Monitoring
47 items tagged with "monitoring"
Best Practices12
RED & USE Monitoring Methodologies
Standard approaches for selecting golden signals (Rate-Errors-Duration / Utilisation-Saturation-Errors).
Service Level Objectives (SLOs)
A target reliability level for a service, expressed as a measurable percentage of good events over a window, used to balance reliability against feature velocity.
The Four Golden Signals
Google SRE's four core metrics for monitoring a user-facing system: latency, traffic, errors, and saturation.
OpenTelemetry Semantic Conventions
Standardized names and attributes for telemetry (spans, metrics, logs) so observability data is consistent and portable across tools and languages.
Structured Logging
Emitting logs as machine-parseable key-value records (typically JSON) with consistent fields, so logs can be searched, filtered, and correlated at scale.
Distributed Tracing Best Practices
Techniques for instrumenting and propagating trace context across services so requests can be followed end-to-end, with sampling and span design that aid debugging.
Prometheus Monitoring Best Practices
Guidance for naming metrics, controlling label cardinality, and writing alerting rules in Prometheus, the CNCF metrics and alerting system.
Symptom-Based Alerting
Alerting on user-visible symptoms (errors, latency, SLO burn) rather than internal causes, to reduce noise and page only on things that matter.
On-Call Best Practices
Sustainable on-call practices covering rotation design, escalation, actionable alerts, runbooks, and workload limits to keep services reliable without burning out engineers.
Observability-Driven Development
Building instrumentation into software as a first-class part of development so engineers can ask new questions of production behavior without shipping new code.
ML Model Monitoring and Drift Detection
Continuously tracking deployed ML models for performance decay, data drift, and concept drift so degradation is caught and corrected before it harms outcomes.
LLM Observability
LLM observability is the practice of tracing, logging, and measuring LLM applications in production to monitor quality, cost, latency, and safety and to debug failures.
Patterns2
Watchdog
An independent supervisor that monitors a system or process and takes corrective action — restart, alert, or failover — when it stops responding.
Distributed Tracing
Tracks a single request as it flows across many services by propagating context, producing an end-to-end timeline that reveals latency and failure sources.
Anti-Patterns4
Cloud Bill Shock
An unexpectedly huge cloud invoice arriving because spend was never tracked, attributed, or governed until the bill landed.
No Monitoring (Flying Blind)
Running production systems with no metrics, logs, or alerts, so problems are invisible until users complain and incidents cannot be diagnosed.
Alert Fatigue
So many low-value or noisy alerts fire that responders become desensitized and start ignoring them, including the ones that actually matter.
Log Everything (Logging Noise)
Logging indiscriminately at high verbosity, burying useful signal in a flood of low-value messages while driving up storage cost and slowing search.
Products5
Reference Architectures1
Stacks4
Elastic Observability Stack
Observability built on the Elastic Stack: Beats and APM agents feed Elasticsearch, with Kibana unifying logs, metrics, and traces in one platform.
Datadog Full Platform
A unified commercial SaaS observability stack covering metrics, traces, logs, RUM, security, and infrastructure monitoring through Datadog agents and integrations.
New Relic Platform
A consumption-based SaaS observability platform unifying APM, infrastructure, logs, and browser monitoring on a single telemetry database with OpenTelemetry support.
Sentry + OpenTelemetry
An application monitoring stack pairing Sentry's error tracking and performance monitoring with OpenTelemetry instrumentation for vendor-neutral traces and metrics.
Comparisons5
Prometheus vs Datadog
Prometheus is an open-source, self-hosted metrics and alerting system; Datadog is a hosted, all-in-one observability SaaS spanning metrics, logs, and traces.
Grafana vs Kibana
Grafana is a source-agnostic visualization and dashboarding tool; Kibana is the visualization layer of the Elastic Stack, optimized for log and search analytics.
OpenTelemetry vs Vendor Agents
OpenTelemetry is an open, vendor-neutral standard for collecting telemetry; proprietary vendor agents are tightly integrated SDKs from a single observability provider.
ELK Stack vs Grafana Loki
The ELK Stack indexes full log content for powerful search; Grafana Loki indexes only labels for cheaper, lighter log aggregation.
Datadog vs New Relic
Two leading observability SaaS platforms covering APM, infrastructure, logs, and more. They differ in breadth, pricing model, and product focus.
FAQs3
What is an SLO, SLI, and error budget?
An SLI (Service Level Indicator) is a measured metric of service health, such as request success rate or latency. An SLO (Service Level Objective) is ...
What is the difference between observability and monitoring?
Monitoring is collecting and alerting on predefined metrics and checks—it answers known questions like 'is CPU above 90%?' Observability is the broade...
What are the three pillars of observability?
The three pillars of observability are metrics, logs, and traces. Metrics are numeric time-series data, such as request rate or error count, that are ...
Glossaries8
Telemetry
The automated collection and transmission of measurement data from a running system to a remote location for monitoring and analysis.
Metric
A numeric measurement of some aspect of a system captured over time, such as request rate, error count, or memory usage, used for monitoring and alerting.
Structured Logging
The practice of emitting log entries as machine-readable structured data, typically key-value pairs or JSON, rather than free-form text strings.
Service Level Objective (SLO)
A target value or range for a service level indicator over a period of time, expressing the desired level of reliability for a service.
Service Level Indicator (SLI)
A quantitative measure of a specific aspect of a service's level of service, such as the proportion of successful requests or requests served within a latency threshold.
Incident Management
The coordinated process for detecting, responding to, mitigating, and resolving unplanned disruptions to a service, then learning from them.
On-Call
An arrangement in which designated engineers are available to respond to alerts and incidents outside normal working hours, usually on a rotating schedule.
Mean Time to Recovery (MTTR)
The average time taken to restore a service after a failure, measured from the start of an incident to its resolution.