Question 1

How to add custom metric types in gotoprom?

Accepted Answer

You can extend gotoprom by registering custom builders using reflection, as demonstrated in the README with TimeHistogram for observing durations. This involves defining a new type and implementing a builder function that integrates with prometheusvanilla.

Question 2

gotoprom vs vanilla Prometheus: which is better for my project?

Accepted Answer

Choose gotoprom if you need compile-time label safety and better maintainability, especially for refactoring. Opt for vanilla Prometheus if you require maximum performance or dynamic label handling, as gotoprom adds overhead and static constraints.

Question 3

What is the real-world performance impact of using gotoprom?

Accepted Answer

gotoprom adds about 600ns per counter increment, making it 3x slower than vanilla Prometheus, with increased memory usage. This is acceptable for most web apps but not for ultra-low-latency systems.

Question 4

How do I define and use labels in gotoprom?

Accepted Answer

Labels are defined as Go structs with `label` tags, such as `type labels struct { Code int `label:"code"` }`. You then pass instances of these structs to metric functions, ensuring type-safe usage without order errors.

Question 5

Can gotoprom handle labels that change at runtime?

Accepted Answer

gotoprom is designed for static label definitions via structs, so handling dynamic labels is limited. You might need to use vanilla Prometheus for those parts or define all possible label combinations upfront.

Question 6

Is gotoprom suitable for high-frequency metric updates?

Accepted Answer

Due to its performance overhead, gotoprom may not be ideal for applications with extreme update rates, like real-time trading systems. However, for typical HTTP request monitoring, the cost is negligible.

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