Custom Queries

You can add root-level Query fields to your GraphQL schema using "Custom Queries". These are PostgreSQL functions, similar to computed columns, that can return return a scalars, records, lists or sets. Sets (denoted by RETURNS SETOF ...) are exposed as connections. The arguments to these functions will be exposed via GraphQL - named arguments are preferred, if your arguments are not named we will assign them an auto-generated name such as arg1.

To create a function that PostGraphile will recognise as a custom query, it must obey the following rules:

  • adhere to common PostGraphile function restrictions
  • if the function accepts arguments, the first argument must NOT be a table type (see computed columns)
  • must NOT return VOID
  • must be marked as STABLE (or IMMUTABLE, though that tends to be less common)
  • must be defined in one of the introspected schemas

For example the functions:

CREATE FUNCTION my_function(a int, b int) RETURNS int AS $$ … $$ LANGUAGE sql IMMUTABLE;
CREATE FUNCTION my_other_function(a int, b int) RETURNS my_table AS $$ … $$ LANGUAGE sql STABLE;

could be queried in GraphQL like this:

  # For a function without arguments

  # For a function with arguments
  myFunction(a: 1, b: 2)

  # For a function that returns a row
  myOtherFunction(a: 1, b: 2) {


Here we write a search query for our forum example using the PostgreSQL LIKE operator variant, ILIKE, which is case insensitive. The custom query we create is included in the forum example’s schema, so if you want to run that example locally you can try it out.

-- Columns unnecessary to this demo were omitted. You can find the full table in
-- our forum example.
create table post (
  headline         text not null,
  body             text,);

-- Create the function named `search_posts` with a text argument named `search`.
-- This will expose `Query.searchPosts(search: String!, ...)` to GraphQL.
create function search_posts(search text)
  -- This function will return a set of posts from the `post` table. The
  -- `setof` part is important to PostGraphile, check out our Functions article
  -- to learn why.
  returns setof post as $$
    -- Write our advanced query as a SQL query!
    select *
    from post
      -- Use the `ILIKE` operator on both the `headline` and `body` columns. If
      -- either return true, return the post.
      headline ilike ('%' || search || '%') or
      body ilike ('%' || search || '%')
  -- End the function declaring the language we used as SQL and add the
  -- `STABLE` marker so PostGraphile knows its a query and not a mutation.
  $$ language sql stable;

And that’s it! You can now use this function in your GraphQL like so:

  searchPosts(search: "Hello world", first: 5) {
    pageInfo {
    nodes {

NOTE: this function will have poor performance because ILIKE specifications of this form (beginning and ending with %) do not utilise indexes. If you're doing this in a real application then it's highly recommended that you look into PostgreSQL's Full Text Search capabilities which can be exposed by a similar function. You may want to check out websearch_to_tsquery in PG11 as part of this.


Though it may be tempting to expose huge collections via a function, it's important to be aware that, when paginating across a function, only LIMIT/OFFSET pagination can be used. (For convenience and consistency we expose cursor pagination over functions, but internally this is just mapped to LIMIT/OFFSET pagination.) Because of this, and because functions are seen as a "black box" by PostgreSQL, if you try and paginate to, say, the 100,000th record then PostgreSQL will literally have to execute the function until all 100,000 records have been generated, and this is often expensive.

One way to solve this is to have your function apply its own internal limits and filters which can be exposed as GraphQL field arguments - if you reduce the amount of data that the function can produce (e.g. to 100 rows) then it reduces the potential cost of having this function in your schema.

Disclaimer: the information in this advice section is not 100% true, for example PostgreSQL can "see through" some SQL functions and has a highly intelligent query planner. If you're an expert on PostgreSQL then you should ignore this advice and go with your own understanding, it's only intended to help beginners from shooting themselves in the foot performance-wise.