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An offline queue can undo a rejected edit only if each queued mutation carries a before-image: the exact fields that existed in the local mirror before the change. A reliable design then needs three more controls: classify failures so only temporary transport problems are retried, serialize replay so two workers cannot send the same mutation, and let the server resolve genuine conflicts. This approach uses a persisted JSON queue, ordered replay, an explicit lock, and one JavaScript flush path shared by foreground and Android background work. It also removes a client-side rule that blocked shared records even though the server already protected them.
The four guarantees an offline queue must provide
Offline editing is not just a matter of storing requests until connectivity returns. A production queue must answer four separate questions:
- What exact local state should be restored if the server rejects an edit?
- Which failures are temporary enough to retry, and which are authoritative refusals?
- How do foreground code, reconnect handlers, and background workers avoid sending one entry twice?
- What happens when another device changed the same record while this device was offline?
The implementation described here answers those questions independently. Rollback data lives in each queue entry, retry policy is explicit, replay is ordered behind a shared lock, and conflict policy is split between client and server. Keeping those responsibilities separate prevents an offline feature from turning every error into an unexplained data loss or duplicate request.
Store a before-image, not just an inverse instruction
The queue is persisted as a JSON array in AsyncStorage. Each pending edit has four fields:
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| Field | Purpose |
|---|---|
id |
Identifies the queued mutation. |
neuronId |
Identifies the record whose local mirror is being changed. |
patch |
The exact partial update sent to the server, without rewriting or normalizing it during replay. |
previous |
The values those fields had in the local mirror before this edit. |
The patch is sent verbatim. The previous object is the rollback payload. If a field did not exist before the edit, it is represented as null; that distinction matters because rollback may need to clear a value rather than leave the newly created value in place.
A deletion uses the same shape instead of inventing a special queue type. Its patch is empty, while previous contains the complete cached row. If the deletion is refused, restoring that full row can recreate the local record exactly as it was. Without this before-image, the client would know that it should undo something but would not know which values to restore.
Capture the before-image at write time
Every writer follows one path. It reads the current mirror values needed by the mutation, keeps those values as previous, sends the request, and updates the cache after a successful response. If the send fails, the writer asks isRetryable whether the failure may be temporary. Only then does it append the entry to AsyncStorage. Capturing previous later is unsafe because another local edit could already have changed the mirror.
For a sequence of edits to the same record, each entry should describe the state immediately before its own edit. Ordered replay then preserves the causal chain: the second patch is not interpreted as if it had been created against the state that existed before the first patch.
Retry transport failures, reject definitive responses
Offline mode is not an excuse to retry every non-success response. The queue treats explicit HTTP refusals as settled decisions:
| Failure | Queue decision | Reason |
|---|---|---|
| 400 Bad Request | Definitive refusal | The server rejected the request’s contents; resending the same body will not fix it. |
| 403 Forbidden | Definitive refusal | The current authorization does not permit the operation. |
| 404 Not Found | Definitive refusal, with a deletion exception | An edit targets a record the server cannot find. A queued deletion receiving 404 is already settled and is dropped without restoring the row. |
| 409 Conflict | Definitive refusal | The server has identified a conflict that this queue entry cannot resolve by blind retry. |
| Other failures, including transport failures | Retry candidate | The condition may clear when connectivity or the service recovers. |
The important distinction is between “the request did not reach a usable server response” and “the server answered and refused this mutation.” A timeout, connection reset, or similar temporary failure remains queued. A 400, 403, 404, or 409 is removed from the queue and handled as a user-visible rollback case.
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How replay rolls the mirror back
Replay walks entries in arrival order. For a successful send, it removes the entry and applies the accepted change to the local mirror. For a definitive refusal, it removes the entry, applies previous to the mirror, and invokes a UI handler that can identify the affected card in a message to the user.
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There is one deliberate exception: if a queued deletion receives 404, the record is already absent on the server. The entry is dropped as settled, and the client does not resurrect the cached row from previous. Applying the normal rollback rule here would produce the opposite of the server’s final state.
Retryable failure stops the pass
If an entry encounters a retryable failure, replay leaves that entry and every later entry queued and stops the pass. This preserves causal order. Sending a later patch while an earlier one is still waiting could apply changes against the wrong server state, especially when both entries affect the same record.
Definitive refusals do not block unrelated later entries. They are removed and rolled back immediately, after which replay can continue in order. The UI callback is separate from queue storage so the storage layer can remain deterministic while the app chooses how to explain the rejection.
Conceptual replay flow
acquire shared lock
flush pending creations first
for each entry in arrival order:
send patch exactly as stored
on success: remove entry and update mirror
on retryable failure: keep entry and stop
on definitive refusal: remove entry and restore previous
release lock in finallyThe actual implementation uses the same decisions for foreground and background triggers; the flow above is a model of those rules, not a second replay implementation.
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Serialize flushes with a lock that always clears
Reconnect handlers and background work can fire close together. Before a shared lock existed, two flushes could read the same queue, both select the same head entry, and send that edit twice. The fix is a process-wide “flush in progress” flag (or equivalent shared lock) checked by every caller.
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A caller that finds the lock held exits without starting another pass. The owner keeps the lock for the entire ordered replay, including queue reads, network sends, mirror updates, and removals. A finally block clears the flag on both success and failure. Without that cleanup, one exception could permanently latch the queue and make every later reconnect appear to do nothing.
The lock prevents overlapping client workers; it does not make a network request globally idempotent. If a process dies after the server accepts a request but before the client removes the entry, a later replay may send it again. The server API therefore still needs normal idempotency or safe repeat semantics where duplicate application would be harmful. The lock addresses the observed overlap between active client passes, not every crash-recovery scenario.
Let the server handle concurrent edits
Client rollback and server conflict resolution solve different problems. The server uses an updated_at guard when accepting an edit. If that compare fails, it re-reads the current row and retries a merge for metadata. This allows independent checklist changes to survive when two clients tick different items while offline.
That merge guarantee is intentionally narrow. Structured metadata can be merged according to the server’s rules, but concurrently rewritten scalar fields remain last-writer-wins and are retained in history. A client must not describe every field as conflict-free merely because checklist metadata merges successfully.
This division keeps the queue simple: it stores the user’s intended patch and the local before-image, while the server decides whether the patch can be applied to the current authoritative row. A 409 remains a definitive refusal from the queue’s perspective; the app rolls back the local fields and tells the user which card needs attention.
Why the shared-record guard was deleted
The removed rule was isSharedFiche. It treated shared or unknown cards as unsafe to edit offline, so a client would refuse a write “to be safe.” That blocked a central use case: ticking items in a shared grocery list while disconnected.
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Commit 3bc82c6 removed the guard after checking what the server already protects. The server’s updated_at compare-and-retry merge safeguards shared metadata, so the client-side rule duplicated protection while denying legitimate offline work. The lesson is not to remove all client validation. It is to name the exact failure a guard prevents and verify whether the server already prevents that failure. If it does, a blanket client refusal may be the bug.
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Use one flush path in the foreground and on Android
When the app is open, a WebSocket reconnect can invoke the JavaScript flush. When Android has closed the app, a native module reports only the pending count and schedules one network-constrained WorkManager item using ExistingWorkPolicy.KEEP. The worker starts a headless JavaScript task, which calls the same flush logic.
Reusing the JavaScript path is a correctness decision. A separate Kotlin replay algorithm would eventually drift in error classification, ordering, rollback, or lock behavior. The native side is responsible for scheduling; JavaScript remains responsible for applying queue semantics.
Flush pending creations before edits
The headless task flushes pending creations first, then pending edits. An edit to a record created while offline could otherwise run before its creation reaches the server. The edit would receive a misleading 404 and be rolled back even though the record was merely waiting in the same local queue. Creation-first ordering removes that false failure mode.
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What the tests prove—and what they do not
Unit tests use mocked storage and send functions to cover the queue store’s ordering, retry behavior, rollback, lock handling, restart recovery, and settled-delete behavior. Those tests exercise the JavaScript semantics without requiring a phone or a live server.
They do not test the Kotlin worker itself. In particular, the Android pending-count report, the KEEP scheduling policy, the network constraint, and starting the headless service are identified as untested integration points. Treat those as separate validation work rather than assuming JavaScript unit coverage proves native scheduling.
Resolve the retention-direction discrepancy
The queue also has an unresolved MAX_ENTRIES policy discrepancy. A comment says the oldest entries should survive, but an implementation using slice(-MAX_ENTRIES) retains the newest entries. Those policies have opposite data-loss behavior:
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- Keeping the newest entries preserves recent intent but can remove an earlier mutation that later entries depend on.
Choose the intended policy explicitly, document why, and add a test that fills the queue beyond the cap. Until that decision is encoded, the cap should be treated as a known risk rather than a settled guarantee.
Implementation checklist
- Persist queue entries as JSON with
id,neuronId, verbatimpatch, and a completepreviousbefore-image. - Represent missing prior fields as
null; store the full cached row for deletions. - Send immediately and update the cache after success; enqueue only failures classified as retryable.
- Treat 400, 403, 404, and 409 as definitive refusals, with 404 deletion treated as already settled.
- Replay in arrival order, stop at the first retryable failure, and roll back definitive refusals.
- Guard the entire flush with one shared lock and clear it in
finally. - Flush pending creations before edits.
- Use the same JavaScript flush for reconnects and Android headless work.
- Keep server conflict policy explicit: metadata may merge after an
updated_atmiss, while scalar rewrites remain last-writer-wins. - Test restart behavior, duplicate-trigger locking, rollback, settled deletes, native scheduling, and the chosen queue-cap direction.
This design makes an offline queue accountable: every retry has a reason, every rollback has the exact state to restore, and every replay trigger follows the same serialized path. Removing the over-broad shared-record guard then becomes a controlled policy change backed by server conflict handling rather than an optimistic relaxation of safety.
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