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Last-write-wins (LWW) is a conflict-resolution rule, not a synchronization strategy. It can be appropriate when a value is safely replaceable, but it may discard a meaningful concurrent edit. An offline-first mobile app also needs a way to store and deliver queued changes, detect or define conflicts, choose an authority and merge unit, and recover from retries or delayed connections.
Choose conflict behavior from the meaning of each field and the loss your users can tolerate. If two offline edits can both be valid, do not let a whole-record LWW rule silently decide which one disappears.
What does last-write-wins mean?
With LWW, a system receives competing versions of the same data and keeps one according to its ordering rule. The other version is discarded. Martin Kleppmann and Alastair R. Beresford describe it plainly in their 2016 paper A Conflict-Free Replicated JSON Datatype: “The simplest way to resolve conflicts is to discard some modifications when a conflict occurs, for example using a ‘last writer wins’ policy.”
Discarding a version is not automatically a problem. If a setting means “use the latest accepted choice,” replacing an earlier value may match the product’s intended behavior. But when both edits carry useful information, losing one is data loss—not merely a synchronization detail.
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The phrase “last” also depends on the implementation. A system might use timestamps or another ordering mechanism; do not assume that a device’s wall clock is authoritative just because a product describes its behavior as LWW. Clocks can be skewed or changed, and tie-breaking rules vary. Android’s offline-first guidance gives timestamp metadata as a conceptual example, not a universal endorsement of client clocks. Check the platform’s documented conflict mechanism.
Why is LWW not enough for offline-first sync?
While a device is disconnected, it can accept local reads and writes that differ from the network’s current state. When the connection returns, synchronization must reconcile those versions before the data can be brought back into alignment. Android Developers puts it this way: “If, when offline, the app writes data locally that is misaligned with the network data, you must resolve the conflict before synchronization can happen.” See its offline-first architecture guidance.
LWW answers only which competing version to keep. A working sync design also decides how changes are stored, delivered, retried, identified as duplicates, and reconciled with the data held by other replicas.
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Choose a synchronization model
Android’s guidance describes three broad patterns. The right fit depends on how long users may be offline, whether the app must support offline writes, and what the product’s infrastructure can support.
- Pull-based sync: Fetch relevant data when the app needs it. This can suit brief or intermediate periods without connectivity, but a long disconnection can leave local data stale or empty.
- Push-based sync: Maintain a local replica and respond to notifications that data may be stale by fetching changes. This supports extended offline use, but requires more careful versioning and write handling.
- Hybrid sync: Choose a pattern for each data type instead of forcing every kind of data into one model.
Make queued writes durable and safe to retry
For offline writes, plan how pending changes survive app restarts and how they are drained after reconnection. Android recommends queuing writes and retrying with exponential backoff; on Android, WorkManager is one common mechanism for persistent work. It is an Android mechanism, not a universal mobile solution.
Retries can deliver a change more than once, and delayed or reordered delivery can complicate reconciliation. Give operations an idempotent handling strategy so duplicate delivery does not accidentally apply the same action twice. Define what happens when a queued write cannot be accepted, and how the app reports or recovers from that outcome.
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When can LWW lose a valid edit?
Consider an illustrative scenario: Device A goes offline and changes a task’s due date. Device B also goes offline and marks the task complete. If both devices upload whole-record snapshots and the system treats the entire record as one conflict unit, LWW may keep one snapshot and erase the other device’s change. A field-aware merge could preserve both edits if due date and completion have independent meanings. If the fields encode mutually exclusive choices, the app may instead need a domain rule or a person to decide.
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Good candidates for LWW
LWW is easiest to justify when a value is genuinely replaceable and users expect one accepted value to supersede another—for example, a preference whose meaning is simply “the current choice.” State that behavior clearly and confirm that the implementation orders writes as intended.
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Warning signs
- Two users or devices can make useful changes to different parts of the same record while offline.
- Discarding either change would violate a user expectation or an important business rule.
- The data represents a collection, workflow, or set of related facts rather than one replaceable value.
- The product has no way to detect, explain, or recover a lost update.
How do documented mobile sync products handle conflicts?
These product behaviors are examples, not rules for every sync system. Their documentation was accessed October 7, 2026; defaults and supported features can change, so confirm the current guidance for the platform and SDK you use.
| Product | Documented offline behavior | Conflict behavior and scope |
|---|---|---|
| Cloud Firestore | The SDK caches actively used data for offline reads, writes, listeners, and queries, then synchronizes local changes after reconnection. Persistence is enabled by default on Android and Apple platforms and disabled by default on web. Web persistence has browser and sensitive-data considerations. | Multiple changes to the same document are resolved using LWW. The documentation also notes a limitation for Enterprise edition pipeline operations. |
| Couchbase Lite | Its product documentation describes local offline data, cloud synchronization, and peer-to-peer options. | Current Android conflict documentation describes LWW as the default update behavior and warns that an update can be overwritten and lost. An app can use failOnConflict to detect a conflict, reread, resolve, and retry, or provide a custom handler to merge document properties. |
Read the platform-specific details in the Cloud Firestore offline persistence documentation, the Couchbase Lite Android conflict documentation, and the Couchbase Lite product introduction. A product’s default is a starting behavior, not proof that it matches your app’s data semantics.
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What are the alternatives to LWW?
Conflict resolution can combine approaches. Pick the one that preserves the meaning of the data, rather than selecting a technique solely because it is convenient to implement.
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| Approach | How it works | When it can fit |
|---|---|---|
| Application- or domain-aware merge | Apply rules based on what fields and operations mean; for example, preserve independent field changes or enforce a domain invariant. | Use when the app can define a correct outcome more precisely than a generic overwrite rule. |
| Surface the conflict to a person | Keep the competing versions available and let a user choose or edit the resolution. | Use when neither automatic choice is safe and the user can make an informed decision. |
| Retain versions | Keep competing states rather than immediately discarding one, so the app can present or resolve them later. | Use when preserving edit history or enabling recovery matters more than choosing an immediate winner. |
| Conflict-free replicated data type (CRDT) | Use a data type with defined merge semantics so replicas can converge despite delayed, reordered, or duplicate messages under the model’s assumptions. | Use when the data type’s semantics fit the application and convergence is valuable; it does not decide every business rule. |
| Reject, reread, resolve, and retry | Detect that the expected version changed, fetch current data, decide how to reconcile, and submit a new write. | Use when an update should not silently overwrite a concurrent change and the app can retry safely. |
What CRDTs do—and do not—guarantee
Kleppmann and Beresford’s 2016 paper presents a JSON CRDT with nested lists and maps, client-side merging, and no dependence on network ordering guarantees. Under its model, replicas can converge despite arbitrary delay, reordering, and duplication, assuming eventual message delivery with retries. The paper also addresses avoiding lost user input from concurrent modifications.
Convergence is not the same as a valid business outcome. A CRDT’s merge rules must fit the data type, and remaining conflicts at leaf values may still require program logic or user input. It does not automatically enforce every application invariant or decide which result users consider correct. The paper establishes algorithmic properties, not a broadly applicable performance benchmark or adoption statistic.
How should you choose a conflict policy?
Work through these decisions per entity or field. Different data in the same app may need different policies.
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- Check whether concurrent operations commute. If applying both operations in either order gives an acceptable result, a merge may fit. If they do not, decide whether to impose a domain rule, retain both versions, or ask a user.
- Choose an authority and conflict unit. Establish whether a central service or peers are authoritative for the data, and whether conflicts are evaluated per field, record, list, or document.
- Choose the least destructive suitable policy. Use LWW only for safely replaceable state. Otherwise consider domain-aware merging, version retention, conflict rejection and retry, or user resolution.
- Account for product constraints. Consider maximum offline duration, offline-write requirements, user expectations, bandwidth and battery, platform and SDK support, operational visibility and recovery, privacy, and security.
What should you test before shipping?
Test the synchronization behavior as a system, not just the merge function. Include the cases that exercise delivery, ordering, and recovery:
- Two devices edit the same data while offline, including independent field edits and genuinely conflicting choices.
- A device reconnects after a brief outage and after an extended offline period.
- A write is retried, delivered twice, or arrives after a newer write.
- The app or device stops while writes are queued, then restarts and resumes synchronization.
- A deletion races with an edit; define whether and how a tombstone or other deletion marker is retained so an old replica does not simply restore removed data.
- Device clocks are skewed or changed if the implementation uses timestamps to order writes.
- The server rejects a write or reports a conflict, and the app must reread, resolve, retry, or explain the outcome.
For each case, verify both the final stored state and what the user sees. A system can technically converge while still losing information or presenting a result that violates product expectations.
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