Last-write-wins (LWW) is a conflict-resolution rule, not a complete synchronization strategy. It picks one version when writes conflict, which can discard another device’s valid edit. Use it only when the data is genuinely safe to replace; otherwise choose a merge, preserve both versions, reject and retry, or ask the user. An offline-first app also needs a plan for storing and delivering writes, detecting concurrency, and deciding which data is authoritative.
What does synchronization need to do before it resolves conflicts?
When a device is offline, it may accept local changes that differ from the network copy. Once connectivity returns, the app must reconcile those versions before synchronization can complete. Android Developers puts the requirement plainly: “If, when offline, the app writes data locally that is misaligned with the network data, you must resolve the conflict before synchronization can happen.” Android’s offline-first guidance treats conflict resolution as one part of a broader data-layer design.
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Start by choosing how replicas stay current. Android’s guidance describes three patterns:
- Pull-based sync: Fetch relevant data when it is needed. This can suit brief or intermediate offline periods, but long disconnections may leave a cache stale or empty.
- Push-based sync: Keep a local replica and respond to notifications that data may be stale, fetching changes as needed. This supports extended offline use, but makes version tracking and write handling more involved.
- Hybrid sync: Use different approaches for different data types, based on product needs and infrastructure.
Then design delivery. Offline writes need a queue that survives the conditions your app promises to support, a way to drain it when connectivity returns, and retry behavior for transient failures. Android recommends queued writes with exponential backoff and identifies WorkManager as a common way to schedule persistent work on Android; it is not a universal mobile-platform mechanism. Retries can also deliver a request more than once, so define how duplicate writes are recognized or made safe. These delivery choices do not themselves resolve conflicting edits.
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What does last-write-wins mean, and when does it lose data?
LWW keeps one version according to an ordering rule and discards the competing version. The rule might use timestamps or another ordering mechanism; the phrase “last write” does not tell you which one a particular product uses. Device clocks can be skewed or changed, so a client’s wall-clock timestamp is not automatically a reliable measure of which edit should win. Verify the platform’s actual mechanism and tie-breaking behavior.
The risk is straightforward: if both concurrent edits contain information the user expects to keep, discarding one is data loss, not merely a technical resolution. Martin Kleppmann and Alastair R. Beresford describe the trade-off 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.”
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Why the conflict unit matters
Consider an illustrative task shared by two devices. Device A, offline, changes its due date; Device B, also offline, marks it complete. If each uploads a whole-record snapshot and LWW compares the record as one unit, the winning snapshot can erase the other edit. If due date and completion are independent fields, field-aware merging may preserve both. If the choices are mutually exclusive, the app may instead need a domain rule or a user-visible conflict. This is a design inference from overwrite behavior, not a claim that a particular product handles this example in a particular way.
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How do documented mobile sync products handle conflicts?
Conflict behavior and offline defaults belong to specific products and platforms; they are not universal properties of offline-first apps.
| Product and scope | Documented behavior | What to check for your app |
|---|---|---|
| Cloud Firestore, Android and Apple platforms | The SDK caches actively used data for offline reads, writes, listens, and queries, then synchronizes local changes after reconnection. Multiple changes to the same document are resolved with LWW. Offline persistence is enabled by default on Android and Apple platforms. | These defaults concern the documented SDKs and document-level behavior. Confirm the current API guidance for your app’s platform and edition. |
| Cloud Firestore, web | Offline persistence is disabled by default. The documentation describes browser and sensitive-data caveats. | Do not assume the native-platform default applies to web; review the persistence guidance for your browser and data sensitivity. |
| Couchbase Lite, current Android documentation | The default update conflict behavior uses LWW, and the docs note that an update can be overwritten and lost. failOnConflict allows an app to detect a conflict, reread, resolve, and retry; custom handlers can merge document properties. |
This is Couchbase Lite-specific behavior. Its product documentation also describes local offline data with cloud-sync and peer-to-peer options; confirm the APIs and behavior for the version you use. |
Sources: Cloud Firestore offline data documentation; Couchbase Lite conflict documentation and product introduction. Product documentation can change; these descriptions reflect the linked guidance accessed October 7, 2026. Firestore’s documentation also notes a limitation for Enterprise edition pipeline operations.
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What are the alternatives to LWW?
| Approach | How it handles competing edits | Use it when | Main trade-off |
|---|---|---|---|
| Domain-aware merge | Applies rules based on the meaning of fields or operations, rather than replacing an entire record indiscriminately. | Concurrent changes can coexist or the product has a clear rule for combining them. | Rules must preserve business invariants and be maintained as the data model evolves. |
| User-visible resolution | Shows competing values or versions and lets a person choose or reconcile them. | The edits are meaningful and no safe automatic choice exists. | It adds friction and requires a useful way to present and retain the alternatives. |
| Retain versions | Preserves competing edits rather than immediately discarding one, so they can be reviewed or reconciled. | Recovery and auditability matter more than silently choosing a winner. | Storage, cleanup, and review behavior need to be designed. |
| Reject and retry | Detects a conflict, fetches the latest state, applies a resolution, and resubmits. | The app can safely recompute the operation against current data. | Retries need bounded failure handling and duplicate-safe writes. |
| CRDT with suitable semantics | Uses a data type whose merge rules allow replicas to converge despite reordered, delayed, or duplicated delivery under the model’s assumptions. | The data type and its merge behavior fit the app’s domain. | Convergence alone does not enforce every business rule or determine a valid outcome for every application-level conflict. |
The 2016 JSON CRDT paper by Kleppmann and Beresford describes nested lists and maps, client-side merging, and convergence without relying on network ordering guarantees. It aims to avoid losing user input from concurrent modifications, but says remaining leaf conflicts can still require program logic or user input. That is a specific algorithmic contribution, not evidence that CRDTs automatically solve every sync problem or a performance benchmark. Read the paper.
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Define correctness per entity and field before choosing an implementation. Ask what the user expects to happen when two devices edit the same record offline, and whether one version can truly replace the other.
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- Specify the meaning of the data. Identify whether each field is replaceable state, an independent value, an accumulated operation, or a choice that must satisfy an invariant.
- Check whether concurrent operations commute. If applying them in either order yields an acceptable same result, automatic merging may fit. If order or mutual exclusivity matters, define an explicit rule or preserve the conflict.
- Choose the authority and conflict unit. Decide whether a server, a peer, or another source is authoritative, and whether conflicts are detected at field, record, list, or document level.
- Match the policy to loss tolerance. Use LWW only for values whose product semantics are “keep one latest accepted value.” Otherwise merge, retain versions, reject and retry, or ask the user.
- Account for operating constraints. Consider how long users may be offline, whether offline writes are required, bandwidth and battery limits, SDK and platform support, privacy and security, and the operational burden of observing and recovering failed sync.
What should you test before shipping offline sync?
Test synchronization as a delivery system and a conflict policy together. The CRDT paper’s model explicitly allows arbitrary delay, reordering, and duplication, with eventual message delivery through retries; these are also useful failure conditions to consider in ordinary sync designs.
- Two devices edit the same field and different fields while offline.
- A device reconnects after a short disconnection and after the maximum offline period your product supports.
- A write is retried or delivered twice, and writes arrive in a different order than they were made.
- A record is deleted on one replica while another replica edits it; define whether deletion uses a tombstone or another recoverable representation.
- A device clock is ahead, behind, or changed while offline, if timestamps affect ordering.
- A queued write repeatedly fails, then succeeds; verify that the queue survives and the user can understand its status.
- Conflict resolution preserves required invariants and offers a recovery path when an automatic merge is not valid.
Choose observability that can distinguish delivery failure from a resolved conflict and from an edit that was discarded. Without that distinction, a sync operation can appear successful while the product has silently lost meaningful input.
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