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A highlight can disappear without a warning when two devices edit the same data while one is offline. In Andy Stanly’s account of a production sync failure, the server compared changes by the order it received them—not when the reader actually made them. A timestamp-based comparison reduced that ordering problem, but it could not resolve every bad device clock or recover every older row.
Why an offline device could lose newer work
Stanly says the highlight feature was built for offline reading and later synchronization across devices. Its original conflict policy treated each highlight as a last-write-wins register: the server assigned a monotonically increasing server_seq, and the larger sequence determined which update prevailed.
That sequence described server processing order, not the reader’s edit order. Consider the account Stanly gives: Device A goes offline for a week while Device B continues syncing. A change made earlier on A waits in a queue. When A reconnects, the server assigns that delayed change a fresh sequence number. The number makes it look newer to the comparison, even though the user made it earlier. A later sync from B can receive an even newer sequence and overwrite A again.
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The defect was in what the ordering signal meant. A server sequence is useful for establishing a consistent order of server events; by itself, it cannot establish the order of edits made independently on disconnected devices.
Why deletes made the failure harder to see
Stanly says deletes could make the conflict especially confusing. A queued delete could mark a row as deleted, while another device briefly resurrected it. After another synchronization cycle, the tombstone—the retained record that marks a row as deleted—could prevail. To a reader, the highlight might seem to vanish and return before disappearing again, with no clear explanation of which device’s action won.
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Stanly reports that a production query found 41,208 rows with deleted_at IS NOT NULL, and that the most affected user had 412 tombstones. These are figures from his account, not independently verified measurements or estimates for other apps. The article does not establish that every such row represented a lost highlight.
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In the revised policy, Stanly says the client captured a timestamp when a change was made. For colliding writes, the later client timestamp won; if timestamps tied, server_seq served as a deterministic fallback. He also says incoming timestamps were clamped to a plausible range around server time, with out-of-range writes logged. These describe his implementation, not a universal sync recipe.
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A client timestamp can better reflect when an offline edit happened, but it depends on the device clock. Stanly explicitly notes that a badly wrong clock can still make ordering uncertain. He reports eleven out-of-range timestamps over two months, attributed in his account to one model of low-cost Android tablet. Neither that attribution nor the count is independently verified, and the publication year is not stated in the source account.
What recovery could—and could not—restore
Because the system used soft deletes, the deleted rows had not necessarily been physically erased. Stanly says a one-off restoration script recovered many highlights from tombstones. He does not claim that every lost highlight was recovered. Older rows without timestamps also limited recovery: without a reliable edit-time value, their intended ordering could remain unknown.
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This distinction matters operationally: a tombstone may preserve a path to recovery, but it is not a backup guarantee. Recovery depends on what metadata remains, whether competing versions still exist, and whether the system can identify which state the user intended.
How to choose a conflict policy for your data
There is no single conflict rule that fits every application. Offline Protocol distinguishes last-writer-wins, retaining versions for user choice, automatic merge, and authority-based decisions as separate approaches. The right choice depends on what a record means and what the application can safely infer.
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- Escape into your books – Your Kindle doesn’t have social media, notifications, or other distracting apps.
- Battery life for your longest novel – A single charge via USB-C lasts up to 12 weeks.
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- Ordering signal: Does the policy use server arrival order, a client-provided edit time, an authority rule, or another signal? What does that signal actually prove when devices are disconnected?
- Offline usability: Can users continue making changes offline, and what happens when those changes later collide?
- Version visibility: Are both versions retained for review, or does one overwrite the other without a user-visible history?
- Merge safety: Can the data type be merged without changing its meaning? A text note, a counter, and a reading highlight may need different rules.
- Delete behavior: How do tombstones interact with updates from devices that have stale copies? Can deletion be reversed, and for how long?
- Errors and recovery: Are failed or rejected sync operations recorded, reviewable, and retryable? Microsoft’s Dataverse documentation provides one official example of sync errors recorded in a table with review and retry workflows.
For a reading highlight, silently choosing a winner may be less trustworthy than keeping conflicting versions available for review. For other data, automatic merge or a designated authority may be safer. The policy should follow the semantics of the data rather than treating every collision as the same problem.
Further reading
For a broader treatment of conflicting writes, replication, unreliable clocks, sync engines, and local-first software, see Designing Data-Intensive Applications, 2nd Edition by Martin Kleppmann and Chris Riccomini. O’Reilly lists the book as published in March 2026; it is background reading, not a requirement for implementing Stanly’s change.
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