`RELATE` statement

The RELATE statement can be used to generate graph edges between two records in the database. This allows you to traverse related records efficiently without needing to pull data from multiple tables and merging that data together using SQL JOINs.

Edges created using the RELATE statement are nearly identical to tables created using other statements, and can contain data. The key differences are that:

Edge tables are deleted once there are no existing relationships left.
Edge tables have two required fields in and out, which specify the directions of the relationships. These cannot be modified in schema declarations except to specify that they must be of a certain record type or to add assertions.

Otherwise, edge tables behave like normal tables in terms of updating, defining a schema or indexes.

Another option for connecting data is using record links. Record links consist of a field with record IDs that serve as unidirectional links by default, or bidirectional links if reference tracking is used. The key differences are that graph relations have the following benefits over record links:

Graph relations are kept in a separate table as opposed to a field inside a record.
Graph relations allow you to store data alongside the relationship.
Graph relations have their own syntax that makes it easy to build and visualize edge queries.

Graph relations offer built-in bidirectional querying and referential integrity. As of SurrealDB 2.2.0, record links also offer these two advantages if they are defined inside a DEFINE FIELD statement using the REFERENCES clause. For more information, see the page on record references.

Statement syntax

SurrealQL Syntax

RELATE [ ONLY ] @from_record -> @table -> @to_record
	[ CONTENT @value
	  | SET @field = @value ...
	]
	[ RETURN NONE | RETURN BEFORE | RETURN AFTER | RETURN DIFF | RETURN @statement_param, ... | RETURN VALUE @statement_param ]
	[ TIMEOUT @duration ]
;

Note

RELATE will create a relation regardless of whether the records to relate to exist or not. As such, it is advisable to create the records you want to relate to before using RELATE, or to at least ensure that they exist before making a query on the relation. If the records to relate to don't exist, a query on the relation will still work but will return an empty array. To override this behaviour and return an error if no records exist to relate, you can use a DEFINE TABLE statement that includes the ENFORCED keyword.

Example usage

Basic usage

The following query shows the basic structure of the RELATE statement, which creates a relationship between a record in the person table and a record in the article table.

CREATE person:aristotle, article:on_sleep_and_sleeplessness;
RELATE person:aristotle->wrote->article:on_sleep_and_sleeplessness;

Response

[
	{
		id: wrote:bpbrj5kd7smu3ahlf55r,
		in: person:aristotle,
		out: article:on_sleep_and_sleeplessness
	}
]

There is no relationship information stored in either the person or article table.

SELECT * FROM person, article;

Response

[
	{
		id: person:aristotle
	},
	{
		id: article:on_sleep_and_sleeplessness
	}
]

Instead, an edge table (in this case a table called wrote) stores the relationship information.

SELECT * FROM wrote;

The structure in -> id -> out mirrors the record IDs from the RELATE statement, with the addition of the automatically generated ID for the wrote edge table.

Response

[
	{
		id: wrote:bpbrj5kd7smu3ahlf55r,
		in: person:aristotle,
		out: article:on_sleep_and_sleeplessness
	}
]

The same structure can be used in a SELECT query, as well as directly from a record ID.

-- Aristotle's id and the articles he wrote
SELECT id, ->wrote->article FROM person:aristotle;
-- Every `person`'s id and written articles
-- Same output as above as the database has a single `person` record
SELECT id, ->wrote->article FROM person;
-- Directly follow the path from Aristotle to his written articles
RETURN person:aristotle->wrote->article;

Response

-------- Query --------

[
	{
		"->wrote": {
			"->article": [
				article:on_sleep_and_sleeplessness
			]
		},
		id: person:aristotle
	}
]

-------- Query --------

[
	article:on_sleep_and_sleeplessness
]

By default, the edge table gets created as a schemaless table when you execute the RELATE statement. You can make the table schemafull by defining a schema.

A common use case is to make sure only unique relationships get created. You can do that by defining an index.

DEFINE INDEX unique_relationships
    ON TABLE wrote
    COLUMNS in, out UNIQUE;

As edge tables are bidirectional by default, there is nothing stopping a query like the following in which an article writes a person instead of the other way around.

RELATE article:on_sleep_and_sleeplessness->wrote->person:aristotle;

To enforce unidirectional relationships, you can restrict the type definition using a DEFINE FIELD definition.

DEFINE FIELD in  ON TABLE wrote TYPE record<person>;
DEFINE FIELD out ON TABLE wrote TYPE record<article>;

Always two records there are - no more, no less

An edge table will always include exactly one record for the in field and one record for the out field.

Knowing this, one would assume that a RELATE statement like the following would fail as it seems to be attempting to insert two cat records at the in field.

CREATE cat:mr_meow, cat:mrs_meow, cat:kitten;
RELATE [cat:mr_meow, cat:mrs_meow]->parent_of->cat:kitten;

However, the query works just fine. Instead of trying to create a single parent_of graph edge, it will create one for each record in the first array: one between cat:mr_meow and cat:kitten, and another between cat:mrs_meow and cat:kitten.

Response

[
	{
		id: parent_of:uahudi4qr68k640fcjbg,
		in: cat:mr_meow,
		out: cat:kitten
	},
	{
		id: parent_of:hi79yfazjppv8b3kyi36,
		in: cat:mrs_meow,
		out: cat:kitten
	}
]

Similarly, a RELATE statement that involves two arrays will return a number of graph edges equal to their product (2 * 2 in this case):

CREATE cat:kitten2;
RELATE [cat:mr_meow, cat:mrs_meow]->parent_of->[cat:kitten, cat:kitten2];

Response

[
	{
		id: parent_of:ysbab20nv5568ogba6ns,
		in: cat:mr_meow,
		out: cat:kitten
	},
	{
		id: parent_of:0ltm6xr94pkblyxf0m6c,
		in: cat:mr_meow,
		out: cat:kitten2
	},
	{
		id: parent_of:71cfl0nvj5frve0r1npv,
		in: cat:mrs_meow,
		out: cat:kitten
	},
	{
		id: parent_of:4gbid7nzo6cwr1t8k090,
		in: cat:mrs_meow,
		out: cat:kitten2
	}
]

Adding data using `SET` and `CONTENT`

Graph edges are standalone tables that can hold other fields besides the default in, out, and id. These can be added during a RELATE statement or during an UPDATE in the same manner as any other SurrealDB table.

Let's look at the two ways you can add record data in the RELATE statement. Both of these queries will produce the same result.

RELATE person:l19zjikkw1p1h9o6ixrg->wrote->article:8nkk6uj4yprt49z7y3zm
    SET 
		metadata.time_written = time::now(),
		metadata.location = "Tallinn";


RELATE person:l19zjikkw1p1h9o6ixrg->wrote->article:8nkk6uj4yprt49z7y3zm
	CONTENT {
		metadata: {
			time_written: time::now(),
			location: "Tallinn"
		}
	};

Response

[
	{
		id: wrote:rva8hentypdu8lcgwjmf,
		in: person:l19zjikkw1p1h9o6ixrg,
		metadata: {
			location: 'Tallinn',
			time_written: d'2024-11-26T01:52:01.169Z'
		},
		out: article:8nkk6uj4yprt49z7y3zm
	}
]

Here is an example of the graph edge being updated in the same way as any other SurrealDB record:

-- Add a small synopsis composed of the table name and article ID
UPDATE wrote SET
    metadata.description = record::tb(out) + ' written by ' + <string>in;

Response

[
	{
		id: wrote:k9d8ynbfxgb8jqjv2ob5,
		in: person:l19zjikkw1p1h9o6ixrg,
		metadata: {
			description: 'article written by person:l19zjikkw1p1h9o6ixrg',
			location: 'Tallinn',
			time_written: d'2024-11-26T01:53:51.350Z'
		},
		out: article:8nkk6uj4yprt49z7y3zm
	}
]

Passing variables in `CONTENT` and `SET`

You can also pass variables in the CONTENT block. This is useful when you want to pass a variable that is not a record ID.

LET $time = time::now();
RELATE person:l19zjikkw1p1h9o6ixrg->wrote->article:8nkk6uj4yprt49z7y3zm
    CONTENT {
        time: {
            written: $time
        }
    };

Response

    {
        "id": "wrote:ctwsll49k37a7rmqz9rr",
        "in": "person:l19zjikkw1p1h9o6ixrg",
        "out": "article:8nkk6uj4yprt49z7y3zm",
        "time": {
            "written": "2021-09-29T14:00:00Z"
        }
    }

Below is an example of how you can pass a variable in the SET block:

LET $time = time::now();

RELATE person:l19zjikkw1p1h9o6ixrg->wrote->article:8nkk6uj4yprt49z7y3zm
    SET time.written = $time;

Response

{
	"id": "wrote:ctwsll49k37a7rmqz9rr",
	"in": "person:l19zjikkw1p1h9o6ixrg",
	"out": "article:8nkk6uj4yprt49z7y3zm",
	"time": {
		"written": "2021-09-29T14:00:00Z"
	}
}

Creating a single relation with the `ONLY` keyword

Using the ONLY keyword, just an object for the relation in question will be returned. This, instead of an array with a single object.

RELATE ONLY person:l19zjikkw1p1h9o6ixrg->wrote->article:8nkk6uj4yprt49z7y3zm;

Response

{
	id: wrote:k9f1rqn3oikolr1560u3,
	in: person:l19zjikkw1p1h9o6ixrg,
	out: article:8nkk6uj4yprt49z7y3zm
}

Using `LET` parameters in RELATE statements

You can also use parameters to specify the record IDs.

-- These two statements store the result of the subquery in a parameter
-- The subquery returns an array of IDs
LET $person =  (SELECT VALUE id FROM person);
LET $article = (SELECT VALUE id FROM article);

-- This statement creates a relationship record for every combination of Record IDs
-- Such that if we have 10 records each in the person and article table
-- We get 100 records in the wrote edge table (10*10 = 100)
-- In this case it would mean that each article would have 10 authors
RELATE $person->wrote->$article SET time.written = time::now();

Modifying output with the `RETURN` clause

By default, the relate statement returns the record value once the changes have been made. To change the return value of each record, specify a RETURN clause, specifying either NONE, BEFORE, AFTER, DIFF, or a comma-separated list of specific fields to return.

-- Don't return any result
RELATE person:l19zjikkw1p1h9o6ixrg->wrote->article:8nkk6uj4yprt49z7y3zm
    SET time.written = time::now()
    RETURN NONE;

-- Return the changeset diff
RELATE person:l19zjikkw1p1h9o6ixrg->wrote->article:8nkk6uj4yprt49z7y3zm
    SET time.written = time::now()
    RETURN DIFF;

-- Return the record before changes were applied
RELATE person:l19zjikkw1p1h9o6ixrg->wrote->article:8nkk6uj4yprt49z7y3zm
    SET time.written = time::now()
    RETURN BEFORE;

-- Return the record after changes were applied (the default)
RELATE person:l19zjikkw1p1h9o6ixrg->wrote->article:8nkk6uj4yprt49z7y3zm
    SET time.written = time::now()
    RETURN AFTER;

-- Return a specific field only from the updated records
RELATE person:l19zjikkw1p1h9o6ixrg->wrote->article:8nkk6uj4yprt49z7y3zm
    SET time.written = time::now()
    RETURN time;

-- Return only the value of a specific field without the field name
RELATE person:l19zjikkw1p1h9o6ixrg->wrote->article:8nkk6uj4yprt49z7y3zm
    SET time.written = time::now()
    RETURN VALUE time;

Using the `TIMEOUT` clause

Adding the TIMEOUT keyword to specify a timeout duration for the statement can be useful when processing a large result set with many interconnected records. If the statement continues beyond this duration, then the transaction will fail, and the statement will return an error.

-- Cancel this conditional filtering based on graph edge properties
-- if not finished within 5 seconds
SELECT * FROM person WHERE ->knows->person->(knows WHERE influencer = true) TIMEOUT 5s;

Using a TIMEOUT is particularly useful when experimenting with complex queries with an extent that is difficult to imagine, especially if the query is recursive.

Deleting graph edges

You can also delete graph edges between two records in the database by using the DELETE statement.

For example the graph edge below:

RELATE person:tobie->bought->product:iphone;

Response

[
	{
		id: bought:ctwsll49k37a7rmqz9rr,
		in: person:tobie,
		out: product:iphone
	}
]

Can be deleted by:

DELETE person:tobie->bought WHERE out=product:iphone RETURN BEFORE;

As mentioned above, a graph edge will also automatically be deleted if it is no longer connected to a record at both in and out.

-- Create three people
CREATE person:one, person:two, person:three;

-- And a love triangle involving them all
RELATE person:one  ->likes->person:two;
RELATE person:two  ->likes->person:three;
RELATE person:three->likes->person:one;

-- Person two moves to Venus permanently, so delete
DELETE person:two;

-- Only one `likes` relationship is left
SELECT * FROM likes;

Output

[
	{
		id: likes:55szjin5yfqwl4sbmy1f,
		in: person:three,
		out: person:one
	}
]

Using RELATE on non-existent records

As mentioned at the top of the page, RELATE can be used for records that do not yet exist. While this behaviour can be overridden by using the ENFORCED keyword, it can be useful in certain situations.

For example, the VALUE clause inside a DEFINE FIELD statement is calculated every time a record is altered (that is, every time it is created or updated). If this value depends on a graph edge, creating the record first will cause VALUE to calculate it based on a nonexistent path.

In the following example, a house table has a field called has_road_access that depends on whether any ->has_road paths return an output that is not empty. Meanwhile, the city has a new road under construction but no houses are present and their details have not been set yet.

-- Returns true if $this->has_road path is not empty
DEFINE FIELD has_road_access ON TABLE house VALUE !!$this->has_road->road;
CREATE road SET name = "Dalhurst Way", length = 10.5;

As the addresses of the upcoming houses have been decided, the ->has_road path can be set ahead of time by giving the house records an ID based on their exact address.

LET $road = SELECT * FROM ONLY road WHERE name = "Dalhurst Way" LIMIT 1;
RELATE [
    house:["Dalhurst Way", 218],
    house:["Dalhurst Way", 222],
    house:["Dalhurst Way", 226],
]->has_road->$road;

Later on, two new houses are completed in the city and registered in the database. As the path to house:["Dalhurst Way", 218] has already been set up, the has_road_access field will evaluate to true, while the other house in the middle of nowhere will evaluate to false.

CREATE house:["Dalhurst Way", 218] SET floors = 2, bedrooms = 5;
CREATE house:["Middle of nowhere", 0] SET floors = 4, bedrooms = 12;

-------- Query --------

[
	{
		bedrooms: 5,
		floors: 2,
		id: house:[
			'Dalhurst Way',
			218
		],
		street: []
	}
]

-------- Query --------

[
	{
		bedrooms: 12,
		floors: 4,
		id: house:[
			'Middle of nowhere',
			0
		],
		street: []
	}
]

Querying graphs

Different ways to reach similar results

For the questions below, each of the queries will give you largely the same answer. Note that whether -> and <- are parsed as in or out depends on their direction in relation to the graph edge wrote. An arrow pointing towards wrote corresponds to in, and vice versa.

The following examples show how to make similar queries in a number of different ways, in the context of a database with one person who wrote two articles.

CREATE 
	person:aristotle,
	article:on_sleep_and_sleeplessness,
	article:on_dreams;
RELATE person:aristotle->wrote->[
		article:on_sleep_and_sleeplessness,
		article:on_dreams
	]
	// Written sometime around the year 330 BC
	SET time_written = d"-0330-01-01";

Who wrote the articles?

-- All queries lead to `person:artistotle` twice,
-- via different paths and thus different field names
-- and/or structure

-- Directly from the `wrote` table
SELECT in FROM wrote;

-- From a single `person` record
SELECT ->wrote.in FROM person;
SELECT ->wrote<-person FROM person;

-- From two `article` records
SELECT <-wrote.in FROM article;
SELECT <-wrote<-person FROM article;

Which articles did the person write?

SELECT out FROM wrote;

SELECT ->wrote.out FROM person;
SELECT ->wrote->article FROM person;

SELECT <-wrote.out FROM article;
SELECT <-wrote->article FROM article;

When was the article written?

SELECT time_written FROM wrote;
SELECT ->wrote.time_written as time_written FROM person;
SELECT <-wrote.time_written as time_written FROM article;

Parsing graph queries

For a more complicated query like the one below you can use a simple rule of thumb: Place the subject in front of the graph selection, then read it backward.

-- This query
SELECT ->purchased->product<-purchased<-person->purchased->product FROM person:tobie

-- Then becomes
person:tobie->purchased->product<-purchased<-person->purchased->product SELECT

Reading this backwards then makes more sense:

Select every product that was purchased by a person who purchased a product that was also purchased by person Tobie.

Alternatively, you can break it down into steps over multiple lines.

-- Starting with Tobie
person:tobie
-- move on to his purchased products
->purchased->product
-- that were also purchased by persons...
<-purchased<-person
-- what are all of those persons' purchased products?
->purchased->product

Putting it all together it would be: based on all the products Tobie purchased, which person also purchased those products and what did they purchase? This sort of query could be used on a social network site to recommend to the user person:tobie a list of people that have similar interests.

Using parentheses to refine graph query logic

Parentheses can be added at any step of a graph query to refine the logic, such as filtering relations based on specific conditions using the WHERE clause.

For example, suppose we want to limit the query to only take recent purchases into account. We can filter purchased graph edge to only include purchases made in last 3 weeks:

-- Select products purchased by people in the last 3 weeks who have purchased the same products that tobie purchased
SELECT 
	->purchased->product
	<-purchased<-person->(purchased WHERE created_at > time::now() - 3w)
	->purchased->product
FROM person:tobie;

If the purchased graph table can lead to both a product or a subscription, they can both be added to the query.

SELECT 
	->purchased->(product, subscription)
	<-purchased<-person
	->purchased->(product, subscription)
FROM person:tobie;

The ? wildcard operator can also be used to search for any and all linked records. The following query will allow purchased product, subscription, insurance, or any other linked records to show up.

SELECT 
	->purchased->(?)
	<-purchased<-person
	->purchased->(?)
FROM person:tobie;

The ? operator on its own can thus be used to see all of the relations that a record has.

CREATE person:hermann_hesse, person:abigail, city:calw, book:demian;
RELATE person:hermann_hesse->wrote->book:demian SET written_in = d'1919-01-01';
RELATE person:hermann_hesse->born_in->city:calw;
RELATE person:abigail->likes->person:hermann_hesse;

SELECT 
	-- all tables in which the record is at `in`
    ->(?).* AS what_hesse_did,
	-- all tables in which the record is at `out`
    <-(?).* AS what_others_did_to_hesse
FROM person:hermann_hesse;

Output

[
	{
		what_hesse_did: [
			{
				id: born_in:k3adylof24a2r5kio8l5,
				in: person:hermann_hesse,
				out: city:calw
			},
			{
				id: wrote:ncbo9w0d8t3xd7lvl4dx,
				in: person:hermann_hesse,
				out: book:demian,
				written_in: d'1919-01-01T00:00:00Z'
			}
		],
		what_others_did_to_hesse: [
			{
				id: likes:6gubmldm14gzasoyypay,
				in: person:abigail,
				out: person:hermann_hesse
			}
		]
	}
]

The ? operator can also be used to find all the relations between one record and another. To do this, use the <-> operator to see all relations in which the record ID in question is either at the in or the out of the graph edge. Follow this with (?) to avoid filtering by graph table name, then use a WHERE filter on the output (an array of record IDs) to see if the record ID is present in either the in or the out field of the graph edge.

A small example of this using some of the relations between Anakin Skywalker (Darth Vader), Palpatine (the Emperor), and Luke Skywalker:

CREATE person:anakin_skywalker, person:luke_skywalker, person:the_emperor;
RELATE person:anakin_skywalker->served->person:the_emperor;
RELATE person:anakin_skywalker->attacked->person:the_emperor SET won = true;
RELATE person:the_emperor->attacked->person:luke_skywalker SET won = false;
RELATE person:luke_skywalker->son_of->person:anakin_skywalker;
RELATE person:the_emperor->fooled->person:anakin_skywalker SET date = "19 BBY";

-- As a SELECT statement
SELECT VALUE <->(?)[WHERE person:the_emperor IN [in, out]] FROM ONLY person:anakin_skywalker;
SELECT VALUE <->(?)[WHERE person:luke_skywalker IN [in, out]] FROM ONLY person:anakin_skywalker;

-- Or returned directly from the record ID
person:anakin_skywalker<->(?)[WHERE person:the_emperor IN [in, out]];
person:anakin_skywalker<->(?)[WHERE person:luke_skywalker IN [in, out]];

Output

-------- Anakin and Emperor relations --------

[
	{
		date: '19 BBY',
		id: fooled:irm2w6jvd1dmppjr7kh2,
		in: person:the_emperor,
		out: person:anakin_skywalker
	},
	{
		id: attacked:r8b4z5yr627wy9i73jkh,
		in: person:anakin_skywalker,
		out: person:the_emperor,
		won: true
	},
	{
		id: served:30oyjvv5uutnj255w4oy,
		in: person:anakin_skywalker,
		out: person:the_emperor
	}
]

-------- Anakin and Luke relations --------

[
	{
		id: son_of:h8oosl7s27n21kh3c2iq,
		in: person:luke_skywalker,
		out: person:anakin_skywalker
	}
]

Parentheses can be used at each point of a graph query. The example below includes person records (authors) connected to book records by the wrote table. As both the person and book tables have fields that can be useful when filtering, they can be isolated with parentheses at this point of the graph query in order to filter using the WHERE clause.

CREATE person:j_r_r_tolkien SET
	name = "J.R.R. Tolkien",
	born = d'1891-01-03';
-- Very approximate date of birth
CREATE person:plato SET 
	name = "Plato", 
	born = "-0428-06-01";

CREATE book:fotr SET 
	name = "The Fellowship of the Ring";
CREATE book:republic SET 
	name = "The Republic",
	original_name = "Πολιτεία";

RELATE person:j_r_r_tolkien->wrote->book:fotr SET written_at = "North Oxford";
RELATE person:plato->wrote->book:republic SET written_at = "Athens";

SELECT 
	name,
	-- Isolate 'wrote' to use WHERE
	->(wrote WHERE written_at = "Athens")->book.* AS books_written_in_athens
FROM person;

SELECT 
	name, 
	-- Isolate 'book' to use WHERE
	->wrote->(book WHERE "Ring" IN name).* AS books_about_rings
FROM person;

Output

-------- Query --------

[
	{
		books_written_in_athens: [],
		name: 'J.R.R. Tolkien'
	},
	{
		books_written_in_athens: [
			{
				id: book:republic,
				name: 'The Republic',
				original_name: 'Πολιτεία'
			}
		],
		name: 'Plato'
	}
]

-------- Query --------

[
	{
		books_about_rings: [
			{
				id: book:fotr,
				name: 'The Fellowship of the Ring'
			}
		],
		name: 'J.R.R. Tolkien'
	},
	{
		books_about_rings: [],
		name: 'Plato'
	}
]

Destructuring can also be used to pick and choose which fields to access inside a graph query. The following query will return the same output as above, except that original_name: 'Πολιτεία' will no longer show up.

SELECT 
	name, 
	->(wrote WHERE written_at = "Athens")->book.{ name, id } AS books_written_in_athens
FROM person;

Bidirectional relation querying

All of the queries up to now have been clear about what sort of record is found at the in and out fields: in is the record that is doing something, while out is the record that has something done to it:

A person who writes an article: the person writes, the article is written.
A person who purchases a product: the person purchases, the product is purchased.

However, sometimes a relation is such that it is impossible to determine which record is located at the in part of a graph table, and which is located at the out part. This is the case when a relationship is truly bidirectional and equal, such as a friendship, marriage, or sister cities:

CREATE city:calgary, city:daejeon;
RELATE city:calgary->sister_of->city:daejeon;

This relation could just as well have been established with the statement RELATE city:daejeon->sister_of->city:calgary.

In such a case, a query on the relationship makes it appear as if one city has a twin city but the other does not.

SELECT id, ->sister_of->city AS sister_cities FROM city;

Response

[
	{
		id: city:calgary,
		sister_cities: [
			city:daejeon
		]
	},
	{
		id: city:daejeon,
		sister_cities: []
	}
]

To solve this, we can use the <-> operator instead of ->. Using <-> will access both the in and out fields, instead of just one.

SELECT id, <->sister_of<->city AS sister_cities FROM city;

This brings up another issue in which a city now appears to be a sister city of itself.

[
	{
		id: city:calgary,
		sister_cities: [
			city:calgary,
			city:daejeon
		]
	},
	{
		id: city:daejeon,
		sister_cities: [
			city:calgary,
			city:daejeon
		]
	}
]

Here we can use the array::complement function to return only items from one array that are not present in another array.

SELECT id, array::complement(<->sister_of<->city, [id]) AS sister_cities FROM city;

Response

[
	{
		id: city:calgary,
		sister_cities: [
			city:daejeon
		]
	},
	{
		id: city:daejeon,
		sister_cities: [
			city:calgary
		]
	}
]

Adding a unique key is a good practice for this sort of relation, as it will prevent it from being created twice. This can be done by defining a field as a unique key based on the ordered record IDs involved, followed by a DEFINE INDEX statement.

DEFINE FIELD key ON TABLE sister_of VALUE <string>array::sort([in, out]);
DEFINE INDEX only_one_sister_city ON TABLE sister_of FIELDS key UNIQUE;

With the index in place, a relation set from one record to the other now cannot be created a second time.

RELATE city:calgary->sister_of->city:daejeon; -- OK
RELATE city:daejeon->sister_of->city:calgary;
-- "Database index `only_one_sister_city` already contains '[city:calgary, city:daejeon]', with record `sister_of:npab0uoxogmrvpwsvfoa`"

Refining the `in` and `out` fields of a relation

As mentioned above, the in and out fields of a graph table are mandatory but can be modified to specify their record type or make assertions.

Thus, the following field declarations will work:

DEFINE FIELD in ON TABLE wrote TYPE record<author>;
DEFINE FIELD out ON TABLE wrote TYPE record<book>;

But any attempt to outright redefine the in or out fields as a different type will be ignored.

DEFINE FIELD in ON TABLE wrote TYPE string;
DEFINE FIELD out ON TABLE wrote TYPE int;

An example of an assertion on one of the fields of a record table for a library which is not yet ready to handle non-English books:

DEFINE FIELD out ON TABLE wrote TYPE record<book> ASSERT $value.language = "English";

CREATE book:demian SET title = "Demian. Die Geschichte von Emil Sinclairs Jugend", language = "German";
CREATE author:hesse SET name = "Hermann Hesse";

RELATE author:hesse->wrote->book:demian;

Output

"Found book:demian for field `out`, with record `wrote:l4xjcgqkgm7vmqqt4iah`, but field must conform to: $value.language = 'English'"

Structure of queries on relations

Using an alias is a common practice in both regular and relation queries in SurrealDB to make output more readable and collapse nested structures. You can create an alias using the AS clause.

CREATE cat:one, cat:two, cat:three;

RELATE cat:one->friends_with->cat:two;
RELATE cat:two->friends_with->cat:three;

SELECT ->friends_with->cat->friends_with->cat FROM cat:one;
-- create an alias for the result using the `AS` clause.
SELECT ->friends_with->cat->friends_with->cat AS friends_of_friends FROM cat:one;

// Output without alias
{
	"->friends_with": {
		"->cat": {
			"->friends_with": {
				"->cat": [
					cat:three
				]
			}
		}
	}
}

// Output with alias
{
	friends_of_friends: [
		cat:three
	]
}

However, an alias might not be preferred in a case where you have multiple graph queries that resolve to the fields of a large nested structure. Take the following data for example:

CREATE country:usa SET name = "USA";
CREATE state:pennsylvania SET population = 12970000;
CREATE state:michigan SET population = 10030000;
CREATE city:philadelphia, city:pittsburgh, city:detroit, city:grand_rapids;

RELATE country:usa->contains->[state:pennsylvania, state:michigan];
RELATE state:pennsylvania->contains->[city:philadelphia, city:pittsburgh];
RELATE state:michigan->contains->[city:detroit, city:grand_rapids];

A query on the states and cities of these records using aliases would return the data in a structure remade to fit the aliases declared in the query.

SELECT
    name,
    ->contains->state AS states,
    ->contains->state->contains->city AS cities
FROM country:usa;

Output

[
	{
		cities: [
			city:philadelphia,
			city:pittsburgh,
			city:grand_rapids,
			city:detroit
		],
		name: 'USA',
		states: [
			state:pennsylvania,
			state:michigan
		]
	}
]

However, opting to not use an alias will return the original graph structure which makes the levels of depth of the query clearer. In addition, the population field is clearly the population for the states.

SELECT
    id,
    ->contains->state.id,
    ->contains->state.population,
    ->contains->state->contains->city.id
FROM country:usa;

The destructuring syntax can be used to reduce some typing. Here is the same query as the last using destructuring syntax instead of one line for each field.

SELECT
    id,
	-- access id and population on a single line
    ->contains->state.{id, population},
    ->contains->state->contains->city.id
FROM country:usa;

Output

[
	{
		"->contains": {
			"->state": {
				"->contains": {
					"->city": {
						id: [
							city:philadelphia,
							city:pittsburgh,
							city:grand_rapids,
							city:detroit
						]
					}
				},
				id: [
					state:pennsylvania,
					state:michigan
				],
				population: [
					12970000,
					10030000
				]
			}
		},
		id: country:usa
	}
]

As the query that uses aliases does not maintain the original graph structure, adding population would require clever renaming such as ->contains->state.population AS state_populations to make it clear that the numbers represent state and not city populations.

Multiple graph tables vs. fields

Being able to set fields on graph tables opens up a large variety of custom query methods, one of which is explored here.

Imagine a database that holds detailed information on the relations between NPCs in a game that are made to be as realistic as possible. Two of the characters have a rocky past but finally end up married. During this period, we might have tracked their relationship by adding and removing graph edges between the two of them as they move from a stage of being friends, to dating, to hating each other, to finally ending up married.

CREATE person:one, person:two;
-- These three relations would end up deleted
RELATE person:one->friends_with->person:two;
RELATE person:one->dating->person:two;
RELATE person:one->hates->person:two;
-- Finally this would be the graph edge connecting the two
RELATE person:one->married->person:two;

This works well to track the current state of the relationship, but creating a more general table such as knows along with a number of fields can be a better method to track the changing relationship over time. The following shows the relationship between the two person records, along with a third record called person:three who went to the same school and once dated person:one.

CREATE person:one, person:two, person:three;
RELATE person:one->knows->person:two SET
    has_been_friends = true,
    has_dated = true,
    has_hated = true,
    married_to = true;

RELATE person:one->knows->person:three SET
    same_high_school = true,
    has_dated = true;

With these fields in place, it is possible to use a WHERE clause to do refined searches on relationships of a certain type.

SELECT 
	->knows->person AS knows,
	->knows[WHERE has_dated]->person AS has_dated,
	->knows[WHERE same_high_school AND has_dated]->person AS dated_and_same_school
 FROM person:one;

Response

[
	{
		dated_and_same_school: [
			person:three
		],
		has_dated: [
			person:two,
			person:three
		],
		knows: [
			person:two,
			person:three
		]
	}
]

Because the WHERE clause simply checks for truthiness (whether a value is present and not empty), these fields do not necessarily need to be booleans and can even be complex objects.

RELATE person:one->knows->person:two SET
	same_high_school = false,
    has_been_friends = true,
    has_dated = {
		from: d'2020-12-25',
		to: d'2023-12-25'
	},
    has_hated = {
		from: d'2023-12-25',
		to: d'2024-03-01'
	},
    married_to = {
		since: d'2024-03-01'
	};

RELATE person:one->knows->person:three SET
    same_high_school = true,
    has_dated = {
		from: d'2019-09-10',
		to: d'2020-12-31'
	};

With these objects, a jealous person:two could do a check on person:one to see how many relationships with has_dated have an end time that overlaps with the has_dated period of person:one and person:two.

SELECT id, ->knows[WHERE same_high_school AND has_dated.to > d'2020-12-25']->person FROM person:one;

Recursive graph queries

Graph edges can also be queried recursively. For a full explanation of this syntax, see the page on recursive paths.

Take the following example which creates five cities, each of which is connected to the next by some type of road of random length.

-- Note: 1..6 used to be inclusive until SurrealDB 3.0.0
-- Now creates 1 up to but not including 6
CREATE |city:1..=6| SET name = <string>id.id() + 'ville';
FOR $pair IN (<array>(1..=5)).windows(2) {
  	LET $city1 = type::record("city", $pair[0]);
    LET $city2 = type::record("city", $pair[1]);
    RELATE $city1->to->$city2 SET 
        type = rand::enum(["train", "road", "bike path"]),
        distance = <int>(rand::float() * 100).ceil()
};

While it is possible to manually move three levels down this road network, it involves a good deal of manual typing.

SELECT ->to->city->to->city->to->city AS fourth_city FROM city:1;

Response

[
	{
		fourth_city: [
			city:4
		]
	}
]

This can be replaced by a @ to refer to the current record, followed by .{3} to represent three levels down the to graph edge. A level between 1 and 256 can be specified here.

SELECT @.{3}->to->city AS fourth_city FROM city:1;

A traditional query to show the final road info from city:1 to the city three stops away would look like this.

SELECT ->to->city->to->city->to.* AS third_journey FROM city:1;

Response

[
	{
		fourth_city: [
			[
				{
					distance: 80,
					id: to:sw2pery99jomfhibzfrh,
					in: city:3,
					out: city:4,
					type: 'train'
				}
			]
		]
	}
]

To use the same query recursively, wrap the part that must be repeated (->to->city) inside parentheses. This will ensure that the .{2} part of the query only repeats ->to->city twice, and not the final ->to.* portion.

SELECT @.{2}(->to->city)->to.* AS third_journey FROM city:1;

A range can be added inside the {} braces. The following query that uses a range of 1 to 20 will follow the ->to->city path up to 20 times, but will stop at the 5th and final depth because the next level returns an empty array.

city:1.{1..20}->to->city;

Response

[
	city:5
]

Ranges can be followed with the destructuring operator to collect fields on each depth, returning them in a single response. The following query goes five depths down the to graph table, returning each city and road along the way.

SELECT @.{1..5}.{ 
    id, 
    next_roads: ->to.*,
    next_cities: ->to->city
} FROM city;

Response

[
	{
		id: city:1,
		next_cities: [
			city:2
		],
		next_roads: [
			{
				distance: 33,
				id: to:bl6i9djau0pg24pqrwd9,
				in: city:1,
				out: city:2,
				type: 'road'
			}
		]
	},
	{
		id: city:2,
		next_cities: [
			city:3
		],
		next_roads: [
			{
				distance: 45,
				id: to:ybugfnlzv6kcrkaj49ig,
				in: city:2,
				out: city:3,
				type: 'road'
			}
		]
	},
	{
		id: city:3,
		next_cities: [
			city:4
		],
		next_roads: [
			{
				distance: 80,
				id: to:sw2pery99jomfhibzfrh,
				in: city:3,
				out: city:4,
				type: 'train'
			}
		]
	},
	{
		id: city:4,
		next_cities: [
			city:5
		],
		next_roads: [
			{
				distance: 29,
				id: to:42hlspf4z5lpqceyv68p,
				in: city:4,
				out: city:5,
				type: 'train'
			}
		]
	},
	{
		id: city:5,
		next_cities: [],
		next_roads: []
	}
]

As noted above, a TIMEOUT can be set for queries that may be computationally expensive. This is particularly useful when experimenting with recursive queries, which, if care is not taken, can run all the way to the maximum possible depth of 256.

Take the following example with two person records that like each other. Following the likes edge will run until the query recurses 256 times and gives up.

CREATE person:one, person:two;
RELATE person:one->likes->person:two;
RELATE person:two->likes->person:one;
-- Open-ended range
person:one.{..}->likes->person;

Response

'Exceeded the idiom recursion limit of 256.'

Take the following example in which three person records of created, each of which likes the other two person records. A query on the ->likes->person path shows that the number of records doubles each time.

CREATE |person:1..4|;
FOR $person IN (SELECT * FROM person) {
  LET $others = (SELECT * FROM person WHERE id != $person.id);
    FOR $other IN $others {
        RELATE $person->likes->$other;
    }
};
RETURN [
	person:1.{2}->likes->person,
	person:1.{3}->likes->person,
	person:1.{4}->likes->person
];

Response

[
	[
		person:1,
		person:2,
		person:1,
		person:3
	],
	[
		person:3,
		person:2,
		person:1,
		person:3,
		person:3,
		person:2,
		person:1,
		person:2
	],
	[
		person:1,
		person:2,
		person:1,
		person:3,
		person:3,
		person:2,
		person:1,
		person:2,
		person:1,
		person:2,
		person:1,
		person:3,
		person:3,
		person:2,
		person:1,
		person:3
	]
]

Since an open-ended range can be specified in a recursive query, this would result in a full 256 attempts to recurse, multiplying the number of results by two each time for a total of 115792089237316195423570985008687907853269984665640564039457584007913129639936 records by the end.

When experimenting with recursive queries, especially open-ended ranges, it is thus recommended to use a timeout.

SELECT @.{..}.{ id, likes: ->likes->person.@ } FROM person TIMEOUT 1s;

Graph clauses

The same clauses available to a SELECT statement can be used inside a graph query. Take the following relations for example:

CREATE person:one, person:two, person:three;
RELATE person:one->knows->person:two SET
	friends = true,
    dated = true,
    married_to = true;

RELATE person:one->knows->person:three SET
    dated = true;

RELATE person:two->knows->person:three SET
	friends = true;

At the knows path, parentheses can be used to insert clauses or an entirely new SELECT statement based on the records turned up at this point. In the following example, the FROM knows portion applies to all the records that a person knows, not the knows table as a whole.

SELECT 
	id, 
	->(SELECT out.id AS counterpart, !!dated AS dated FROM knows) AS acquaintances
FROM person;

Output

[
	{
		acquaintances: [
			{
				counterpart: person:three,
				dated: true
			},
			{
				counterpart: person:two,
				dated: true
			}
		],
		id: person:one
	},
	{
		acquaintances: [],
		id: person:three
	},
	{
		acquaintances: [
			{
				counterpart: person:three,
				dated: false
			}
		],
		id: person:two
	}
]

In some cases, the dot or destructuring operator can produce the same output. The following queries are equivalent.

SELECT ->(SELECT * FROM knows) FROM person:one;
SELECT ->knows.* FROM person:one;

Output

[
	{
		"->knows": [
			{
				dated: true,
				id: knows:2tsz3aomelegp060ii7d,
				in: person:one,
				out: person:three
			},
			{
				dated: true,
				friends: true,
				id: knows:g54z9zapdkssxb4p4pjc,
				in: person:one,
				married_to: true,
				out: person:two
			}
		]
	}
]

However, clauses available in SELECT statements such as WHERE, LIMIT, GROUP BY, aliases and so on can be used, making a graph clause a most flexible option.

SELECT ->(SELECT *, time::now() AS queried_at FROM knows LIMIT 1) FROM person:one;

Output

[
	{
		"->knows": [
			{
				dated: true,
				id: knows:2tsz3aomelegp060ii7d,
				in: person:one,
				out: person:three,
				queried_at: d'2025-01-24T02:16:31.811Z'
			}
		]
	}
]

Some other examples of possible graph clauses:

CREATE |person:1..4|;

RELATE person:1->likes->person:2 SET like_strength = 20, know_in_person = true;
RELATE person:1->likes->person:3 SET like_strength = 5,  know_in_person = false;
RELATE person:2->likes->person:1 SET like_strength = 10, know_in_person = true;
RELATE person:2->likes->person:3 SET like_strength = 12, know_in_person = false;
RELATE person:3->likes->person:1 SET like_strength = 2,  know_in_person = false;
RELATE person:3->likes->person:2 SET like_strength = 9,  know_in_person = false;

SELECT ->likes AS likes FROM person;
SELECT ->(SELECT like_strength FROM likes) AS likes FROM person;
SELECT ->(SELECT like_strength FROM likes WHERE like_strength > 10) AS likes FROM person;
SELECT ->(likes WHERE like_strength > 10) AS likes FROM person;
SELECT ->(SELECT like_strength, know_in_person FROM likes ORDER BY like_strength DESC) AS likes FROM person;
SELECT ->(SELECT count() as count, know_in_person FROM likes GROUP BY know_in_person) AS likes FROM person;
SELECT ->(likes LIMIT 1) AS likes FROM person;
SELECT ->(likes START 1) AS likes FROM person;

Multiple graph tables can be selected by separating each table with a comma, in the same way as in any other SELECT statement. In addition, all tables can be selected by using ? as a wildcard operator.

CREATE person:one SET name = "Þor";
CREATE dog:one SET name = "Fenrir";
CREATE cat:one SET name = "Jólakötturinn";
RELATE person:one->feeds->cat:one SET at = time::now();
RELATE dog:one->plays_with->cat:one SET at = time::now();

-- Select from both 'feeds' and 'plays_with'
SELECT <-(SELECT * FROM feeds, plays_with ORDER BY at) FROM cat:one;
-- Or any graph table
SELECT <-(SELECT * FROM ? ORDER BY at) FROM cat:one;

Ranges inside graph queries

Range syntax can also be used on the edges of a graph query.

CREATE person:one, person:two, person:three, person:four;

RELATE person:one->likes:1->person:two;
RELATE person:one->likes:2->person:three;
RELATE person:one->likes:3->person:four;

person:one->likes:2..=4->person;

Output

[
	person:three,
	person:four
]

A common usage of range syntax on edges is when their ID has been defined as a ULID, making the id field random yet sortable and significant in terms of time.

RELATE character:one->speaks_to:ulid()->character:two SET content = "Greetings, adventurer!";
RELATE character:one->speaks_to:ulid()->character:two SET content = "Can you please help me? My sheep have run amok.";

SELECT
	// Grab the latter part of the record ID, turn it into a datetime
    time::from_ulid(id.id()) AS at,
    content
FROM
    // ULID from 2025-04-25, well before today's date
    character:one->speaks_to:01JSNG0KZSY3HJ5QSZ7JSMQMGR..;

Output

[
	{
		at: d'2025-04-25T03:37:53.246Z',
		content: 'Greetings, adventurer!'
	},
	{
		at: d'2025-04-25T03:37:53.248Z',
		content: 'Can you please help me? My sheep have run amok.'
	}
]

Array-based record IDs also work well inside range queries on edges.

CREATE planet:venus, telescope:one;

RELATE telescope:one->observed:[d'2025-04-24T02:02:18.204Z']->planet:venus CONTENT { 
      temperature_profile: {
        surface: 735.0,
        upper_atmosphere: 300.0
      },
      composition: {
        CO2: 96.5,
        N2: 3.5,
        SO2: 0.015
      },
};

RELATE telescope:one->observed:[d'2025-04-25T02:02:18.204Z']->planet:venus CONTENT {
      temperature_profile: {
        surface: 737.0,
        upper_atmosphere: 298.5
      },
      composition: {
        CO2: 96.6,
        N2: 3.4,
        SO2: 0.015
    }
};

SELECT id, (<-observed:[d'2025-04-24']..).{
    at: id[0], 
    surface: temperature_profile.surface,
    atmosphere: temperature_profile.upper_atmosphere
} AS observations FROM planet;

Output

[
	{
		id: planet:venus,
		observations: [
			{
				at: d'2025-04-24T02:02:18.204Z',
				atmosphere: 300,
				surface: 735
			},
			{
				at: d'2025-04-25T02:02:18.204Z',
				atmosphere: 298.5f,
				surface: 737
			}
		]
	}
]

RELATE statement

Statement syntax

Note

Example usage

Basic usage

Always two records there are - no more, no less

Adding data using SET and CONTENT

Passing variables in CONTENT and SET

Creating a single relation with the ONLY keyword

Using LET parameters in RELATE statements

Modifying output with the RETURN clause

Using the TIMEOUT clause

Deleting graph edges

Using RELATE on non-existent records

Querying graphs

Different ways to reach similar results

Parsing graph queries

Using parentheses to refine graph query logic

Bidirectional relation querying

Refining the in and out fields of a relation

Structure of queries on relations

Multiple graph tables vs. fields

Recursive graph queries

Graph clauses

Ranges inside graph queries

`RELATE` statement

Adding data using `SET` and `CONTENT`

Passing variables in `CONTENT` and `SET`

Creating a single relation with the `ONLY` keyword

Using `LET` parameters in RELATE statements

Modifying output with the `RETURN` clause

Using the `TIMEOUT` clause

Refining the `in` and `out` fields of a relation