The continuation of the The AWS Well-Architected Framework and it's 5 Pillars Series.
For the others who haven't yet read the initial part of this series, please read the previous parts above or below this article.
Let's continue on diving deep and this time let's venture the Performance Efficiency realm to gain knowledge on what are the key areas, what questions you must conquer and what are the key phases involve in this pillar for us to satisfy it's standards.
... a data-driven approach to building a high-performance architecture.. ensure that you are taking advantage of the continually-evolving AWS Cloud.
In other words, We need to take advantage of the latest innovations that move our builds closer to its business goals.
To satisfy this pillar, your architecture is critically needs to remove bottlenecks, reduce waste by leveraging these key areas:
- Let AWS do the work whenever possible
- Reduce latency through regions and the AWS edge
- Serverless, Serverless, SERVERLESS
- Experiment as new services are released
- Think about the user, not your tech stack
Is this the optimal solution for this workload? Evaluate it:
- What type of compute best suits?
- Which data store is ideal for this workload?
- Does your network design complement compute and data store choices
Continuously ensure choices that works for your workload:
- Is infrastructure stored as code?
- Are deployments simple and automated?
- Can benchmarks be taken automatically?
- Does load testing interfere with production?
What's happening right now? an hour ago? Know your metrics:
- Use active and passive monitoring where appropriate
- Understand the 5 phases of monitoring (Generation, Aggregation, Real-time processing, Storage, Analytics)
- Create actionable metrics
You cannot have it all:
- Will caching help?
- Should you partition or shard your workload?
- Can compression improve performance?
- Is buffering an option?
The key takeaways in this pillar is you need to understand that building an performance efficient system is a continuous and long term process.
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