Collecting Prometheus metrics from multi-process web servers, the Ruby case | Michal Kazmierczak

08/09/2023
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In Prometheus, metrics collection must follow concrete rules. For example, counters must be either always monotonically increasing or reset to zero. Violating this rule will result in collecting nonsensical data.It is a challenge with multi-process web servers (like Unicorn or Puma in Ruby or Gunicorn in Python) where each scrape might reach a different instance of the app which holds a local copy of the metric[1]. These days, horizontal autoscaling and threaded web servers only increase the complexity of the problem. Typical solutions - implementing synchronization for scrapes or adding extra labels to initiate new time series for every instance of the app - can’t always be implemented.In this article I describe a rebellious solution of the problem which combines StatsD for metric collection and aggregation with Prometheus for time series storage and data retrieval.
Collecting Prometheus metrics from multi-process web servers, the Ruby case | Michal Kazmierczak #ruby #rubydeveloper #rubyonrails #servers, #web https://rubyonrails.ba/single/collecting-prometheus-metrics-from-multi-process-web-servers-the-ruby-case-michal-kazmierczak