How to design your queues so they actually hit ...
Your slow queue isn't a Redis problem, it's a math problem. Let me show you a 60 second way to design your queues so that they actually hit their SLAs. When jobs pile up, devs like to add more workers and then hope things will work out. Instead, design your queues by SLA and then size them with Little's Law. It's two simple steps. First, split your jobs by how fast results are needed, not by feature team. For instance, real-time, near real-time, and batching. Each bucket gets its own queue, its own concurrency, its own retry back off, and its own SLO dashboards. No more noisy neighbors. Little's Law says that the jobs in the system is equal to the arrival rate times the average time in the system. Let's walk through an example. If your peak arrival rate is five jobs per second, your SLA is less than or equal to 60 seconds, and the maximum WIP to stay under the SLA is 300 jobs. If you see more than that in your dashboard, you're breaking the SLA. No guessing required. This is a pretty effective strategy, but don't forget to use the same best practices. Make jobs item potent, use a dead letter queue, exponential back off, and don't forget to set per-queue rate limits so real-time traffic never waits behind batch. Follow for more backend dev tips.
Summary
To effectively manage queues and meet SLAs, categorize jobs by urgency and apply Little's Law for sizing. Implement best practices to optimize performance.
Key Points
- Design queues based on SLA requirements, not feature teams.
- Use Little's Law to determine maximum work in progress.
- Split jobs into categories: real-time, near real-time, and batching.
- Each job category should have its own queue and concurrency settings.
- Monitor your dashboard to ensure you stay under SLA limits.
- Implement best practices like idempotency and dead letter queues.
Tags
Repurpose Ideas
- LinkedIn post: How to categorize jobs for optimal queue management
- Tweet: Key principles for designing effective queues
- Checklist: Best practices for queue management in backend systems
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