What Is an ORM: Pros, Cons and Popular Examples
Basics3 minInnovialabs editorial team
In short
An ORM is a library that lets you work with database tables as objects in code and generates the SQL for you. It speeds up routine development, but you still need to know which queries hit the database, or you will run into the N+1 problem and slow pages.
What an ORM is
ORM (Object-Relational Mapping) is a layer between application code and a relational database. You define models — User, Order, Product — and the ORM maps them to tables, turns method calls into SQL and returns results as objects.
Instead of writing SELECT * FROM users WHERE is_active = true, you write something like User.objects.filter(is_active=True), and the ORM builds the query, binds parameters and creates the objects.
Popular ORMs
| ORM | Language | Highlights |
|---|---|---|
| Prisma | TypeScript, JavaScript | Schema in a separate file, generated type-safe client, built-in migrations |
| Django ORM | Python | Part of Django, with migrations and an admin panel out of the box |
| SQLAlchemy | Python | Flexible library: a high-level ORM plus a SQL expression builder |
| Hibernate | Java | Implementation of the JPA standard, models defined with @Entity annotations |
Here is "active users with their orders" in Prisma:
const users = await prisma.user.findMany({
where: { isActive: true },
include: { orders: true },
});
Pros
- Development speed. Routine create, read and update operations take one line.
- Safety. Parameters are bound automatically, which protects against SQL injection in normal use.
- Migrations. Schema changes live in code and are applied the same way in every environment.
- Types and autocomplete. Especially with Prisma and modern SQLAlchemy: typos in field names show up before you run the code.
- Portability. Switching from PostgreSQL to MySQL is easier, though in practice it is rarely free.
Cons
- Hidden queries. One line of code can trigger dozens of SQL queries.
- Suboptimal SQL for complex reports, window functions and database-specific features.
- Another abstraction. To debug performance you still need to understand SQL and indexes.
The N+1 problem
The best-known ORM trap. You fetch a list of N records in one query, then access a related object inside a loop — and the ORM runs one more query per record. That is N+1 queries instead of one or two.
# Django: 1 query for orders + 1 query per customer
for order in Order.objects.all():
print(order.customer.name)
# Fixed: orders and customers loaded in one query with a JOIN
for order in Order.objects.select_related("customer"):
print(order.customer.name)
On a local database with ten orders you will not notice. In production, with thousands of rows and network latency, the page starts taking seconds to load.
How different ORMs solve it:
- Django —
select_relatedfor one-to-one and many-to-one relations,prefetch_relatedfor one-to-many. - SQLAlchemy — the
selectinloadandjoinedloadloader options. - Prisma —
includeorselectwith related models. - Hibernate —
JOIN FETCHin a JPQL query, or entity graphs.
The key habit: turn on SQL logging in development and check how many queries each page makes.
When raw SQL is better
- Reports and analytics: complex aggregations, window functions, CTEs.
- Bulk operations: updating or inserting hundreds of thousands of rows at once.
- Database-specific features: full-text search, JSONB operators, upserts with special conditions.
- Performance-critical queries where you need full control over the execution plan.
Almost every ORM lets you run raw SQL where needed. A healthy approach: the ORM for the bulk of routine work, SQL for the bottlenecks.