This course covers the core learning objective to meet the requirements of the 'Designing Database solutions in AWS - Level 3' skill
- Analzy targert AWS database platforms when performing a migration
- Create and deploy an enterprise-wide scalable RDS Database solition to meet and exceed workload performance expectations
- Create an AWS database slution to withstand AWS global infrastructure outages with minimal data loss
AWS regularly announces price cuts to its services and features. Because of this, costs and how they're calculated are subject to change. While the numbers may change, how they're calculated remains consistent.
In the case of Performance Insights, it has a free tier. Once usage has exceeded this tier, costs are calculated based on consumption.
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Regarding cost, Performance Insights has a free tier that includes the most recent seven days of performance data history. Long term retention. Any data kept longer than seven days is priced per VCPU per month for each database instance in which it is enabled. The total cost depends on the RDS instance type and can vary by region.
API pricing is a little more complex. The first one million requests are part of the free tier. There's no charge for them. After that first million has been consumed, usage is charged at one cent per 1,000 requests. A million API requests seems like a large number. However, a single custom dashboard using both available API calls will consume over 1 million requests every month.
The two API calls are
GetResourceMetrics. Consider a dashboard that calls both of these metrics every five seconds. Two API calls, times 30 days, times 24 hours, times 60 minutes, times 60 seconds divided by five second intervals is equal to 1,036,800 API calls. For this situation, the billing works like this. The first one million API calls per account are free. Subtracting one million from the total number of API calls is 36,800. Every 1,000 requests costs one cent. 36,800 divided by 1,000 is equal to 36.8. 36.8 times one cent is equal to 37 cents for the month. If two instances are being monitored using the same custom dashboard, 1,036,800 API calls times two is equal to 2,073,600. Subtract the million free requests from this number to get 1,073,600. Every 1,000 requests cost one cent. Divide 1,073,600 by 1,000. The result is 1,073.6. Multiply this by one cent to get 10.736. This rounds up to $10.74 for the month.
The free tier includes seven days of performance history. To analyze performance trends lasting more than seven days, activate Long Term Retention. This allows Performance Insights data older than seven days to be retained for up to two years. The cost for Long Term Retention is based on multiple factors, the AWS region, the type and number of CPUs in the RDS instance, and the number of hours retained.
Notice that the amount of data stored has no impact on the cost of longterm retention. To illustrate what this means, longterm retention of data for two RDS instances of the same type, but with a different number of CPUs works like this.
If one instance has four CPUs and the other has one CPU, the instance with four CPUs costs four times as much as the same instance type with only one CPU. When longterm retention is turned off, performance data older than seven days is deleted. Billing is prorated based on the number of hours data has been retained.
Billing considerations can be dull and a little dry, but since AWS costs are based on consumption, it's good to know in general how they're calculated, and how they can impact you and your organization.
This wraps up the section on cost and billing for RDS Performance Insights. In the next lecture, I'll summarize what I've covered in this course and share a few final thoughts.
Stephen is the AWS Certification Specialist at Cloud Academy. His content focuses heavily on topics related to certification on Amazon Web Services technologies. He loves teaching and believes that there are no shortcuts to certification but it is possible to find the right path and course of study.
Stephen has worked in IT for over 25 years in roles ranging from tech support to systems engineering. At one point, he taught computer network technology at a community college in Washington state.
Before coming to Cloud Academy, Stephen worked as a trainer and curriculum developer at AWS and brings a wealth of knowledge and experience in cloud technologies.
In his spare time, Stephen enjoys reading, sudoku, gaming, and modern square dancing.