What is SQL for APIs?

SQL for APIs is a design pattern that projects API operations onto relational semantics: list and get operations become SELECT, create operations become INSERT, modify operations become UPDATE or REPLACE, and remove operations become DELETE. The API's response objects become rows; their attributes become columns. The pattern gives any REST or RPC API the ergonomics of a database without requiring the API provider to change anything.

How it works

The mapping is mechanical, which is what makes it generalizable:

API concept	Relational concept
Provider (AWS, GitHub)	Schema namespace
Service (EC2, repos)	Sub-schema
Resource collection	Table
List / get operation	SELECT method
Create operation	INSERT method
Patch / put operation	UPDATE / REPLACE method
Delete operation	DELETE method
Other lifecycle actions (start, stop, reboot)	EXEC method
Required path or query parameters	Required WHERE clause predicates
Request body fields	data__ prefixed insert/update columns

In StackQL, this mapping is generated from extended OpenAPI specifications held in the open StackQL provider registry. The required API parameters are discoverable at runtime: SHOW METHODS IN aws.ec2.instances returns each method's SQL verb and its required parameters, so a human or an AI agent can derive a correct query without reading provider documentation.

Example

A read is a SELECT whose WHERE clause carries the API's required parameters:

SELECT name, language, stargazers_count
FROM github.repos.repos
WHERE org = 'stackql';

A create is an INSERT whose columns carry the request body (prefixed data__) and routing parameters:

INSERT INTO google.storage.buckets(
  project,
  data__name,
  data__location
)
SELECT
  'my-project',
  'my-new-bucket',
  'US';

SHOW INSERT INTO google.storage.buckets generates this template mechanically, including all writable fields.

Why it exists

SQL is the most widely deployed query language in existence, with fifty years of tooling: drivers, ORMs, BI platforms, notebooks, and - increasingly important - LLM training data. Every mainstream AI model can already read and write competent SQL. Projecting APIs into SQL means all of that tooling and model capability applies to any API surface immediately. The alternative - one SDK per provider per language - multiplies integration work and gives agents nothing reusable. The argument is developed further in Why SQL is a Strong Interface for Cloud APIs.

Related concepts

• What is StackQL? - the reference implementation of this pattern for cloud and SaaS APIs
• What is Queryable Infrastructure? - the operating model the pattern enables
• Why SQL is a Strong Interface for Cloud APIs - the design rationale in depth
• StackQL Architecture Overview - how the mapping is implemented
• How to query GitHub repositories with StackQL - the pattern applied to a SaaS API