To make a React app work offline, combine separate pieces: TanStack Query manages server-state caching and network behavior, IndexedDB can retain structured data across reloads, and a service worker can cache app assets or selected request responses. Persisting a query cache helps users read previously fetched data; it does not, by itself, make edits durable or synchronize them with a server.
Decide what “offline” needs to mean
Offline support can mean several different things. Decide which outcomes your app promises before choosing storage or network settings:
- Read previously fetched data: show server data that was cached in memory or restored from persistent storage.
- Keep user changes while disconnected: save drafts or other local intent durably, rather than relying on a query result as the editable record.
- Reconcile changes with the server: queue and replay writes, then handle validation, duplicates, authentication, ordering, and conflicts.
These are related but distinct capabilities. A persisted TanStack Query cache primarily supports the first. Durable offline editing needs an explicit local write model; synchronization needs a defined contract between that model and the server.
Choose a TanStack Query network mode for each workload
Current TanStack Query documentation describes three network modes. They control how queries and mutations behave in relation to the library’s online state; none of them stores data durably.
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| Mode | When it fits | Behavior to account for |
|---|---|---|
online (default) |
Work that needs a network, such as a normal API query. | Queries and mutations pause when TanStack Query considers the app offline. A query can be pending while its fetch status is paused, so inspect fetch status as well as query status when deciding what the UI should show. |
always |
A query function that reads local data and does not need a network. | It ignores online state. Use it only when running the function while disconnected is useful and safe. |
offlineFirst |
A request that may be fulfilled locally first, for example through a service worker or HTTP cache. | The query function runs once; after a failure, retries pause while offline. This can allow a cache-backed first request without treating every retry as immediately available. |
Set modes at the query or mutation level when workloads differ rather than treating one global setting as an offline architecture. Reconnect behavior is useful only if the underlying function can reach the right source and the app knows how to refresh or reconcile its data. A browser’s navigator.onLine signal should not be treated as proof that a particular server is reachable; use TanStack Query’s online-state abstraction, and consult the current OnlineManager reference if the app needs custom events.
Persist and restore the query cache deliberately
TanStack Query’s persistence integration stores dehydrated query and mutation state, restores that state later, and subscribes to cache changes so subsequent changes can be saved. It is a way to retain Query state, not a domain database or synchronization protocol.
Keep in-memory garbage collection aligned with persistence
Set the QueryClient’s gcTime to at least the persistence maxAge when the goal is to keep restored cache entries available for that full period. The current persistence guide notes that hydration’s default gcTime is five minutes while persistence maxAge defaults to 24 hours. Without an appropriate gcTime, restored data can be garbage-collected from memory sooner than the persisted age suggests. These are configuration defaults, not a guarantee that the browser will retain the backing storage.
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Make startup order explicit
- Create one stable QueryClient. Keep it stable across renders so restoration and cache subscriptions operate on the same client.
- Start restoration before dependent work. Use the persistence provider or an explicit restore flow, and show an intentional loading or restoration state where needed.
- Decide how restored data and refetches interact. Avoid allowing eager queries to race an incomplete restore unless the app has deliberately chosen which result should win.
- Version persisted state when necessary. Use a cache buster or build identifier if a deployment makes older stored state incompatible. Expired, busted, erroneous, or empty persisted state is removed by the persistence flow.
The official offline integration example illustrates waiting for restoration before some router fetches and resuming paused mutations afterward. That example is hosted in the v4 documentation; check current v5 documentation for version-sensitive API names and defaults before adopting its code.
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TanStack Query’s persistence abstraction is storage-agnostic. An IndexedDB-backed persister can save dehydrated QueryClient state, or the app can keep domain records in IndexedDB and have query functions read from that local source. These choices solve different problems.
| Approach | Best suited to | What the app still owns |
|---|---|---|
| Persist the Query cache | Restoring previously fetched Query state after a reload, with limited additional storage modeling. | Cache age and compatibility, restoration order, and any separate design for offline edits and server reconciliation. |
| Store domain data in IndexedDB | Structured records, indexed lookups, durable local edits, and a deliberate local data model. | Schema migrations, relationships and transactions, the Query functions that read local data, and synchronization and conflict rules. |
The core Query library does not create an IndexedDB schema for the app. If users must edit data offline, make the local records and queued intent explicit rather than assuming a persisted server-state cache is the source of truth for every workflow.
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Use IndexedDB for structured local data
IndexedDB is an asynchronous browser database for structured records. It supports versioned schemas, transactions, and indexes, making it a better fit than string-only Web Storage for many larger or more structured data models.
- Open the database at a known version. Handle the upgrade event to create or update object stores when the schema version changes.
- Choose stores around the records the app owns. Define the shape and identifiers for local entities and, where needed, queued changes.
- Use a transaction for related operations. Start a transaction on the relevant store or stores, issue reads or writes, and handle request errors and transaction completion.
- Add indexes for frequent lookups. Index fields used to find records efficiently instead of repeatedly scanning an entire store.
- Make migrations and failure states part of the design. Schema upgrades can affect existing local data, so test how the app handles upgrade errors and incompatible records.
For a local-only query, use a query function that reads the appropriate IndexedDB records and select a network mode that does not pause that local read while offline. For a server-backed query, decide separately whether to read restored cached state, request a response that might be served by a service worker, or wait for connectivity.
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Add a service worker for assets and request-response caching
A service worker can intercept requests and serve cached app assets or selected responses when a network is unavailable. Its install event can populate a cache. It complements IndexedDB and TanStack Query: it does not automatically persist arbitrary API data as domain records or resolve conflicting edits.
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| Layer | Natural role | Lifecycle concern |
|---|---|---|
| Service worker and Cache API | App assets and request-response caching. | Choose safe request rules, decide how stale responses are refreshed, and retire old cache versions. During updates, old and new worker versions may coexist until activation. |
| IndexedDB | Structured records, transactions, indexes, and local data models. | Manage database versions, schema upgrades, and the retention of locally owned data. |
Service workers require a secure context: generally HTTPS, with localhost treated as secure for development. Pairing a service worker with offlineFirst can make sense when the initial fetch may be fulfilled from a local request cache; still define which requests may be cached and what happens when a cached response is stale.
Design offline writes as a synchronization feature
TanStack Query can restore paused mutations and resume them, but a mutation saved before reload needs a mutation function available again afterward. The official example sets a default mutation function, waits for persistence restoration to succeed, then resumes paused mutations and invalidates queries. That mechanism does not choose a safe sync policy for a particular product.
Before promising that offline edits will “sync,” specify the behavior for each of these cases:
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- Durable intent: save the user’s intended change locally before reporting it as safely queued.
- Visible state: distinguish saved locally, queued, syncing, synced, and failed states so users can understand what has happened.
- Retries and duplicate protection: decide what is retried and use idempotency keys or an equivalent server-supported mechanism to avoid applying a repeated request twice.
- Authentication and authorization: define what happens when credentials expire while a change is queued, and revalidate permissions on the server when sending it.
- Validation and ordering: handle server rejection and determine whether dependent changes must be sent in a particular order.
- Conflicts: define whether the server, latest edit, or user decides the result when local and remote records diverge. Consequential or collaborative records need an explicit server contract and conflict policy.
Do not resume queued work until restoration has completed and the app has a valid execution path for the mutation. Resuming after reload is a delivery mechanism; application-specific retry, authentication, and conflict decisions remain necessary.
Account for browser retention and privacy
Browser storage is best-effort by default. Quotas and eviction policies vary, users can clear stored data, and private browsing may impose different limits or remove data when the session ends. Treat locally stored data as useful persistence, not permanent storage.
An app can call navigator.storage.persist() to request stronger retention. A browser may prompt, approve automatically, or deny the request according to its policy, so the app must still handle data being unavailable.
Do not store secrets or sensitive records without a deliberate threat model and retention plan. Include cleanup for logout and tenant changes, and use cache versioning when old state is incompatible. Local cleanup helps prevent data from crossing account boundaries on a shared browser, but it does not replace server-side authorization.
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Plan the layers together, but test their failure boundaries
For a read-only offline experience, a persisted cache may be enough if the UI communicates that it is showing previously fetched data and can recover when storage is missing. If the app also needs an offline shell or cached responses, add a service worker with deliberate cache rules. If users can create or edit records offline, model those records and queued changes in durable storage, then specify how they are validated and reconciled when connectivity returns.
Test more than the happy path: first launch without a connection, reload after a local change, expired or incompatible persisted cache, browser-cleared storage, failed database upgrades, an expired login, duplicate delivery, and a server-side conflict. The expected result for each case should follow from the product’s offline promise—not from assuming that a cache, network mode, or service worker solves it automatically.
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