Cloud Native Observability
Cloud native observability instruments containerized, distributed systems to make their internal state visible through metrics, logs, and traces. It addresses the challenge of understanding behavior across many short-lived, independently deployed services.
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Intro
Cloud Native Observability
Cloud native systems spread work across services, containers, nodes, networks, and managed platforms. A user sees one request. The system may handle that request through many components that change independently.
Observability helps you understand the internal state of that system from its outputs. Those outputs are telemetry: traces, metrics, logs, and related context. You use telemetry to answer two different questions:
- Is the service meeting user expectations?
- If it is not, where did the behavior change?
The first question keeps your work tied to reliability. The second guides investigation. A useful observability system must support both.
Observability is not a dashboard product or a collection of charts. It is a capability built from instrumentation, telemetry pipelines, storage, analysis, and operational practice. A tool can store data without making a system observable. A team can also collect huge volumes of telemetry and still lack the context needed to explain an incident.
Start with user-visible behavior
A healthy host does not prove that a service works for its users. A service can have low CPU use while returning incorrect results. It can also have busy nodes while serving every request within its reliability target.
Start with service level indicators, or SLIs. An SLI measures service behavior from a user's perspective. Common examples include the proportion of successful requests and the proportion of requests completed within a latency threshold.
A service level objective, or SLO, gives an SLI a target over a time window. The gap between perfect performance and the objective is the error budget. That budget makes reliability a measurable product decision instead of a vague desire for uptime.
Use user-centered indicators for detection. Then use detailed telemetry to explain the cause. Google SRE guidance separates these roles: SLO metrics show that users are affected, while diagnostic metrics help explain why.
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Sources
- https://opentelemetry.io/docs/concepts/observability-primer/
Supports
- Observability, telemetry, reliability, SLI, and SLO definitions
- Metric, log, span, and distributed trace concepts
- Traces as related spans following one request
- https://opentelemetry.io/docs/concepts/signals/
Supports
- Current OpenTelemetry signal categories
- Metrics as runtime measurements, logs as event records, and traces as request paths
- Baggage and profiles as distinct signal-related concepts
- https://opentelemetry.io/docs/what-is-opentelemetry/
Supports
- Observability as understanding internal state from outputs
- OpenTelemetry as a framework for telemetry generation, collection, and export
- OpenTelemetry is not a storage or visualization backend
- APIs, SDKs, protocol, semantic conventions, automatic instrumentation, and Collector roles
- https://opentelemetry.io/docs/concepts/instrumentation/
Supports
- Instrumentation as making components emit telemetry
- Code-based and zero-code instrumentation approaches
- Domain context complementing automatic framework instrumentation
- https://opentelemetry.io/docs/concepts/context-propagation/
Supports
- Context propagation across service boundaries
- Trace context connecting spans into one distributed trace
- https://opentelemetry.io/docs/concepts/sampling/
Supports
- Sampling as a way to control trace volume and observability cost
- Head and tail sampling as different trace-selection points
- Sampling policies can retain traces based on error or latency conditions
- https://opentelemetry.io/docs/specs/otel/logs/
Supports
- Trace and span identifiers used to correlate logs with traces
- Resource context used to correlate telemetry by origin
- Existing logging libraries integrated with OpenTelemetry log records
- https://opentelemetry.io/docs/concepts/signals/metrics/
Supports
- Metrics as runtime measurements with time and metadata
- Metrics used for availability, performance, alerting, and resource use
- Cardinality as unique attribute combinations and a driver of memory cost
- https://opentelemetry.io/docs/collector/components/
Supports
- Receiver, processor, exporter, connector, and extension responsibilities
- https://opentelemetry.io/docs/collector/configuration/
Supports
- Service pipelines enabling configured Collector components
- Metrics, logs, and traces pipeline structure
- Processor order affecting processing order
- https://opentelemetry.io/docs/collector/transforming-telemetry/
Supports
- Collector filtering, transformation, and resource enrichment
- Telemetry transformation for data quality, governance, cost, and security
- Advanced processing can affect Collector performance
- https://kubernetes.io/docs/concepts/cluster-administration/observability/
Supports
- Kubernetes metrics, logs, and traces across control plane, add-ons, and applications
- Prometheus-format component metrics and typical metrics pipelines
- Cluster, workload, and application visibility needs
- https://prometheus.io/docs/concepts/data_model/
Supports
- Time series identified by metric name and label set
- A changed label value creates a new time series
- Labels as dimensions for filtering and aggregation
- https://prometheus.io/docs/practices/alerting/
Supports
- Symptom-based alerting tied to user pain
- Pages should require meaningful human action
- External blackbox monitoring can detect monitoring-system failures
- Capacity conditions can justify timely intervention
- https://sre.google/workbook/monitoring/
Supports
- SLI metrics as the first evidence for SLO-based alerts
- Diagnostic metrics used to explain the cause of SLO violations
- Monitoring resource limits for capacity planning and intervention
- https://sre.google/workbook/alerting-on-slos/
Supports
- Alerting based on SLI measurements and error-budget consumption
- Trade-offs among precision, recall, detection time, and reset time
- Multi-window alerting patterns for significant error-budget consumption
- https://opentelemetry.io/docs/demo/
Supports
- A multi-service system for learning OpenTelemetry instrumentation and signals
- Collector and backend integrations used for hands-on exploration