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Optimistic locking is usually the better starting point when simultaneous edits are uncommon and the application can recover from a conflict. Pessimistic locking can be a better fit when contention is frequent and waiting is cheaper than repeatedly rejecting and retrying work. Neither is universally faster or safer: the right choice depends on the workload, transaction design, and the specific database and ORM behavior.
What is the difference?
With optimistic concurrency control, transactions read data without first reserving it. When a transaction tries to write, the system checks whether the data changed since it was read. If it did, the write is rejected or otherwise reported as a conflict; the application must decide whether to retry, reload, or reconcile the changes. Microsoft Learn summarizes the distinction in its Transaction Locking and Row Versioning Guide: “In optimistic concurrency control, transactions don’t lock data when they read it.” This describes the optimistic approach, not a promise that the database uses no locks at all.
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With pessimistic locking, a transaction obtains a lock to protect data while it works. Other transactions that need incompatible access may have to wait until the lock is released, usually when the transaction ends. For example, PostgreSQL supports row-locking reads such as SELECT ... FOR UPDATE; its PostgreSQL 17 documentation on explicit locking explains that conflicting updates and locking reads can wait for the lock holder’s transaction to finish.
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| Decision factor | Optimistic locking | Pessimistic locking |
|---|---|---|
| Likely fit | Conflicts are uncommon, and handling an occasional rejected update is acceptable. | Conflicts are frequent or predictable, and waiting is preferable to discarding and repeating work. |
| What happens under contention | A write detects that the observed data changed; the application handles the failed update, rollback, retry, or reconciliation. | Competing operations may wait for a protected resource; lock management and waiting can constrain throughput. |
| Application responsibility | Check versions consistently and define a clear response to conflicts. | Keep transactions bounded, choose the intended lock scope, and handle lock timeouts and deadlocks. |
| Typical mechanism | A version or timestamp included in the update condition. | An explicit lock, such as a database row-locking read. |
| Key question | Does every relevant write verify the version the transaction originally read? | Does this database’s lock mode protect exactly the rows and operations the invariant requires? |
These are workload heuristics, not universal performance guarantees. Isolation level, database defaults, indexes, transaction shape, and ORM dialect support all affect concrete behavior. Explicit locks are only one part of a database’s concurrency model. PostgreSQL’s application-level consistency guidance distinguishes ordinary MVCC behavior from cases where explicit locking is needed to protect an application invariant.
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How does version-based optimistic locking work?
- Read the record and its version. The version may be an integer or a timestamp, provided the application updates and compares it consistently.
- Write only if that version is still current. The update condition includes both the record identifier and the version that was read. If another transaction has already changed the record, the condition no longer matches.
- Treat a non-matching update as a conflict. Do not silently overwrite newer data. Depending on the task, reload and retry, present both versions for reconciliation, or tell the user the change could not be applied.
For example, if an application reads a profile at version 12, its update should apply only while the stored version is still 12, and should advance the version when it succeeds. If another update has already advanced it, the first application’s write must not proceed as if its copy were current.
ORM support does not automatically protect writes that bypass its version protocol. A raw SQL update or another service that changes the row without maintaining the version can undermine the check. Hibernate’s locking guide describes version checks for managed entities and explains that the ORM ultimately relies on database concurrency mechanisms. Verify the details for the Hibernate version, database, and write paths in use.
How does pessimistic locking work?
- Start a transaction and lock the target data. Use the database’s supported locking operation for the rows the transaction must protect. In PostgreSQL, a locking read can use
SELECT ... FOR UPDATE. - Perform the necessary work while holding the lock. Keep the protected section short. A lock held through a slow external call—or while waiting for a person to respond—can make other transactions wait unnecessarily.
- Commit or roll back promptly. Ending the transaction releases its locks. If the transaction fails because it waited too long or encountered a deadlock, handle the failure explicitly.
When a transaction needs multiple locks, acquiring them in a consistent order can reduce deadlocks. PostgreSQL detects deadlocks and aborts one participant; an application may retry the aborted transaction when doing so is safe. A retry should repeat the whole transaction against current data, not continue with assumptions from the failed attempt. PostgreSQL also notes that row locking can cause disk writes, so lock operations are not cost-free.
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- Estimate conflict frequency. If simultaneous updates are rare, optimistic checks often avoid making every reader wait. If conflicts are frequent and predictable, compare the cost of waiting with the cost of failed work and retries.
- Account for the cost of a rejected write. A user editing a long form may need a useful reconciliation path. A short, repeatable background transaction may be easier to retry, if its side effects are safe.
- Consider latency and transaction duration. Pessimistic locking can delay other work while a transaction is open. Optimistic locking can surface a conflict late, after work has already been done.
- Protect the actual invariant. Decide which rows and operations must be coordinated. A row lock may not, by itself, protect a rule involving multiple rows or a broader condition.
- Check the full stack. Confirm the behavior for the database engine and version, isolation level, ORM, and any writes that bypass the ORM. SQL Server’s locking and row-versioning guidance is specific to SQL Server; PostgreSQL and Hibernate document their own semantics and implementation considerations.
There is no evidence-based universal conflict threshold or performance multiplier for choosing between the approaches. Measure the relevant workload and make conflict handling, lock waits, and transaction failures visible in the application’s operational signals.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What locking does not guarantee
Optimistic locking detects concurrent changes; it does not prevent them or decide how a conflict should be resolved. Pessimistic locking makes competing operations wait when their requested access conflicts; it does not replace understanding the database’s isolation level or the invariant the application needs to preserve. Row locks and transaction isolation are related but distinct parts of concurrency control. Check the relevant database documentation and test the application’s actual transaction patterns rather than assuming that a lock name has identical effects across engines.
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