Query custom SQL Functions

What are custom SQL functions?

Custom SQL functions are user-defined SQL functions that can be used to either encapsulate some custom business logic or extend the built-in SQL functions and operators.

Hasura GraphQL engine lets you expose certain types of custom functions over the GraphQL API to allow querying them using both queries and subscriptions.

Supported SQL functions

Currently, only functions which satisfy the following constraints can be exposed over the GraphQL API (terminology from Postgres docs):

  • Function behaviour: ONLY STABLE or IMMUTABLE
  • Return type: MUST be SETOF <table-name>
  • Argument modes: ONLY IN

Creating & exposing SQL functions

Custom SQL functions can be created using SQL which can be run in the Hasura console:

  • Head to the Data -> SQL section of the Hasura console
  • Enter your create function SQL statement
  • Select the Track this checkbox to expose the new function over the GraphQL API
  • Hit the Run button

Note

If the SETOF table doesn’t already exist or your function needs to return a custom type i.e. row set, create and track an empty table with the required schema to support the function before executing the above steps.

Querying custom functions using GraphQL queries

Let’s see how we can query custom functions using a GraphQL query as via the below examples:

Example: Text-search functions

Let’s take a look at an example where the SETOF table is already part of the existing schema.

In our article/author schema, let’s say we’ve created and tracked a custom function, search_articles, with the following definition:

CREATE FUNCTION search_articles(search text)
RETURNS SETOF article AS $$
    SELECT *
    FROM article
    WHERE
      title ilike ('%' || search || '%')
      OR content ilike ('%' || search || '%')
$$ LANGUAGE sql STABLE;

This function filters rows from the article table based on the input text argument, search i.e. it returns SETOF article. Assuming the article table is being tracked, you can use the custom function as follows:

query {
  search_articles(
    args: {search: "hasura"}
  ){
    id
    title
    content
  }
}
query { search_articles( args: {search: "hasura"} ){ id title content } }
{ "data": { "search_articles": [ { "id": 1, "title": "first post by hasura", "content": "some content for post" }, { "id": 2, "title": "second post by hasura", "content": "some other content for post" } ] } }

Example: Fuzzy match search functions

Let’s look at an example of a street address text search with support for misspelled queries.

First install the pg_trgm PostgreSQL extension:

CREATE EXTENSION pg_trgm;

Next create a GIN (or GIST) index in your database for the columns you’ll be querying:

CREATE INDEX address_gin_idx ON property
USING GIN ((unit || ' ' || num || ' ' || street || ' ' || city || ' ' || region || ' ' || postcode) gin_trgm_ops);

And finally create the custom SQL function in the Hasura console:

CREATE FUNCTION search_property(search text)
RETURNS SETOF property AS $$
    SELECT *
    FROM property
    WHERE
      search <% (unit || ' ' || num || ' ' || street || ' ' || city || ' ' || region || ' ' || postcode)
    ORDER BY
      similarity(search, (unit || ' ' || num || ' ' || street || ' ' || city || ' ' || region || ' ' || postcode)) DESC
    LIMIT 5;
$$ LANGUAGE sql STABLE;

Assuming the property table is being tracked, you can use the custom function as follows:

query {
  search_property(
    args: {search: "Unit 2, 25 Foobar St, Sydney NSW 2000"}
  ){
    id
    unit
    num
    street
    city
    region
    postcode
  }
}
query { search_property( args: {search: "Unit 2, 25 Foobar St, Sydney NSW 2000"} ){ id unit num street city region postcode } }
{ "data": { "search_property": [ { "id": 1, "unit": "UNIT 2", "num": "25", "street": "FOOBAR ST", "city": "SYDNEY", "region": "NSW", "postcode": "2000" }, { "id": 2, "unit": "UNIT 12", "num": "25", "street": "FOOBAR ST", "city": "SYDNEY", "region": "NSW", "postcode": "2000" } ] } }

Example: PostGIS functions

Let’s take a look at an example where the SETOF table is not part of the existing schema.

Say you have 2 tables, for user and landmark location data, with the following definitions (this example uses the popular spatial database extension, PostGIS):

-- User location data
CREATE TABLE user_location (
  user_id INTEGER PRIMARY KEY,
  location GEOGRAPHY(Point)
);

-- Landmark location data
CREATE TABLE landmark (
  id SERIAL PRIMARY KEY,
  name TEXT,
  type TEXT,
  location GEOGRAPHY(Point)
);

In this example, we want to fetch a list of landmarks that are near a given user, along with the user’s details in the same query. PostGIS’ built-in function ST_Distance can be used to implement this use case.

Since our use case requires an output that isn’t a “subset” of any of the existing tables i.e. the SETOF table doesn’t exist, let’s first create this table and then create our location search function.

  • create and track the following table:

    -- SETOF table
    CREATE TABLE user_landmarks (
      user_id INTEGER,
      location GEOGRAPHY(Point),
      nearby_landmarks JSON
    );
    
  • create and track the following function:

    -- function returns a list of landmarks near a user based on the
    -- input arguments distance_kms and userid
    CREATE FUNCTION search_landmarks_near_user(userid integer, distance_kms integer)
    RETURNS SETOF user_landmarks AS $$
      SELECT  A.user_id, A.location,
      (SELECT json_agg(row_to_json(B)) FROM landmark B
       WHERE (
         ST_Distance(
           ST_Transform(B.location::Geometry, 3857),
           ST_Transform(A.location::Geometry, 3857)
         ) /1000) < distance_kms
       ) AS nearby_landmarks
      FROM user_location A where A.user_id = userid
    $$ LANGUAGE sql STABLE;
    

This function fetches user information (for the given input userid) and a list of landmarks which are less than distance_kms kilometers away from the user’s location as a JSON field. We can now refer to this function in our GraphQL API as follows:

query {
  search_landmarks_near_user(
    args: {userid: 3, distance_kms: 20}
  ){
    user_id
    location
    nearby_landmarks
  }
}
query { search_landmarks_near_user( args: {userid: 3, distance_kms: 20} ){ user_id location nearby_landmarks } }
{ "data": { "search_landmarks_near_user": [ { "user_id": 3, "location": { "type": "Point", "crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::4326" } }, "coordinates": [ 12.9406589, 77.6185572 ] }, "nearby_landmarks": [ { "id": 3, "name": "blue tokai", "type": "coffee shop", "location": "0101000020E61000004E74A785DCF22940BE44060399665340" }, { "id": 4, "name": "Bangalore", "type": "city", "location": "0101000020E61000005396218E75F12940E78C28ED0D665340" } ] } ] } }

Aggregations on custom functions

You can query aggregations on a function result using the <function-name>_aggregate field.

For example, count the number of articles returned by the function defined in the text-search example above:

query {
  search_articles_aggregate(
    args: {search: "hasura"}
  ){
    aggregate {
      count
    }
  }
}

Using arguments with custom functions

As with tables, arguments like where, limit, order_by, offset, etc. are also available for use with function-based queries.

For example, limit the number of articles returned by the function defined in the text-search example above:

query {
  search_articles(
    args: {search: "hasura"},
    limit: 5
  ){
    id
    title
    content
  }
}

Using argument default values for custom functions

If you omit an argument in the args input field then the GraphQL engine executes the SQL function without the argument. Hence, the function will use the default value of that argument set in its definition.

For example: In the above PostGIS functions example, the function definition can be updated as follows:

-- input arguments distance_kms (default: 2) and userid
CREATE FUNCTION search_landmarks_near_user(userid integer, distance_kms integer default 2)

Search nearby landmarks with distance_kms default value which is 2 kms:

query {
  search_landmarks_near_user(
    args: {userid: 3}
  ){
    user_id
    location
    nearby_landmarks
  }
}
query { search_landmarks_near_user( args: {userid: 3} ){ user_id location nearby_landmarks } }
{ "data": { "search_landmarks_near_user": [ { "user_id": 3, "location": { "type": "Point", "crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::4326" } }, "coordinates": [ 12.9406589, 77.6185572 ] }, "nearby_landmarks": [ { "id": 3, "name": "blue tokai", "type": "coffee shop", "location": "0101000020E61000004E74A785DCF22940BE44060399665340" } ] } ] } }

Permissions for custom function queries

Access control permissions configured for the SETOF table of a function are also applicable to the function itself.

For example, in our text-search example above, if the role user doesn’t have the requisite permissions to view the table article, a validation error will be thrown if the search_articles query is run using the user role.