Learn TanStack - Building a Full-stack Data App

#Introduction

All the material from episodes 0 through 22 now converges at one point: building a real full-stack data app. Episode 23 designs the end-to-end architecture with TanStack, combines query caching, table rendering, and routing in one application, weighs performance and UX tradeoffs, and closes with production-ready deployment and maintenance.

The case study app is an inventory dashboard: a product table with sorting and filtering, per-route product details, stock mutations with optimistic updates, and a statistics summary. Three classic data-driven app problems you've practiced in the previous episodes.

By the end of the episode, you'll see how small, consistent decisions across 24 episodes produce an app that is fast, type-safe, and easy to maintain.

#End-to-end Architecture with TanStack

#Mapping Features to Libraries

Every part of the app is mapped to the most fitting library: Query for server state and caching, Router for navigation and per-page data loading, Table for the inventory datagrid, and Virtual for long product lists.

Arsitektur satu halaman dashboard

const inventarisRoute = createRoute({
  getParentRoute: () => rootRoute,
  path: "inventaris",
  validateSearch: z.object({
    halaman: z.number().default(1),
    q: z.string().optional(),
  }),
  loader: ({ context, search }) =>
    context.queryClient.ensureQueryData({
      queryKey: ["inventaris", search],
      queryFn: () => ambilInventaris(search),
    }),
  component: HalamanInventaris,
})

The route's loader calls ensureQueryData so data is available before the component renders. The halaman and q search params are validated with Zod, so the URL always carries valid state and its types flow all the way into the components.

#Single Source of Truth

The Query cache is the only source of data on the client. Components never store their own copies of server data; they read the cache and wait for mutations to update it through invalidation.

#Combining Query Caching, Table Rendering, and Routing

#Data Fills the Table, the URL Stores State

The table doesn't store its own filters and sorting; it reads them from the Router's search params and writes changes back to the URL. Query uses those values as the query key, so navigation and fetching are always aligned.

Table membaca state dari URL

const table = useReactTable({
  data: inventaris?.items ?? [],
  columns,
  state: {
    sorting: search.sorting,
    pagination: { pageIndex: search.halaman - 1, pageSize: 10 },
  },
  onSortingChange: (updater) =>
    navigate({ search: { ...search, sorting: result(updater) } }),
})

The table's pagination and sorting read their values from search. Every change is written back to the URL, so refreshing the page doesn't send users back to the initial state.

#Mutations and Optimistic Updates

When stock changes, useMutation updates the UI optimistically first, then invalidating the inventory query syncs the data with the server. A failed mutation triggers automatic rollback thanks to TanStack's optimistic state.

#Performance and UX Tradeoffs

#Choosing How Much Data per Page

Not all data can be served by a single pattern. Per-page queries give fast responses for tables, while massive lists use virtual scrolling. This decision affects the whole architecture.

Prefetch halaman berikutnya

const prefetchHalamanBerikutnya = () => {
  void queryClient.prefetchQuery({
    queryKey: ["inventaris", { ...search, halaman: search.halaman + 1 }],
    queryFn: () => ambilInventaris({ ...search, halaman: search.halaman + 1 }),
  })
}

prefetchQuery untuk halaman berikutnya makes pagination feel instant because the data is already in the cache before the user presses the button. The tradeoff is extra bandwidth spent on data that may never be opened.

#Keeping UX Light

Use proportionate loading skeletons, keep old data during refetch (placeholderData), and give clear feedback while mutations run. Together these make the app feel responsive even when the backend is slow.

#Production-Ready Deployment and Maintenance

#Full Pipeline and Monitoring

This app uses every foundation from episodes 19 through 21: building with Vite and TypeScript, a CI pipeline that runs lint and typecheck, deployment to a hosting platform, and observability that sends query metrics and errors.

npm run typecheck && npm run build
git tag v1.0.0 && git push origin main

npm run typecheck && npm run build is the last gate before release. The version tag then marks a release point that can be rolled back at any time.

#Ongoing Maintenance

Document architectural decisions, keep the query key factory centralized, and schedule gradual library upgrades. An app that's easy to maintain is an app that can be reworked without fear.

#Conclusion

Episode 23 closes the series with an end-to-end inventory dashboard app: an architecture mapping features to TanStack libraries, table state synced to the URL, prefetch and optimistic updates for UX, and a pipeline with observability for production.

Key takeaways:

• Mapping features to the right libraries keeps the architecture simple.
• The Query cache is the only source of truth for data on the client.
• Table state lives in the URL so navigation and fetching stay aligned.
• Prefetch and optimistic updates keep the UX responsive.
• Typecheck and build are the last gates before release.
• A query key factory and documentation keep the app easy to maintain.

The Learn TanStack series is complete. From the pre-requisites in episode 0 to the full-stack app in episode 23, you've traveled the whole journey: understanding the architecture of Query, Table, Router, Virtual, and Charts, mastering advanced patterns, and shipping an app to production with healthy observability and maintenance. Apply this foundation to your projects, keep following the ecosystem's updates, and make TanStack a part of your daily toolkit. Happy building!