Monitoring and observability

Here’s a complete overview of Monitoring and Observability in a DevOps / SRE context — including strategies, tools, and best practices 👇

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⚙️ 1. Core Concepts

Term	Meaning	Goal
Monitoring	Collecting, visualizing, and alerting on system metrics (e.g., CPU, latency, errors).	Detect issues early and maintain uptime.
Observability	Understanding why something is happening by correlating metrics, logs, and traces.	Identify root causes quickly and improve system behavior.

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📊 2. Three Pillars of Observability

Pillar	Description	Common Tools
Metrics	Numeric measurements of system performance (e.g., CPU %, request rate, memory).	Prometheus, CloudWatch, Datadog
Logs	Text-based event records that describe what happened.	Loki, ELK Stack (Elasticsearch, Logstash, Kibana), CloudWatch Logs
Traces	Track a request as it flows through distributed systems.	Jaeger, OpenTelemetry, AWS X-Ray

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🧠 3. Monitoring Strategy

Objective	Strategy
Define SLIs, SLOs, and SLAs	Define Service Level Indicators (e.g., latency < 200ms), Service Level Objectives (target 99%), and SLAs (agreements with users).
Set Up Real-Time Alerts	Use alerting rules (Prometheus Alertmanager, PagerDuty, Opsgenie) for high latency, error rates, or downtime.
Create Dashboards	Use Grafana or Datadog to visualize trends and track system health.
Automate Incident Response	Integrate alerting tools with Slack or Teams for instant collaboration.
Perform Regular Reviews	Conduct postmortems after incidents to learn and improve reliability.

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🔍 4. Observability Best Practices

Area	Best Practice
Instrumentation	Use OpenTelemetry SDKs for consistent tracing and metrics collection.
Context-Rich Logs	Include correlation IDs and metadata to trace logs through systems.
Anomaly Detection	Implement AI/ML-based alerting to reduce noise.
Dashboards by Ownership	Create team-based dashboards (e.g., frontend latency, DB health).
Error Budgets	Balance reliability and innovation using SLO error budgets.

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🧰 5. Common Tool Stack

Category	Tools
Metrics & Monitoring	Prometheus, CloudWatch, Datadog, New Relic
Visualization	Grafana
Logging	Loki, ELK Stack, Fluentd, CloudWatch Logs
Tracing	Jaeger, Tempo, OpenTelemetry, AWS X-Ray
Alerting & Incident Response	Alertmanager, PagerDuty, Opsgenie, VictorOps
Synthetic Monitoring	Pingdom, Uptrends, Grafana Synthetic Monitoring

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🔄 6. Example Observability Flow

1. App emits metrics/logs/traces using OpenTelemetry or exporters.

2. Prometheus scrapes metrics → stores time series data.

3. Loki collects logs.

4. Jaeger/Tempo collects traces.

5. Grafana visualizes all three.

6. Alertmanager triggers alerts → sends to Slack / PagerDuty.

7. Incident review → update dashboards / alert thresholds.

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📈 7. Example Metrics to Track

Layer	Metrics
Infrastructure	CPU, memory, disk, network I/O
Application	Request rate (RPS), latency (P95/P99), error rate
Database	Query latency, connection pool, cache hit ratio
Kubernetes	Pod restarts, node usage, pending pods
Business	Orders per minute, checkout success rate

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🧩 8. Example Tools Integration

flowchart LR
App -->|Metrics| Prometheus
App -->|Logs| Loki
App -->|Traces| Tempo
Prometheus --> Grafana
Loki --> Grafana
Tempo --> Grafana
Grafana -->|Alerts| Alertmanager --> Slack

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Would you like me to create a Grafana + Prometheus + Loki + Tempo production-ready observability setup guide (with architecture, Helm install commands, and alerting integration)? That would make this even more practical for your EKS setup.