High latency can lead to significant revenue loss as impatient customers abandon your service.
Average response times can mask severe delays experienced by a small but significant percentage of users.
Selecting the right number of bins is crucial, as too few can hide data patterns and too many can introduce noise.
Statistical models like the Sturges' formula or Freedman-Diaconis rule can provide a starting point for determining the optimal number of bins.
Percentile-based alerts (like P99) provide faster and more relevant notifications about poor user experiences compared to mean-based alerts.
Understanding whether your data has a normal, skewed, or bimodal distribution is key to correctly interpreting performance metrics.
Default bucket configurations in Prometheus can be inaccurate, so proper placement is critical for creating meaningful SLOs and metrics.
Actively testing your service's latency profile allows you to define precise bucket boundaries and SLOs that reflect real-world performance.
Focus on P99 over averages, choose bin widths carefully, and probe your services to configure buckets for precise monitoring.
Jobs that call for the skills explored in this talk.