Wrong SLOs — and the error budgets they inform — can cost millions of dollars and waste significant resources. At Circonus, we use OpenHistogram for computing SLOs because it ensures the greatest accuracy and flexibility. Histograms are a data structure for representing large quantities of samples (such as the latencies of all API requests). OpenHistogram is a vendor-neutral log-linear histogram technology that allows you to efficiently store an unlimited volume of high frequency data for years at low cost.
“We use Circonus’ histograms extensively to surface issues before users notice them. We also have alerting set up such that if we start to burn error budget in latency for a particular service too quickly, we alert the team. The histograms enable us to identify these issues and make adjustments right away.”
Inform your latency SLOs with powerful visualizations and analytics
- Surface issues before users notice them. Alert on real-time SLO compliance issues, or if you are burning through your error budgets too quickly.
- Add histograms together for quick analysis of historical latency data. Identify changes in service performance, or compare latencies from one software release to another.
- Visualize real-time latency data to assess probability distributions, find disruptions and concentrations, discover extreme system loads or drop-offs, and more.
- Leverage the Circonus Analytics Query Language (CAQL) to create customized and powerful queries against metric data stored as OpenHistogram. Use CAQL to power Circonus or Grafana-based dashboards.
“Using Circonus’ histograms, it’s a huge benefit to be able to go back and sort of redraw the graph at the appropriate SLA to prove to our partners what our delivery time is. We’re able to do this because we are recording the latency of every operation at all times.”
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