Bottom line up front (BLUF)
- Telemetry is the raw signal that keeps you honest when your gut insists everything is fine.
- Observability (often abbreviated into the numeronym o11y) is how you turn that signal into answers, fast, when production feels wobbly.
- Tracing stitches the story together span by span, letting a single timestamp expose the shape of a failure.
Background
I have yet to see a seriously scaled system that survived on intuition alone. At some point, usually 2 AM during an outage, you stop guessing and start measuring. When traffic spikes or the deploy train derails, the difference between a five-minute wobble and a multi-hour outage is whether someone can point to actual numbers. Telemetry is that lifeline. Without it, we improvise; with it, we investigate.
Telemetry is any structured data you emit out of a running system so you can observe it from afar: a counter for failed checkouts, a gauge for queue depth, a log line when a retry exhausts itself. Metrics, logs, heartbeats, counters, and timers comprise telemetry. Good telemetry is intentional: it carries the minimum fields you need to ask, "What just happened?" and it shows up quickly enough to matter. If it's captured inside your service and shipped elsewhere for interpretation, it's telemetry.
Observability is the ability to explain what your system is doing without having to ship a fresh build. In practice, it is shorthand for combining telemetry streams--metrics for trends, logs for context, and traces for causality--so you can answer novel questions. Observability is not just dashboards; it is the muscle memory of instrumenting code paths before you need them, and it builds the guardrails that keep deploys fast without feeling reckless.
This will be the first in a short series about how we are implementing and using telemetry / o11y at Gearflow. Gearflow is on a mission to simplify the parts ordering process for heavy equipment fleets by providing better parts supplier access, communication, and reporting, in one easy-to-use platform.
The three pillars: metrics, logs, and traces
Observability rests on three complementary data types:
Metrics are aggregated numbers over time—request rates, error counts, latency percentiles. They are cheap to store and query, perfect for dashboards and alerts. Metrics tell you *something broke*.
Logs are timestamped event records with context—error messages, stack traces, business logic breadcrumbs. They are expensive to store but essential for explaining *why it broke*.
Traces follow a single request across services, capturing timing and causality. They connect the dots between symptoms and root causes, showing you *where it broke*.
You need all three. Metrics for detection, logs for context, traces for causality.
Tracing: the request-level storyboard
Tracing follows a single request or workflow as it hops across services. Each hop emits two timestamps: a start and a stop. The span between them is your latency budget, and the metadata hanging off that span is your breadcrumb trail. Stack enough spans together and you have a full request storyboard that shows where time slips away or where retries pile up.
OpenTelemetry.Tracer.with_span "checkout.submit", %{
attributes: [{"tenant", "blue"}]
} do
do_checkout_work()
end
The `with_span` block records the moment work began and automatically captures the finish when the block completes. Most tracers also compute the duration automatically, but the raw timestamps are the real treasure--especially when clocks wobble or services exist in hostile environments.
The power of a single timestamp
A lone timestamp seems mundane, but paired with context it becomes a spotlight. Say checkout latency spikes to 10 seconds at exactly 14:03:21.427 UTC. Cross-reference that against your deploy timeline and you discover a database migration started 30 seconds earlier. Now you know: the migration saturated the connection pool. Without that timestamp, you're grasping frantically for datapoints, hoping to find clues in the dark. A span that starts at 14:03:21.427 UTC and ends ten seconds later tells you more than "checkout is slow." Overlay that timestamp against deployment markers, CPU graphs, or database wait events and you can see the exact second a connection pool saturated or a feature flag turned toxic. Tracing gives you that richness without waiting for someone to reproduce the bug locally.
Telemetry feels expensive until you need it..Standing up telemetry does feel like a questionable expense the first time through. You thread span IDs across services, wrangle exporters, and teach engineers how to instrument the scary parts of the codebase. Once the pipeline is live and emitting quality data, though, buyer's remorse evaporates. Teams almost never regret the work, especially when production starts gasping and the dashboards light up with the clues you need to stabilize quickly.
Putting telemetry to work today
- Emit spans around every external dependency (databases, queues, third-party APIs) and tag them with the identifiers you page on.
- Add metric counters and gauges for the critical business flows the same way you add unit tests: as part of every feature.
- Ship logs that explain *why* a path was taken, not just *what* happened, so you can marry them to spans later.
- Store all of it where engineers can self-serve: a tracing backend, a metrics TSDB, and a log search tool.
Getting started:
1. Pick one painful workflow and instrument it end-to-end with spans and a couple of lightweight metrics.
2. Wire those spans into alerts that page you when duration blows past your comfort zone.
3. Make the telemetry visible--dashboards, runbooks, still photos in incident channels.
4. Bake instrumentation reviews into code review so new code never ships blind again.
Telemetry is not a vanity project; it is the circuit breaker between curiosity and chaos. Define your terms, wire up tracing so every important path emits start and stop timestamps, and let observability turn those raw measurements into narrative. The next time production jitters, you will have more than a hunch--you will have the data to prove what broke and the breadcrumbs to fix it all before anyone even notices.