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Is Joist the Best ORM, Ever?

· 10 min read

I've been working on the Joist docs lately, specifically a Why Joist? page, which ended up focusing more on "why Domain Models?" than a feature-by-feature description of Joist.

Which is fine, but a good friend (and early Joist user) proofread it, and afterward challenged me that I was being too humble, and I should be more assertive about Joist being "THE BEST ORM FOR TYPESCRIPT AND POSTGRES" (his words), as he listed off his own personal highlights:

  1. If it compiles, it works. "If you love TypeScript, you'll love Joist."
  2. It's "really effing fast" (no N+1s, ever).
  3. We solve many common problems for you (auto-batching updates, handling the insertion order of related entities, and have many patterns for enums, polymorphic relations, etc.)
  4. Factories make testing amazing.

All of these are true.

But in thinking about his challenge, of pitching Joist specifically as "the best ORM for TypeScript & Postgres", I actually think I can be even more bullish and assert Joist is, currently, the best ORM, in any language, ever, TypeScript or otherwise.

Which is crazy, right? How could I possibly assert this?

I have three reasons; admittedly the first two are not technically unique to Joist, but both foundational to its design and implementation, and the third that is one of Joist's "special sauces":

  1. JavaScript's ability to solve N+1s via the event loop, and
  2. TypeScript's ability to model loaded-ness in its type system.
  3. Joist's "backend reactivity"

No N+1s: JavaScript's Event Loop

I've used many ORMs over the years, going back to Java's Hibernate, Ruby's ActiveRecord, and a few bespoke ones in between.

Invariably, they all suffer from N+1s.

I don't want to repeat Joist's existing Avoiding N+1s docs, but basically "entities are objects with fields/methods that incrementally lazy-load their relations from the database" is almost "too ergonomic", and tempts programmers into using the abstraction when they shouldn't (i.e. in a loop), at which point N+1s are inevitable.

Again as described in "Avoiding N+1s", JavaScript's event loop forcing all I/O calls to "wait just a sec", until the end of the event loop tick, gives Joist an amazing opportunity, of course via dataloader, to de-dupe all the N+1s into a single SQL call.

For everything.

This works so well, that personally I don't know that I ever want to work in a programming language/tech stack that cannot use this trick (at least to build backend/line-of-business applications).

Granted, JavaScript is not the only language with an event loop--async Rust is a thing, Python has asyncio, and even Vert.x on the JVM provides it (I prototyped "dataloader ported to Vert.x" several years ago), and either Rust or the JVM (Scala!) would be pretty tempting just in terms of "faster than JavaScript" performance.

But the event loop is only part of the story--another critical part is TypeScript's type system.

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Other TypeScript ORMs like Prisma & Drizzle "solve" N+1s by just not modeling your domain as entities (with lazy-loaded relations), and instead force/assume a single/large up-front query that returns an immutable tree of POJOs.

This does remove the most obvious N+1 footgun (lazy-loaded relations), but it also fundamentally restricts your ability to decompose business logic into smaller/reusable methods, because now any logic that touches the database must be done "in bulk" directly by your code, and often crafted in SQL specifically to how each individual endpoint is accessing the data.

(Concretely, if you had a saveAuthor endpoint with logic/queries to validate "this author is valid", and now write a batch saveAuthors endpoint, you could not reuse the "written for one entity" logic without rewriting it to work at the new endpoint's grouped/batch level of granularity. Or similar for saveBook logic that you want to use within a saveAuthor that also upserts multiple children books.)

Instead, Joist's auto-batching lets you ergonomically write code at the individual entity abstraction level (whether in a loop, or in per-entity validation rules or lifecycle hooks), but still get performant-by-default batched queries.

Loaded Subgraphs: TypeScript's Type System

After solving N+1s with the event loop, the next biggest ergonomic problem in traditional, entity-based ORMs is tracking (or basically not tracking) loaded-ness in the type system.

Because you can't have your entire relational database in memory, domain models must incrementally load their data from the database, as your business logic's codepaths decide which parts they need to read.

This was another downfall of the Hibernate/ActiveRecord ORMs: there was no notion of "is this relation loaded yet?", and so any random relation access could trigger the surprise of an expensive database I/O call, as that relation was lazy-loaded from the database.

Joist solves this by statically typing all relations as "unloaded" by default, i.e. accessing an Author's books requires calling a1.books.load(), which returns a Promise (which is also key to the N+1 prevention above).

Which is great, I/O calls are now obvious, but "do an await for every relation access" would really suck (we tried that), so Joist goes further and uses TypeScript's type system to not only track individual relation loaded-ness (like author1.books or book2.authors), but mark entire subgraphs of entities as populated/loaded relations and hence synchronously accessible:

// Load the Author plus the specific books + reviews subgrpah
const a1 = await em.load(Author, "a:1", {
populate: { books: { reviews: "comments" } },
});

// a1 is typed as Loaded<Author, { books: { reviews: "comments" } }>
// Tada, no more await Promise.all
a1.books.get.forEach((book) => {
book.reviews.get.forEach((review) => {
console.log(review.comments.get.length);
});
})

This combination of:

  • Explicit .load() / await calls for any I/O, but leveraging
  • Mapped types to allow compiler-checked synchronous access

For me, is also something that I never want to work without again. It's just so nice.

Unlike JavaScript not having a monopoly on the event loop, for these mapped types I believe TypeScript effectively does have a lock on this capability, from a programming language/type system perspective.

Creating "new types" in other programming languages is generally handled by macros (Scala and Rust), or I suppose Haskell's higher-kinded-types. But, as far as I know, none of them can combine TypeScript "mapped type + conditional type" features in a way that would allow this "take my user-defined type (Author)" and "this user-defined populate hint type" and fuse them together into a new type, that is "the author with this specific subgraph of fields marked as loaded".

I'm happy to be corrected on this, but I think TypeScript is the only mainstream programming language that can really power Joist's Loaded<Author, { books: "reviews" }>-style adhoc typing of subgraphs, or at least this easily.

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Other TypeScript ORMs (Prisma, Drizzle, Kysley, etc.) also leverage TypeScript's mapped types to create dynamic shapes of data, which is legitimately great.

However, they all have the fundamental approach of issuing "one-shot" queries that return immutable trees of POJOs, directly mapped from your SQL tables, and not subgraphs of entities that can have non-SQL abstractions & be further incrementally loaded as/if needed (see Why Joist for more on this).

You can generally see, for both issues covered so far (N+1s and statically-typed loaded-ness), most TypeScript ORMs have "solved" these issues by just removing the features all together, and restricting themselves to be "sophisticated query builders".

Joist's innovation is keeping the entity-based, incremental-loading mental model that is historically very popular/idiomatic for ORMs (particularly Ruby's ActiveRecord), and just fundamentally fixing it to not suck.

Joist's Backend Reactivity

This 3rd section is the first feature that is unique to Joist itself: Joist's "backend reactivity".

Many ORMs have lifecycle hooks (this entity was created, updated, or deleted--which Joist does as well), to organize side effects/business logic of "when X changes, do Y".

But just lifecycle hooks by themselves can become tangled, complicated, and a well-known morass of complexity and "spooky action at a distance".

This is because they're basically "Web 1.0" imperative spaghetti code, where you have to manually instrument each mutation that might trigger a side effect.

(Concretely, lets say you have a rule that needs to look at both an author and its books. With raw lifecycle hooks, you must separately instrument both the "author update" and "book update" hooks to call your "make sure this author + books combination is still valid" logic. This can become tedious and error-prone, to get all the right hooks instrumented.)

Instead, Joist's reactive fields and reactive validation rules take the lessons of "declarative reactivity" from the Mobx/Solid/reactivity-aware frontend world, and bring it to the backend: reactive rules & fields declare in one place what their "upstream dependencies" are, and Joist just handles wiring up the necessary cross-entity reactivity.

This brings a level of ease, specificity, and rigor to what are still effectively lifecycle hooks under the hood, that really makes them pleasant to work with.

info

The declarative nature of Joist's domain model-wide reactivity graph is also very amenable to DX tooling & documentation generation, but we've not yet deeply explored/delivered any functionality that leverages it.

Conclusion: Best ORM Ever?

So, these three features are what back up my exaggerated "best ORM ever" assertion.

If tomorrow, I suddenly could not use Joist, and had to find another ORM to use (or, in general, build any sort of application backend on top of a relational database), in any current/mainstream programming language, without a doubt I would want:

  1. Bullet-proof N+1 prevention,
  2. Tracking loaded relation/subgraph state in the type system, and
  3. Backend reactivity, for declarative cross-entity validation rules and reactive fields.

And Joist is the only ORM that does all three of these: two of which are uniquely enabled by the JavaScript/TypeScript stack, and the third just part of Joist's own innovation.

Disclaimer 1: Uncomfortably Bold Claims

I usually don't like making bold/absolutist claims, like "this or that framework is 'the best'" or "technology x/y/z is terrible" or what not.

I did enough of that early in my career, and at this point I'm more interested in "what are the trade-offs?" and "what's the best tool for this specific use case?"

So, I hold two somewhat incongruent thoughts in my head, as I am both:

  • Very confident that Joist is "the best" way to build application backends on top of a relational database, for a large majority of use cases/teams/codebases, but I also
  • Recognize it's "framework" / entity approach (see Why Joist) might be either too opinionated or too much abstraction for some people's tastes, and just in general choices & alternatives are always great to have.

My guess is if you tried Joist, you would quickly come to like it, but it's also perfectly fine if not!

Disclaimer 2: Still a Lot To Do

Similar to the two incongruent thoughts above, another two semi-contradictory thoughts is the disclaimer that:

  • Joist's core is very solid and vetted by 4+ years of production usage & continual iteration at Homebound, but also
  • There's still a lot of work to do, obviously supporting other databases, but also the myriad fun, incremental improvement ideas we're tracking in the issue tracker, and of course even more that we've not thought of yet.

Feedback

If you have thoughts, questions, or feedback, please let us know! Feel free to join the Joist discord, or file issues on the GitHub repo if you try Joist and run into any issues.

Despite all the hubris in this post, we are still a very small project & community, and so have a lot of growth and improvement ahead of us.

Thanks for the read!

New NextJS Sample App

· 7 min read

We've added a new NextJS + Joist sample app that shows how Joist can be used in a NextJS application, with several benefits:

  • Automatic N+1 Prevention
  • JSON Payload/Props Creation
  • Optional Join-based Preloading

This post gives a short overview; if you'd like to watch a video, we also have a YouTube video that walks through the sample app.

Two Render Tree Approaches

While building the sample app, we found two fundamental ways of structuring a NextJS app's render tree:

  1. Fewer React Server Components, that prop drill data to the Client Components
    • Shown on the left, see author-rcc-card.tsx and book-rcc-preview.tsx
  2. Mostly React Server Components, with Client Components only at the bottom
    • Shown on the right, see author-rsc-card.tsx and book-rsc-preview.tsx

The top-level Table / table.tsx component renders each of these side-by-side, so we can see the differences, and observe some pros/cons of each approach.

  • With mostly RSC components, it's easy to decompose data loading away from the top-level component.

    For example, the AuthorRscCard can make its own data loading calls, and even if it's render many pages on the page, Joist will de-dupe across the N sibling AuthorRscCards, and batch into a single SQL call.

    type AuthorCardProps = {
    /** RSCs can accept the domain model enities as a prop. */
    author: Author;
    addBook: (id: string) => Promise<void>;
    };

    /** The RSC version of AuthorCard can load it's own data. */
    export async function AuthorRscCard({ author, addBook }: AuthorCardProps) {
    // This will be auto-batched if many cards render at once
    const books = await author.books.load();
    // Or if you wanted a tree of data, this will also be auto-batched
    const loaded = await author.populate({ books: { reviews: "ratings" } });
    return <div>...jsx</div>;
    }

    This is nice because it allows the AuthorRscCard to be more self-sufficient, and allow the parent table component to be unaware of its children loading details.

  • With mostly Client components, the opposite happens, and only the parent can make database / EntityManager calls, and so is responsible for loading all the data for its children, and passing it as JSON via props:

    type AuthorCardProps = {
    /** RCCs must accept a POJO of `Author` + all nested data. */
    author: AuthorPayload;
    addBook: (id: string) => Promise<void>;
    };

    /** The RCC version of AuthorCard accepts the `AuthorPayload`. */
    export function AuthorRccCard({ author, addBook }: AuthorCardProps) {
    // can only use data already available on `author`
    }

    Even though the up-front data load can become awkward, it does give more opportunities for optimizations; for example Joist can use join-based preloading to load a single tree of Author + Book + Review entities in a single SQL call, which is even better optimization than the "one query per layer" N+1 prevention of the RSC-based approach.

Automatic N+1 Prevention

In either approach, Joist's N+1 prevention auto-batches database calls, even if they are made across separate component renders. I.e. in the RSC components:

  • The top-level Table component makes 1 SQL call for all Author entities.
  • All 2nd-level AuthorRscCard cards each make their own author.books.load() (or author.populate(...)) call, but because they're all rendered in the same event loop, Joist batches all the load calls into 1 SQL call
  • Any 3rd-level components would have their load calls batched as well.

In the React Client Component approach, this auto-batching is admittedly not as necessary, assuming a singular top-level component, like Table, loads all the data at once anyway (although, as mentioned later, Joist can optimize that as well).

See the Avoiding N+1s section of our docs for more information.

JSON Payload/Props Creation

Since the client components cannot make their own async data calls, the top-level Table components is responsible for loading all the data into a JSON payload, and passing it down to the children as props.

Joist entities have an easy way of doing this is, via a toJSON method that takes the shape of data to create:

// Define the shape of data to create
export const authorHint = {
id: true,
firstName: true,
books: {
id: true,
title: true,
reviews: ["id", "rating"],
},
customField: (a) => a.id + a.title,
} satisfies JsonHint<Author>;

// This typedef can be used in the client-side props, or to match any
// endpoint-based respones types like for REST/OpenAPI.
export type AuthorPayload = JsonPayload<Author, typeof authorHint>;

const payload = await a.toJSON(authorHint);

The toJSON implementation will:

  • Load any relations that are not yet loaded from the database
  • Output only the keys that are requested in the authorHint
  • Call any lambdas like customField to generate custom values

As with previous examples, all data loading is N+1 safe, and also potentially join-based preloaded.

See the toJSON docs for more information.

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This recursive toJSON payload generation is a relatively new feature of Joist, so if you have feature ideas that would make it more useful, please let us know!

Join-Based Preloading

The last optimization that Joist can do is join-based preloading, which can be used in either the RSC or RCC approach.

This is also a newer feature that requires opt-ing in to, but in em.ts you can add a preloadPlugin:

/** Returns this request's `EntityManager` instance. */
export const getEm = cache(() => {
// Opt-in to preloading
const preloadPlugin = new JsonAggregatePreloader();
return new EntityManager({}, { driver, preloadPlugin });
});

This will allow Joist to load a deep tree/subgraph of entities in a single SQL call.

For example, normally a Joist em.find a call like:

const a = await em.find(
Author,
{ id: 1 },
{populate: { books: "reviews" } },
);
// Now access all the data in memory
console.log(a.books.get[0].reviews.get[0].rating)

Will issue three SQL calls:

SELECT * FROM authors WHERE id = 1;
SELECT * FROM books WHERE author_id = 1;
SELECT * FROM reviews WHERE book_id IN (1, 2, 3, ...);

But with the preloadPlugin enabled, it will use a single SQL call that uses CROSS JOIN LATERAL and json_agg to return the author's books, and the book's reviews (omitted for brevity) in a single row:

select a.id, _b._ as _b from authors as a
cross join lateral
-- create a tuple for each book, and aggregate then into an array of books
select json_agg(json_build_array(_b.id, _b.title, _b.foreword, _b.author_id) order by _b.id) as _
from books _b
where _b.author_id = a.id
) _b
where a.id = ? limit ?
info

Joist's join-based preloading is still a beta feature, so if you run into any issues, please let us know!

What about Complex Queries?

So far, our queries have focused on loading "just entities", and then putting those on the wire (or rendering them to HTML).

This is because Joist's focus is on building robust domain models, and specifically helping solve the "write-side" of your application's business logic (running the correct validation rules, lifecycle hooks, reactive updates), and less so on the "read-side" of complex queries (i.e. that using aggregates using GROUP BY, multiple nested subqueries/projections/etc.).

As such, Joist does not yet have a sophisticated query builder that can create arbitrary SQL queries, like Kysley or Drizzle.

Instead, Joist encourages an approach that uses its robust write-side features to create materialized columns in the database, such that the majority of your pages/responses really can be served by "super simple SELECT statements", instead of using complicated queries to calculate aggregates on-the-fly.

Although you can of course use both approaches, and just use a lower-level query builder where needed.

Sample App Feedback

Joist's roots come from the GraphQL world, so this sample app was our first foray into using it for a NextJS application. If we've missed any key features that would make it easier to use Joist in a NextJS app, please let us know!