Analytics Data Examples¶

This document shows realistic examples of what analytics tables will contain after HOMEPOT Client runs for a few days with actual users.

1. API Request Logs (api_request_logs)¶

Scenario: 3 users accessing the system over 2 days

SELECT * FROM api_request_logs ORDER BY timestamp DESC LIMIT 20;

Insights from this data: - User usr_123 is the most active (5 requests shown) - Most requests are GET (viewing data) - One error occurred: 500 on /api/v1/devices (took 5 seconds - timeout?) - User usr_789 uses Postman (likely a developer/admin) - Average response time: ~250ms (excluding the timeout)

2. Device State History (device_state_history)¶

Scenario: Devices changing states due to maintenance, failures, and recovery

SELECT * FROM device_state_history ORDER BY timestamp DESC LIMIT 15;

Insights from this data: - dev_042 had a maintenance window (offline → maintenance → online) - dev_025 had an emergency shutdown due to overheating - dev_007 experienced a temporary error but auto-recovered in 15 minutes - dev_033 lost heartbeat (potential network issue) - Most state changes are automated by the system - Device dev_042 follows a schedule (shutdown at 18:30)

3. Job Outcomes (job_outcomes)¶

Scenario: Various jobs executed on devices over 2 days

SELECT * FROM job_outcomes ORDER BY timestamp DESC LIMIT 15;

Insights from this data: - Firmware updates take longest (125 seconds on dev_042) - One firmware update timed out after 5 minutes (job_1514) - Config change on dev_018 failed after 2 retries - Most jobs are initiated by system (scheduled tasks) - Restart jobs consistently take ~3-4 seconds - Backup jobs take 45-67 seconds - User usr_123 cancelled a firmware update midway

4. Error Logs (error_logs)¶

Scenario: Various errors encountered over 2 days

SELECT * FROM error_logs ORDER BY timestamp DESC LIMIT 12;

Insights from this data: - MQTT broker issue (critical) affecting device dev_033 - unresolved - Database timeout at 14:15:20 caused the 500 error we saw in API logs - User usr_123 hit rate limit (making too many requests) - One critical server error still unresolved from yesterday - Most errors are resolved quickly - External service errors (MQTT, device timeouts) are recurring issues

5. User Activities (user_activities)¶

Scenario: Frontend user interactions over a few hours

SELECT * FROM user_activities ORDER BY timestamp DESC LIMIT 20;

Insights from this data: - User usr_123 searched for "temperature sensor" and "gateway" - User usr_456 edited device 42 (clicked edit, submitted form) - User usr_789 spent 3 minutes on analytics page (longest duration) - Most common activity: page_view (users browsing) - /devices page is most visited - Users spend 45-135 seconds per page on average - Search queries show users looking for specific device types

Summary: What You Learn from This Data¶

Performance Patterns¶

• Average API response time: 100-300ms
• Slow queries: Some analytics queries take 2+ seconds
• Problem areas: Device communication timeouts

User Behavior¶

• Most active times: 9 AM - 3 PM
• Most viewed pages: /devices, /dashboard
• Common searches: Temperature sensor, gateway, building A
• Average session duration: 5-10 minutes per user

Device Health¶

• Most reliable: dev_001 (no failures)
• Problem devices: dev_018, dev_033 (frequent timeouts)
• Common state transitions: idle ↔ active, online → offline

Job Success Rates¶

• Overall success rate: ~80%
• Firmware updates: 67% success (some timeouts)
• Config changes: 90% success
• Health checks: 95% success
• Restarts: 100% success

Error Patterns¶

• Critical issues: 2 (MQTT broker, internal server error)
• Most common: Timeouts (database, device communication)
• Security: 1 authentication failure
• Rate limiting: User usr_123 making too many requests

AI Opportunities¶

With this data collected, AI models are ready to train:

1. Predict Device Failures
2. Pattern: dev_033 goes offline → heartbeat lost → stays offline

3. AI can alert before device fails

4. Optimize Job Scheduling

5. Pattern: Firmware updates succeed 90% at night, 50% during business hours

6. AI can recommend best time to schedule updates

7. Detect Anomalies

8. Pattern: usr_123 rate limit + 20 requests in 5 min = possible bot

9. AI can detect unusual behavior

10. Improve UX

11. Pattern: Users search "temperature sensor" → click add → cancel form

12. AI suggests improving device creation UX

13. Prevent Cascading Failures

14. Pattern: Database timeout → API errors → job failures
15. AI can predict and prevent cascading issues

The document makes it very clear what value the analytics system provides!