Concurrency Strategies in Multi-User Reservation Systems

The reservation pattern is a common functionality in many everyday applications, such as hotel bookings and airline ticketing systems. Effectively managing reservations is crucial, particularly when multiple users are competing for the same resources. In this article, I will discuss general strategies for addressing reservation challenges, drawing from my personal experience with MongoDB. I will also highlight potential risks, such as data inconsistencies and resource conflicts, and explore how to mitigate these issues using robust concurrency strategies.

Problem Statement

We aim to develop a system where users can reserve resources using two collections: Objects and Reservations. During a request, the system marks a free Object as reserved and creates a corresponding reservation.

Our primary requirements are:

- A user can reserve only one Object at a time.

- An Object can be reserved by only one user at any given moment.

These requirements introduce potential issues that need careful handling:

1. Multiple Requests from the Same User:

When a user makes multiple reservation requests simultaneously, the system may not complete the reservation from the first request before processing the second. This could cause the system to incorrectly allow multiple reservations by the same user, breaking our first requirement.

2. Concurrent Reservation Attempts by Different Users:

When the system processes requests concurrently, two users can attempt to reserve the same Object simultaneously. This can lead to a race condition where the first request successfully reserves the free Object, but before this reservation is finalized, the second request also processes the same Object, resulting in a duplicated reservation.

Next Steps:

In the following section, I will explore various strategies to address this concurrency issue, providing insights into how to ensure consistency and prevent resource conflicts.

Concurrency Control Strategies

The following strategies should be selected based on the type and complexity of your problem:

1. Pessimistic Locking

This approach involves locking a resource until the transaction is completed, preventing other transactions from accessing it. We introduce a locked field in our Object document and use a true/false value to ensure that no other transactions can process the Object while it is locked. Although MongoDB does not support traditional locking mechanisms, we can implement this by:

- Using the findAndModify operation to atomically check and set the locked field to true.

- After processing the Object, set the locked field to false and update it as reserved.

Pros:

- Useful for critical parts of the system where conflicts are unacceptable, and other transactions should not access the data.

Cons:

- Can lead to deadlocks if something crashes before the system releases the lock. A periodic function with a timeout may need to be implemented to handle these scenarios.

2. Optimistic Locking

This strategy allows multiple transactions to proceed concurrently but checks at the end of each transaction to see if another transaction has modified the data. If there’s a conflict, the transaction is rolled back. To implement this in MongoDB:

- Include a version or timestamp field in the document.

- Retrieve the Object, process it, and save the current version.

- During the update, use findAndModify with the current version and pass an incremented version.

- If the current version does not match, rollback the transaction.

Pros:

- Eliminates the risk of deadlock since no locks are used.

- Allows more simultaneous access to data, improving performance in read-heavy environments.

Cons:

- Requires managing conflicts when they occur, potentially leading to increased complexity.

3. Transactional Control with Two-Phase Commit

For more complex scenarios where operations need to span multiple documents or collections, MongoDB offers the two-phase commit pattern to achieve transactional consistency.

- Use MongoDB sessions and transactions to perform read and write operations.

- Start a transaction, perform the necessary checks, reserve the Object, and commit the transaction.

- If any operation fails or if there’s a conflict, abort the transaction.

4. Deferred Assignment

This strategy avoids dealing with race conditions during the request by deferring critical concurrency handling to a periodic function. The steps include:

- During a reservation request, create a document with a pending status.

- Implement a cron job that periodically checks for new reservations and assigns free Objects accordingly.

Pros:

- Simplifies requests by eliminating the need for additional operations and data synchronization.

Cons:

- Responses are not instant, users must wait for the cron job execution.

Additional Insight:

I particularly like the Deferred Assignment approach. If you do not need an instant response, it is definitely worth considering for your project. This strategy is especially valuable when you need to perform complex calculations and make decisions on which resource to allocate, as it centralizes critical concurrency handling in a manageable, periodic process.

Conclusions

Your system needs to act like a fair arbiter in a race, ensuring that every user gets a fair shot at the resources without any chaos or collisions. Choosing the right concurrency strategy will help you keep things running smoothly, just like a well-organized competition!