Why this matters
As a Platform Engineer building an observability platform, you will coordinate incident response and transform outages into improvements. Strong postmortems ensure your telemetry, alerts, runbooks, and deployment practices actually get better over time.
- Turn incidents into durable fixes and safer systems.
- Reduce repeat outages and alert noise.
- Build a learning culture across product and infrastructure teams.
Concept explained simply
A postmortem is a structured, blame-free review of an incident. It documents what happened, why it happened, what helped, and what you will change so it is less likely to happen again or is easier to detect and resolve.
Key qualities of a good postmortem
- Blameless: focuses on systems and processes, not individuals.
- Evidence-based: timelines from logs, metrics, traces, alerts, and chat transcripts.
- Actionable: clear owners, deadlines, and expected outcomes.
- Shareable: concise, searchable, and easy for new teammates to learn from.
Mental model
Think of incidents as falling dominos. A postmortem identifies the dominos, maps why they fell, and removes or spaces them out so next time they do not topple the whole chain. We analyze both triggering events and system conditions (like weak alerts or missing runbooks) that let small issues become outages.
The anatomy of a practical postmortem
- Incident summary: One paragraph: what broke, customer impact, duration, severity.
- Timeline: Minute-by-minute events from detection to resolution, with sources (alerts, dashboards, traces, tickets).
- Impact and detection: Who/what was affected; how it was detected; time to detect/mitigate/resolve.
- Contributing factors: Conditions that amplified the issue (e.g., missing SLOs, noisy alerts, config drift).
- Root cause analysis (RCA): Use 5 Whys and/or a causal tree; include evidence screenshots or metric names.
- What went well: Tools, decisions, or docs that sped up recovery.
- Action items: SMART tasks with owners and due dates; tie to risks and expected impact.
- Follow-up: Date to review actions, verify results, and close the postmortem.
Tip: SMART action items checklist
- Specific: Clear change (e.g., "Add alert for error ratio > 2% over 10 min").
- Measurable: Define success metric (e.g., reduce mean time to detect by 30%).
- Achievable: Fits team capacity and skills.
- Relevant: Directly addresses a contributing factor.
- Time-bound: Concrete due date and review point.
Worked examples
Example 1: Noisy alert hid real outage
Scenario: Checkout errors spiked for 25 minutes. Teams missed the signal because a high-volume CPU alert dominated the channel.
- Impact: 9% checkout failures; approx. 1,200 failed orders.
- Detection: Manual report from Support at 12:10; actionable alert fired at 12:18.
- Contributing factors: Alert fatigue; missing SLO-based alert; ungrouped alerts.
- RCA (5 Whys): Error spike → downstream payment API timeout → retry storm → thread pool exhaustion → missing backoff configuration and circuit breaker.
- Actions: Implement circuit breaker and exponential backoff; add SLO alert on checkout error ratio; group CPU alerts; audit alert priorities.
Example 2: Latency regression after deploy
Scenario: p95 latency increased 2x after a feature flag rollout.
- Impact: Slower responses for 30% of users for 40 minutes.
- Detection: SLO burn-rate alert fired 8 minutes post-deploy.
- Contributing factors: Missing canary metric guardrail; unknown N+1 query in new code path.
- RCA (Causal tree): New feature → calls user profile repeatedly → ORM eager loading disabled → DB under provisioned for surge.
- Actions: Add canary with query count budget; enable query sampling trace; add load test step in CI for this endpoint.
Example 3: Config drift broke logs pipeline
Scenario: Log ingestion dropped by 40%. Dashboards showed gaps; tracing still intact.
- Impact: Reduced visibility; delayed detection of a separate error spike.
- Detection: Dashboard gap alert fired; on-call confirmed missing logs from specific nodes.
- Contributing factors: Manual node configuration; missing config drift detection; no health check for log agent.
- RCA (5 Whys): Missing logs → agent not running → upgrade replaced systemd unit → custom flags lost → configuration not managed declaratively → no CI validation for agent config.
- Actions: Manage agent config via IaC; add startup health probe; create synthetic log heartbeat; add unit test validating flags.
Hands-on exercises
These mirror the exercises below. Do them in a doc or ticket template. Everyone can take the test; only logged-in users get saved progress.
- Exercise ex1: Draft a blameless postmortem from a given incident story.
- Exercise ex2: Run 5 Whys and propose 4 SMART action items with owners and due dates.
- Checklist: Include summary, timeline, detection, impact, contributing factors, RCA, what went well, actions, follow-up.
Common mistakes and self-check
Frequent pitfalls
- Blame-focused language. Replace with system/process framing.
- No evidence. Always reference metrics, traces, logs, and alert IDs.
- Vague actions. Make them SMART and assign an owner.
- Forgetting follow-up. Schedule a review to verify outcomes.
- Too long; didn’t read. Keep it skimmable and structured.
Self-check prompts
- Can a new teammate understand what happened in 2 minutes?
- Is every action tied to a specific contributing factor?
- Would these actions prevent or shorten a similar incident?
- Is there at least one detection/observability improvement?
Practical projects
- Build a postmortem template with embedded guidance for your org; pilot it on 2 past incidents.
- Create an SLO-centered alerting review: map current alerts to SLOs and remove 10% noisy rules.
- Automate timeline capture: export alert, page, and deploy events into a single timeline view.
Who this is for
- Platform Engineers responsible for incident response, observability, and reliability.
- Backend Engineers who on-call for services and want durable improvements.
- Engineering Managers facilitating blameless reviews.
Prerequisites
- Basic observability signals (metrics, logs, traces) and alerting concepts.
- Familiarity with incident severity levels and runbooks.
- Ability to read deployment, infra, and service change logs.
Learning path
- Learn SLOs and burn-rate alerts to improve detection quality.
- Practice incident timelines from raw telemetry and chat transcripts.
- Perform RCA using 5 Whys and causal trees with evidence.
- Design SMART actions that address contributing factors and detection gaps.
- Institutionalize learning: templates, tagging, and quarterly review of themes.
Mini challenge
Pick a past incident. In 20 minutes, write a 5-sentence blameless summary and list 3 contributing factors you can mitigate this quarter. Share with a peer for feedback.