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What the queue must guarantee
An offline-first client has two copies of a record: the local mirror that the user sees and the server copy that ultimately governs shared data. A write can succeed immediately, fail because connectivity disappeared, or reach the server and be rejected for a definite reason. Treating those outcomes alike creates data loss, duplicate requests, or records that appear edited when the server rejected them.
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The queue described here is designed around five guarantees:
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- The original request body is preserved exactly for replay.
- The values replaced by that request are preserved so the local mirror can be restored.
- Only failures that might succeed later enter the queue.
- Only one replay pass runs at a time, and it processes entries in arrival order.
- Foreground reconnects and Android background work invoke the same JavaScript flush routine.
Those guarantees are related. A before-image without ordered replay can still be applied over a newer local change; ordered replay without a lock can still send the same request twice; and a perfect rollback payload is useless if the client queues a permanent 400 or 403 refusal forever.
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The mutation record: request plus before-image
The queue is persisted as a JSON array in AsyncStorage. Each PendingEdit contains id, neuronId, patch, and previous. The patch is the exact object sent to the server. The previous object records the values that were in the local mirror immediately before the optimistic edit.
Missing properties are represented as null, rather than omitted. That distinction matters: assigning null during rollback clears a field that the failed edit had added or changed, while an omitted property would leave the optimistic value behind.
{
"id": "queue-entry-123",
"neuronId": "card-42",
"patch": { "checked": true, "note": "Buy oats" },
"previous": { "checked": false, "note": null }
}The request and its inverse are therefore stored together. The queue does not try to reconstruct an inverse later from the current cache, because the cache may have changed again by the time replay occurs. As the implementation comment puts it: “Without this a rollback is impossible: we would know to undo without knowing what to restore.”
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A deletion uses an empty patch and stores the complete cached row as previous. If the server refuses that deletion, the client has enough information to recreate the row exactly in its local mirror. Storing only an identifier would permit a tombstone, but not a faithful restoration of the user’s data.
Keep the payload immutable
Do not regenerate patch from the current UI state during replay. A later edit may have changed the form, and replacing the original body would destroy the causal sequence represented by the queue. Treat the persisted entry as the authoritative request until it is acknowledged, rejected, or explicitly removed.
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Write path: send first, then mirror; queue only retryable failures
Every writer follows one path: attempt the network request first, update the cache after success, and enqueue only when isRetryable classifies the failure as potentially temporary.
- Read the current local row and construct the exact patch.
- Capture the affected fields in
previous; for a deletion, capture the full row. - Send the request immediately.
- If the request succeeds, apply the server-confirmed result to the local mirror.
- If it fails, classify the error before deciding whether to enqueue.
- Persist the queue entry only for a retryable failure, leaving the optimistic mirror in place until replay decides its fate.
Explicit HTTP 400, 403, 404, and 409 responses are definitive refusals in this design. They are not interpreted as “the device is offline.” Queueing them would cause repeated requests that cannot become valid merely because the network returns.
Other failures are retry candidates. This category is intentionally broader than “no Wi-Fi”: a timeout, connection reset, or unavailable transport may be temporary, but the implementation still leaves the final decision to the retry classifier. The important property is that classification happens once, consistently, on both the foreground send path and replay.
Replay: one lock, one order, and a clear tail policy
Replay walks the persisted array in arrival order. A shared lock prevents a foreground flush and a background flush from reading the same queue simultaneously and sending an edit twice.
How a flush proceeds
- Acquire the shared in-process flush lock.
- Reload the current queue from AsyncStorage rather than relying on a stale in-memory copy.
- Process entries from the head of the array.
- On success, remove the acknowledged entry and continue.
- On a retryable failure, stop the pass and leave that entry and every later entry queued.
- On a definitive refusal, remove the entry, restore the local mirror from
previous, and notify the UI. - Release the lock in a
finallyblock, regardless of success, rejection, or an unexpected exception.
Stopping at the first retryable failure preserves causal order. If an earlier edit has not reached the server, sending a later edit for the same record could produce a state that never existed in the user’s intended sequence. The tail remains queued for a later attempt.
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Definitive refusal and user feedback
When replay receives a definitive refusal, the refused entry is dropped, the mirror is patched back to the pre-edit fields, and an application-provided handler names the affected card in a user message. This gives the user a visible explanation instead of silently leaving an optimistic value that the server rejected.
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Why the finally block is essential
The lock is state, not a mutex supplied by the operating system. If an exception leaves the flag set, every future flush can be blocked indefinitely. Clearing it in finally ensures that a failed pass does not permanently disable synchronization.
Server conflict handling: merge what is structured, last-write-wins for scalars
The client queue does not attempt to resolve all conflicts locally. The server applies an updated_at guard. When that compare fails, the server re-reads the current record and retries a merge for metadata so independent checklist changes can survive.
This is a selective policy, not a promise that every field merges safely:
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| Field category | Conflict behavior | Implication |
|---|---|---|
| Checklist or other structured metadata | Server re-reads after an updated_at miss and merges changes |
Independent item-level changes can survive a concurrent update |
| Scalar fields rewritten concurrently | Last-writer-wins | The later accepted value replaces the earlier one; no automatic field-level merge is claimed |
The distinction explains why a client-side rule that blocks all shared records was removed. The deleted rule, isSharedFiche, treated shared or unknown cards as unsafe to edit offline. It was removed in commit 3bc82c6 because shared grocery-list ticks are a central offline use case and the server already protects structured metadata with its compare-and-retry merge. The remaining scalar last-writer-wins behavior is explicit and retained in history; removing the guard did not make scalar conflicts disappear.
The practical rule is narrower: a client guard should name the exact failure it prevents, and the team should verify whether the server already prevents that failure. A broad “shared means unsafe” rule can block legitimate offline work without adding protection that the backend does not already provide.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.One flush path for reconnects and Android background work
Foreground and background synchronization must share semantics. WebSocket reconnect invokes the JavaScript flush directly. When Android is closed, a native module reports only the pending count and schedules one network-constrained WorkManager item with ExistingWorkPolicy.KEEP.
The worker starts a headless JavaScript task. That task runs the same JavaScript flush used by the foreground path rather than reimplementing queue logic in Kotlin. Reusing one routine prevents retry classification, rollback, ordering, and refusal handling from drifting between app-open and app-closed behavior.
Creation-before-edit ordering
The headless task flushes pending creations before pending edits. An edit for a record created while offline can otherwise run first, receive a misleading 404, and roll back even though its record has simply not been created on the server yet. Creation-first ordering establishes the remote identity before dependent edits are replayed.
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What the native scheduler does—and does not—decide
The Android layer is responsible for wake-up conditions and deduplication of scheduled work: it exposes a count, requires network availability, and keeps an existing work item instead of adding another. It does not define mutation semantics. The JavaScript store remains responsible for queue order, error classification, rollback, and lock handling.
Failure cases and the expected result
| Event | Queue action | Local mirror action |
|---|---|---|
| Transport failure while sending | Keep or add the entry for replay | Leave the optimistic edit until a later result |
| Successful replay | Remove the acknowledged entry | Keep the server-confirmed state |
| 400, 403, or 409 refusal | Drop the entry | Restore fields from previous and notify the UI |
| 404 for a queued edit | Drop the entry as definitive | Restore the before-image, because the edit target was refused |
| 404 for a queued deletion | Drop the entry as already settled | Do not resurrect the deleted row |
| Retryable failure during replay | Leave the failed entry and tail queued; stop the pass | Keep optimistic local changes pending |
Testing the store and identifying the untested boundary
Unit tests cover the queue store’s ordering, retry behavior, rollback, lock, restart persistence, and settled-delete behavior. These tests exercise mocked storage and send functions, so they validate JavaScript semantics without claiming that a physical phone will wake, schedule, or start the native worker exactly as configured.
The native boundary remains an explicit limitation. The Kotlin pending-count bridge, WorkManager KEEP policy, network constraint, and headless-service start are not covered by those unit tests. They require device or integration testing if those operational guarantees matter to a release.
The retention-direction discrepancy
The queue also has a documented MAX_ENTRIES ambiguity. Its comment says the oldest entries should survive, but the implementation uses slice(-MAX_ENTRIES), which retains the newest entries. Those policies have opposite data-loss implications:
- Keeping the oldest entries preserves causal history but may delay newer user actions.
- Keeping the newest entries favors recent actions but can discard the head of an ordered chain.
A test should state the intended policy and assert it directly before a queue cap is relied upon in production. Until then, do not describe the retention direction as settled behavior.
Implementation checklist
- Persist the exact outbound patch, not a reconstructed form value.
- Capture every pre-edit field, using
nullfor fields that were absent. - Store the complete cached row for deletions.
- Centralize retry classification and treat 400, 403, 404, and 409 as definitive refusals.
- Serialize replay with a shared lock and clear that lock in
finally. - Process entries in arrival order and stop at the first retryable failure.
- Restore the mirror and notify the UI after a definitive refusal.
- Drop a 404 deletion without resurrecting the row.
- Flush pending creations before edits that depend on them.
- Use the same JavaScript flush for reconnect and Android headless work.
- Document which fields the server merges and which remain last-writer-wins.
- Test native scheduling separately from store-level unit tests.
- Resolve the
MAX_ENTRIESretention policy with an executable test.
The resulting queue is not merely a list of failed HTTP requests. It is a persisted sequence of intent, each entry carrying both the operation to retry and the state required to undo it. Ordered, single-worker replay protects causality; selective retry avoids futile loops; server-side compare-and-retry handles the conflicts the client cannot see; and removing an overbroad shared-record guard lets offline use follow the backend’s actual guarantees.
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