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For application updates, optimistic concurrency is usually the better starting point when two writes rarely collide and the application can handle a conflict. Pessimistic locking is a better fit when contention is frequent, the protected database work is brief, and making other operations wait is acceptable. Neither is universally faster or safer: the right choice depends on conflict frequency, transaction duration, retry cost, conflict policy, and the database’s behavior.
What problem do these approaches solve?
A lost update can happen when one user reads a record, another user changes it, and the first user later saves an edit based on the older version. The second save may unintentionally erase the first change. Microsoft describes this common editing scenario in its ASP.NET Core concurrency tutorial.
Concurrency control determines how the application detects or prevents incompatible changes. Optimistic control lets work proceed without reserving the record, then checks for a conflict when saving. Pessimistic control acquires a lock so incompatible work must wait while the protected operation runs.
How do optimistic and pessimistic control differ?
| Dimension | Optimistic concurrency | Pessimistic locking |
|---|---|---|
| When conflict is detected | At write or validation time, often by comparing a version token or original values. | Before or during work, when a lock blocks incompatible access. |
| Workload fit | Conflicts are infrequent and retries are affordable. | Contention is common, with a short critical section and acceptable waiting. |
| Main cost | Failed writes, retries, and application conflict-handling complexity. | Wait time, lock management, resource use, and possible performance degradation. |
| Long user-driven edit | Can avoid holding a transaction open while a person edits; a stale save still needs detection and handling. | Usually a poor fit if the lock would remain held during user interaction. |
| Multi-item operation | Needs a database-appropriate transaction or conditional-write design. | A transaction can provide multi-row atomicity, but the locking strategy depends on the database. |
| Failure and edge cases | A stale write must not be blindly retried if business assumptions may have changed. | Long or poorly managed locks can block other work; implementation support and behavior vary. |
This is workload guidance, not a universal performance result. The official guidance does not establish that one approach is categorically faster. To choose for a particular deployment, test the same workload and measure conflicts, wait time, retries, and throughput.
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When is optimistic concurrency the right choice?
Use optimistic control when conflicting updates are uncommon, retrying or resolving a failed save is manageable, and the application should not reserve a record while someone is thinking or editing. A version token lets the database reject a write based on stale state rather than silently accepting it.
In EF Core, configure a concurrency token, load it with the entity, and have the update or delete compare against the originally read token. If another change means no row matches, EF Core reports a DbUpdateConcurrencyException; application code then decides how to proceed. See Microsoft’s EF Core concurrency documentation.
Typical optimistic update flow
- Read the row and its concurrency token.
- On save, update only if both the row key and original token still match.
- Treat a zero-row update or provider-reported concurrency exception as a real conflict.
- Load the current values and apply the product’s conflict policy. Reapply or retry only after checking that the operation’s assumptions still hold.
SQL Server’s rowversion is a database-generated token that changes when the row changes and can support whole-row conflict detection. It is SQL Server-specific, so do not assume another provider offers identical behavior; see Microsoft’s SQL Server provider documentation.
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When is pessimistic locking the right choice?
Pessimistic locking can suit frequently contested operations when competing work should wait rather than proceed and later discover a conflict. Keep the protected database operation short: acquire the appropriate lock within a database operation or transaction, perform the change, and release it promptly.
Do not hold a lock while waiting for a user or an external service. Longer lock duration increases the chance that other work will be blocked and can degrade performance. Exact syntax, lock scope, isolation behavior, and deadlock or timeout handling are database-specific; check the documentation for the database and provider you deploy. The cited EF Core guidance discusses the trade-offs of locking and isolation in its isolation-level section.
How should an application resolve a detected conflict?
A conflict is a point where the application must choose what to preserve; it is not automatically a safe retry. Microsoft’s ASP.NET Core tutorial describes two broad policies:
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- Store wins: show the current stored values, then let the user decide whether to reapply their changes.
- Client wins: write the submitted values over the stored values. This can be deliberate, but it may overwrite someone else’s work.
Updating only properties the user changed can preserve another user’s edits to different fields. It does not prevent a lost update if both users changed the same property. The application should make that behavior explicit rather than implying the database automatically resolves the conflict.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Are concurrency tokens and isolation levels the same thing?
No. A token comparison is one way to detect stale writes; an isolation level governs the behavior of a transaction more broadly. They are not interchangeable labels.
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What changes with a particular database or provider?
EF Core
EF Core uses configured concurrency tokens loaded with an entity and checked during update or delete. The application is responsible for deciding what to do when a concurrent change prevents the operation from matching. See the EF Core documentation.
PostgreSQL with the Npgsql EF Core provider
Npgsql documents PostgreSQL’s hidden xmin system column as a value that changes when a row changes and can be mapped as a concurrency token. This is provider-specific; see Npgsql’s concurrency documentation.
Amazon DynamoDB
AWS documents version attributes with conditional writes for optimistic locking, transactions for multi-item atomicity, and a lock client for some long-running distributed coordination needs. These are distinct tools: use transactions when the operation needs multi-item atomicity, rather than treating a single-item version check as a transaction. See AWS’s optimistic-locking guidance and DynamoDB transaction documentation.
DynamoDB global tables reconcile concurrent changes across regions using last-writer-wins. AWS warns that version-based optimistic locking does not work as expected across regions, so applications using global tables need a conflict design suited to that behavior. See AWS’s global-tables caveat.
How should you choose?
- Estimate how often writes to the same data actually conflict.
- Consider how long the critical section lasts and whether user or external-system interaction occurs inside it.
- Compare the cost of a retry or rollback with the cost of waiting and lock management.
- Decide what users should see when edits conflict, including what happens when both changed the same field.
- For multi-row or multi-item work, choose the transaction and conditional-write behavior the database supports.
- Confirm provider-specific token, isolation, and lock semantics in the documentation for the versions you deploy.
- Measure conflicts, wait time, retry frequency, and throughput under a representative workload before optimizing for performance.
There is no general benchmark in the cited official guidance that picks a winner for every workload. The useful choice is the one whose conflict behavior and operational costs fit the application’s actual update pattern.
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