Leveraging S3 for Distributed Concurrency Control in Data Processing

In distributed systems, managing concurrency—ensuring that only a set number of processes run in parallel—is crucial to maintain system performance and prevent data inconsistencies. This is particularly important when performing data-intensive operations where multiple workers might attempt to process or modify data simultaneously.

In this blog, we’ll explore how to manage parallel executions using Amazon S3 as a locking mechanism and define a concurrency control strategy that limits the number of concurrent executions in a distributed environment. Let’s dive into the concept, the workflow, and how you can implement this pattern to handle concurrency with minimal infrastructure overhead.

Video guide

Understanding Concurrency in Distributed Systems

Concurrency refers to the ability of a system to handle multiple tasks or processes simultaneously. In data processing, especially with large-scale systems, you may have multiple workers (or processes) running in parallel, attempting to access and modify data. However, too many parallel executions can cause issues like race conditions, resource contention, and inefficient use of resources.

In scenarios where only a limited number of tasks can run simultaneously, concurrency control becomes essential. This ensures that, at any given time, only a certain number of tasks are executing, preventing system overload and ensuring that shared resources are managed efficiently.

Why Use S3 for Locking?

Amazon S3 is often used as a storage backend in distributed data processing systems. Its durability, availability, and ease of use make it a great candidate for implementing locking mechanisms.

Here’s why we use S3 as a locking mechanism:

1. Scalability: S3 is designed to handle a large number of requests and can scale horizontally, which is useful in distributed systems.
2. Durability: Data in S3 is replicated across multiple devices and regions, making it highly reliable.
3. Simplicity: Using S3’s basic object storage features, you can manage locks and concurrency control without complex infrastructure.

By creating an object (a file) in S3, we can use it to represent a lock. The object’s presence or absence indicates whether a particular resource is locked or available for processing.

How S3 Locking and Concurrency Control Works

The general idea is to use a file in S3 as a "counter" that tracks the number of currently active locks. The system will attempt to acquire a lock before starting a task, and once the task is complete, the lock is released.

Pseudocode Logic

Let’s break down the core logic of how this works using pseudocode:

How the Code Works

Below is a step-by-step breakdown of how the Python code implements this pseudocode using boto3 (the AWS SDK for Python):

1. Initialize the Lock: The class S3Lock is initialized with the bucket name, lock name, and concurrency limit. The lock is represented by an object in S3, and the concurrency count is tracked using another object (the active_locks.json file).
2. Check Active Locks (Concurrency Control): The method _check_concurrency_limit() reads the active_locks.json file from S3 to determine how many tasks are currently active. If the number of active locks is below the concurrency limit, the lock can be acquired.
3. Acquire the Lock: The acquire_lock() method tries to create a lock object (empty file) in S3. If the file already exists (indicating that the lock is already held), the method retries after a brief interval. If successful, it increments the active lock counter and the task can begin.
4. Work: Once the lock is acquired, the job or task proceeds. This is where the actual data processing or other task logic would go.
5. Release the Lock: After the task is complete, the release_lock() method deletes the lock object in S3, signaling that the resource is now free. The active lock count is decremented accordingly.

Code Implementation

Output

Screenshot 1: Job1 acquires Lock

Screenshot 2: Job2 acquires Lock job1 release lock

Code: https://github.com/soumilshah1995/s3-concurrency-lock/tree/main

Conclusion

By using S3 as a lock and managing the concurrency count with simple object storage, you can effectively control the number of parallel executions in a distributed system. This technique is especially useful in environments where you want to process large datasets with a limited number of workers while maintaining system stability. The best part is that this approach requires minimal infrastructure overhead, relying only on S3's object storage and basic concurrency control mechanisms.

Feel free to adapt this technique for your own use cases, and don’t hesitate to explore more advanced locking mechanisms if you need stronger guarantees for your applications!