ash_postgres/documentation/topics/advanced/expressions.md

# Expressions

In addition to the expressions listed in the [Ash expressions guide](https://hexdocs.pm/ash/expressions.html), AshPostgres provides the following expressions

# Fragments

Fragments allow you to use arbitrary postgres expressions in your queries. Fragments can often be an escape hatch to allow you to do things that don't have something officially supported with Ash.

### Examples

#### Simple expressions

```elixir
fragment("? / ?", points, count)
```

#### Calling functions

```elixir
fragment("repeat('hello', 4)")
```

#### Using entire queries

```elixir
fragment("points > (SELECT SUM(points) FROM games WHERE user_id = ? AND id != ?)", user_id, id)
```

> ### a last resport {: .warning}
>
> Using entire queries as shown above is a last resort, but can sometimes be the best way to accomplish a given task.

####  In calculations

```elixir
calculations do
  calculate :lower_name, :string, expr(
    fragment("LOWER(?)", name)
  )
end
```

#### In migrations

```elixir
create table(:managers, primary_key: false) do
  add :id, :uuid, null: false, default: fragment("UUID_GENERATE_V4()"), primary_key: true
end
```

## Like and ILike

These wrap the postgres builtin like and ilike operators.

Please be aware, these match *patterns* not raw text. Use `contains/1` if you want to match text without supporting patterns, i.e `%` and `_` have semantic meaning!

For example:

```elixir
Ash.Query.filter(User, like(name, "%obo%")) # name contains obo anywhere in the string, case sensitively
```

```elixir
Ash.Query.filter(User, ilike(name, "%ObO%")) # name contains ObO anywhere in the string, case insensitively
```

## Trigram similarity

To use this expression, you must have the `pg_trgm` extension in your repos `installed_extensions` list.

This calls the `similarity` function from that extension. See more https://www.postgresql.org/docs/current/pgtrgm.htmlhere: https://www.postgresql.org/docs/current/pgtrgm.html

For example:

```elixir
Ash.Query.filter(User, trigram_similarity(first_name, "fred") > 0.8)
```