Backend Engineering
Optimistic vs Pessimistic Locking: Choosing the Right Concurrency Strategy
Key Takeaway
“Choose optimistic locking for read-heavy, low-contention workloads where fast reads matter and choose pessimistic locking for write-heavy, high-contention scenarios where retry overhead would degrade performance.”
Understanding the Two Approaches
Optimistic locking assumes conflicts are rare and only validates changes at commit time using version numbers. When a transaction completes, it checks if the version matches the original read value. If another transaction modified the data first, the write fails and the application retries.
Pessimistic locking takes the opposite approach by acquiring exclusive locks before reading. This prevents any concurrent modifications from the start, ensuring serializable access to critical resources. The database holds the lock until the transaction completes.
Performance Trade-offs Under Load
| Scenario | Optimistic Locking | Pessimistic Locking | Decision Factor |
|---|---|---|---|
| Low Contention | Minimal overhead, fast commits | Lock acquisition delays | Use Optimistic |
| High Contention | Many retries, wasted computation | Guaranteed single-pass success | Use Pessimistic |
| Read-Heavy Workloads | Excellent throughput, few conflicts | Unnecessary lock contention | Use Optimistic |
| Write-Heavy Workloads | Retry storms under load | Serialized writes, predictable latency | Use Pessimistic |
Optimistic locking shines when collision probability remains low because reads never block. However, as contention increases, retry loops consume CPU and database resources, eventually degrading performance below pessimistic approaches. Pessimistic locking trades throughput for consistency by serializing access but guarantees no wasted retries.
Deadlock and Failure Modes
Pessimistic locking introduces deadlock risk when locks are acquired in inconsistent orders across transactions. For example, one transaction locks Table A then Table B, while another locks Table B then Table A. Both wait indefinitely. Careful lock ordering and timeout policies mitigate this risk.
Optimistic locking avoids deadlocks entirely since no locks are held during execution. The only failure mode is a write conflict, which simply triggers application-level retry logic. This makes optimistic approaches more resilient in complex, multi-step workflows.
Extending Control Beyond Tables
Database advisory locks and Redis-based distributed locks enable pessimistic patterns across multiple tables or services. Redis atomic operations like SETNX let you implement mutex semantics across microservices. Database advisory locks (Postgres advisory locks, MySQL named locks) provide fine-grained control within a single database.
Use application-level locking when consistency spans multiple resources or when database-native mechanisms fall short. However, introduce additional complexity: network failures in distributed locks, cache invalidation challenges, and increased operational overhead. Optimistic locking remains simpler for single-resource conflicts.
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