One day, your system crashes. You open the dashboard and scan through the logs—only to be hit by a wall of noise:
10,000 lines of “User created.”
8,000 lines of “Processing…”
And somewhere, buried deep: Error: something went wrong.
You have no idea where the problem is. No clue which request each line belongs to. Everything blurs into static.
And then you realize: Unstructured logging is just noise.
Logging isn’t about writing down everything that comes to mind. It’s the art of collecting clues—so that when something breaks, you can trace what happened, where, and why.
First: Use Log Levels Properly
DEBUG: For low-level detail during development. Lines like “Received input…” or “Loop i = 4” belong only in dev or when debug is enabled.
INFO: For meaningful actions during normal operation: user login, order placed, message processed. This is your standard, low-anxiety log level.
WARN: For things that aren’t broken, but aren’t ideal: slow API responses, retries, missing configs that fall back to defaults. Worth watching—not panicking.
ERROR: Real problems:
Exceptions, failed transactions, connection drops, malformed data.
These should light up your dashboards, ping Slack, trigger PagerDuty—and demand attention when they spike.
Second: Always Tag Logs with request_id or trace_id
In modern systems, a single request may span 5–10 services. Without a common identifier linking logs, the trail goes cold.
You’ll see an error in Service D, but not know where it started. You’ll see “Processed” in Service A, but not know if it relates to the error.
Attaching request_id is your only way to trace the full journey. Add trace_id and span_id (via OpenTelemetry), and now you can measure how long each step took.
Third: Embrace Structured Logging
Don’t just log raw strings—use structured logs (e.g., JSON):
{
"timestamp": "2025-05-06T14:23:12Z",
"level": "INFO",
"event": "user_created",
"user_id": 1234,
"request_id": "abc-xyz-123"
}
Structured logging lets you:
Search logs by user_id, event, or request_id
Filter, group, and visualize with ELK, Datadog, Sentry…
Build dashboards tracking event rates, error spikes, etc.
In a distributed system, structured logs are essential—you can’t rely on eyeballing plaintext anymore.
Finally: The Most Dangerous Log Is the One That Lies. It’s not the missing logs that hurt most. It’s thinking you have logs—when they’re wrong.
Logging errors in the wrong context
Logging without stack traces
Logging sensitive data (passwords, tokens)
Overlogging in production and choking memory or I/O
Logging just enough to mislead you in a crisis
A single careless log can stall your entire investigation—or worse, compromise your system.
Logging is not about memory. It’s about visibility. When you can’t "see" your system with your own eyes, logs become your lens.
✅ Log less, but log right.
✅ Log with context, structure, and clarity.
✅ Log like you’re telling a teammate what just happened—so they know where to look, what to fix, and why.
Further Reading
If you found this article helpful and you’re keen on optimizing algorithms and honing your algorithmic thinking, I highly recommend the book Algorithm Mindset. It doesn’t just walk you through real-world problem-solving, it also helps you cultivate an algorithmic mindset in the most intuitive way.
Top comments (1)
This is such a great point—logging should be about clarity, not just quantity. I've found that watching developer talks or system logging tutorials on YouTube helps deepen my understanding of structured logging and debugging strategies. When I want to save those videos for offline study or reference, I use Y2mate. It’s a useful tool for keeping a local library of insightful content.