ノートブックの例と活用例

# Production API Outage - October 15, 2024

**Incident Start**: 2024-10-15 14:32 UTC
**Incident End**: 2024-10-15 15:18 UTC
**Duration**: 46 minutes
**Impact**: 15% of API requests failed

## Summary
Our main API experienced elevated error rates starting at 14:32 UTC...

SELECT count(*) as 'Total Requests',
       filter(count(*), WHERE httpResponseCode >= 400) as 'Errors'
FROM Transaction
WHERE appName = 'api-production'
TIMESERIES 1 minute
SINCE '2024-10-15 14:00:00' UNTIL '2024-10-15 16:00:00'

SELECT count(*) as 'Error Count',
       average(duration) as 'Avg Duration (ms)'
FROM Transaction
WHERE appName = 'api-production'
  AND httpResponseCode >= 400
FACET request.uri
SINCE '2024-10-15 14:00:00' UNTIL '2024-10-15 16:00:00'
ORDER BY count(*) DESC

## Root Cause Analysis

### Timeline of Events
- **14:32**: Error rates began climbing for `/api/users` endpoint
- **14:35**: Database connection pool exhaustion detected
- **14:45**: Database scaling initiated
- **15:18**: Service fully recovered

### Contributing Factors
1. Unusual traffic spike during product launch
2. Database connection pool too small for peak load
3. Missing rate limiting on user registration endpoint

SELECT average(duration) as 'Query Duration (ms)',
       count(*) as 'Query Count'
FROM DatabaseSample
WHERE host = 'prod-db-01'
TIMESERIES 5 minutes
SINCE '2024-10-15 14:00:00' UNTIL '2024-10-15 16:00:00'

# Weekly Performance Review - Week of October 14, 2024

## Objectives
- Review application response times across all services
- Identify performance regressions
- Track progress on optimization initiatives

## Key Metrics
- P95 response time target: < 500ms
- Error rate target: < 0.1%
- Apdex score target: > 0.85

SELECT percentile(duration, 50) as 'P50',
       percentile(duration, 95) as 'P95',
       percentile(duration, 99) as 'P99'
FROM Transaction
WHERE appName IN ('web-frontend', 'api-backend', 'auth-service')
FACET appName
TIMESERIES 1 day
SINCE 7 days ago

SELECT percentage(count(*), WHERE error IS true) as 'Error Rate %'
FROM Transaction
WHERE appName IN ('web-frontend', 'api-backend', 'auth-service')
FACET appName
TIMESERIES 1 day
SINCE 7 days ago
COMPARE WITH 1 week ago

## Key Findings

### Performance Improvements ✅
- API backend P95 improved from 650ms to 420ms
- Authentication service error rate down 50%

### Areas for Attention ⚠️
- Web frontend P95 increased 15% week-over-week
- Database query timeouts up 25%

### Action Items
1. Investigate frontend asset loading delays
2. Optimize top 5 slowest database queries
3. Implement caching for user profile data

# New Dashboard Feature Adoption - 30 Days Post-Launch

**Launch Date**: September 15, 2024
**Analysis Period**: September 15 - October 15, 2024

## Feature Description
New interactive dashboard builder with drag-and-drop widgets...

## Success Metrics
- **Primary**: 25% of active users create at least one custom dashboard
- **Secondary**: Average 3 widgets per custom dashboard
- **Tertiary**: < 5% bounce rate on dashboard creation page

SELECT uniqueCount(userId) as 'Users Creating Dashboards'
FROM PageView
WHERE pageUrl LIKE '%/dashboard/create%'
TIMESERIES 1 day
SINCE 30 days ago

SELECT funnel(userId,
  WHERE pageUrl LIKE '%/dashboard/create%' as 'Started Creation',
  WHERE pageUrl LIKE '%/dashboard/create%' AND eventType = 'widget_added' as 'Added Widget',
  WHERE eventType = 'dashboard_saved' as 'Saved Dashboard'
)
FROM PageView, UserAction
SINCE 30 days ago

# Weekly Security Review - October 21, 2024

## Monitoring Scope
- Failed authentication attempts
- Unusual API access patterns
- Database query anomalies
- File system access violations

## Alert Thresholds
- Failed logins: > 100/hour from single IP
- API rate limiting: > 1000 requests/minute
- Suspicious queries: containing SQL injection patterns

SELECT count(*) as 'Failed Attempts',
       latest(remoteAddr) as 'Source IP'
FROM Log
WHERE message LIKE '%authentication failed%'
FACET remoteAddr
SINCE 7 days ago
HAVING count(*) > 50
ORDER BY count(*) DESC

SELECT count(*) as 'Request Count',
       uniqueCount(userAgent) as 'Unique User Agents'
FROM Transaction
WHERE appName = 'api-gateway'
FACET request.headers.x-forwarded-for
SINCE 24 hours ago
HAVING count(*) > 10000
ORDER BY count(*) DESC

# Monthly Business & Technical Review - October 2024

## Business Objectives
- Increase user engagement by 15%
- Reduce customer support tickets by 20%
- Improve conversion rate to 3.5%

## Technical Performance Targets
- 99.9% uptime
- < 2 second page load times
- Zero security incidents

SELECT count(*) as 'Page Views',
       uniqueCount(userId) as 'Active Users',
       average(duration) as 'Avg Session Duration'
FROM PageView
TIMESERIES 1 day
SINCE 30 days ago

SELECT funnel(userId,
  WHERE pageUrl = '/signup' as 'Signup Page',
  WHERE pageUrl = '/signup/verify' as 'Email Verified',
  WHERE pageUrl = '/onboarding/complete' as 'Onboarding Complete',
  WHERE eventType = 'subscription_created' as 'Converted'
)
FROM PageView, UserAction
SINCE 30 days ago

# Investigation: [ISSUE DESCRIPTION]

**Date**: [TODAY'S DATE]
**Investigator**: [YOUR NAME]
**Priority**: [HIGH/MEDIUM/LOW]

## Problem Statement
[Describe what you're investigating and why]

## Hypothesis
[What do you think might be causing the issue?]

## Investigation Plan
1. [First thing to check]
2. [Second thing to check]
3. [Third thing to check]

## Findings
_[Update this section as you discover information]_

## Next Steps
_[What actions should be taken based on your findings?]_

-- Replace with your initial query
SELECT count(*)
FROM [YourEventType]
WHERE [YourConditions]
SINCE 1 hour ago