What Is Observability? Logs, Metrics, Traces Explained

Bottom line up front: Observability is the ability to understand what’s happening inside your software by examining the data it produces. It rests on three pillars: logs (timestamped records of events), metrics (numbers measured over time), and traces (the end-to-end journey of a single request). Together they let you debug failures you never predicted — not just the ones you planned for. OpenTelemetry (OTel) is the industry-standard, vendor-neutral framework for collecting all three. In this guide, we break down observability logs metrics traces in plain English — what it is, how it works, and how to use it.

Observability Logs Metrics Traces: Why Do Developers Need Observability?

Modern apps rarely live on one server anymore. A single “checkout” click can travel through an API gateway, an authentication service, an order service, a database, a cache, and a message queue — often across different containers and regions. When something goes wrong, you can’t just open the hood and look.

Traditional monitoring only watches for problems you anticipated: CPU over 90%? Alert. Error rate over 1%? Alert. That’s useful, but it tells you something is wrong, not why. One of observability’s main goals is to minimize the amount of prior knowledge needed to debug an issue, so you can investigate failures nobody predicted — without shipping new code first.

What Are the Three Pillars of Observability?

Observability stands on three types of telemetry data. Each answers a different question:

1. Logs — What happened?

Logs are timestamped text records of discrete events: errors, exceptions, user logins, payment failures. They’re the diary of your system — how engineers replay history and find the “why” behind an incident.

2025-01-21T09:25:00Z ERROR OrderService Payment timeout after 3s

2. Metrics — How healthy is the system?

Metrics are numerical measurements aggregated over time: requests per second, error rate, CPU usage, p99 latency. They’re cheap to collect, easy to aggregate, and ideal for dashboards and alerting. Their limitation: they tell you something broke, not why.

3. Traces — Where did the request go?

A trace follows one request’s journey across every service it touches. Each step in that journey is called a span. If a request that normally takes 200ms suddenly takes 2 seconds, traces show you exactly which hop ate the time.

Trace: "Place Order" (250ms total)
  |-- Span: HTTP POST /orders (250ms)
      |-- Span: OrderService.Create (180ms)
          |-- Span: SQL INSERT INTO Orders (40ms)
          |-- Span: HTTP call to InventoryService (120ms)

How Do Logs, Metrics, and Traces Compare?

Individually they’re useful; together they provide the full picture. OpenTelemetry unifies them so a single trace ID can connect a log line to the exact request that produced it.

What’s the Difference Between Monitoring and Observability?

Think of it this way: monitoring tells you something is wrong; observability tells you why — and where. Monitoring watches known failure modes with dashboards and alerts. Observability lets you ask new questions about your running system — exploring properties and patterns you didn’t define in advance. In microservices, high latency can have dozens of possible causes; without traces and correlated logs, teams are left guessing, restarting pods, and scaling blindly.

What Is OpenTelemetry?

OpenTelemetry (often shortened to OTel) is a vendor-neutral, open-source framework for instrumenting, generating, collecting, and exporting telemetry data. It was formed from the merger of two earlier projects, OpenTracing and OpenCensus, and is now an industry standard supported by more than 90 observability vendors.

Its moving parts are simple: APIs (language-specific interfaces for instrumenting code), SDKs (implementations for many programming languages), the Collector (a vendor-agnostic pipeline that receives, processes, and exports telemetry), and exporters that ship data to backends like Jaeger, Prometheus, Grafana, or commercial vendors. Because OTel is vendor-neutral, you can switch backends with a config change instead of re-instrumenting your code — no vendor lock-in.

One important clarification: OpenTelemetry is not a dashboard, not a storage backend, and not an alerting platform. It’s the layer that sits between your code and whichever backend you send data to. To learn why open standards like this matter, see our What Is Open Source Software? A Beginner’s Guide guide.

How Do You Actually Collect These Signals?

In practice, getting started looks like this:

1. Instrument your code with an OpenTelemetry SDK (many languages, including Python 3.15: What’s New and Why It Matters Python, are supported) or use zero-code auto-instrumentation agents.
2. Run the OTel Collector to receive, process, and forward telemetry in a vendor-agnostic way.
3. Pick a backend for storage, dashboards, and alerts — open-source options like Prometheus, Grafana, Jaeger, Loki, and Tempo, or platforms like OpenObserve.
4. Correlate signals using shared context (like trace IDs) so a metric spike leads you to the right traces, and traces lead you to the right logs.

Why Is OpenObserve Trending?

OpenObserve is an open-source observability platform that has been gaining attention for its cost claims. According to OpenObserve, its architecture stores data in Apache Parquet columnar format on any S3-compatible object storage, achieving roughly 40x compression and up to 140x lower storage costs than index-heavy backends like Elasticsearch — which the company says translates to 60–98% lower total observability costs versus tools like Datadog or Splunk. (These are OpenObserve’s own figures; independent verification is scarce, so treat them as vendor claims.)

Its pitch: one unified, self-hostable platform covering logs, metrics, traces, dashboards, alerts, and RUM — built in Rust on Apache DataFusion, deployable as a single binary, and queryable with SQL and PromQL — replacing the multi-tool stacks teams normally stitch together. If you self-host, note you’ll want a solid Linux for Beginners: COSMIC Desktop Guide 2026 Linux foundation to run it on.

Frequently Asked Questions

What is observability in simple terms?

Observability is the ability to figure out what’s happening inside your software just by looking at the data it produces — its logs, metrics, and traces. If you can diagnose any problem without opening the code or guessing, your system is observable.

What are the three pillars of observability?

The three pillars are logs (timestamped records of what happened), metrics (numbers measured over time, like CPU or error rates), and traces (the end-to-end journey of a single request through your system, made of spans).

What’s the difference between monitoring and observability?

Monitoring watches for known problems using dashboards and alerts you configured in advance. Observability goes further: it lets you investigate unexpected failures by exploring live data, so you can answer questions you never thought to ask ahead of time.

What is OpenTelemetry?

OpenTelemetry (OTel) is a vendor-neutral, open-source framework for collecting logs, metrics, and traces from your applications. It provides APIs, SDKs, and a Collector to generate and export telemetry to any backend you choose — so you’re never locked into one vendor.

Why is OpenObserve cheaper than Datadog, according to OpenObserve?

OpenObserve claims up to 140x lower storage costs than index-heavy backends like Elasticsearch, because it stores telemetry in compressed Apache Parquet files on cheap S3-compatible object storage instead of maintaining expensive search indexes. These are the vendor’s own figures.

Do I really need all three signals — aren’t metrics and logs enough?

For a simple monolith, metrics plus logs can be enough. But in distributed systems with many services, metrics tell you something is slow and logs are hard to connect across services — only traces show you exactly which service in the chain caused the problem. That’s why the three are called pillars: each covers the others’ blind spots.