Intermediate everything in DevOps & Cloud, page 19
OpenTelemetry Protocol (OTLP): The Universal Adapter for Telemetry
OTLP is the universal adapter for observability, providing a single vendor-neutral protocol for traces, metrics, and logs. Use it to send data from an OpenTelemetry SDK to a collector.
OpenTelemetry Semantic Conventions: A Shared Vocabulary
Think of Semantic Conventions as a universal dictionary for telemetry. They standardize attribute names like http.method across all services, letting you correlate data from different languages, libraries, and platforms seamlessly.
Span: The Building Block of a Distributed Trace
A Span is a single unit of work in a request's journey, like one leg of a flight. Spans capture the duration and context of individual operations (e.g., a DB query), helping you debug latency in distributed systems.
OpenTelemetry Collector: The Swiss Army Knife for Telemetry Data
The OpenTelemetry Collector is a universal adapter for your telemetry data. It receives data in one format, processes it, and exports it to multiple backends, letting you centralize configuration and avoid vendor lock-in.
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.
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.
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.
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.
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.
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.
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.
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.
Kubebuilder: Build Kubernetes APIs the Canonical Way
Kubebuilder is a framework for scaffolding custom Kubernetes APIs, letting you define your own resources like MyWebApp. Use it to extend Kubernetes with declarative APIs, making your app a first-class citizen.
OPA Gatekeeper: Enforce Kubernetes Policies as Code
OPA Gatekeeper is a Kubernetes admission controller using OPA to enforce policies on resources. Use it to mandate labels or block insecure images. The footgun is thinking it's just OPA; Gatekeeper adds K8s-native CRDs, auditing, and mutation capabilities.
Pod Topology Spread: Spreading Pods for High Availability
Pod Topology Spread Constraints prevent putting all your pods in one basket. They instruct the scheduler to distribute a service's pods evenly across nodes or zones, improving availability. The main footgun is that it's a soft preference by default.
CSI Volume Cloning: `cp` for Kubernetes Volumes
Think of volume cloning as cp for your Kubernetes data. It creates a new, independent volume pre-populated with data from an existing one, offloading the copy operation to your storage provider.
Kubernetes Secrets: Encrypting Data at Rest
By default, Kubernetes Secrets are only base64-encoded, not encrypted. Encryption at rest makes the API server encrypt Secret data before saving to etcd, protecting against compromised backups.
Kubernetes Gateway API: The Successor to Ingress
The Gateway API replaces Kubernetes Ingress with a role-oriented model, separating infrastructure from application routing. Use it when different teams need to manage their own traffic rules.
kube-apiserver: The Front Door to Your Kubernetes Cluster
The kube-apiserver is the front door to your Kubernetes control plane. All requests to query or modify the cluster's state must pass through it, from kubectl commands to automated controller actions. The footgun is bypassing it to modify etcd directly.
Kubernetes' Declarative Model: Desired vs. Actual State
The declarative model is like telling Kubernetes your destination, not giving it turn-by-turn directions. You define the desired state in a file, and Kubernetes works to make it a reality. This enables self-healing and GitOps.
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