> ## Documentation Index > Fetch the complete documentation index at: https://docs.nofire.ai/llms.txt > Use this file to discover all available pages before exploring further. # Change Management > See what changed in your infrastructure and code, and when it happened 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. Ask about pod restarts, deployments, and scaling across your entire cluster Get a unified view of infrastructure + code changes for a specific service ## 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 ## How to Use It Tag `@NOFire AI` in Slack and ask in plain language. NOFire figures out what you need. No query syntax required. ### Cluster-Wide Queries Use when you want to see what happened across the entire cluster, without focusing on a specific service. ``` @NOFire AI what pods restarted in the otel-demo cluster in the last hour? ``` ``` @NOFire AI show me all deployments in the otel-demo cluster today ``` ``` @NOFire AI what scaling events happened in the otel-demo cluster in the last 24 hours? ``` ``` @NOFire AI show me all changes in the otel-demo cluster in the last 24 hours ``` ### Entity-Specific Timeline Use when you're investigating a specific service, deployment, or pod. @-mention the service to get faster, more precise results. ``` @NOFire AI show me all changes for @checkout in the last 2 hours ``` ``` @NOFire AI what changed in @cart in the last 6 hours? ``` ``` @NOFire AI show me infrastructure and code changes for @payment in the last 4 hours ``` ### Combined Queries NOFire can correlate infrastructure and code changes together. Be specific about what signals you want and over what time window. ``` @NOFire AI show me pod changes and recent deployments for @frontend in the last hour ``` ``` @NOFire AI what code changes correlate with @recommendation restarts in the last hour? ``` ## Understanding the Output NOFire returns a timeline with `Time` and `Event` columns, ordered chronologically: ``` Change timeline for: otel-demo cluster (last 24 hours) 47 changes across 18 entities Time Event ───────────────────────────────────────────────────── 2026-02-17 14:32:11 checkout: deployment rolled out (v2.1.4 → v2.1.5) 2026-02-17 14:28:45 cart: 3 pods restarted (OOMKilled) 2026-02-17 14:15:02 payment: autoscaled 2 → 5 pods (CPU 87%) 2026-02-17 13:55:18 product-catalog: config update (feature flag changed) 2026-02-17 13:40:00 frontend-proxy: deployment rolled out (nginx config) 2026-02-17 13:22:34 kafka: replication factor updated ``` ## Common Scenarios Start broad, then narrow down. ``` @NOFire AI what changed in the last hour across all services? ``` Then follow up on anything suspicious: ``` @NOFire AI show me the deployment timeline for @checkout in the last 2 hours ``` ``` @NOFire AI show me all infrastructure and code changes for the cart service before 14:30 today ``` NOFire returns a unified timeline combining infrastructure events (restarts, scaling, config updates) and recent commits/PRs in the same window. ``` @NOFire AI show me pod changes, scaling events, and recent deployments for the payment service in the last 3 hours ``` Useful when traces show latency but you need to understand what infrastructure or code change preceded it. ``` @NOFire AI show me changes in the otel-demo namespace in the last 7 days ``` Understand deployment frequency and stability before releasing a new version of the recommendation or fraud-detection service. ``` @NOFire AI show me all changes between Feb 16 18:00 and Feb 17 08:00 in the otel-demo cluster ``` Use this to populate your postmortem timeline without manual log digging across infrastructure events and Git history. ``` @NOFire AI understand seasonality patterns of the otel-demo cluster from December 2025 to February 2026 ``` NOFire analyzes deployment cycles, scaling windows, maintenance patterns, and peak activity periods to help plan releases and on-call coverage. ## 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 understand seasonality patterns of this cluster from December 2025 till February 16th 2026 ``` 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 ### 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. ## 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. ## Next Steps Start collecting Kubernetes change events Correlate code changes with infrastructure events