Turbocharged Postgres
data layer
We wanted a database with hierarchical queries, advanced data modeling, and great DX. There were none, so we built EdgeDB, based on PostgreSQL, 100% open source.
It's become so much more.
N+1, solved
EdgeDB solves the problems that ORMs exist to workaround.
A comparison that speaks for itself:
SELECT
movie.title,
(
SELECT avg(rating)
FROM reviews
WHERE movie_id = movie.id
) AS avg_rating,
(SELECT
array_agg(q.v)
FROM
(SELECT
person.name AS v
FROM
actors
INNER JOIN persons AS person
ON (actors.person_id = person.id)
WHERE
actors.movie_id = movie.id
) AS q
WHERE q.v IS NOT NULL
) AS actors
FROM
movies AS movie
WHERE
id = $1;const movieRatingQuery = db
.select({
id: schema.reviews.movieId,
avgRating: avg(schema.reviews.rating).mapWith(Number),
})
.from(schema.reviews)
.groupBy(schema.reviews.movieId)
.where(eq(schema.reviews.movieId, sql.placeholder("movieId")))
.prepare("movieRating");
const movieQuery = db.query.movies
.findFirst({
columns: {
title: true
},
extras: {
avg_rating: sql`${sql.placeholder("avgRating")}`.as("avg_rating"),
},
with: {
actors: {
columns: {
name: true
}
}
},
where: eq(schema.movies.id, sql.placeholder("movieId")),
}).prepare('movie');
const result = await db.transaction(async () => {
const rating = await movieRatingQuery.execute({ id });
return await movieQuery.execute({
id,
avgRating: rating[0].avgRating
});
});const _result = await client.$transaction([
client.movies.findUnique({
where: {
id: id,
},
select: {
title: true,
actors: {
select: {
person: {
select: {
name: true
}
}
}
}
}),
client.reviews.aggregate({
_avg: {
rating: true,
},
where: {
movie: {
id: id,
},
},
}),
]);
_result[0].avg_rating = _result[1]._avg.rating;
const result = _result[0];# assuming "Movie model" is defined with this
# "expression column":
#
# avg_rating = orm.column_property(
# select(func.avg(Review.rating))
# .where(Review.movie_id == id)
# .correlate_except(Review)
# .scalar_subquery()
#)
stmt = (
sa.select(m.Movie)
.options(
orm.selectinload(m.Movie.actors_rel).joinedload(
m.Actors.person_rel
),
)
.filter_by(id=id)
)
movie = session.scalars(stmt).first()
actors = [rel.person_rel for rel in movie.actors_rel]
result = {
"title": m.title,
"actors": [
[{"name": a.name} for a in actors]
],
"avg_rating": avg_rating
}
# EdgeQL is the main query language of EdgeDB.
select Movie {
title,
actors: {
name
},
rating := math::mean(.reviews.score)
} filter .id = <uuid>$idconst query = e.params({id: e.uuid}, ($) =>
e.select(e.Movie, (movie) => ({
title: true,
actors: {
name: true
},
rating: e.math.mean(movie.reviews.score),
filter: e.op(movie.id, '=', $.id),
}));
);
const result = await query.run(client, {id});Composable
Write complex queries at the speed of thought.
Ergonomic
Fully type-safe query language and schema.
Universal
Learn once and build with any software stack.
Fast
EdgeDB's client libraries, network protocol, and query language are designed for performance.
Guillermo Rauch, Vercel, Founder & CEO
EdgeDB fixes fundamental flaws of SQL and legacy DBs in serverless. The perfect fit for Next.js and Vercel.
James Tamplin, Firebase, Founder
EdgeDB's powerful querying is what we wanted to build into Firebase and never got there. I'm excited!
David Cramer, Sentry, Founder
The most high tech companies in the world run graph architectures on top of SQL. Now you, too, can be high tech.
Charlie Marsh, Astral, Founder
I've been continually impressed by both the ideas behind EdgeDB and the way they've been brought to life.
Theo - t3.gg
As a long time hater of MongoDB, I'm pumped to see a real challenger for SQL that goes the other direction. Relations are good!
Sebastián Ramírez, FastAPI
If you want to use a relational database but you wish there was something beyond SQL, you should check out EdgeDB. As it generates strict types, has async drivers, and has great performance, it's a great DB option for FastAPI apps. And it's built by the same people that created the async Python underpinnings that enable things like FastAPI.
AI-Ready
EdgeDB can automatically generate embeddings for your data.
Works with OpenAI, Mistral AI, and Anthropic.
Ship AI-enabled apps in three steps.
Drop this line into your schema:
using extension ai;Add an AI index to your type:
type Astronomy {
content: str;
deferred index
ext::ai::index(...) on (.content);
}Configure which LLM you want to use.
Query your new RAG endpoint via HTTP or our client SDKs. EdgeDB will handle the rest.
Simple Stack
Store and query your application and vector data in one database.
Flexible
Use the full RAG or only the deferred AI index functionality.
Graphical UI
Chat with your database via the built-in graphical UI.
Seamless Auth
EdgeDB comes with a complete built-in auth solution. OAuth, passwords, passwordless, email, notifications — all covered.
Login UI
Enable the built-in login UI flow, or craft your own with our SDK.
Access Policies
Auth works with Schema Access Policies for robust data security.
Free
Included in the core with no hidden charges — support unlimited users.
OAUTH
EdgeDB sends emails for password resets, registration, and more.
Passwordless
Passkeys, WebAuthn, magic links... we've got it all.
Cloud
EdgeDB Cloud is designed alongside both EdgeDB and its tooling to deliver the very best cloud database experience.
Vertically integrated
EdgeDB, its core tools, SDKs, and our Cloud are designed to function cohesively.
Admin panel
Includes performance metrics, schema browser, REPL, query IDE, and a data editor for your app.
Secure by default
TLS is seamless and enabled by default. We handle certificates and connection strings for you.
Vercel & GitHub integration
Auto-configure Vercel and enable preview deployments for GitHub PRs.
Regional replicas
Create read-only copies in various regions and route queries to the nearest replica.
Performance insights
Slow query log, automatic EXPLAIN, and tailored optimization advice.
