# Observability
All Observability notes →What is Loki
Grafana Labs' log aggregation system — 'like Prometheus, but for logs': index only labels, store compressed chunks in object storage, query with LogQL. Shares its distributor/ingester/compactor architecture with Mimir and Tempo.
What is Mimir
Grafana Labs' horizontally-scalable, multi-tenant long-term storage for Prometheus metrics — the 2022 successor to Cortex, and the actual system serving every PromQL query and remote-write in a Grafana Cloud metrics stack.
What is Tempo
Grafana Labs' distributed tracing backend — the radical simplification vs. Jaeger's classic architecture: no dedicated index, just object storage and a trace-ID lookup, queried with TraceQL and linked from metrics via exemplars.
# Grafana Cloud
All Grafana Cloud notes →Chapter 1 — Introduction to Grafana Cloud
Grafana Cloud's ecosystem, how OSS/Enterprise/Cloud editions differ, the core platform components, and how regions, availability, and pricing plans shape architecture decisions.
Chapter 2 — Organizations & Stack Management
Organizations, stacks, users, teams, RBAC, access policies, service accounts, and authentication as the building blocks of a Grafana Cloud tenancy.
Chapter 3 — Grafana User Interface
Home, Explore, Dashboards, Drilldowns, Connections, Plugins, and Administration as the day-to-day navigation surface, plus navigation best practices.
Chapter 1 — Grafana Alloy
Alloy's architecture, River configuration language, installation, pipelines, receivers, processors, exporters, and integrations as the unified telemetry collector.
Chapter 2 — OpenTelemetry Integration
How metrics, logs, traces, and profiles flow into Grafana Cloud via OpenTelemetry, semantic conventions, and auto vs. manual instrumentation.
Chapter 3 — Integrations
Out-of-the-box Grafana Cloud integrations for Kubernetes, Azure, AWS, GCP, Linux, Windows, databases, message brokers, and third-party systems.
Chapter 1 — Grafana Mimir
Mimir's architecture, remote-write ingestion, storage, high availability, replication, and retention as Grafana Cloud's horizontally-scalable metrics backend.
Chapter 2 — PromQL
Instant and range queries, functions, aggregations, histograms, recording rules, and query optimization for querying Mimir-backed metrics.
Chapter 3 — Metrics Management
Label strategy, cardinality management, Adaptive Telemetry, and cost/performance best practices for keeping a metrics pipeline sustainable at scale.
Chapter 1 — Grafana Loki
Loki's architecture, label-based indexing, chunks, storage, and retention as Grafana Cloud's cost-efficient log aggregation backend.
Chapter 2 — LogQL
LogQL's query language, parsing and pipeline stages, deriving metrics from logs, and log correlation and performance optimization.
Chapter 3 — Log Management
Log collection, processing, filtering, retention policies, and cost optimization across a Grafana Cloud logging pipeline.
Chapter 1 — Grafana Tempo
Tempo's distributed tracing architecture, trace collection and storage, TraceQL, and sampling strategy.
Chapter 2 — Grafana Pyroscope
Continuous profiling with Pyroscope — CPU and memory profiling, flame graphs, and using profiles for performance analysis.
Chapter 3 — Correlations
Navigating between metrics, logs, traces, and profiles as one investigation flow, and using cross-signal correlation for root cause analysis.
Chapter 1 — Dashboards
Dashboard design, variables, panels, transformations, library panels, and dashboard provisioning in Grafana.
Chapter 2 — Explore & Drilldowns
Using Explore and Drilldowns for ad hoc metrics, log, and trace investigation, correlation, and saved queries.
Chapter 3 — Alerting
Grafana's unified alerting — alert rules, contact points, notification policies, silences, templates, and the alert lifecycle.
Chapter 4 — Reporting & Sharing
Snapshots, public dashboards, scheduled reporting, and PDF export for sharing Grafana Cloud dashboards.
Chapter 1 — Application Observability
Automatic service discovery, RED metrics, application performance, and error/latency analysis in Grafana Cloud's Application Observability solution.
Chapter 2 — Entity Catalog
Entity discovery, metadata, ownership, and labeling as the inventory layer underneath Grafana Cloud's observability graph.
Chapter 3 — Entity Graph
Infrastructure and service relationships, dependency mapping, and topology visualization across the entity graph.
Chapter 4 — Service Graph
Service dependencies, request flows, critical paths, and bottleneck analysis derived from trace data.
Chapter 1 — Kubernetes Monitoring
Cluster, node, workload, container, networking, and storage monitoring via the Kubernetes integration and grafana-k8s-monitoring.
Chapter 2 — Frontend Observability
Grafana Faro, real user monitoring, Web Vitals, session analysis, and JavaScript error tracking for browser-side observability.
Chapter 3 — Synthetic Monitoring
HTTP, DNS, and ping checks, browser tests, and private probes for proactively monitoring endpoint availability.
Chapter 4 — k6 Performance Testing
Load, stress, spike, and browser testing with k6, plus cloud execution and result analysis.
Chapter 1 — Grafana SLO
Defining SLIs and SLOs, tracking error budgets, and configuring burn-rate alerts and reliability reporting in Grafana Cloud's SLO app.
Chapter 2 — Grafana Incident
Incident lifecycle, timeline, collaboration, runbooks, and postmortems in Grafana Incident.
Chapter 3 — Grafana OnCall
Escalation policies, on-call schedules, alert routing, and integrations in Grafana OnCall.
Chapter 4 — Incident Response & Management (IRM)
Incident coordination, response automation, analytics, and operational workflows in Grafana IRM.
Chapter 1 — GCX CLI
Installation, authentication, context management, and resource/dashboard/alerting/SLO/synthetic-monitoring workflows via the gcx CLI, including AI agent integration, GitOps, CI/CD, and migration from grafanactl.
Chapter 2 — Grafana Cloud APIs
Authenticating against and automating Grafana Cloud's REST APIs — access policies, service accounts, pagination, and rate limits.
Chapter 3 — Terraform Provider
Provisioning dashboards, data sources, alerting, teams, RBAC, SLOs, and synthetic checks through the Grafana Terraform provider.
Chapter 4 — Observability as Code
GitOps for dashboards and alerting, provisioning, promotion pipelines, and drift detection across Grafana Cloud environments.
Chapter 1 — Security
RBAC, authentication, SSO, access policies, service accounts, and secrets management across a Grafana Cloud organization.
Chapter 2 — Billing & Cost Management
Usage metrics, billing, quotas, retention, and cost optimization levers in Grafana Cloud.
Chapter 3 — Adaptive Telemetry
Cardinality reduction, drop rules, sampling, and data-governance controls for keeping telemetry cost under control.
Chapter 4 — Fleet Management
Managing an Alloy agent fleet — configuration distribution, remote configuration, policy management, and upgrades.
Chapter 5 — Grafana Assistant
AI-assisted investigations, dashboard and query generation, alert analysis, and root-cause assistance via Grafana Assistant.
Chapter 6 — Platform Governance
Naming standards, folder strategy, multi-tenancy, and operational standards for running Grafana Cloud as a shared platform.
Chapter 1 — Azure Reference Architecture
Reference architecture for Grafana Cloud alongside Azure Monitor, AKS, Container Apps, Functions, SQL, and Cosmos DB.
Chapter 2 — AWS Reference Architecture
Reference architecture for Grafana Cloud alongside EKS, ECS, Lambda, EC2, and CloudWatch.
Chapter 3 — Kubernetes Platform Architecture
Multi-cluster GitOps, the Prometheus Operator, and Alloy deployment patterns at platform scale.
Chapter 4 — Hybrid & Multi-Cloud
Hybrid and multi-region architecture, and disaster recovery/high-availability design for Grafana Cloud deployments spanning multiple clouds.
Chapter 5 — Production Best Practices
Scalability, performance, security, reliability, and operational excellence checklists for running Grafana Cloud in production.
Chapter 6 — Troubleshooting Playbook
A playbook for missing metrics/logs/traces, broken dashboards, alert issues, query performance, and data collection problems.
Chapter 1 — CLI & Utilities Reference
A quick reference across gcx, the Grafana HTTP API, the Terraform provider, the Foundation SDK, Alloy, Mimirtool, LogCLI, Tempo CLI, k6 CLI, Faro SDK, and the OpenTelemetry Collector/Operator.
Chapter 2 — Query Language Cheat Sheets
Side-by-side cheat sheets for PromQL, LogQL, TraceQL, the River language, and common regex patterns.
Chapter 3 — Grafana Cloud APIs Reference
A terse reference for authentication, the resource model, API endpoints, pagination, error codes, and rate limits.
Chapter 4 — Observability Patterns
Dashboard design patterns, alert design patterns, labeling strategy, entity modeling, and multi-tenancy patterns as a pattern-library appendix.
Chapter 5 — Reference Architectures
A rollup of deployment-size reference architectures — small team, enterprise, multi-region, SaaS, Kubernetes, and hybrid cloud.
Chapter 6 — Certification & Interview Preparation
Best-practice checklists, troubleshooting scenarios, incident walkthroughs, architecture review checklists, interview Q&A, hands-on labs, and capstone projects for Grafana Cloud certification and interview prep.
Grafana Cloud
A book-shaped table of contents for Grafana Cloud: platform foundations through telemetry collection, Mimir/Loki/Tempo/Pyroscope, visualization, application observability, reliability tooling, developer experience, governance, and enterprise reference architectures — cross-linking existing notes instead of duplicating them.