Why I Don’t Like Implementing Queues in the Database

Over the years, I’ve seen many times when developers decide to use the database itself as a queue.

At first glance, this may seem like a convenient and simple solution. 👍

Nothing complicated to set up, no need to introduce additional infrastructure. Just create a db table, insert “tasks” as rows, introduce process status, and run a cron job to check for unprocessed items.

But in practice, I realized that this approach often leads to more problems than it actually solves.

Let me explain why I always prefer proper message queue systems over queues implemented in databases.

Using Wrong Tool to Solve the Problem

It makes sense to mention in the beginning, that probably everybody understands that databases are not designed to be message queues. And we have plenty of specialized tools for that purpose: RabbitMQ, Kafka, AWS SQS, etc.

One major issue is performance. Databases are optimized for data storage and retrieval, not for the high-throughput, low-latency operations that queues require. But this argument becomes valid only when you have a significant load with a lot of messages.

In case your application does not have high load, should you then use database as a queue? 🤔

Let’s explore this question and I will give you more reasons why this approach is problematic.

Locking and Concurrency Issues

The number one reason why database is not efficient queue is because of concurrency problems. If you application is a distributed system with multiple workers processing tasks from the queue, you will run into issues.

The problem in this scenario is that multiple workers will try to read and write to the same table concurrently. Fetching either already processed tasks or overwriting each other. So you will either make sure only one worker always works at a time, or you will have to deal with implementing some locking mechanisms. 🔒

This also defeats the purpose of having multiple workers in the first place. You can not reduce processing time and scale workers anymore. 🐌

Additionally, polling overhead from cron job, updating tasks status and locking can lead to some load to database, which could be easily avoided by using proper queue.

Out of the Box Features

Dedicated message queue systems come with a variety of features that are specifically designed to handle queuing scenarios. These include:

• Message Acknowledgment: Ensures that messages are processed successfully before being removed from the queue.
• Retry Mechanisms: Automatically retries failed messages, reducing the risk of data loss.
• Dead Letter Queues: Captures messages that cannot be processed after a certain number of attempts, allowing for later analysis and reprocessing.
• Scalability: Easily scales horizontally to handle increased load without significant changes to the application architecture.

In contrast, implementing a queue in a database often requires building these features from scratch and you end up reinventing the wheel. This not only increases development time but also introduces potential bugs and maintenance challenges.