Everything in Monitoring & SRE, page 8
Counterfactual Reasoning in Incident Analysis
Counterfactual reasoning asks what would have prevented or mitigated an incident: if this alert had existed, if this check had run. Used well it finds systemic gaps, but it is a trap when it implies a single person should have just acted differently in…
Actionable Remediation Items
Actionable remediation items are specific, owned, trackable follow-ups from a postmortem that prevent recurrence or improve response. Good ones are concrete and assigned with a due date; vague items like be more careful are non-actionable and predictably…
Postmortem Report Template
A postmortem template is a standard structure for documenting an incident: summary, impact, timeline, root cause, what went well and poorly, and action items.
Incident Timeline Reconstruction
Timeline reconstruction is the after-the-fact process of merging evidence from logs, metrics, deploys, and chat into one accurate sequence of what happened.
War Room in Incident Response
A war room is a dedicated space, physical or virtual, where responders coordinate during a major incident. It centralizes communication and decision-making under a defined incident commander, cutting confusion and duplicated effort, but should be reserved for…
Runbooks
A runbook is a documented, step-by-step procedure for handling a specific operational task or known failure, such as responding to an alert. It captures expert knowledge so any on-call engineer can act quickly and consistently, reducing reliance on tribal…
Incident Timeline
An incident timeline is a chronological, timestamped record of what happened during an incident: detection, key events, actions taken, and resolution. It anchors the postmortem in facts, separates symptoms from causes, and reveals detection and response…
Baggage in Distributed Tracing
Baggage is key-value context propagated alongside a trace across service boundaries, so downstream services can read values set upstream. It enables cross-cutting context like tenant id, but it travels in headers on every hop, so overuse adds latency and leak…
Auto-Instrumentation
Auto-instrumentation automatically adds telemetry to an application without manual code changes, by hooking into libraries, frameworks, or the runtime. It gives broad baseline observability fast, but produces generic spans that often need manual…
Metrics in Observability
Metrics are numeric measurements aggregated over time, like counters, gauges, and histograms. They are cheap to store and fast to query, making them ideal for dashboards and alerting, but their pre-aggregation discards per-event detail needed for deep…
Observability vs Monitoring
Monitoring watches predefined metrics and alerts on known failure modes you anticipated. Observability is the property of being able to ask new questions about a system's internal state from its outputs, letting you debug unknown failures you never predicted…
SLOs Tied to User Journeys, Not APIs
A user-journey SLO measures the full flow a person experiences, not one microservice's health. If checkout is 99.9% up but payments fail, the metric lied. Teams drown in green per-service dashboards while users are furious.
DevOps Is Culture, SRE Is Engineering
DevOps is a cultural philosophy for fast, safe delivery; SRE is the engineering discipline that implements it with error budgets and SLOs. They are complementary, not rival job titles. The footgun is hiring SREs and declaring DevOps done.
AWS Fault Injection Simulator
AWS Fault Injection Simulator is a controlled chaos button: it breaks resources on purpose to prove your failover works before real disasters. Run it before peak traffic to validate auto-healing.
Load Balancing Algorithms: How to Pick a Server
Load balancing algorithms are the rules a client uses to pick one server from a pool of identical backends. They're used by web proxies routing user traffic and by microservices calling each other.
Post-Incident Review: Learning from Failure, Blamelessly
A Post-Incident Review (PIR) is a blameless process to learn from an outage, not to assign blame. Use it after a production incident to identify systemic flaws and create action items to prevent repeats.
Escalation Policy: When to Stop Shipping and Start Fixing
An escalation policy is a pre-agreed plan for when to divert engineers from feature work to fix reliability. When a service's error budget burns too fast, the policy's thresholds trigger specific actions. The footgun is thinking a quick rollback is enough.
Time-Series Compression: Storing More with Less
Time-series compression stores data more efficiently by saving the *difference* between consecutive points, not the full values. It's key for managing terabyte-scale monitoring and IoT data, often saving over 90% on storage.
Service Maps: A Live Blueprint of Your Architecture
A service map is a live blueprint of your distributed system, generated from telemetry data. It visualizes service dependencies and health, letting you instantly see an incident's blast radius instead of hunting through Slack for tribal knowledge.
Capacity Planning: Don't Run Out of Room
Capacity planning matches your system's resources to user demand, crucial for handling traffic spikes or budgeting cloud spend. The main footgun is planning for theoretical 'design capacity' instead of realistic 'effective capacity' which accounts for…
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