Discuss Optimistic vs. Pessimistic Locking

Optimistic and pessimistic locking are two distinct strategies for handling concurrency, each with specific trade-offs regarding performance and consistency. Pessimistic locking operates on the assumption that conflicts will occur frequently. It acquires exclusive locks on resources immediately upon access, preventing other transactions from reading or writing until the lock is released. This ensures strong consistency but significantly reduces system throughput because threads block waiting for resources. It is ideal for scenarios with high contention, such as deducting items from a shared warehouse inventory where multiple users might try to buy the last item simultaneously. In contrast, optimistic locking assumes conflicts are rare. It does not acquire locks during the read phase. Instead, it records a version number or timestamp of the data when read. When a transaction attempts to update the record, the system checks if the version number has changed since it was read. If it has, the transaction fails and must be retried. This approach maximizes parallelism and throughput for read-heavy workloads but introduces complexity in handling retry logic and potential livelocks. For Amazon's scale, I would choose optimistic locking for most product catalog updates or user profile changes where writes are infrequent relative to reads. However, for critical operations like payment processing or seat reservation on a flight, I would lean toward pessimistic locking or a hybrid approach to guarantee data integrity without the risk of cascading failures from constant retries.