🎯 Overview
Even with a well-designed system, real-world operations introduce unexpected issues.
This post covers:
- Common failure scenarios
- Root cause analysis
- Practical debugging strategies
🧠 Troubleshooting Approach
We follow a structured approach:
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1. Detect issue
2. Identify affected layer
3. Trace data flow
4. Validate assumptions
5. Fix and monitor
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🏗️ System Layers
Understanding where the issue occurs is critical:
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[Collection Layer]
[Processing Layer]
[Storage Layer]
[Alert Layer]
[Analytics Layer]
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⚠️ Scenario 1 — Missing Data in Dashboard
Symptom
- Dashboard shows gaps
- Missing metrics in charts
Possible Causes
- Telegraf stopped
- Python collector failed
- InfluxDB write failure
Debugging Steps
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1. Check Telegraf status
2. Verify Python process is running
3. Query InfluxDB directly
4. Check timestamps
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Solution
- Restart collection agents
- Validate write API
- Fix scheduling issues
⚠️ Scenario 2 — High CPU Usage (Collector)
Symptom
- Python process consumes high CPU
- System slowdown
Possible Causes
- Tight loop (no sleep)
- Excessive data processing
- Inefficient queries
Debugging Steps
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1. Inspect Python process
2. Check loop interval
3. Analyze query frequency
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Solution
- Add sleep interval
- Optimize query logic
- Reduce processing frequency
⚠️ Scenario 3 — InfluxDB Write Failure
Symptom
- No new data in InfluxDB
- Write errors
Possible Causes
- Invalid token
- Network issue
- Incorrect bucket
Debugging Steps
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1. Check API token
2. Verify endpoint URL
3. Test connection manually
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Solution
- Regenerate token
- Fix configuration
- Restart service
⚠️ Scenario 4 — Alert Not Triggered
Symptom
- Threshold exceeded
- No Slack alert
Possible Causes
- Alert condition incorrect
- Slack webhook failure
- Cooldown logic blocking
Debugging Steps
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1. Validate threshold logic
2. Test webhook manually
3. Check alert logs
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Solution
- Fix condition logic
- Update webhook URL
- Adjust cooldown settings
⚠️ Scenario 5 — Duplicate Alerts
Symptom
- Same alert triggered repeatedly
Possible Causes
- No deduplication
- Missing state tracking
Debugging Steps
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1. Check alert frequency
2. Inspect last alert timestamp
3. Verify state logic
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Solution
- Add cooldown period
- Store last alert state
Symptom
- Dashboard loads slowly
- Queries take too long
Possible Causes
- Querying raw data
- Missing indexes (MySQL)
- Large dataset
Debugging Steps
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1. Analyze query performance
2. Check MySQL indexes
3. Reduce time range
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Solution
- Use aggregated tables
- Add indexes
- Optimize queries
⚠️ Scenario 7 — Data Misalignment
Symptom
- Data points do not align
- Incorrect aggregation
Possible Causes
- Different time intervals
- Timezone mismatch
Debugging Steps
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1. Check timestamps
2. Verify timezone settings
3. Align aggregation windows
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Solution
- Standardize timezone
- Align time intervals
🧠 Debugging Best Practices
1. Layer-by-layer Analysis
- Do not jump to conclusions
- Identify exact failure point
2. Log Everything
- Collection logs
- Processing logs
- Alert logs
3. Reproduce Issues
- Use controlled test cases
4. Monitor Fixes
- Ensure issue does not recur
🧪 Debugging Checklist
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✔ Is data being collected?
✔ Is data written to InfluxDB?
✔ Is aggregation working?
✔ Is MySQL updated?
✔ Are alerts triggered?
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🎯 Key Takeaways
- Most issues occur at boundaries between layers
- Logging and visibility are critical
- Simple checks solve most problems
🚀 Next Step
In the final post, we will cover:
- Project summary
- Lessons learned
- Future improvements