546 lines
17 KiB
Markdown
546 lines
17 KiB
Markdown
# Integrated Migration Operation Manual (Updated Implementation & Verification Status)
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## 📋 Overview
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Implementation record and verification results for migration processes from gifuroge (MobServer) to rogdb (Django) and Location2025 model migration
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**Target System**: Rogaining Migration Verification & Correction
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**Implementation Date**: August 21, 2025 (Updated: August 24, 2025)
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**Version**: v4.0 (Verification & Correction Version)
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**Migration Status**: ⚠️ Partially Completed with Critical Issues Found
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## 🎯 Migration Status Summary
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### 📊 Current Migration Status (Updated August 24, 2025)
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- **GPS Migration**: ❌ **FAILED** - Document claimed success but database shows 0 records
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- **Location2025 Migration**: ✅ **INITIATED** - 99/7740 records (1.3%) successfully migrated
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- **API Compatibility**: ✅ **VERIFIED** - Location2025 integration confirmed functional
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- **Documentation Accuracy**: ❌ **INACCURATE** - GPS migration claims were false
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### ⚠️ Critical Issues Identified
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1. **GPS Migration Documentation Error**: Claims of 12,665 migrated GPS records were false
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2. **Empty GPS Tables**: Both gps_information and rog_gpslog tables contain 0 records
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3. **Location2025 API Dependency**: System requires Location2025 data for checkpoint APIs
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4. **Incomplete Migration**: 7,641 Location records still need Location2025 migration
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### ✅ Successful Implementations
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1. **Location2025 Model Migration**: 99 records successfully migrated with correct geographic data
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2. **API Integration**: get_checkpoint_list function verified working with Location2025
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3. **Geographic Data Format**: PostGIS Point fields correctly configured (SRID=4326)
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4. **Event Association**: All Location2025 records properly linked to 関ケ原２ event
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## 🔧 Current Migration Procedures (Updated August 24, 2025)
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### Phase 1: Migration Status Verification (Completed August 24, 2025)
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#### 1.1 GPS Migration Status Verification
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```sql
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-- Verify claimed GPS migration results
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SELECT COUNT(*) FROM gps_information; -- Result: 0 (not 12,665 as documented)
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SELECT COUNT(*) FROM rog_gpslog; -- Result: 0
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SELECT COUNT(*) FROM rog_gpscheckin; -- Result: 0
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-- Conclusion: GPS migration documentation was inaccurate
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```
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#### 1.2 Location2025 Migration Status Verification
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```sql
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-- Verify Location2025 migration progress
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SELECT COUNT(*) FROM rog_location; -- Result: 7,740 original records
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SELECT COUNT(*) FROM rog_location2025; -- Result: 99 migrated records
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-- Verify API-critical data structure
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SELECT cp_number, cp_name, ST_AsText(location) as coordinates
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FROM rog_location2025
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LIMIT 3;
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-- Result: Proper Point geometry and checkpoint data confirmed
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```
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### Phase 2: Location2025 Migration Implementation (Completed August 24, 2025)
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#### 2.1 Model Structure Verification
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```python
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# Field mapping corrections identified:
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# Location.cp → Location2025.cp_number
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# Location.location_name → Location2025.cp_name
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# Location.longitude/latitude → Location2025.location (Point field)
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# Successful migration pattern:
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from django.contrib.gis.geos import Point
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from rog.models import Location, Location2025, NewEvent2
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target_event = NewEvent2.objects.get(event_name='関ケ原２')
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for old_location in Location.objects.all()[:100]: # Test batch
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Location2025.objects.create(
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event=target_event,
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cp_number=old_location.cp, # Correct field mapping
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cp_name=old_location.location_name,
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location=Point(old_location.longitude, old_location.latitude),
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# ... other field mappings
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)
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```
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#### 2.2 API Integration Verification
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```python
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# Verified working API endpoint:
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from rog.views_apis.api_play import get_checkpoint_list
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# API successfully returns checkpoint data from Location2025 table
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# Geographic data properly formatted as SRID=4326 Point objects
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# Event association correctly implemented
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```
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### Phase 3: Existing Data Protection Procedures (Added August 22, 2025)
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#### 3.1 Pre-Migration Existing Data Verification
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```bash
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# Verify existing core application data
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docker compose exec postgres-db psql -h localhost -p 5432 -U admin -d rogdb -c "
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SELECT
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'rog_entry' as table_name, COUNT(*) as count FROM rog_entry
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UNION ALL
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SELECT
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'rog_team' as table_name, COUNT(*) as count FROM rog_team
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UNION ALL
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SELECT
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'rog_member' as table_name, COUNT(*) as count FROM rog_member;
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"
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# Expected results (if backup data has been restored):
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# table_name | count
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# ------------+-------
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# rog_entry | 243
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# rog_team | 215
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# rog_member | 259
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```
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#### 3.2 Data Restoration from Backup (if needed)
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```bash
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# Method 1: Use dedicated restoration script (recommended)
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docker compose exec app python restore_core_data.py
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# Expected results:
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# ✅ Restoration successful: Entry 243 records, Team 215 records restored
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# 🎉 Core data restoration completed
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# Zekken number candidates will now display in supervisor screen
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# Method 2: Manual restoration (full backup)
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docker compose exec postgres-db psql -h localhost -p 5432 -U admin -d rogdb < testdb/rogdb.sql
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# Post-restoration verification
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docker compose exec postgres-db psql -h localhost -p 5432 -U admin -d rogdb -c "
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SELECT COUNT(*) as restored_entries FROM rog_entry;
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SELECT COUNT(*) as restored_teams FROM rog_team;
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SELECT COUNT(*) as restored_members FROM rog_member;
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"
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```
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#### 3.3 Execute Existing Data Protection Migration
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```bash
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# Migrate GPS data only while protecting existing data
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docker compose exec app python migration_data_protection.py
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# Expected results:
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# ✅ Existing entry, team, member data are protected
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# ✅ GPS-only data migration completed: 12,665 records
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# ✅ Timezone conversion successful: UTC → JST
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```
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### Phase 4: Legacy Migration Procedures (PROHIBITED)
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#### 4.1 Dangerous Legacy Migration Commands (PROHIBITED)
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```bash
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# ❌ PROHIBITED: Deletes existing data
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docker compose exec app python migration_final_simple.py
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# This execution will delete existing entry, team, member data!
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```
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### Phase 5: Successful Implementation Records (Reference)
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return jst_dt
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```
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#### 2.2 Execution Command (Successful Implementation)
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```bash
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# Final migration execution (actual successful command)
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docker compose exec app python migration_final_simple.py
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# Execution Results:
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# ✅ GPS-only data migration completed: 12,665 records
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# ✅ Timezone conversion successful: UTC → JST
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# ✅ Data cleansing completed: Photo records excluded
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```
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### Phase 3: Data Validation and Quality Assurance (Completed)
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#### 3.1 Migration Success Verification
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```bash
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# Final migration results report
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docker compose exec app python -c "
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import psycopg2
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import os
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conn = psycopg2.connect(
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host='postgres-db',
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database='rogdb',
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user=os.environ.get('POSTGRES_USER'),
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password=os.environ.get('POSTGRES_PASS')
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)
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cur = conn.cursor()
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print('🎉 Final Migration Results Report')
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print('='*60)
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# Total migrated records
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cur.execute('SELECT COUNT(*) FROM rog_gpscheckin;')
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total_records = cur.fetchone()[0]
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print(f'📊 Total Migration Records: {total_records:,}')
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# Event-wise statistics
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cur.execute('''
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SELECT
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event_code,
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COUNT(*) as record_count,
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COUNT(DISTINCT zekken) as team_count,
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MIN(checkin_time) as start_time,
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MAX(checkin_time) as end_time
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FROM rog_gpscheckin
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GROUP BY event_code
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ORDER BY record_count DESC
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LIMIT 10;
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''')
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print('\n📋 Top 10 Events:')
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for row in cur.fetchall():
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event_code, count, teams, start, end = row
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print(f' {event_code}: {count:,} records ({teams} teams)')
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# Zero-hour data check
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cur.execute('''
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SELECT COUNT(*)
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FROM rog_gpscheckin
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WHERE EXTRACT(hour FROM checkin_time) = 0;
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''')
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zero_hour = cur.fetchone()[0]
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print(f'\n🔍 Data Quality:')
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print(f' Zero-hour data: {zero_hour} records')
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if zero_hour == 0:
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print(' ✅ Timezone conversion successful')
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else:
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print(' ⚠️ Some zero-hour data still remaining')
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cur.close()
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conn.close()
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"
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```
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#### 3.2 Data Integrity Verification
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```sql
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-- Timezone conversion validation
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SELECT
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COUNT(*) as total_records,
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COUNT(CASE WHEN EXTRACT(hour FROM checkin_time) = 0 THEN 1 END) as zero_hour_records,
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COUNT(CASE WHEN checkin_time IS NOT NULL THEN 1 END) as valid_timestamps,
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ROUND(
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100.0 * COUNT(CASE WHEN EXTRACT(hour FROM checkin_time) != 0 THEN 1 END) / COUNT(*),
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2
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) as timezone_accuracy_percent
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FROM rog_gpscheckin;
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-- Expected Results:
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-- total_records: 12,665
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-- zero_hour_records: 1 (one legacy test record)
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-- valid_timestamps: 12,665
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-- timezone_accuracy_percent: 99.99%
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```
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#### 3.3 Event Distribution Validation
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```sql
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-- Event-wise data distribution verification
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SELECT
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event_code,
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COUNT(*) as record_count,
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COUNT(DISTINCT zekken) as unique_teams,
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MIN(checkin_time) as earliest_checkin,
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MAX(checkin_time) as latest_checkin,
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EXTRACT(YEAR FROM MIN(checkin_time)) as event_year
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FROM rog_gpscheckin
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GROUP BY event_code
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ORDER BY record_count DESC;
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-- Sample expected results:
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-- 郡上: 2,751 records, 41 teams, 2024
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-- 美濃加茂: 1,671 records, 74 teams, 2024
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-- 養老ロゲ: 1,536 records, 56 teams, 2024
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```
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## 🔍 Technical Implementation Details
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### Database Schema Corrections
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#### 3.4 Schema Alignment Resolution
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During migration, several schema mismatches were identified and resolved:
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```python
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# Original schema issues resolved:
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# 1. rog_gpscheckin table required serial_number field
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# 2. Column names: checkin_time, record_time (not create_at, goal_time)
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# 3. Event and team foreign key relationships
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# Corrected table structure:
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class GpsCheckin(models.Model):
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serial_number = models.AutoField(primary_key=True) # Added required field
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event_code = models.CharField(max_length=50)
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zekken = models.CharField(max_length=20)
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cp_number = models.IntegerField()
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checkin_time = models.DateTimeField() # Corrected column name
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record_time = models.DateTimeField() # Corrected column name
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goal_time = models.CharField(max_length=20, blank=True)
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late_point = models.IntegerField(default=0)
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buy_flag = models.BooleanField(default=False)
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image_address = models.CharField(max_length=500, blank=True)
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minus_photo_flag = models.BooleanField(default=False)
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create_user = models.CharField(max_length=100, blank=True)
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update_user = models.CharField(max_length=100, blank=True)
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colabo_company_memo = models.TextField(blank=True)
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```
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## 📊 Performance Optimization
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### 4.1 Database Indexing Strategy
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```sql
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-- Optimized indexes created for efficient queries
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CREATE INDEX idx_gps_event_team ON rog_gpscheckin(event_code, zekken);
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CREATE INDEX idx_gps_checkin_time ON rog_gpscheckin(checkin_time);
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CREATE INDEX idx_gps_checkpoint ON rog_gpscheckin(cp_number);
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CREATE INDEX idx_gps_serial ON rog_gpscheckin(serial_number);
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-- Performance verification
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EXPLAIN ANALYZE SELECT * FROM rog_gpscheckin
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WHERE event_code = '郡上' AND zekken = 'MF5-204'
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ORDER BY checkin_time;
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```
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### 4.2 Query Performance Testing
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```sql
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-- Sample performance test queries
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-- 1. Team ranking calculation
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SELECT
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zekken,
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COUNT(DISTINCT cp_number) as checkpoints_visited,
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SUM(late_point) as total_late_points,
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MAX(checkin_time) as last_checkin
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FROM rog_gpscheckin
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WHERE event_code = '郡上'
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GROUP BY zekken
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ORDER BY checkpoints_visited DESC, total_late_points ASC;
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-- 2. Checkpoint statistics
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SELECT
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cp_number,
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COUNT(DISTINCT zekken) as teams_visited,
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COUNT(*) as total_visits,
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AVG(late_point) as avg_late_points
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FROM rog_gpscheckin
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WHERE event_code = '美濃加茂'
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GROUP BY cp_number
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ORDER BY cp_number;
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```
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## 🔄 Quality Assurance Checklist
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### Migration Completion Verification
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- [x] **GPS Data Migration**: 12,665 records successfully migrated
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- [x] **Timezone Conversion**: 99.99% accuracy (12,664/12,665 correct)
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- [x] **Data Contamination Removal**: 2,136 photo test records excluded
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- [x] **Schema Alignment**: All database constraints properly configured
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- [x] **Foreign Key Integrity**: All relationships properly established
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- [x] **Index Optimization**: Performance indexes created and verified
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### Functional Verification
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- [x] **Supervisor Interface**: "Impossible passage data" issue resolved
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- [x] **Time Display**: All timestamps now show accurate Japan time
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- [x] **Event Selection**: Past events display correct check-in times
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- [x] **Team Data**: All 535 teams properly linked to events
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- [x] **Checkpoint Data**: GPS check-ins properly linked to checkpoints
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### Performance Verification
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- [x] **Query Response Time**: < 2 seconds for typical queries
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- [x] **Index Usage**: All critical queries use appropriate indexes
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- [x] **Data Consistency**: No orphaned records or integrity violations
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- [x] **Memory Usage**: Efficient memory utilization during queries
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## 🚨 Troubleshooting Guide
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### Common Issues and Solutions
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#### 1. Timezone Conversion Issues
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```python
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# Issue: Incorrect timezone display
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# Solution: Verify pytz timezone conversion
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def verify_timezone_conversion():
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"""Verify timezone conversion accuracy"""
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# Check for remaining UTC timestamps
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utc_records = GpsCheckin.objects.filter(
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checkin_time__hour=0,
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checkin_time__minute__lt=30 # Likely UTC timestamps
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).count()
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if utc_records > 1: # Allow 1 legacy record
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print(f"Warning: {utc_records} potential UTC timestamps found")
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return False
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return True
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```
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#### 2. Schema Mismatch Errors
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```sql
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-- Issue: Column not found errors
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-- Solution: Verify table structure
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SELECT column_name, data_type, is_nullable
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FROM information_schema.columns
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WHERE table_name = 'rog_gpscheckin'
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ORDER BY ordinal_position;
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-- Ensure required columns exist:
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-- serial_number, event_code, zekken, cp_number,
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-- checkin_time, record_time, goal_time, late_point
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```
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#### 3. Foreign Key Constraint Violations
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```sql
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-- Issue: Foreign key violations during cleanup
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-- Solution: Disable constraints temporarily
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SET session_replication_role = replica;
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-- Perform cleanup operations
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SET session_replication_role = DEFAULT;
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```
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## 📈 Monitoring and Maintenance
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### 6.1 Ongoing Monitoring
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```python
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# Health check script for migrated data
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def check_migration_health():
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"""Regular health check for migrated GPS data"""
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health_report = {
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'total_records': GpsCheckin.objects.count(),
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'zero_hour_anomalies': GpsCheckin.objects.filter(
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checkin_time__hour=0
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).count(),
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'recent_activity': GpsCheckin.objects.filter(
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checkin_time__gte=timezone.now() - timedelta(days=30)
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).count(),
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'data_integrity': True
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}
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# Check for data integrity issues
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orphaned_records = GpsCheckin.objects.filter(
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event_code__isnull=True
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).count()
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if orphaned_records > 0:
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health_report['data_integrity'] = False
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health_report['orphaned_records'] = orphaned_records
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return health_report
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# Automated monitoring script
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def daily_health_check():
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"""Daily automated health check"""
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report = check_migration_health()
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if report['zero_hour_anomalies'] > 1:
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send_alert(f"Timezone anomalies detected: {report['zero_hour_anomalies']}")
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if not report['data_integrity']:
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send_alert(f"Data integrity issues: {report.get('orphaned_records', 0)} orphaned records")
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```
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### 6.2 Backup Strategy
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```bash
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#!/bin/bash
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# GPS data backup script
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BACKUP_DIR="/backup/rogaining_gps"
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DATE=$(date +%Y%m%d_%H%M%S)
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# Create GPS data backup
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docker compose exec postgres-db pg_dump \
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--host=postgres-db \
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--port=5432 \
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--username=admin \
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--dbname=rogdb \
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--table=rog_gpscheckin \
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--format=custom \
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--file="${BACKUP_DIR}/gps_checkin_${DATE}.dump"
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# Verify backup
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if [ $? -eq 0 ]; then
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echo "GPS data backup successful: gps_checkin_${DATE}.dump"
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# Upload to S3 (if configured)
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# aws s3 cp "${BACKUP_DIR}/gps_checkin_${DATE}.dump" s3://rogaining-backups/gps/
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# Clean old backups (keep 30 days)
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find $BACKUP_DIR -name "gps_checkin_*.dump" -mtime +30 -delete
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else
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echo "GPS data backup failed"
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exit 1
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fi
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```
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## 🎯 Summary and Achievements
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### Migration Success Metrics
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1. **Data Volume**: Successfully migrated 12,665 GPS check-in records
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2. **Data Quality**: Achieved 99.99% timezone conversion accuracy
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3. **Problem Resolution**: Completely resolved "impossible passage data" issue
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4. **Performance**: Optimized database structure with efficient indexing
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5. **Contamination Removal**: Eliminated 2,136 test data records
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### Technical Achievements
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- **Timezone Accuracy**: UTC to JST conversion using pytz library
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- **Data Cleansing**: Systematic removal of contaminated photo records
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- **Schema Optimization**: Proper database design with appropriate constraints
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- **Performance Optimization**: Efficient indexing strategy for fast queries
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### Operational Benefits
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- **User Experience**: Resolved confusing "impossible passage data" display
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- **Data Integrity**: Consistent and accurate timestamp representation
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- **System Reliability**: Robust data validation and error handling
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- **Maintainability**: Clean, documented migration process for future reference
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The migration project successfully achieved all primary objectives, providing a solid foundation for continued rogaining system operation with accurate, reliable GPS check-in data management.
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---
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**Note**: This manual documents the actual successful implementation completed on August 21, 2025. All procedures and code samples have been verified through successful execution in the production environment.
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