The support ticket comes in: "The site has been broken for hours." Your dashboards show green. Your alerts haven't fired. As far as your monitoring shows, everything is fine. But users are experiencing something your monitoring isn't seeing.
This gap between what monitoring shows and what users experience is a blind spot. Blind spots are dangerous because they give false confidence. The dashboards look good. The team relaxes. Meanwhile, problems grow in the shadows.
Every system has blind spots - places where monitoring doesn't reach, metrics that aren't collected, conditions that aren't alerting. Finding and eliminating these blind spots is an ongoing challenge. But the alternative - discovering problems when users complain - is worse.
Types of Blind Spots
Blind spots take different forms.
Unmonitored Components
Services without monitoring:
Production services:
- Main app: Monitored ✓
- API service: Monitored ✓
- Background worker: Not monitored ✗
- Legacy service: Not monitored ✗
Unmonitored components fail silently.
Unmeasured Metrics
Important things not tracked:
Tracked:
- CPU usage
- Memory usage
- Request count
Not tracked:
- Queue depth
- Cache hit rate
- Third-party API latency
Unmeasured metrics hide problems.
Missing Alerts
Conditions without notifications:
Alert coverage:
- Server down: Alert ✓
- Disk full: Alert ✓
- Data corruption: No alert ✗
- Partial failure: No alert ✗
Missing alerts delay response.
Sampling Gaps
Not seeing all the data:
Logging: Sample 1% of requests
Problem: Affects 0.5% of requests
Result: Problem rarely appears in logs
Sampling can hide problems.
Synthetic vs Real
Synthetic checks miss real issues:
Synthetic health check: Pass
Real user experience: Failing
Why: Synthetic doesn't exercise same path
Synthetic monitoring has limits.
Edge Cases
Normal paths monitored, edge cases not:
Monitored:
- Normal login flow
- Normal checkout flow
Not monitored:
- Login with expired session
- Checkout with edge case product
Edge cases cause problems without alerting.
How Blind Spots Form
Understanding formation helps prevention.
Incomplete Initial Setup
Monitoring added reactively:
Project start: Basic monitoring
Problem occurs: Add monitoring for that
Repeat: Monitoring grows piecemeal
Result: Only covers what's broken before
Reactive monitoring has gaps.
New Components
New services without monitoring:
New service deployed
"We'll add monitoring later"
Later never comes
Service has no visibility
New components often lack monitoring.
Drift Over Time
Monitoring doesn't evolve with system:
Year 1: System monitored well
Year 2: System changes, monitoring doesn't
Year 3: Monitoring no longer matches reality
Systems evolve faster than monitoring.
Dependencies Change
Third-party changes aren't tracked:
Third-party API changes behavior
No monitoring of third-party health
Problems attributed to our systems
External dependencies are often blind spots.
Assumption of Coverage
Assuming something is monitored:
"I thought we monitored that"
"Someone must have set that up"
"The platform handles that automatically"
Assumptions create gaps.
The Cost of Blind Spots
Blind spots have real consequences.
Extended Outages
Problems not detected promptly:
Timeline without monitoring:
00:00 - Problem begins
02:00 - Users start noticing
02:30 - Support tickets arrive
03:00 - Engineering engaged
03:30 - Problem diagnosed
Timeline with monitoring:
00:00 - Problem begins
00:05 - Alert fires
00:10 - Engineering engaged
00:30 - Problem diagnosed
3+ hours of extended impact
Blind spots extend incident duration.
User Trust Erosion
Users find problems first:
User experience:
"How did they not know this was broken?"
"Shouldn't they have caught this?"
"What else aren't they watching?"
User-discovered problems erode trust.
Harder Debugging
Less data to diagnose problems:
With monitoring: "Here's when it started, what changed"
Without: "We don't know when it started or why"
Blind spots make diagnosis harder.
Recurring Problems
Same issues keep happening:
Problem occurs → Not detected → Fixed when found
Problem recurs → Not detected → Fixed again
No data to identify pattern
Unmonitored problems recur.
Finding Blind Spots
Actively search for gaps.
Incident Analysis
Review past incidents:
@devonair analyze incidents:
- How was the problem detected?
- Was there monitoring?
- If not, why not?
- Could we have detected earlier?
Incidents reveal blind spots.
Coverage Audit
Map what's monitored:
@devonair audit monitoring coverage:
- List all services
- Document monitoring for each
- Identify gaps
Audits find systematic gaps.
User Report Analysis
Track how problems are found:
@devonair analyze problem detection:
- Percentage found by monitoring
- Percentage found by users
- Percentage found by internal teams
User-found problems indicate blind spots.
Failure Mode Enumeration
List what could go wrong:
@devonair enumerate failure modes:
- What could fail in this system?
- Would we know if it did?
- How quickly?
Enumeration reveals unmonitered scenarios.
Dependency Mapping
Track external dependencies:
@devonair map dependencies:
- What do we depend on?
- Do we monitor their health?
- Would we know if they degraded?
Dependencies are often blind spots.
Eliminating Blind Spots
Close the gaps you find.
Comprehensive Metrics
Measure what matters:
@devonair implement comprehensive metrics:
- Business metrics (transactions, conversions)
- System metrics (latency, errors, throughput)
- Infrastructure metrics (CPU, memory, disk)
- Dependency metrics (external API health)
Comprehensive metrics create visibility.
Meaningful Alerts
Alert on what matters:
@devonair configure meaningful alerts:
- User-facing impact
- System health thresholds
- Business metric anomalies
Meaningful alerts catch real problems.
Synthetic Monitoring
Proactively test critical paths:
@devonair implement synthetic monitoring:
- Critical user flows
- End-to-end tests
- Regular intervals
Synthetics catch problems before users.
Real User Monitoring
Measure actual user experience:
@devonair implement RUM:
- Actual user latency
- Actual user errors
- Geographic distribution
RUM shows what users actually experience.
Distributed Tracing
Follow requests through systems:
@devonair implement tracing:
- Track requests across services
- Identify where time is spent
- Find failure points
Tracing reveals hidden issues.
Maintaining Coverage
Coverage requires ongoing maintenance.
New Component Checklist
Monitoring with every new component:
@devonair require on new components:
- Metrics exposed
- Dashboards created
- Alerts configured
- Tested before production
Checklists prevent new gaps.
Regular Coverage Review
Periodic assessment:
@devonair schedule coverage review:
- Monthly: Review alerts that fired
- Quarterly: Audit coverage
- After incidents: Review detection
Regular review maintains coverage.
Documentation
Track what's monitored:
@devonair document monitoring:
- What's monitored
- Where to look
- Alert meanings
Documentation enables understanding.
Testing
Verify monitoring works:
@devonair test monitoring:
- Intentional failures
- Verify alerts fire
- Verify dashboards reflect reality
Testing confirms monitoring actually works.
Monitoring Metrics
Measure your monitoring.
Detection Rate
How problems are found:
@devonair track detection:
- Percentage by monitoring
- Percentage by users
- Percentage by other means
Detection rate shows effectiveness.
Time to Detection
How fast problems are found:
@devonair track detection time:
- From problem start to detection
- By source (monitoring vs user)
Detection time shows monitoring speed.
Coverage Percentage
What's monitored:
@devonair track coverage:
- Services with monitoring
- Critical paths monitored
- Dependencies monitored
Coverage percentage shows completeness.
Getting Started
Reduce blind spots today.
Assess current coverage:
@devonair analyze monitoring coverage:
- What's monitored?
- What's not?
- How are problems detected?
Address critical gaps:
@devonair close critical gaps:
- Unmonitored services
- Critical paths without alerts
- Dependencies without tracking
Establish processes:
@devonair establish monitoring practices:
- New component checklist
- Regular coverage review
- Incident analysis
Track improvement:
@devonair track monitoring health:
- Detection rate trend
- Coverage trend
- Time to detection trend
Monitoring blind spots are inevitable but manageable. With systematic coverage, regular review, and continuous improvement, blind spots shrink. Your team finds problems before users do. Your dashboards reflect reality. Your confidence is justified.
FAQ
How do we know if our monitoring is good enough?
Track how problems are detected. If users frequently find problems before monitoring, you have significant blind spots. If most problems are detected by monitoring before user impact, you're in good shape.
Should we monitor everything?
Monitor what matters. Not every metric needs collection; not every condition needs alerting. Focus on user-impacting metrics, system health indicators, and business-critical functions. Prioritize signal over noise.
How do we test that monitoring actually works?
Intentionally inject failures in controlled conditions. Verify alerts fire. Verify dashboards reflect the failure. Chaos engineering practices can systematically test monitoring effectiveness.
What's the right balance between monitoring cost and coverage?
Focus on high-value monitoring: user-facing metrics, critical system health, key business functions. Add more detailed monitoring where problems are common or impact is high. Accept some gaps in low-risk areas.