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Whether you’re debugging an incident, verifying a deployment, or building a postmortem, the first question is always: what changed? NOFire correlates infrastructure events and code changes into a single timeline so you don’t have to piece it together manually.

Cluster-Wide Changes

Ask about pod restarts, deployments, and scaling across your entire cluster

Entity Timeline

Get a unified view of infrastructure + code changes for a specific service

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What NOFire Tracks

Infrastructure changes:
  • Deployment rollouts and rollbacks
  • Autoscaling events (scale up / scale down)
  • Pod restarts and lifecycle events
  • Configuration updates (ConfigMaps, Secrets, feature flags)
  • Node and host-level maintenance events
  • Cloud resource changes (load balancers, DNS, security groups)
  • Namespace and environment-wide changes
Code changes (from VCS webhooks):
  • Recent commits and merged PRs
  • Deployment-correlated code changes
  • File-level change history

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How to Use It

Tag @NOFire AI in Slack and ask in plain language. NOFire figures out what you need. No query syntax required.

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Cluster-Wide Queries

Use when you want to see what happened across the entire cluster, without focusing on a specific service.

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Entity-Specific Timeline

Use when you’re investigating a specific service, deployment, or pod. @-mention the service to get faster, more precise results.

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Combined Queries

NOFire can correlate infrastructure and code changes together. Be specific about what signals you want and over what time window.

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Understanding the Output

NOFire returns a timeline with Time and Event columns, ordered chronologically:

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Common Scenarios

Start broad, then narrow down.
Then follow up on anything suspicious:
NOFire returns a unified timeline combining infrastructure events (restarts, scaling, config updates) and recent commits/PRs in the same window.
Useful when traces show latency but you need to understand what infrastructure or code change preceded it.
Understand deployment frequency and stability before releasing a new version of the recommendation or fraud-detection service.
Use this to populate your postmortem timeline without manual log digging across infrastructure events and Git history.
NOFire analyzes deployment cycles, scaling windows, maintenance patterns, and peak activity periods to help plan releases and on-call coverage.

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Seasonality Analysis

For longer time windows, NOFire goes beyond a change timeline and analyzes patterns: when your team deploys, when your infrastructure scales, and when the riskiest change windows are.
NOFire AI building an investigation plan for seasonality patterns, pulling cluster change data
NOFire identifies daily deployment cycles, weekly activity patterns, and monthly trends, then surfaces actionable recommendations for scheduling changes and on-call coverage.
Seasonality patterns analysis showing daily deployment windows, weekly patterns, and monthly trends
Predictive seasonality insights with expected patterns for the next 30 days and operational recommendations

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What the analysis covers

  • Daily deployment cycles: peak deployment windows and quiet periods
  • Weekly patterns: high-activity weekdays vs. low-activity weekends
  • Monthly trends: deployment volume growth and trajectory
  • Maintenance windows: certificate rotations, system updates, scaling event frequency
  • Risk periods: highest and lowest change density windows
  • Operational recommendations: when to schedule critical maintenance, when to avoid changes, and where to increase capacity
This is useful for capacity planning, on-call scheduling, and setting deployment policies based on actual historical behavior rather than assumptions.

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Time Windows

By default, NOFire looks back 24 hours. You can specify any window in your query:
  • in the last hour
  • in the last 6 hours
  • today
  • yesterday
  • in the last 7 days
  • between Feb 16 and Feb 17
For patterns and seasonality analysis, longer windows (30-90 days) surface deployment cycles, scaling behavior, and maintenance windows.

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Next Steps

Install NOFire Edge

Start collecting Kubernetes change events

Connect GitHub

Correlate code changes with infrastructure events