22 posts · page 1 of 1
Set up centralized log aggregation with Elasticsearch, Logstash, and Kibana. Learn to collect, parse, and visualize logs from multiple services in one dashboard.
Understand the three pillars of observability. Learn how logs, metrics, and distributed traces work together to give you full visibility into production systems.
Set up centralized Kubernetes logging with Fluent Bit, Elasticsearch, and Kibana. Covers DaemonSets, parsers, filters, and production tuning.
Replace unstructured log lines with structured JSON logging. Covers log levels, correlation IDs, context propagation, and integration with observability tools.
Build automated rollback systems that use health checks, error rate thresholds, and observability signals to detect failures and revert deployments without human intervention.
A practical guide to designing Grafana dashboards that surface the right metrics, from panel types to dashboard-as-code workflows.
A practical guide to instrumenting applications with OpenTelemetry for traces, metrics, and logs across distributed systems.
Learn the four golden signals of monitoring from Google SRE and how to implement them with Prometheus for reliable production systems.
Configure Docker logging drivers to route container logs to files, syslog, or centralized systems. Includes monitoring with Prometheus and cAdvisor.
Use the diagnostics_channel API to add zero-overhead observability to your Node.js applications without modifying library code.
Move beyond print-style logging. Learn structured logging with structlog, correlation IDs, context binding, and integrating with observability tools.
Learn Go's slog package for structured, leveled logging with JSON output, custom handlers, and context-aware log enrichment.
Build a meaningful CloudWatch setup with custom metrics, composite alarms, and dashboards that catch real incidents without paging on noise.
A practical tour of monitoring services with Prometheus for metrics collection and Grafana for dashboards, alerts, and SLO tracking.
A tour of the modern observability stack: metrics, logs, traces, and events. Learn how the pillars fit together and how to choose tooling without drowning in dashboards.
Service Level Indicators, Objectives, and error budgets demystified: how to pick the right metric, set a target, and use the budget as a decision tool.
Profile Go programs with pprof: enable the HTTP endpoint, capture CPU and heap profiles, read flame graphs, and find the hot spot that is actually costing you latency.
Master systemd's journal: query logs with structured filters, tail in real time, persist across reboots, and forward to a central collector.
A practical guide to attributing, monitoring, and controlling LLM spend per user, per feature, and per request without slowing down delivery.
Set up structured, high-performance logging in Node.js with Pino, including child loggers, redaction, and pretty-printing for development.
Use LangSmith to trace, debug, and evaluate RAG pipelines step by step, from instrumentation to dataset replay and regression detection.
How to set up Python logging properly: loggers vs handlers, structured logs, contextual fields, log levels that scale, and how to avoid the classic print-debug trap.