Learn Observability with the LGTM Stack - History, Background & Why You Need Observability

#Introduction

Before building the stack, we need to understand why observability became a buzzword in the cloud-native era. The term is not just a cooler replacement for "monitoring" — it brings a fundamental change in how we think about complex systems.

This episode traces the evolution from traditional monitoring toward observability, the concrete problems it solves, introduces the LGTM Stack, then compares it with popular alternatives like the ELK Stack and commercial solutions. By the end of the episode, you'll know when and why to choose LGTM.

#The Evolution of Observability

#From Traditional Monitoring to Observability

Traditional monitoring works on the premise "we know what to measure": CPU, memory, disk, and a few well-known application metrics. This approach was born in the era of monoliths and static servers, when failure prediction was relatively easy because systems were deterministic.

The cloud-native era changed everything. Microservices exploded the number of components, containers come and go dynamically, and failures can come from many combinations. The concept of monitoring vs observability then emerged: monitoring asks "is this system healthy?", while observability asks "why does this system behave this way?" — even for questions you've never thought to ask before.

That difference is not just wordplay. Monitoring depends on a list of known metrics, so you can only find problems you already expected. Observability focuses on the ability of the system to be explored, so even new, unanticipated problems can be investigated from the data already collected.

#Three Pillars and Cloud-Native Challenges

Modern observability practice is grounded in the three pillars of observability: metrics, logs, and traces. You'll explore all three in depth in episode 2. What matters now is understanding that these pillars complement each other — metrics tell you what is happening, logs tell you exactly what happened, and traces tell you where the problem is.

Observability tools themselves evolved rapidly: from SNMP and Nagios, to Prometheus and Grafana, and then the rise of scalable backends like Loki, Tempo, and Mimir. The biggest challenges of this era are the growing volume of data and the need for correlation between telemetry signals.

#Problems It Solves

The complexity of distributed systems makes manual debugging impossible. When a request passes through twenty services, where do you look for the source of the latency? This is where observability answers.

• Distributed systems complexity: many components, many failure points, hard to reason about without data.
• Microservices debugging: without traces, you're only guessing which service is failing.
• Black box vs white box monitoring: white box looks inside the system through internal metrics, rather than just guessing from the outside.
• MTTD and MTTR: observability shortens the time to detection (Mean Time To Detection) and time to resolution (Mean Time To Resolution).
• Unknown unknowns: failures you never thought of before — observability lets you investigate them.
• Correlation across telemetry signals: connecting metrics, logs, and traces into a single investigation flow.

The terms MTTD and MTTR will come up again in episode 21 alongside incident response. For now, understand that both are the business measures most often used to sell the importance of observability.

The core of all the problems above is the same: the larger and more distributed a system is, the more expensive ignorance becomes. Observability is an investment to eliminate that cost early, not after a major incident happens.

#LGTM Stack Overview

#Components and Why Choose It

The LGTM Stack consists of four open-source products developed by Grafana Labs:

• Loki: cost-effective log aggregation that uses Prometheus-style labels and requires no full indexing.
• Grafana: the visualization, dashboard, and query platform that unifies all data sources.
• Tempo: a distributed tracing backend based on object storage with native OTLP support.
• Mimir: a long-term metric storage that is Prometheus-compatible and horizontally scalable.

The main advantage of LGTM is seamless integration: all three are natively plugged into Grafana, share a data model, and support unified querying. There is no vendor lock-in because everything is open-source and communicates via standard protocols like OTLP and remote write.

grafana:  http://localhost:3000
loki:     http://localhost:3100  -> logs from Alloy
tempo:    http://localhost:3200  -> traces from OTel SDK
mimir:    http://localhost:9009  -> metrics from remote write

The address scheme above will be made real in episode 4. For now, just remember the four characteristic ports of each component.

#Comparison with Alternatives

• LGTM vs ELK Stack (Elasticsearch, Logstash, Kibana): ELK excels at full-text search, but is expensive because it indexes every log line. LGTM trades full search capability for much lower storage costs.
• LGTM vs PLG Stack (Prometheus, Loki, Grafana): PLG uses Prometheus for short-term metrics. Mimir replaces Prometheus for long retention and large-scale needs.
• LGTM vs commercial solutions (Datadog, New Relic, Dynatrace): commercial solutions save setup time and have support, but are expensive per-host and create lock-in.

For teams already familiar with Grafana, with low operational costs and high scaling needs, LGTM is a very attractive choice. Commercial solutions remain worth considering if you're a small team that wants to go live as quickly as possible.

The final decision is usually driven by three factors: team maturity, budget, and compliance requirements. For teams that have already adopted OpenTelemetry, LGTM becomes the most natural choice because all its components natively accept data via standard protocols. The same is not always easy to do with commercial solutions that close off their ecosystems.

#Common Use Cases

The LGTM Stack handles a wide range of production scenarios:

• Application performance monitoring (APM): tracking latency, error rate, and throughput per service.
• Infrastructure monitoring: monitoring CPU, memory, disk, and network across all hosts.
• Distributed tracing: mapping the journey of requests across microservices.
• Log aggregation and analysis: bringing logs from thousands of containers into one place.
• Incident response and debugging: accelerating root cause analysis.
• SLO and SLA monitoring: continuously monitoring service reliability targets.
• Capacity planning: using historical data to plan resource needs.

curl -s http://localhost:3000/api/health | jq .

The curl command above will be used over and over to check component health. Practice it now to get used to validating APIs from the terminal.

#Closing

In episode 1 you understood that observability is the evolution of traditional monitoring to answer cloud-native complexity, got to know the problems it solves, understood the role of each LGTM Stack component, and compared it with ELK, PLG, and commercial solutions.

The key takeaways:

• Observability answers "why", not just "is it healthy".
• The three pillars of metrics, logs, and traces complement each other.
• The LGTM Stack consists of Loki, Grafana, Tempo, and Mimir.
• LGTM offers lower costs than ELK and commercial solutions.
• MTTD and MTTR are the main business measures observability reduces.
• Use cases include APM, infrastructure, tracing, logs, incidents, and SLOs.

In the next episode 2 we'll dissect the three pillars of observability — the concepts and philosophy behind metrics, logs, and traces, including the USE and RED methods, up to how to correlate all three. Get your notes ready, because this episode becomes the foundation of all the technical discussions that follow.