Autometrics leverages metaprogramming tools like macros, wrappers, and decorators, along with existing metrics libraries to provide a seamless experience for developers instrumenting their code.
Easily explore your application's performance from within your editor. Autometrics automatically inserts links to generated PromQL queries directly in your function's doc comments.
All metrics data Autometrics generates has standard names, enabling you to use dashboards without any configuration. You can grab any of the already-created dashboards, connect your Prometheus with Autometrics data, and start exploring.
- Autometrics Overview (opens in a new tab)
- Autometrics Function Explorer (opens in a new tab)
- Autometrics Service-Level Objectives (SLOs) (opens in a new tab)
Autometrics can track your software versions and will writes queries that can help pinpoint where and when issues were introduced.
Under the hood Autometrics produces a
build_info metric and uses labels to
expose the version, commit and branch information of your app to Prometheus.
Autometrics allows you to group instrumented functions in to Service-Level Objectives (SLO) and create useful alerts on them all while keeping the context in your application source code.
Alerts for any Autometrics SLO can be enabled using a single alerting rules
autometrics.rules.yml (opens in a new tab).
By default these rules are dormant, and are enabled only when metrics with
Autometrics SLO labels are registered.
The default set of rules will work for any project that uses the following objective percentiles: 90%, 95%, 99%, 99.9%. If you want to use other percentiles you can generate a new rules file, see here for instructions (opens in a new tab)
Autometrics can be incrementally adopted to work with most open-source metrics libraries that you already use.