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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →To prevent lost status updates, make each change conditional on the state the client actually read—or serialize the change inside a short database transaction. Optimistic locking checks a version when the client writes; pessimistic locking holds a database lock while the server validates and commits a transition. Neither is universally faster: choose based on how often changes overlap, the cost of a conflict, and how long protection must last.
How lost updates happen
A lost update occurs when two clients read the same status, then each submits a change based on that older value. If the server accepts both writes without checking whether the record changed in between, the later write can replace the earlier one. The result depends on which write arrives last, and one client’s change disappears. MDN’s guide to conditional requests describes this overwrite problem.
For example, two staff members read a request as pending. One approves it while the other rejects it. A blind assignment can leave the record with whichever status was written last, without telling either person that their view was stale.
Optimistic locking vs. pessimistic locking
| Decision point | Optimistic version check | Pessimistic row lock |
|---|---|---|
| Where protection happens | At write time: compare the submitted version token with the current resource. | During the database transaction: hold a lock that blocks conflicting writers or lockers. |
| What the other client experiences | A stale update is rejected, typically with HTTP 412 Precondition Failed; the client must refresh or reconcile. |
A competing operation may wait until the lock-holding transaction ends. |
| How long protection lasts | No database lock is held while a person edits. | Only for the lock-holding transaction; commit or rollback releases the row lock. |
| Typical concerns | Conflict response and a clear recovery path for the client. | Waiting, timeouts, deadlocks, retries, and transaction isolation behavior. |
| Often suits | Edits that may take time, where collisions are manageable and can be explained. | Short atomic transitions that need serialization and can tolerate waiting. |
This comparison describes the mechanisms, not a performance benchmark. The right choice depends on expected overlap, conflict cost, transaction duration, and the experience you want to provide.
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How to prevent lost updates with an ETag and If-Match
HTTP If-Match lets a client submit the entity tag (ETag) it received when reading a representation. The server applies the requested change only if that tag still strongly matches the current representation. RFC 9110 identifies this state-changing precondition as a way to prevent lost updates when user agents act in parallel. See RFC 9110, Section 13.1.1.
- Read the resource. Return its current representation and a strong ETag, such as
"v17". The example token is illustrative; the format is defined by the API. - Submit the intended change with its validator. Include the exact ETag from that read in the request’s
If-Matchheader. For example:If-Match: "v17". - Check and mutate atomically. The server must compare the submitted validator with current state as part of the write path. A client-side read-then-compare followed by an unguarded update still leaves a race between the comparison and mutation.
- Reject a failed precondition. If the representation no longer matches, do not perform the requested method. RFC 9110 permits a
412 Precondition Failedresponse for this case; it is the usual response for anIf-Matchmismatch. - Refresh or reconcile. Return enough information for the client to fetch current state and recover according to the policy described below.
If-Match requires strong entity-tag comparison. A weak ETag, prefixed with W/, is not suitable as the validator for this precondition. The API should issue and compare tokens that represent the version relevant to the requested update.
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What should a client do when an update returns 412?
Treat 412 as evidence that the client’s version is no longer current—not as a transient error to replay blindly. The application should make the conflict visible and give the person a route to a valid decision.
- Reload and retry: fetch the latest representation, show the updated status, and ask the person to make or confirm a new choice.
- Show both values: present the current status alongside the attempted status so the person can decide how to proceed.
- Reconcile only when rules allow it: the server may accept a new request after checking whether the intended transition remains valid against current state.
Automatically resending the same stale status intent with a fresh ETag is not conflict resolution: it can simply overwrite the change that caused the first request to fail. An ETag mismatch is a version conflict; a separate domain-rule violation can use an API-defined response such as 409 Conflict. Keep those meanings distinct in the contract.
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How to serialize a status transition with a PostgreSQL row lock
When a short operation must inspect the current row and change it as one serialized unit, PostgreSQL can lock the selected row with SELECT ... FOR UPDATE. Conflicting writers or lockers wait until the transaction ends. The PostgreSQL 17 explicit-locking documentation describes row-lock behavior and deadlocks.
- Begin a transaction.
- Select and lock the target row. Use
SELECT ... FOR UPDATEfor the row whose status is changing. - Validate the transition against the locked current state. For example, verify that
pendingmay transition toapproved. - Update the status and commit. The lock is released when the transaction ends.
Keep the transaction short. Do not call external services or wait for user input while holding the lock. PostgreSQL warns that waiting with transactions open for long periods is a bad idea. If a transaction must lock multiple records, acquire them in a consistent order where practical; PostgreSQL identifies consistent ordering as an important deadlock defense. A deadlock can still occur, and PostgreSQL aborts one participant, so the application needs a bounded, operation-appropriate retry policy.
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PostgreSQL isolation-level caveat
With PostgreSQL Repeatable Read, snapshot timing matters: a transaction’s snapshot may predate a lock acquired after its first query or data-modification command. PostgreSQL’s application-level consistency guidance advises using Read Committed or obtaining needed locks before queries when relying on explicit locks for consistency. This is PostgreSQL-specific guidance; check the relevant database’s locking and isolation documentation before applying the same assumptions elsewhere.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make status transitions explicit and atomic
Prefer an operation that expresses a domain transition—such as “approve this pending request”—over a blind instruction to assign approved. The server can then verify that the current state permits the transition, rather than treating every status value as interchangeable.
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Whichever strategy you choose, the version check or row lock, the validation, and the mutation must meet at the server/database boundary as one atomic operation. Otherwise another writer can change the row between checking it and saving the new status.
Also define what a duplicate request means. If a retry arrives after the first request already took effect, the API may treat the operation as already successful in some cases. RFC 9110 cautions that permissive success treatment can be risky for non-cooperative state changes, so make this behavior deliberate rather than silently accepting every mismatch.
Quick Recap
Which locking strategy should you choose?
- Favor optimistic checks when a user may spend time deciding or editing before saving, conflicts are manageable, and the application can explain and resolve a rejected update.
- Favor a pessimistic row lock when the transition is short, must validate against current state and commit as a unit, and waiting for a competing operation is acceptable.
- Measure the actual workload if throughput is decisive. The relevant factors are overlap frequency, conflict cost, lock duration, and the client recovery experience; there is no universal contention threshold or evidence-backed performance winner.
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