Ruby Server Timing vs Thanos: What are the differences?
Ruby Server Timing: Brings Rails server-side performance metrics to Chrome's Developer Tools via the Server Timing API. Bring Ruby on Rails server-side performance metrics 📈 to Chrome's Developer Tools (and other browsers that support the Server Timing API) via the servertiming gem Metrics are collected from the scoutapm gem. A Scout account is not required.; Thanos: Highly available Prometheus setup with long term storage capabilities. Thanos is a set of components that can be composed into a highly available metric system with unlimited storage capacity. It can be added seamlessly on top of existing Prometheus deployments and leverages the Prometheus 2.0 storage format to cost-efficiently store historical metric data in any object storage while retaining fast query latencies. Additionally, it provides a global query view across all Prometheus installations and can merge data from Prometheus HA pairs on the fly.
Ruby Server Timing and Thanos can be primarily classified as "Monitoring" tools.
Ruby Server Timing and Thanos are both open source tools. It seems that Thanos with 3.83K GitHub stars and 429 forks on GitHub has more adoption than Ruby Server Timing with 495 GitHub stars and 6 GitHub forks.
What is Ruby Server Timing?
What is Thanos?
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Why do developers choose Ruby Server Timing?
Why do developers choose Thanos?
What are the cons of using Ruby Server Timing?
What are the cons of using Thanos?
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We recently implemented Thanos alongside Prometheus into our Kubernetes clusters, we had previously used a variety of different metrics systems and we wanted to make life simpler for everyone by just picking one.
Prometheus seemed like an obvious choice due to its powerful querying language, native Kubernetes support and great community. However we found it somewhat lacking when it came to being highly available, something that would be very important if we wanted this to be the single source of all our metrics.
Thanos came along and solved a lot of these problems. It allowed us to run multiple Prometheis without duplicating metrics, query multiple Prometheus clusters at once, and easily back up data and then query it. Now we have a single place to go if you want to view metrics across all our clusters, with many layers of redundancy to make sure this monitoring solution is as reliable and resilient as we could reasonably make it.
If you're interested in a bit more detail feel free to check out the blog I wrote on the subject that's linked.