What is otel?

Otel is short for OpenTelemetry, an open‑source project that provides a standard way to collect data about what your software is doing. It lets you capture traces, metrics, and logs from applications so you can see how they perform and where problems occur.

Let's break it down

  • Telemetry: information like timing, errors, and resource usage that a program can report while it runs.
  • OpenTelemetry: a set of APIs, libraries (called SDKs), and agents that you add to your code to automatically gather this telemetry.
  • Traces: step‑by‑step records of a request moving through different services.
  • Metrics: numeric data such as CPU usage, request count, or latency.
  • Logs: textual messages that describe events or errors. All of these are sent to a backend (e.g., Jaeger, Prometheus, Grafana Cloud) where you can visualize and analyze them.

Why does it matter?

Without visibility, you’re guessing why an app is slow or failing. Otel gives you concrete data to:

  • Find bottlenecks and errors quickly.
  • Understand user experience across microservices.
  • Make informed decisions about scaling or fixing code.
  • Reduce downtime and improve reliability, which saves time and money.

Where is it used?

  • Cloud‑native applications running in containers or Kubernetes.
  • Microservice architectures where many services talk to each other.
  • Traditional monolithic apps that need performance monitoring.
  • Serverless functions, mobile apps, and even IoT devices can use otel libraries.
  • Companies of all sizes use it to feed data into observability platforms like Grafana, Datadog, New Relic, or custom backends.

Good things about it

  • Vendor‑neutral: works with many monitoring tools, so you’re not locked into one provider.
  • Unified: one framework for traces, metrics, and logs.
  • Open source: free to use, community‑driven, and constantly improving.
  • Language support: libraries for Go, Java, Python, JavaScript, .NET, Ruby, and more.
  • Automatic instrumentation: many popular frameworks (e.g., Spring, Express) can be instrumented with minimal code changes.

Not-so-good things

  • Learning curve: understanding concepts like spans, context propagation, and exporters can be confusing at first.
  • Configuration overhead: setting up collectors, exporters, and backends may require extra effort.
  • Performance impact: adding instrumentation can add slight latency or CPU/memory usage if not tuned.
  • Evolving spec: the project is still maturing, so APIs can change between versions, requiring updates.