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Site Reliability Engineering (SRE): Ops as a Software Problem
Site Reliability Engineering (SRE) treats operations as a software problem, using engineering to automate and scale system management. It's crucial for massive services like Google Search, ensuring availability, latency, and capacity.
Toil: The Repetitive Work That Kills Engineering Velocity
Toil is manual, repetitive work that scales with your service, stealing time from real engineering. It's the operational treadmill of handling the same alerts or manually running scripts.
Embracing Risk: Reliability Is a Budget, Not a Goal
Instead of chasing 100% uptime, SRE treats reliability as a budget. The acceptable downtime, or 'error budget,' is permission to take calculated risks like shipping features faster. The footgun is wasting resources on reliability users won't even notice.
Service Level Indicators: Measuring What Matters
An SLI is a direct measurement of your service's performance, like request latency or error rate. It's the raw data that forms the basis for reliability goals (SLOs). The main footgun is measuring system internals instead of the actual user experience.
Error Budgets: Balancing Reliability and Innovation
An error budget is your service's allowance for unreliability, calculated as 1 minus the SLO. It provides a data-driven signal for when to halt new feature releases and focus on stability, protecting users from repeated SLO misses.
Blameless Postmortems: Fix Systems, Not Blame
A blameless postmortem treats human error as a symptom of a system flaw, not a root cause. It's used after major incidents to find systemic causes and prevent recurrence, assuming everyone acted with good intentions.
Service Level Agreement (SLA): The Contract Behind Uptime
An SLA is a business contract, not a technical target. It defines the minimum service quality a provider promises a customer, with financial penalties for failure. You see them in every cloud provider contract.
SRE Engagement Models: From Gatekeeper to Platform Builder
SRE engagement models define how reliability experts help product teams, evolving from gatekeeping existing services to providing reliable platforms. This applies when scaling an SRE team's impact.
Code Instrumentation: Making Your App Observable
Instrumentation is like adding a flight recorder to your app, emitting telemetry about its internal state. It's how you generate traces, metrics, and logs for observability tools. The main footgun is over-instrumenting, creating noisy and expensive data.
Health Checks: Is Your Service Alive or Just Running?
A health check answers 'Can you do your job?', not just 'Are you running?'. Load balancers and orchestrators use this API endpoint to stop routing traffic to sick instances.
The Four Golden Signals of Service Monitoring
The Four Golden Signals provide a simple framework for service health: Latency, Traffic, Errors, and Saturation. Use them for top-level dashboards and alerts. The biggest footgun is ignoring saturation, the leading indicator of future latency issues.
Pull vs. Push: How Your Metrics Get to the Collector
Push vs. Pull metrics is about who starts the conversation. In a push model (like OTLP), the app sends metrics to a collector. In a pull model (like Prometheus), the collector scrapes metrics from the app. The footgun: pull can miss short-lived jobs.
Cardinality: The Hidden Cost of Time-Series Metrics
Cardinality is the number of unique label combinations in your metrics. High cardinality, from labels like user IDs, is the silent killer of monitoring systems like Prometheus, exploding memory and cost. The footgun is adding a label with unbounded values.
Sampling: Tracing Everything Without Storing Everything
Sampling makes high-volume observability affordable by deciding which traces to keep and which to discard. It's essential in distributed systems where capturing every request is too costly.
Error Budgets: The Currency of Reliability
An error budget is the acceptable amount of downtime or errors your service can have over a period. Calculated as (1 - SLO), it's a currency for balancing risk (new features) and reliability work. The main footgun is treating it as a target to spend.
SLO Time Windows: Choosing Your Measurement Period
An SLO's time window is the calendar for grading reliability. A short window forces rapid fixes, while a long one smooths out blips for strategic planning. The footgun is using calendar months, which have unequal lengths and complicate budget math.
SLO Document: Your Service's Reliability Contract
An SLO document is the source of truth for a service's reliability promises, acting as a formal contract between teams on what 'good enough' looks like. It defines measurable targets for availability and latency, guiding engineering priorities.
SLO Burn Rate: Alerting on Budget Consumption
SLO burn rate measures how fast you're using your error budget. A burn rate of 2 means you're consuming budget twice as fast as allowed. It's used to trigger alerts for both fast outages and slow-burning problems to prevent SLO breaches.
Error Budget Burndown: Your Service's Reliability Fuel Gauge
An error budget burndown chart is a fuel gauge for your service's reliability. It shows how much 'allowable downtime' you've used, helping you decide when to ship features versus fix bugs. The footgun is ignoring a steep burn rate until it's too late.
OpenTelemetry API: The Stable Interface for Your Code
The OpenTelemetry API provides stable interfaces for your code to generate telemetry. This lets you instrument your application once, while the SDK implementation handles the actual data processing and export, which can be swapped out later.