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Quick Tour

This page gives a quick overview/scan of "what using Joist looks like". Joist's docs dive into these features in more detail, and see Installation for a true "getting started".

With Joist, you start by creating/updating your database schema, using node-pg-migrate or whatever migration tool you like:

# Start your postgres database
docker-compose up db --wait
# Apply the latest migrations
npm run migrate

Then invoke Joist's code generation:

npm run joist-codegen

To automatically get super-clean domain objects created (see Code Generation):

// src/entities/Author.ts
export class Author extends AuthorCodegen {
// ...empty placeholder for your custom methods/business logic...

// src/entities/AuthorCodegen.ts
export class AuthorCodegen {
// ...all the boilerplate fields & m2o/o2m/m2m relations generated for you...
readonly books: Collection<Author, Book> = hasOne(...);
get firstName(): string { ... }
set firstName(): string { ... }

Joist generates both sides of relations, and will keep them automatically in sync (see Relations):

const a1 = em.load(Author, "a:1", "books");
// Create a new book for a1
const b1 = new Book(em, { title: "b1", author: a1 });
// a1.books already has b1 in it, so your view of data is always consistent

You can create your own derived relations for common paths in your domain:

class Author extends AuthorCodegen {
// Use hasManyThrough for simple paths that include everything
readonly reviews: Collection<Author, Review> = hasManyThrough((a) =>;

// Use hasManyDerived to do filtering if needed
readonly publicReviews: Collection<Author, Review> = hasManyDerived(
{ books: "reviews" },
(a) => a.flatMap(a.books.get).flatMap(b => => r.isPublic)

Or derived fields that will be reactively calculated (and updated in the database) when their dependencies change (see Reactive Fields):

class Author extends AuthorCodegen {
readonly numberOfBooks: ReactiveField<Author, number> =
(a) => a.books.get.length,

// Now we can filter/sort by numberOfBooks in queries b/c its a column in the db
const prolificAuthors = await em.find(Author, { numberOfBooks: { gt: 100 } });

You write validation rules that can be per-field, per-entity or even reactive across multiple entities, i.e. in Author.ts (see Validation Rules):

import { authorConfig as config } from "./entities";

export class Author extends AuthorCodegen {}

// Required rules for `NOT NULL` columns are automatically added in AuthorCodegen

// Anytime a book is associated/disassociated to/from this author, run this rule
config.addRule("books", (author) => {
if (author.books.get.length > 10) {
return "Too many books";

You load/save entities via a per-request EntityManager that acts as a Unit of Work and on em.flush will batch any changes made during the current request in an atomic transaction, only after running all validation rules & updating any derived values (see Entity Manager):

const a1 = em.load(Author, "a:1");
a1.firstName = "Allen";
a2.lastName = "Zed";
// Runs validation against all created/updated entities, calls lifecycle hooks,
// updates derived values, and issues bulk INSERTs/UPDATEs in a transaction
await em.flush();

To avoid tedious await / Promise.all, you can use deep load a subgraph via populate hints (see Load-Safe Relations):

// Use 1 await to preload a tree of data
const loaded = await a1.populate({
books: { reviews: "comments" },
publisher: {},

// No more await Promise.all
loaded.books.get.forEach((book) => { => {

Loading any references or collections within the domain model is guaranteed to be N+1 safe, regardless of where the populate / load calls happen within the code-path (see Avoiding N+1 Queries).

To find entities, you can use an ergonomic em.find API that combines joins and conditions in a single "join literal" (see Finding Entities):

const books = await em.find(
author: { publisher: { name: "p1" } },
status: BookStatus.Published,
{ orderBy: { name: "desc" } }

Or if you have complex conditions, you can use dedicated conditions to do cross-table ANDs and ORs (also see Finding Entities):

const [p, b] = aliases(Publisher, Book);
const books = await em.find(
{ as: b, author: { publisher: p } },
conditions: { or: ["p1"), b.status.eq(BookStatus.Published)] },
orderBy: { name: "desc" },

For lower-level, complex queries that do sums, group bys, etc., Joist currently defers to existing query builder libraries like Knex.

You can test all of your behavior with integrated test factories (see Test Factories):

import {  newEntityManager } from "./setupTests";

describe("Author", () => {
it("can have reactive validation rules", async () => {
const em = newEntityManager();
// Given the book and author start out with acceptable names
const a1 = new Author(em, { firstName: "a1" });
const b1 = new Book(em, { title: "b1", author: a1 });
await em.flush();
// When the book name is later changed to collide with the author
b1.title = "a1";
// Then the validation rule is ran even though it's on the author entity
await expect(em.flush()).rejects.toThrow(
"Validation error: Author:1 A book title cannot be the author's firstName",

And tweak your factories to provide "valid by default" data to keep your tests succinct:

export function newAuthor(em: EntityManager, opts: FactoryOpts<Author> = {}): DeepNew<Author> {
return newTestInstance(em, Author, opts, {
// firstName has a unique constraint, so make it unique
firstName: `a${testIndex}`,
// Authors should be popular by default, but only in tests, not prod
isPopular: true,

Finally, Joist has a number of other nifty features, like Tagged Ids, automatic handling of Soft Deletes, support for Class Table Inheritance, and more.