Hey everyone, I'm currently managing a critical live production workload on Amazon Aurora MySQL (version 8.0.mysql_aurora.3.05.2) and I'm in urgent need of advice on cost optimization. Last month, my RDS bill jumped to $966, and my management team has asked me to find ways to reduce costs. I initially tried switching to Aurora Serverless V2 with ACUs ranging from 1 to 16, but it was quite unstable with frequent connection drops. After that, I increased it to 22 ACUs, which ended up being more costly and did not perform well during idle times.
As a result, I've reverted to a provisioned db.r5.2xlarge instance, which is stable but also expensive. I considered the t4g.2xlarge option, but it couldn't handle the workload, and even the db.r5.large instance struggles under pressure.
I'm facing some constraints: I can't reduce the instance size without impacting performance, this is a critical real-time database, and as the 'cloud expert' on my team, I'm feeling the pressure to optimize costs effectively.
Here are a few questions I have:
- Has anyone dealt with similar cost issues in Aurora and found a successful solution?
- Would implementing a read replica help reduce costs meaningfully, or would it just increase expenses?
- Are there any important considerations regarding I/O-Optimized instances that I should be aware of?
- Lastly, what other strategies might help optimize costs for a real-time, production-grade database?
I appreciate any suggestions without ego—I'm here to learn and improve!
5 Answers
It seems you've made some attempts already, and honestly, for a critical real-time database, $1,000 a month doesn’t sound out of the ordinary. Maybe your application's performance could use a closer look instead of just focusing on the database costs.
Make sure to evaluate your database schema. A poorly optimized schema can waste resources and hinder performance. You might find opportunities to streamline things that could lead to cost savings.
Your time in optimizing may have already cost more than any potential savings. You should dig deeper into what's causing high resource usage. For instance, I managed to downsize from an xlarge to a large instance simply by blocking excessive traffic from crawlers using AWS WAF.
Consider why you're on db.r5. You might get better performance and lower costs with newer generations like db.r8.large. Performance insights are a must. Also, while read replicas can help, you'll need to tweak your code to utilize them effectively. Don’t forget to set up multi-AZ replicas for better resilience and further optimization. We once started with a read and a write replica that were over-provisioned and then trimmed down once we optimized our queries using performance insights. Just keep an eye on complex queries that might be dragging your costs up.
Great advice! Performance insights have helped me pinpoint areas to optimize in the past.
Thanks for the detailed breakdown! I'm definitely looking into performance insights.
If you haven't yet, try enabling performance insights. It could reveal if there's anything performing poorly that you can optimize or fix.
Absolutely, schema design can make a huge difference!