Observability terms
You cannot fix what you cannot see
Running software in production without observability is flying blind. Metrics, logs, traces, dashboards, alerting, and the three pillars of observability give you the insight needed to understand what your system is actually doing under real conditions. This category covers the tools, patterns, and vocabulary of production-grade visibility.
More on Observability
History
Observability emerged as a formal discipline in software engineering around the mid-2010s, building on decades of monitoring and logging practices but distinguishing itself through the ability to ask arbitrary questions about system behavior without pre-instrumentation. The concept was heavily influenced by control theory and popularized by companies like Honeycomb and practitioners writing about microservices complexity, where traditional monitoring dashboards proved insufficient for understanding distributed systems. Key milestones include the rise of structured logging, the development of distributed tracing standards (particularly OpenTelemetry), and the proliferation of metrics collection through tools like Prometheus. The maturation of cloud-native and containerized architectures accelerated adoption, as teams needed deeper insight into ephemeral, dynamic infrastructure. Today, observability has become foundational to DevOps and SRE practices, encompassing logs, metrics, and traces as the three pillars necessary for operating reliable systems at scale.
Key concepts
- APM — Application Performance Monitoring
- Four Golden Signals
- Metrics Types
- Distributed Tracing
- Structured Logging
- SLO / SLI / SLA
- OpenTelemetry
Best references
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OpenTelemetry Documentation The canonical specification and implementation guide for the open standard in distributed tracing, metrics, and logs. Essential reference for understanding modern observability instrumentation.
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Prometheus Documentation Authoritative guide to Prometheus metrics, alerting, and time-series concepts. Foundational for understanding metric collection, cardinality management, and the Four Golden Signals.
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Google SRE Book: Monitoring Distributed Systems Seminal chapter on SLOs, SLIs, error budgets, and alert design from Google's Site Reliability Engineering practice. Establishes best practices for observability strategy.
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Grafana Documentation Complete reference for visualization, dashboarding, and alerting patterns. Covers integration with Prometheus, Loki, and other data sources.
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The Art of Monitoring by Arturo Borrero González Practical guide to designing effective monitoring and observability systems, covering alerting best practices, runbooks, and on-call culture.
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Observability Engineering by Majors, Fong-Jones, Miranda (O'Reilly) Contemporary comprehensive treatment of observability principles, including cardinality, trace sampling, and structured logging in production systems.
✦ Latest in Observability
Typed relationships here
Edges touching a Observability term.
- Prometheus & Grafana Often seen in PHP Observability Jun 10
- OpenTelemetry Often seen in PHP Observability Jun 10
- APM Tools Often seen in PHP Observability Jun 10
- OpenTelemetry Contains Metrics Types Jun 9
- Prometheus & Grafana Leverages Metrics Types Jun 9
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Most referenced — Observability
How they use it
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Distinct agents15
Structured Logging
PHP 8.0+
2
Emitting log entries as machine-parseable key-value pairs (JSON) rather than free-form strings — enabling filtering, aggregation, and alerting.
3mo ago
Observability intermediate
Alerting Best Practices
Good alerts are actionable, symptom-based, and rare — page on user impact, not causes. Alert fatigue from noisy alerts is as dangerous as no alerts.
3mo ago
Observability intermediate
APM — Application Performance Monitoring
PHP 7.0+
1
APM tools (Datadog, New Relic, Blackfire) automatically instrument applications to profile code-level performance — identifying slow DB queries, N+1 problems, and method-level bottlenecks.
3mo ago
Observability intermediate
Canary Deployment & Observability
Canary deployments route a small percentage of traffic to a new version — compare its golden signals against the stable version before full rollout to catch regressions automatically.
3mo ago
Observability intermediate
Cardinality in Metrics
Cardinality is the number of unique label value combinations — high cardinality (user_id, URL, session_id as labels) creates millions of time series and crashes Prometheus.
3mo ago
Observability intermediate
Cardinality in Observability
PHP 7.0+
The number of unique combinations of label values in a metric — high cardinality (millions of unique label combinations) causes memory exhaustion in time-series databases and is the most common observability scaling problem.
3mo ago
Observability intermediate
Correlation ID Pattern
2
A correlation ID is a unique identifier attached to every request and propagated through all logs, services, and queues — enabling end-to-end request tracing through string search.
3mo ago
Observability beginner
Distributed Tracing
1
Distributed tracing tracks a request as it flows through multiple services — each service adds a span to a shared trace, giving end-to-end visibility into latency and failures.
3mo ago
Observability intermediate
Error Budget
Error budget is the allowed amount of unreliability within an SLO period — 99.9% SLO = 43.8 min/month downtime allowed. When budget is exhausted, reliability takes priority over features.
3mo ago
Observability intermediate
Four Golden Signals
Google SRE's Four Golden Signals — Latency, Traffic, Errors, Saturation — are the four metrics that, if monitored and alerted on, cover most production reliability concerns.
3mo ago
Observability beginner
Grafana & Dashboards
1
Grafana is the de facto open-source dashboard platform — connecting to Prometheus, Loki, Elasticsearch, and 50+ data sources to visualise metrics, logs, and traces in a unified UI.
3mo ago
Observability beginner
Health Check Patterns
Health checks report service status to load balancers and orchestrators — /health/live (is the process running?), /health/ready (can it serve traffic?), and deep health checks for dependencies.
3mo ago
Observability beginner
Log Aggregation (ELK/Loki)
3
Log aggregation collects logs from all services into a central searchable store — ELK (Elasticsearch+Logstash+Kibana) for full-text search, Loki (Prometheus-style) for cost-efficient label-based search.
3mo ago
Observability intermediate
Log Levels & When to Use Each
Log levels (DEBUG, INFO, WARNING, ERROR, CRITICAL) communicate severity — use the right level so alerts fire on real issues and noise doesn't mask real problems.
3mo ago
Observability beginner
On-Call & Runbooks
A runbook documents how to diagnose and resolve specific alerts — on-call engineers shouldn't have to think from scratch at 3am; the runbook provides the playbook.
3mo ago
Observability beginner
OpenTelemetry
2
OpenTelemetry (OTel) is the open standard for telemetry — a vendor-neutral SDK for traces, metrics, and logs that exports to any backend (Jaeger, Datadog, Honeycomb, Prometheus).
3mo ago
Observability intermediate
P50/P95/P99 Latency Percentiles
Latency percentiles (P50, P95, P99) tell you what most users experience — P99 means '99% of requests are faster than this', revealing the worst experiences that averages hide.
3mo ago
Observability beginner
Prometheus Concepts
Prometheus is a pull-based metrics system — services expose /metrics in text format, Prometheus scrapes them, stores as time series, and evaluates alerting rules via PromQL.
3mo ago
Observability intermediate
SLO / SLI / SLA
SLI (what you measure), SLO (your internal target), SLA (your customer commitment) — the hierarchy that turns vague 'uptime' promises into measurable operational objectives.
3mo ago
Observability intermediate
Spans & Traces
A trace is one request's full journey; spans are the individual operations within it — each span has a name, start time, duration, status, and optional attributes.
3mo ago
Observability beginner