October 6, 2022

Robotic Notes

All technology News

JSON Schema support for Postgres

4 min read


Released on the Supabase platform today, pg_jsonschema is a Postgres extension which adds support for JSON Schema validation json and jsonb data types.

The use-case for JSON validation

Despite Supabase being an SQL shop, even our most zealous relational data model advocates (begrudgingly) recognize some advantages to the document data model. Mainly, if some complex data will always be consumed together, a document data type can be a good fit.

For Example:

If our application receives data via a webhook:

{
  "status_code": 200,
  "checksum": "89b623f6332d2b9b42b4e17eaf1bcc60"
  "headers": {
    "Content-Type": "application/json",
    "Last-Modified": "Tue, 09 Aug 2022 09:14:10 GMT"
  },
  "payload": {
    {
      "sucess": true,
      "content": {
        "account_id": "d928b484-16bd-4f10-a827-3eb959b4cc14",
        "event": "SUBSCRIBED",
        "subscriptions": [
          {"subscription_id": 481, "exp": 1660050940},
          {"subscription_id": 121, "exp": 1660041852},
        ]
      }
    }
  }
}

A reasonable swing at normalizing that data into tables might look like this:

pg_jsonschema_erd.png

That’s a lot of architecting! Moreover, the query to recover the original input requires 5 joins!

A solution that aligns better with our intent would be to persist whatever we receive from the external service as long as it meets a minimum set of requirements. With Postgres’ json data type we can achieve half of that goal.

pg_jsonschema_erd2.png

Treating the webhook contents as a json document simplifies our data model. It is also robust to changing payloads and more efficient to query, update, and delete.

Now what about this part?

as long as it meets a minimum set of requirements

Challenges

The flexibility of document types also comes with some downsides.

The schema of the json payload from the previous example is a little intense for a blog post, so let’s instead say we intend for a table’s json column to hold objects with a string attribute named foo and no additional attributes.

Without constraints, the setup would be:

create table some_table (
  id serial primary key,
  metadata json not null
);

insert into some_table (metadata)
values (<SQL Input>);

But the resulting schema is much more permissive than our intention. When inserting a mix of correct and incorrect values:

Check constraints

Only 2 of our 8 test cases were handled appropriately by our data model.

A core strength of SQL databases is their ability to constrain data’s types, nullability, referential integrity, uniqueness, and even arbitrary developer-defined rules. Those constraints are a lot to sacrifice to gain the convenience of document types.

Fortunately, the challenge of validating json documents is not specific to SQL databases. NoSQL/Document databases, like MongoDB, optionally enforce data constraints so there’s plenty of prior art for us to draw from.

JSON Schema

JSON Schema is a specification for validating the shape and contents of json documents. It can describe constraints for documents similar to those applied by relational databases.

Translating our constraints from the previous example into a JSON Schema we get:

// objects with a string attribute
// named foo and no additional attributes
{
  "type": "object",
  "properties": {
    "foo": {
      "type": "string"
    }
  },
  "required": ["foo"],
  "additionalProperties": false
}

Which is a formal and human-readable description of our intent. A tutorial on the JSON Schema language is out-of-scope for this article, but you can find a full introduction in their guide.

So now we have:

✅ flexible document data type → json

✅ a language to describe constraints on json documents → JSON Schema

❌ a way to enforce JSON Schema constraints on json documents in Postgres

pg_jsonschema

pg_jsonschema is a Postgres extension that can validate json and jsonb data types against a JSON Schema. The extension offers two functions:

-- Validates a json *instance* against a JSON Schema *schema*
json_matches_schema(schema json, instance json) returns bool

-- Validates a jsonb *instance* against a JSON Schema *schema*
jsonb_matches_schema(schema json, instance jsonb) returns bool

We can use those functions in combination with a check constraint to more completely describe our data model.

create table some_table(
  id serial primary key,
  metadata json not null,

  check (
    json_matches_schema(
      schema :='{
         "type": "object",
         "properties": {
          "foo": {
           "type": "string"
          }
         },
         "required": ["foo"],
         "additionalProperties": false
      }',
      instance := metadata
    )
  )
);

insert into some_table(metadata)
values
  (<SQL input>);

With that check constraint in place, we re-run the same test cases:

Check constraints 2

Now all 8 tests are handled correctly. In cases where records failed to insert, Postgres throws an error referencing the failing constraint.

ERROR: new row for relation “some_table” violates check constraint “some_table_metadata_check”

DETAIL: Failing row contains (1, null).

SQL state: 23514

With these tools you can wield the flexibility of json/jsonb data types without sacrificing the guarantees of a well-specified data model!

To get started with pg_jsonschemafire up a new supabase project and enable the extension with

create extension pg_jsonschema;

or follow the Docker Compose instructions in the repo’s README.



Source link