Optimistic Locking - What, When, Why, and How?

Summary

In this video, the speaker explains the concept of optimistic locking in multi-threaded programming, contrasting it with the more common pessimistic locking. Pessimistic locking can create bottlenecks by requiring threads to wait for access to critical sections, reducing system throughput. Optimistic locking, on the other hand, allows multiple threads to attempt updates simultaneously, letting one succeed while others fail gracefully. The video also covers the implementation details of optimistic locking, highlighting its advantages in low-contention scenarios, as well as the trade-offs and challenges involved, including its reliance on CPU support for atomic operations.

Takeaways

• 😀 Pessimistic locking ensures correctness by allowing only one thread to execute in the critical section at a time, but reduces throughput under high contention.
• 😀 Optimistic locking allows multiple threads to attempt execution without locks, using a compare-and-swap (CAS) approach, which gives better throughput when there are fewer conflicts.
• 😀 The core idea of optimistic locking is that if two threads try to modify the same variable, only one will succeed, and the other will fail and can retry or take other actions.
• 😀 Optimistic locking is implemented using the 'compare and swap' semantic, which checks the current value before applying the update, ensuring atomic operations.
• 😀 The CAS operation is atomic, meaning once it starts, no context switching occurs, ensuring that the operation is completed without interruption, crucial for performance.
• 😀 Optimistic locking provides flexibility in handling contention failures — threads can retry, ignore the failure, or handle it with custom logic depending on the scenario.
• 😀 While pessimistic locking uses mutexes to manage access to the critical section, optimistic locking avoids locks and only uses CAS, which leads to lower overhead and better concurrency.
• 😀 Optimistic locking works well in systems with rare conflicts and fewer contentions, providing higher throughput by allowing more threads to execute concurrently.
• 😀 Pessimistic locking becomes a choke point when many threads try to access the same critical section, leading to performance degradation in highly parallel applications.
• 😀 For optimistic locking to be efficient, it relies on underlying hardware support for atomic operations like CAS. If not supported, mutex-based fallback implementations are used.
• 😀 Optimistic locking implementations can be more complex due to the need for explicit conflict handling and retry logic, unlike the simpler mutex-based pessimistic locking.

Q & A

What is the main purpose of using locks in multi-threaded programs?

Locks are used to protect critical sections in multi-threaded programs, ensuring that only one thread executes a critical section at a time to maintain correctness and prevent data corruption.

How does pessimistic locking work?

In pessimistic locking, a thread acquires a lock before entering a critical section and releases it after execution. Only one thread can execute the critical section at a time, while others wait, ensuring correctness but potentially reducing throughput.

What is the main drawback of pessimistic locking?

The main drawback is that it can become a bottleneck in high-concurrency scenarios. Multiple threads waiting to acquire the lock can reduce system throughput and prevent efficient utilization of CPU cores.

What is optimistic locking and how does it differ from pessimistic locking?

Optimistic locking allows multiple threads to attempt operations concurrently, assuming conflicts are rare. If a conflict occurs, only one thread succeeds, while others can handle failure as needed. Unlike pessimistic locking, it doesn’t block threads with locks.

What is the 'Compare and Swap' (CAS) operation in optimistic locking?

CAS is an atomic operation that updates a variable only if it has an expected current value. If the current value matches the expected value, the update occurs; otherwise, it fails. This mechanism helps resolve conflicts in optimistic locking.

Why is optimistic locking considered more performant in low-contention scenarios?

Because threads are not blocked waiting for a lock, multiple threads can attempt operations simultaneously. When conflicts are rare, this leads to better throughput compared to pessimistic locking, which forces threads to wait sequentially.

What are some advantages of optimistic locking over pessimistic locking?

Advantages include higher throughput in low-contention environments, lower locking overhead since it relies on atomic operations instead of mutexes, and flexibility in conflict resolution, allowing retries, ignores, or errors as needed.

What are the disadvantages or limitations of optimistic locking?

Disadvantages include more complex implementation compared to mutexes, potential inefficiency in high-contention scenarios due to repeated retries, and it is not suitable for all types of operations, especially those involving multiple dependent steps.

How is optimistic locking implemented at the CPU level?

Optimistic locking leverages atomic instructions provided by the CPU, such as CMPXCHG (Compare and Exchange). These instructions ensure that an update occurs only if the current value matches the expected value, preventing context switches and ensuring atomicity.

What happens if the CPU does not support native atomic instructions for optimistic locking?

If the CPU doesn’t support native atomic instructions, implementations of optimistic locking fall back to using traditional mutexes or locks internally, essentially behaving like pessimistic locking but still providing the same logical behavior.

Why might optimistic locking be preferred in databases or other high-level systems?

In databases and high-level systems where conflicts are infrequent, optimistic locking allows multiple operations to proceed concurrently, improving performance and throughput. Conflicts can be handled programmatically, reducing unnecessary waiting.

Can you give a simple example of using CAS for incrementing a counter in optimistic locking?

To increment a counter, a thread first reads the current value (old value), computes the new value (old value + 1), and then uses CAS to set the counter to the new value only if the current value equals the old value. If CAS fails due to another thread’s update, the thread can retry.