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Loading Entities

Joist has several ways to load entities, and which to use depends on how much control you need over the query.


Joist's primary focus is not "never having to hand-write SQL", so it is not a full-fledged query builder (like Knex or Kysely); instead it focuses on robust domain modeling, with validation rules, reactive derived values, etc.

So it's expected to, for advanced/complicated queries, occasionally use a 3rd party query builder in addition to Joist, as covered in Approach 3.


Loading entities is a core feature of ORMs, and Joist supports several ways of doing this:

1. Object Graph Navigation

This is the bread & butter of ORMs, and involves just "walking the graph" from some entity you already have, to other entities that are related to it. Examples are:

// Calling .load() methods directly
const author = await;
// Using a lens
const reviews = await publisher.load(p =>;
// Using populate + gets
const loaded = author.populate({ books: "reviews" });
loaded.books.get.flatMap(b =>;

This pattern will likely be ~90% of the queries in your app, and are so pervasive/ergonomic that you likely won't even think of them as "making SQL queries".

  • Pro: The most succinct way of loading entities.
  • Pro: Joist guarantees these will not N+1, even if called in a loop.
  • Pro: Works with non-database/domain model-only relations like Joist's hasOneDerived, hasOneThrough, AsyncProperties, etc.
  • Con: Generally object graph navigation loads all entities within the sub-graph you're walking, i.e. you can't say "return only out of stock books" (see find queries next)

2. Find Queries

EntityManager.find queries are a middle-ground that allow database-side filtering of rows, and so return only a subset of data (instead of the full subgraph like approach 1). Examples are:

const r1 = await em.find(Book, { author: { firstName: "b1" } });
const r2 = await em.find(Publisher, { authors: { firstName: "b1" } });
const r3 = await em.find(Author, { firstName: { like: "%a%" } });
const r4 = await em.find(Author, { publisher: p1 });

If object graph navigation is ~80% of your application's queries (because they are all implicit), em.find queries will likely be ~15% of your queries.

See Find Queries for more documentation and examples.

  • Pro: Still succinct because joins are implicit in the object literal
  • Pro: Supports WHERE-based filtering/returning a subset of entities
  • Pro: N+1 safe even when called in a loop
  • Con: Cannot use domain model-level relations like Joist's hasOneDerived, hasOneThrough, AsyncProperties, etc.
  • Con: Loads only full entities, not cross-table aggregates/group bys/etc.

3. Other Query Builders

For queries that grow outside what em.find can provide, i.e. the last ~5% of your application's queries that are truly custom, then it's perfectly fine to use a 3rd-party query builder like Knex or Kysely.

Knex would be a natural choice, because Joist uses Knex as an internal dependency, but Kysely would be fine too.

In particular, any queries that need to:

  • Group bys/aggregates
  • Select custom fragments of data (not just an entity)

Are best done via Knex or Kysely.


Joist provides a buildQuery method that allows blending approaches 2 and 3: you can pass an em.find-style join literal to buildQuery (with either inline or complex conditions), and get back a Knex QueryBuilder with all the joins and conditions added, to which you can do your own further joins or filters.

const query = buildQuery(knex, Book, {
where: { author: [a1, a2] },
// Use knex methods to continue building the query
// Then load the entities with the customizing query
const books = await em.loadFromQuery(Book, query);

These three options all focus on loading entities, which your code will then iterate over to perform business/view logic.

If you need to load bespoke, non-entity fragments of data across several tables (i.e. with aggregates/group bys/etc.), that is currently not a feature that Joist provides, so you must use a separate raw query builder, as per the "option 3" in the above list.