6 Best Queue Systems for Backend Developers: An In-Depth Comparison

As a backend developer, few things are more important than picking the right queue system. The queue you choose can make or break your architecture and end up being a lifelong commitment.

In this comprehensive guide, I‘ll share my insights as a backend engineer and queueing expert to help you find the ideal system for your needs.

Why Queues Are Essential

Before jumping into the options, it‘s worth stepping back and truly understanding why queues have become indispensable in modern applications.

Keeping User Experiences Fast

Imagine you‘re checking out on an ecommerce site. You click "Place Order" and…nothing happens. The site hangs for 30 seconds before showing you a confirmation.

This painful experience is common on sites that do work like sending emails, processing payments, generating PDF receipts, etc. directly during checkout. All this blocks the response back to you.

With queues, these time-consuming tasks get immediately deferred to background workers. The site returns a response quickly while work finishes asynchronously.

This difference in user experience is staggering. Queues mean keeping user interactions snappy even with computationally intensive workloads.

Decoupling System Components

Queues allow different services in your infrastructure to run independently.

Take an ad tech platform. The web app needs to call the notifications microservice to send emails when events occur. Without queues, the web app must directly call notifications. This tight coupling makes development and scaling harder.

With queues, the web app just emits an event to the queue. Notifications consumes events and sends emails in the background. The services are now decoupled.

Loose coupling through queues means increased flexibility and scalability. You can modify, maintain, and scale services independently.

Building Resilient Systems

Finally, queues make it easier to build reliable systems.

Consider direct external API calls from the web layer. If the API is down, so is your site. Retries and error handling get complex very quickly.

With queues, web servers emit events, assuming background workers will eventually process them. Failures handled gracefully in the background without affecting users.

Queues combined with retries construct resilient systems. Critical workflows continue even when parts of the system fail.

In summary, queues enable:

Fast frontends – Defer slow work away from user interactions
Decoupled services – Build independently scalable components
Resiliency – Retry failures gracefully in the background

Now let‘s compare leading options for implementing queueing.

1. Redis

Redis is often the first solution developers try for queueing. As a fast in-memory data store, Redis works well for basic queueing tasks.

Redis provides list structures that can be used as FIFO queues. Clients push onto the tail of a list to enqueue jobs and pop items from the head to dequeue.

Producer:
  RPUSH myqueue "job1"
  RPUSH myqueue "job2" 

Consumer:
  LPOP myqueue

Redis is great for simple use cases given:

Blazing fast performance since data is in memory
Easy to spin up locally without a dedicated cluster
Flexible data structures beyond just queues

However, there are downsides to be aware of:

No reliability guarantees – Jobs can be lost if Redis crashes
No cluster support – Limited to single instance without add-ons
Overhead of managing – You must configure, monitor, and manage Redis

For mission critical queues at scale, Redis lacks important reliability and clustering features available in systems like Kafka or RabbitMQ.

All in all, Redis is a convenient option for getting started locally or simple production use cases. But you‘ll quickly hit limits as queueing needs grow.

2. RabbitMQ

RabbitMQ is a long-standing open source message broker with robust queueing capabilities.

RabbitMQ uses the AMQP messaging protocol. Producers send messages containing job data to queues. Consumers receive messages by subscribing to queues.

A major difference compared to Redis is reliable delivery. RabbitMQ confirms messages are received and handles redelivery if workers fail. Configurable message TTLs can automatically expire stale jobs.

And while Redis lives on one box, RabbitMQ easily scales across clusters of servers. This enables huge volumes of messages across distributed systems.

In summary, RabbitMQ brings:

Reliability – Acking, delivery guarantees, auto-expires
Scale – Distributed clusters with independent scaling
Monitoring – Management UI and metrics for visibility
Flexibility – Pub-sub, routing schemes, fanout exchanges

However, this power comes at the cost of complexity. Running a RabbitMQ cluster takes more effort than say SQS:

Operation overhead – You need to provision, configure, and monitor a RabbitMQ cluster
Learning curve – Deep library of queueing concepts to master
Maintenance needs – Upgrades, patching, failure handling

For complex queueing needs at scale, RabbitMQ shines. But simpler use cases may not warrant the overhead.

3. Amazon SQS

Amazon SQS offers fully managed queues via AWS.

With SQS, you simply interact with AWS APIs to produce and consume messages. SQS handles queue and infrastructure management behind the scenes.

SQS removes the operational burden of running your own queue cluster. Useful features like message timers, delays, and long polling help implement robust queues.

But with great simplicity comes some limitations:

Vendor lock-in – Difficult to migrate off of AWS
Limited semantics – Less routing, binding, etc. compared to RabbitMQ
Cost at scale – Can get expensive at higher msg volumes

SQS is likely the best blend of simplicity and queueing capabilities for applications on AWS. But it may prove too simplistic for complex use cases.

4. Kafka

Apache Kafka is a distributed streaming platform that also shines as a queue system.

Kafka brings a unique push-based approach. Producers write messages to Kafka topics. Consumers subscribe to topics they are interested in. Kafka pushes messages to consumers in order.

This architecture allows Kafka to achieve remarkable throughput at scale. Kafka is battle-tested with trillions of messages per day across companies like Netflix, Uber, and LinkedIn.

Kafka also provides stronger delivery guarantees than competing solutions:

Ordering – Messages in a partition are strictly ordered
Durability – Messages are persisted to disk as soon as received
Availability – Consumers can read messages as long as a single replica is alive

The benefits make Kafka a powerful choice for large-scale queueing. But simplicity is not one of them. Operating Kafka adds complexity:

Operation expertise – Kafka clusters take skill to properly tune and run
More moving pieces – More components like ZooKeeper coordinate Kafka
Overkill for simpler use cases – Kafka is likely excessive if you just need basic queues

For high throughput queueing applications, however, Kafka is likely the most robust and performant solution available today.

5. Azure Queue Storage

If you operate primarily on Azure, Azure Queue Storage is a handy option.

As with SQS, Queue Storage allows sending and receiving messages without managing infrastructure. Useful features like poison message handing help build reliable queues.

Queue storage integrates tightly with other Azure services like Functions and Logic Apps. And icy cold storage keeps costs low during inactivity.

But reliance on Azure services leads to vendor lock-in:

Azure only – Can‘t run on other clouds or on-prem
Learning curve – New service-specific concepts to master
Young product – More maturity needed compared to SQS

For simple queueing needs exclusively on Azure, Queue Storage hits the spot. More complex use cases may necessitate alternatives.

6. ActiveMQ

ActiveMQ is a veteran open source message broker supporting robust queueing workflows.

ActiveMQ serves as a broker, running as its own service. Producers write messages to queues registered on the broker. Consumers connect and subscribe to those queues.

ActiveMQ supports branching features beyond just queues:

Topics – Pub-sub messaging with subscriptions
Rules – Routing messages based on filters
Protocols – AMQP, MQTT, STOMP, OpenWire

This flexibility allows broader messaging architectures. However, open source means more operational responsibility:

Self managed – You run ActiveMQ servers vs fully managed offerings
Legacy technology – Eclipsed in hype by newer solutions
Java centric – Best support remains in the Java ecosystem

For self-managed queueing on open standards, ActiveMQ delivers. But cloud-based solutions require far less Ops work.

Honorable Mentions

There are a few other capable options beyond the leaders above:

Beanstalkd – Simple, fast work queue good for small loads
Google Cloud Tasks – Fully managed queues on Google Cloud
Amazon MQ – AWS managed message broker based on ActiveMQ

Each brings its own pros and cons depending on the use case.

Key Differences Summary

To recap, here are some key points on when you may choose one option over others:

Redis – Great to start locally but lacks features for complex queueing
RabbitMQ – Powerful but requires operational expertise to run at scale
SQS – Simple yet limited queues fully managed by AWS
Kafka – High throughput and strong delivery guarantees but very complex
Queue Storage – Simple Azure-based option lacking advanced features
ActiveMQ – Self-managed broker for open protocol needs

There is no one-size-fits-all perfect queueing solution. Choosing requires understanding your specific needs around scale, delivery guarantees, team skills, and more.

Conclusion

Queues enable key scalability, resiliency, and user experience patterns for modern applications.

Choosing a queue system is an important long-term decision requiring careful analysis. I hope this guide provides useful insights into the leaders of the queueing world.

When evaluating options, consider:

Your current infrastructure – Are you already on AWS/Azure/GCP?
Needed delivery guarantees – Do you need strict ordering?
Scale requirements – Do you need to distribute across clusters?
Team skills – Do you have expertise to operate a complex system?

There are great options for simple queueing like SQS or Redis. But complex needs at scale warrant powerful brokers like Kafka or RabbitMQ.

Understanding your specific requirements and constraints will help narrow the choices and allow picking the best queueing solution for your applications.