Learn Observability with the LGTM Stack - Production Deployment Checklist & Best Practices

#Introduction

Congratulations, you've completed 34 episodes of building and understanding the LGTM Stack. This final episode isn't just adding knowledge — it summarizes everything into a production checklist you can use directly at go-live.

This episode covers pre-production checks, ongoing operational practices, meta-monitoring, the most common pitfalls that break observability stacks, and a list of modern features for a mature stack.

#Pre-Production Checklist

#Checks Before Go-Live

Capacity planning completed: the capacity calculations from episodes 22-24 have been done.
HA configuration validated: replication and zone-aware setup have been tested (episode 22).
Backup procedures tested: backup and restore are tested, not just configured.
Security hardening applied: TLS, authentication, and redaction are active (episode 33).
Monitoring of monitoring: the observability stack monitors itself.
Disaster recovery plan: recovery procedures are documented and tested.
Documentation and runbooks: every alert has a runbook (episode 19).

Pre-production checklist order

capacity -> HA -> backup -> security -> meta-monitoring -> DR

The order capacity -> HA -> backup -> security ensures the foundation is in place before advanced features.

#Operational Practices

#Ongoing Routines

Regular capacity reviews: review capacity as traffic grows.
Cost monitoring: monitor the cost per data type (episode 26).
Performance tuning: adjust parameters based on measurements.
Upgrade procedures: plan and test upgrades for each component.
Data retention review: check retention policies periodically.
Alert rule maintenance: audit alerts that are no longer relevant.

Check the health of the entire stack

docker compose ps
curl -s http://localhost:9009/ready
curl -s http://localhost:3100/ready
curl -s http://localhost:3200/ready

The docker compose ps command and health endpoint checks are the most basic meta-monitoring routine.

#Monitoring the Monitoring

#Proper Meta-Monitoring

Component health checks: the status of every stack component.
Ingestion lag monitoring: the gap between data arriving and data being available for queries.
Query performance tracking: monitor slow queries.
Storage utilization alerts: alert when storage capacity runs low.
Pipeline health: make sure collectors and agents keep sending.

Warning

The most common irony in the field: the observability stack is down, and no one knows because the observability is dead too. Meta-monitoring with alerts sent to a different channel — for example email — is the last safety net.

#Common Pitfalls

#What to Avoid

High cardinality explosion: high-value labels that flood Mimir (episode 6).
Unbounded log volume: logs without limits and without sampling (episode 23).
Missing sampling strategies: every trace is stored without consideration (episode 24).
Inadequate retention policies: data stored too long or too short.
Alert fatigue: too many alerts that get ignored (episode 19).
Lack of SLOs: no measurable reliability targets (episode 20).

The six main pitfalls

cardinality | volume | sampling | retention | alert-fatigue | slo

The pattern cardinality | volume | sampling | retention is the list to check when observability costs balloon.

#Modern Feature Checklist

#A Mature Observability Stack

OpenTelemetry adoption: standard instrumentation across all services (episode 3).
Grafana Alloy deployment: vendor-neutral collector on all nodes (episode 11).
TraceQL for trace queries: attribute-based trace search (episode 15).
Exemplars configuration: correlating metrics to traces (episode 18).
Native histograms: Mimir's histogram storage efficiency.
Adaptive metrics: Mimir's automatic cardinality management.
Grafana OnCall integration: on-call connected to alerts.
Grafana Incident: unified incident management.
Grafana SLO plugin: SLOs managed in one interface (episode 20).
Continuous profiling (Pyroscope): the fourth pillar active (episode 34).

Production stack layers

OTel + Alloy -> LGTM -> correlation -> SLO + alerting -> on-call

The pattern OTel + Alloy -> LGTM -> correlation represents the entire architecture you've built throughout this series.

#Closing

In episode 35 you have a complete go-live guide: pre-production checklist, operational practices, meta-monitoring, common pitfalls to avoid, and a modern feature checklist for a mature stack.

The key takeaways:

Test backup and HA, don't just configure them.
Monitor the observability stack itself.
Avoid the pitfalls of cardinality, volume, and alert fatigue.
SLOs without measurement tools are just wishes.
Modern features: OTel, Alloy, TraceQL, exemplars, Pyroscope.

And so ends the journey of Learning Observability with the LGTM Stack from episode 0 to episode 35. You've built a local stack, delved into the Loki, Grafana, Tempo, and Mimir architectures, mastered PromQL, LogQL, and TraceQL, and understood production operations from scaling and security to continuous profiling. Now it's time to bring this knowledge into real systems: apply the production checklist, maintain meta-monitoring, and keep growing observability with your team. The observability journey is never truly finished — it grows together with the systems you maintain. Happy production!