Modern Rails applications don’t fail slowly.
They spike, stall, cascade, and recover — often within minutes.
That’s why DeadBro is built as a pragmatic APM for real-world Rails apps: not just to collect metrics, but to help you understand incidents while they’re happening — and explain them after the fact.
DeadBro gives you visibility into:
- Web request performance
- Background job execution
- Error rates and exceptions
- Memory and resource pressure
All without drowning you in dashboards you never look at.
And one of its most powerful tools for incident analysis is Time Bro.
What Is Time Bro?
Time Bro lets you zoom into a single minute of your application’s life and inspect exactly what happened during that window.
Not averages.
Not smoothed graphs.
Not “roughly around that time”.
A precise, minute-level snapshot of:
- Web request performance
- Worker execution behavior
- Error occurrences
- Load and traffic anomalies
If something went wrong at 14:32, Time Bro shows you 14:32 — in detail.
Why Minute-Level Visibility Matters
Most monitoring tools answer questions like:
- “Is performance worse today than yesterday?”
- “Is error rate trending up?”
Those are useful — until you’re dealing with:
- Sudden traffic spikes
- A single deploy that broke something
- A bad job rollout
- An external dependency failing briefly
- A memory or CPU spike that resolved itself
These problems often happen fast, and by the time you look at hourly or five-minute averages, the signal is already diluted.
Time Bro exists because incidents happen in minutes, not dashboards.
Analyze Traffic Spikes Without Guesswork
When your app suddenly receives 2× or 10× its normal traffic, the question is never just “did traffic increase?”.
You want to know:
- Which endpoints slowed down?
- Did response times degrade evenly or only for specific requests?
- Did background workers start backing up?
- Did error rates spike — and if so, where?
With Time Bro, you can:
- Select the exact minute of the spike
- See web performance metrics for that minute
- Compare worker activity before and after
- Correlate traffic with errors and slowdowns
Instead of guessing, you reconstruct the moment.
Pinpoint Error Spikes in Context
Error tracking is useful — but errors without context are frustrating.
Time Bro lets you answer questions like:
- Did errors appear alongside a deploy?
- Did they correlate with increased load?
- Were workers failing or retrying excessively?
- Did performance degrade before errors started?
By viewing errors inside the same minute snapshot as performance and worker activity, you can see:
- Cause and effect
- Primary failures vs secondary symptoms
- Whether the system was already under stress
This turns error debugging from log archaeology into pattern recognition.
One View for Web and Workers
Rails apps don’t just fail in controllers.
Often the real damage happens in:
- Long-running background jobs
- Queues backing up
- Workers competing for memory
- Retry storms after transient failures
Time Bro shows web requests and workers side by side for the same minute, so you can see:
- If workers slowed down web requests
- If job spikes followed traffic spikes
- If failures in workers cascaded into user-facing errors
This unified view is critical for understanding Rails systems as systems — not isolated components.
Built for Post-Incident Clarity
Time Bro is just as valuable after things calm down.
It helps you:
- Explain incidents to clients or stakeholders
- Verify whether a fix actually worked
- Learn where your app is fragile under load
- Identify capacity or architecture limits
Instead of “it seemed slow for a bit”, you get:
“At 14:32, traffic increased by 4×, worker execution time doubled, queue latency spiked, and errors appeared on two endpoints.”
That’s actionable insight.
Monitoring That Matches How Rails Apps Actually Behave
DeadBro is not about more charts.
It’s about sharper questions — and clearer answers.
Time Bro gives you:
- Precision when it matters
- Context when debugging
- Confidence when explaining incidents
If you’re running a Rails app in production, this is the difference between watching graphs move and understanding what happened.
