELECT mh FROM minhash_a_2)
    );

This query shows the approximate similarity of the two similar tables (ta and tb):

SELECT APPROXIMATE_SIMILARITY(mh)
  FROM (
    (SELECT mh FROM minhash_a)
    UNION ALL
    (SELECT mh FROM minhash_b)
  );

+-----------------------------+
| APPROXIMATE_SIMILARITY (MH) |
|-----------------------------|
|                        0.75 |
+-----------------------------+

This query shows the approximate similarity of the two very different tables (ta and tc):

SELECT APPROXIMATE_SIMILARITY(mh)
  FROM (
    (SELECT mh FROM minhash_a)
    UNION ALL
    (SELECT mh FROM minhash_c)
  );

+-----------------------------+
| APPROXIMATE_SIMILARITY (MH) |
|-----------------------------|
|                           0 |
+-----------------------------+