gem-hunter
主要言語: TypeScript
An ecommerce store built with Zero, TanStack, Drizzle, and PlanetScale for Postgres.
A sync-based ecommerce app with 88k artists and 200k albums from the 1990's.
Built with:
Live demo: https://ztunes.rocicorp.dev
cp .env.sample .env, then fill in all the required varspnpm i pnpm dev
The database is Postgres, run under Docker during dev (see scripts/dev-db.ts).
In production, point it at any Postgres database using the ZERO_UPSTREAM_DB and PG_URL env vars.
The schema is managed using Drizzle (see db/schema.ts).
drizzle-zero is used to generate the Zero schema (in zero/schema.gen.ts). You do not need to worry about the Zero schema at all - it is completely automated.
The Zero schema is auto-generated during dev when the Drizzle schema changes.
The Drizzle schema is not automatically pushed to your dev db because that could destroy data. To push your Drizzle schema changes during dev just use the normal npx drizzle-kit push.
This app uses TanStack Start in SPA mode. There is no server-side rendering. Zero doesn't currently support SSR and SSR'ing even the shell results in a slower overall startup. See the comment in auth/auth.ts for more information about this setup.
However, that doesn't mean the app is client-side only. TanStack's API routes are used for the various API endpoints needed by both Zero and Better Auth. See app/routes/api.
The auth setup is slightly non-standard. We encode the userID, email, and JWT in the browser cookie and make it client-visible. See auth/auth.ts for more information.
This is not ideal for security (it would be better to not expose credentials to client JS because of XSS risks), but it is basically required by Zero right now. Doing other things makes the app much slower without improving security significantly.
The Zero team is working on improving this and getting the same perf w/o needing to expose credentials to the client.
This app uses Zero mutators (see zero/mutators.ts) to enforce that a user can only add items to their own cart. Note that there is no need for an explicit permission check anywhere. Instead, the mutator gets the userID out of the session and only writes to that users' data. There is no need for a check because there is no way for the client to specify a different userID. Basically it works just like a normal web app would.
Read permissions are implemented similarly: the userID is used to generate a query that filters cart items to only the allowed rows.
An important consideration in any Zero app is what to preload. Our general advice is "any data the app needs within one click".
In TanStack we can mostly achieve this elegantly via TanStack's built-in preload feature.
Each Route defines a free query function which it uses in TanStack's loader method:
function query(zero: Zero, artistID: string | undefined) { return zero.query.artist .where('id', artistID ?? '') .related('albums', album => album.related('cartItems')) .one(); } export const Route = createFileRoute('/_layout/artist')({ // ... loader: async ({context, deps: {artistId}}) => { const {zero} = context; query(zero, artistId).run(); }, });
Then this same query function is used during render like normal:
const [artist] = useQuery(query(zero, id));
The default TTL for Zero queries is 5m, so if user visits link within 5m, data will already be synced to client and nav will be instant.
Additionally:
See https://zero.rocicorp.dev/docs/reading-data#ttls for more information on ttls.
TanStack's built-in preloading only helps with links. We want instant search too.
To support this, ztunes preloads a subset of artist data at app startup, so that it can be searched over locally.
This is something most apps will want to to do, but the question is what data to preload. We want enough data to cover the most common things users will search for, but not so much that app startup begins to suffer.
ztunes' choice is to preload the first 1k (out of 88k) most popular artists (by a popularity field in the artist table). This 1k records is the dataset that is searched locally.
This works out nicely because it is very likely that anything the user searches is going to be contained in that most popular 1k.
Asynchronously, just like all Zero queries, the search also goes to the server. So if the user picks something obscure, they will still get the result, just more slowly.
We want to provide instant results over local data, but we don't want to "jostle" (reorder) those results when server result comes in asynchronously. This would make the UX very hard to read and negate any benefit from instant results.
To prevent this jostle, ztunes sorts all query results by popularity descending. This way any local results are by definition the most popular and are at the top of the list. Other results pop in below.
In general you will want to preload data in the same order you will be displaying in search results.
Zero currently lacks first-class text indexing. This means searches are going to be worst-case O(n). This worst case occurs when there are fewer matching records in the database than the app requests with limit. It also occurs if the sort of the query doesn't match the sort order of an index.
So for example, if the user searches f, that's going to get fast results because the UI requests 20 results and there are many more than 20 matches for this string.
But if the user searches foo fighters, it will be O(n) because Zero will scan all aritsts trying to come up with 20 matches.
In the future Zero will have first-class search that will improve this. For now, 88k records is not a large enough amount of records to search to make a significant difference in query performance.
If you apply this pattern to your own apps keep in mind this performance quirk. If you need better text search let us know, as we have some ideas for quick fixes.
We run a live deployment of ztunes, continuously updated, on Supabase, Vercel, and Cloud Zero.
We continuously deploy ztunes using Github CI. See deploy.yml for how this is done.
One important thing to observe here is that the order of updates does matter. We recommend, as a default:
zero-cacheBecause of (3) being at the end, UI changes can be deployed atomically that depend on new pg schema or new version of zero-cache.
In the case of removing features from your schema you must roll out the UI first, wait for users to update, then separately roll out the schema change. See the Zero docs for more information on schema changes.