🔓Distributed Locking

Wake Up to the Reality of Production Environments

• "No better place than 127.0.0.1" is a phrase that holds true when working locally, but production environments are a different story. Issues can arise when multiple instances of your application are running.

The Problem with Concurrent Requests

• Imagine a scenario where you need to handle a payment process:

  1. Fetch user data from the DB.

  2. Check if the user is free or paid.

  3. Update the user's status in the DB if not paid.

  4. Send a response based on the user's payment status.

• Issue: If two requests come in simultaneously—one to cancel the payment and another to mark it as paid—the system could incorrectly allow a user access to your product without payment.

Locks to the Rescue

• Locks ensure that only one process can access a critical section of code at a time.

  • Steps:

    1. Acquire the lock.

    2. Execute the transaction.

    3. Release the lock.

The Problem with In-Memory Locks

• In-memory locks are limited to a single instance of your application. They don't work well in a distributed environment where multiple instances might be running.

Introduction to Distributed Locking

• Distributed locking ensures that if one instance acquires a lock, other instances cannot access the same critical section.

  • Example Key: In a payment service, the key could be "PAYMENT_" + user_id + amount, ensuring that only one action (payment or cancellation) can proceed.

Using Redis for Distributed Locking

• Single Redis Instance:

  • Can fail, causing locks to remain unreleased.

  • Communication between master-replica can lead to inconsistent lock states.

• Redlock Algorithm:

  • Acquires locks on multiple Redis instances with expiration times.

  • Lock is considered acquired if the majority of instances successfully lock.

  • Releases locks across all instances.

Conclusion

• Distributed locking is crucial for ensuring data consistency in production environments with multiple application instances. The Redlock algorithm with Redis provides a robust solution.

Code with detailed explanation:-

refer:- https://dev.to/lovepreetsingh/how-to-implement-a-distributed-lock-using-redis-he