Operators

A variety of operators in SurrealQL allow for complex manipulation of data, and advanced logic.

&& or AND

Checks whether both of two values are truthy.

SELECT * FROM 10 AND 20 AND 30;

30

|| or OR

Checks whether either of two values are truthy.

SELECT * FROM 0 OR false OR 10;

10

!

Reverses the truthiness of a value.

SELECT * FROM !(TRUE OR FALSE)

false

SELECT * FROM !"Has a value";

false

!!

Determines the truthiness of a value (simply an application of the ! operator twice).

SELECT * FROM !!"Has a value";

true

??

Check whether either of two values are truthy and not NULL.

SELECT * FROM NULL ?? 0 ?? false ?? 10;

0

?:

Check whether either of two values are truthy.

SELECT * FROM NULL ?: 0 ?: false ?: 10;

10

= or IS

Check whether two values are equal.

SELECT * FROM true = "true";

false

SELECT * FROM 10 = "10";

false

SELECT * FROM 10 = 10.00;

true

SELECT * FROM 10 = "10.3";

false

SELECT * FROM [1, 2, 3] = [1, 2, 3];

true

SELECT * FROM [1, 2, 3] = [1, 2, 3, 4];

false

SELECT * FROM { this: "object" } = { this: "object" };

true

SELECT * FROM { this: "object" } = { another: "object" };

false

!= or IS NOT

Check whether two values are equal.

SELECT * FROM 10 != "15";

true

SELECT * FROM 10 != "test";

true

SELECT * FROM [1, 2, 3] != [3, 4, 5];

true

==

Check whether two values are exact. This operator also checks that each value has the same type.

SELECT * FROM 10 == 10;

true

SELECT * FROM 10 == "10";

false

SELECT * FROM true == "true";

false

?=

Check whether any value in an array equals another value.

SELECT * FROM [10, 15, 20] ?= 10;

true

*=

Check whether all values in an array equals another value.

SELECT * FROM [10, 10, 10] *= 10;

true

~

Compare two values for equality using fuzzy matching.

SELECT * FROM "test text" ~ "Test";

true

SELECT * FROM "true" ~ true;

false

SELECT * FROM ["test", "thing"] ~ "test";

false

!~

Compare two values for inequality using fuzzy matching.

SELECT * FROM "other text" !~ "test";

true

SELECT * FROM "test text" !~ "Test";

false

?~

Check whether any value in a set is equal to a value using fuzzy matching.

SELECT * FROM ["true", "test", "text"] ?~ true;

false

SELECT * FROM [1, 2, 3] ?~ "2";

false

*~

Check whether all values in a set are equal to a value using fuzzy matching.

SELECT * FROM ["TRUE", true, "true", "TrUe"] *~ true;

false

SELECT * FROM ["TEST", "test", "text"] *~ "test";

false

<

Check whether a value is less than another value.

SELECT * FROM 10 < 15;

true

<=

Check whether a value is less than or equal to another value.

SELECT * FROM 10 <= 15;

true

>

Check whether a value is less than another value.

SELECT * FROM 15 > 10;

true

>=

Check whether a value is less than or equal to another value.

SELECT * FROM 15 >= 10;

true

+

Add two values together.

SELECT * FROM 10 + 10;

20

SELECT * FROM "test" + " " + "this";

"test this"

SELECT * FROM 13h + 30m;

"13h30m"

-

Subtracts a value from another value.

SELECT * FROM 20 - 10;

10

SELECT * FROM 2m - 1m;

"1m"

* or ×

Multiplies a value by another value.

SELECT * FROM 20 * 2;

40

/ or ÷

Divides a value with another value.

SELECT * FROM 20 / 2;

10

**

Raises a base value by another value.

SELECT * FROM 20 ** 3;

8000

CONTAINS or ∋

Check whether a value contains another value.

SELECT * FROM [10, 20, 30] CONTAINS 10;

true

SELECT * FROM "this is some text" CONTAINS "text";

true

SELECT * FROM {
	type: "Polygon",
	coordinates: [[
		[-0.38314819, 51.37692386], [0.1785278, 51.37692386],
		[0.1785278, 51.61460570], [-0.38314819, 51.61460570],
		[-0.38314819, 51.37692386]
	]]
} CONTAINS (-0.118092, 51.509865);

true

CONTAINSNOT or ∌

Check whether a value does not contain another value.

SELECT * FROM [10, 20, 30] CONTAINSNOT 15;

true

SELECT * FROM "this is some text" CONTAINSNOT "other";

true

SELECT * FROM {
	type: "Polygon",
	coordinates: [[
		[-0.38314819, 51.37692386], [0.1785278, 51.37692386],
		[0.1785278, 51.61460570], [-0.38314819, 51.61460570],
		[-0.38314819, 51.37692386]
	]]
} CONTAINSNOT (-0.518092, 53.509865);

true

CONTAINSALL or ⊇

Check whether a value contains all of multiple values.

SELECT * FROM [10, 20, 30] CONTAINSALL [10, 20, 10];

true

CONTAINSANY or ⊃

Check whether a value contains any of multiple values.

SELECT * FROM [10, 20, 30] CONTAINSANY [10, 15, 25];

true

INSIDE or ∈ or IN

Check whether a value is contained within another value.

SELECT * FROM 10 INSIDE [10, 20, 30];

true

SELECT * FROM "text" INSIDE "this is some text";

true

SELECT * FROM (-0.118092, 51.509865) INSIDE {
	type: "Polygon",
	coordinates: [[
		[-0.38314819, 51.37692386], [0.1785278, 51.37692386],
		[0.1785278, 51.61460570], [-0.38314819, 51.61460570],
		[-0.38314819, 51.37692386]
	]]
};

true

NOTINSIDE or ∉ or NOT IN

Check whether a value is not contained within another value.

SELECT * FROM 15 NOTINSIDE [10, 20, 30];

true

SELECT * FROM "other" NOTINSIDE "this is some text";

true

SELECT * FROM (-0.518092, 53.509865) NOTINSIDE {
	type: "Polygon",
	coordinates: [[
		[-0.38314819, 51.37692386], [0.1785278, 51.37692386],
		[0.1785278, 51.61460570], [-0.38314819, 51.61460570],
		[-0.38314819, 51.37692386]
	]]
};

true

ALLINSIDE or ⊆

Check whether all of multiple values are contained within another value.

SELECT * FROM [10, 20, 10] ALLINSIDE [10, 20, 30];

true

ANYINSIDE or ⊂

Check whether any of multiple values are contained within another value.

SELECT * FROM [10, 15, 25] ANYINSIDE [10, 20, 30];

true

NONEINSIDE or ⊄

Check whether none of multiple values are contained within another value.

SELECT * FROM [15, 25, 35] NONEINSIDE [10, 20, 30];

true

OUTSIDE

Check whether a geometry value is outside another geometry value.

SELECT * FROM (-0.518092, 53.509865) OUTSIDE {
	type: "Polygon",
	coordinates: [[
		[-0.38314819, 51.37692386], [0.1785278, 51.37692386],
		[0.1785278, 51.61460570], [-0.38314819, 51.61460570],
		[-0.38314819, 51.37692386]
	]]
};

true

INTERSECTS

Check whether a geometry value intersects another geometry value.

SELECT * FROM {
	type: "Polygon",
	coordinates: [[
		[-0.38314819, 51.37692386], [0.1785278, 51.37692386],
		[0.1785278, 51.61460570], [-0.38314819, 51.61460570],
		[-0.38314819, 51.37692386]
	]]
} INTERSECTS {
	type: "Polygon",
	coordinates: [[
		[-0.11123657, 51.53160074], [-0.16925811, 51.51921169],
		[-0.11466979, 51.48223813], [-0.07381439, 51.51322956],
		[-0.11123657, 51.53160074]
	]]
};

true

MATCHES

Checks whether the terms are found in a full-text indexed field.

SELECT * FROM book WHERE title @@ 'rust web';


[
	{
		id: book:1,
		title: 'Rust Web Programming'
	}
]

Using the matches operator with a reference checks whether the terms are found, highlights the searched terms, and computes the full-text score.

SELECT id,
		search::highlight('<b>', '</b>', 1) AS title,
		search::score(1) AS score
FROM book
WHERE title @1@ 'rust web'
ORDER BY score DESC;

[
	{
		id: book:1,
		score: 0.9227996468544006f,
		title: '<b>Rust</b> <b>Web</b> Programming'
	}
]

#KNN

Available since: v1.3.0

K-Nearest Neighbors (KNN) is a fundamental algorithm used for classifying or regressing based on the closest data points in the feature space, with its performance and scalability critical in applications involving large datasets.

In practice, the efficiency and scalability of the KNN algorithm are crucial, especially when dealing with large datasets. Different implementations of KNN are tailored to optimize these aspects without compromising the accuracy of the results.

SurrealDB supports different K-Nearest Neighbors methods to perform KNN searches, each with unique requirements for syntax. Below are the details for each method, including how to format your query with examples:

Brute Force Method

Best for smaller datasets or when the highest accuracy is required.

SurrealQL Syntax
<|K,DISTANCE_METRIC|>

• K: The number of nearest neighbors to retrieve.
• DISTANCE_METRIC: The metric used to calculate distances, such as EUCLIDEAN or MANHATTAN.

CREATE pts:3 SET point = [8,9,10,11];
SELECT id FROM pts WHERE point <|2,EUCLIDEAN|> [2,3,4,5];

MTREE Index Method

Available since: v1.3.0

Ideal for larger datasets where performance is crucial, and a consistent distance metric can be predefined.

SurrealQL Syntax
<|K|>

• K: The number of nearest neighbors. The distance metric is predefined in the index, simplifying the syntax.

CREATE pts:3 SET point = [8,9,10,11];
DEFINE INDEX mt_pts ON pts FIELDS point MTREE DIMENSION 4 DIST EUCLIDEAN;
SELECT id FROM pts WHERE point <|2|> [2,3,4,5];

HNSW Method

Available since: v1.5.0

Recommended for very large datasets where speed is essential and some loss of accuracy is acceptable.

SurrealQL Syntax
<|K,EF|>

• K: The number of nearest neighbors.
• EF: The size of the dynamic candidate list during the search, affecting the search’s accuracy and speed.

CREATE pts:3 SET point = [8,9,10,11];
DEFINE INDEX mt_pts ON pts FIELDS point HNSW DIMENSION 4 DIST EUCLIDEAN EFC 150 M 12;
SELECT id FROM pts WHERE point <|10,40|> [2,3,4,5];