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How compare-and-swap prevents lost updates
A lost update happens when two clients read the same status, then each submits a change based on that older state. If the server accepts both writes unconditionally, the later one can silently overwrite the earlier one.
Compare-and-swap avoids that race by treating the version a client observed as an expectation: apply the requested change only if the current version still matches it. The comparison and mutation must happen together. A separate read, comparison in application code, and later unconditional write leaves time for another update to slip in. The IETF describes HTTP’s If-Match condition as a way to prevent accidental overwrites in this situation (RFC 9110, Section 13.1.1).
Implement it with HTTP ETags and If-Match
- Return a validator with the resource. When the client reads a status resource, return its representation and an ETag that changes when the relevant representation changes.
- Require the observed ETag on updates. The client sends its intended change with
If-Matchset to the ETag it received. - Check the condition before applying the method. RFC 9110 requires strong comparison for
If-Matchand says the method must not be performed when the condition is false. Do not use a weak ETag for this concurrency check. - Return the new representation and validator on success. If the condition fails, respond with
412 Precondition Failed, a standard response for an unmet HTTP precondition. The response can include current state or direct the client to fetch it.
This illustrative exchange shows the protocol shape, not tested application code:
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GET /items/42HTTP/1.1 200 OK
ETag: "v17"
Content-Type: application/json
{"status":"pending"}PATCH /items/42
If-Match: "v17"
Content-Type: application/json
{"status":"approved"}If another update changes the selected representation before the PATCH is evaluated, the server rejects this request without applying it. The client should reload the resource and decide what its intended change means against the newer state.
Implement it with an atomic database version check
For a versioned row, compare the expected version in the update predicate and increment the stored version in that same write:
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UPDATE items
SET status = :new_status,
version = version + 1
WHERE id = :id
AND version = :expected_version;This is generic SQL pseudocode; exact syntax and transaction behavior depend on the database. Inspect the affected-row count: one row means the expected version matched and the update was applied; zero rows means the record is missing or its version changed. Handle the latter as a conflict, while distinguishing a missing resource according to the API’s information-disclosure policy.
In DynamoDB, the analogous pattern uses a ConditionExpression, such as Version = :expected_v. A failed condition produces ConditionalCheckFailedException. AWS describes optimistic locking as detecting conflicts at write time rather than preventing them (DynamoDB Developer Guide).
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Handle conflicts without replaying stale data
A failed comparison is not a successful update. The client should fetch the latest state and then choose an appropriate resolution:
- Show the conflict and let the user decide which status or action is intended.
- Merge or recompute an intent-based change using the fresh state, if the operation’s meaning is clear.
- Retry a bounded number of times only when the operation can be safely recomputed. AWS advises limiting retries because each retry adds a read (DynamoDB optimistic locking guidance).
Do not blindly replay a stale whole-record replacement. Keep the server authoritative for both current status and version: a client-provided version is an expectation, not permission to bypass validation. Independently validate that the requested transition is allowed; compare-and-swap prevents stale overwrites but does not enforce business rules about valid status transitions. Also distinguish version conflicts from authorization failures, invalid requests, missing resources, and infrastructure errors.
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Choose the concurrency strategy for the work
| Situation | Approach | Trade-off |
|---|---|---|
| Conflicts are infrequent, retries are inexpensive, and one item is being updated | Optimistic locking with a version and conditional write | Detects conflicts at write time without coordinating a lock in advance. |
| Several items must change together | Database transaction | Provides all-or-nothing semantics across the grouped writes. |
| Long-running critical work or high contention makes retries costly | Evaluate locking or another coordination strategy | Adds coordination complexity but may be preferable to repeated failed writes. |
| DynamoDB global tables receive writes in multiple Regions | Explicit application-level conflict handling | AWS documents last-writer-wins reconciliation for global tables; version-based optimistic locking does not provide the expected cross-region protection. |
AWS discusses these trade-offs in its DynamoDB guidance on concurrent updates. If an operation also triggers non-idempotent side effects, the version check alone does not protect those effects; design their handling separately.
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