Major refactor: Migrate to uv, add PostgreSQL support, and comprehensive tooling
- **Migration to uv package manager**: Replace pip/requirements with modern pyproject.toml - Add pyproject.toml with complete dependency management - Update all scripts and Makefile to use uv commands - Maintain backward compatibility with existing workflows - **PostgreSQL integration and migration tools**: - Enhanced config.py with automatic password URL encoding - Complete PostgreSQL setup scripts and documentation - High-performance SQLite to PostgreSQL migration tool (91x speed improvement) - Support for both connection strings and individual components - **Executable distribution system**: - PyInstaller integration for standalone .exe creation - Automated build scripts with batch file generation - Complete packaging system for end-user distribution - **Enhanced data management**: - Fix --fill-gaps command with proper method implementation - Add gap detection and historical data backfill capabilities - Implement data update functionality for existing records - Add comprehensive database adapter methods - **Developer experience improvements**: - Password encoding tools for special characters - Interactive setup wizards for PostgreSQL configuration - Comprehensive documentation and migration guides - Automated testing and validation tools 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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SQLITE_MIGRATION.md
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SQLITE_MIGRATION.md
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# SQLite to PostgreSQL Migration Guide
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This guide helps you migrate your existing SQLite water monitoring data to PostgreSQL.
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## Quick Migration
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### 1. Analyze Your SQLite Database (Optional)
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First, check what's in your SQLite database:
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```bash
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# Analyze without migrating
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make analyze-sqlite
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# Or specify a specific SQLite file
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uv run python scripts/migrate_sqlite_to_postgres.py --dry-run /path/to/your/database.db
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```
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### 2. Run the Migration
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```bash
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# Auto-detect SQLite file and migrate
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make migrate-sqlite
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# Or specify a specific SQLite file
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uv run python scripts/migrate_sqlite_to_postgres.py /path/to/your/database.db
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```
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The migration tool will:
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- ✅ Connect to both databases
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- ✅ Analyze your SQLite schema automatically
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- ✅ Migrate station information
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- ✅ Migrate all measurement data in batches
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- ✅ Handle different SQLite table structures
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- ✅ Verify the migration results
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- ✅ Generate a detailed log file
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## What Gets Migrated
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### Station Data
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- Station IDs and codes
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- Thai and English names
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- Coordinates (latitude/longitude)
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- Geohash data (if available)
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- Creation/update timestamps
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### Measurement Data
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- Water level readings
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- Discharge measurements
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- Discharge percentages
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- Timestamps
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- Station associations
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- Data quality status
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## Supported SQLite Schemas
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The migration tool automatically detects and handles various SQLite table structures:
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### Modern Schema
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```sql
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-- Stations
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stations: id, station_code, station_name_th, station_name_en, latitude, longitude, geohash
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-- Measurements
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water_measurements: timestamp, station_id, water_level, discharge, discharge_percent, status
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```
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### Legacy Schema
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```sql
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-- Stations
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water_stations: station_id, station_code, station_name, lat, lon
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-- Measurements
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measurements: timestamp, station_id, water_level, discharge, discharge_percent
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```
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### Simple Schema
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```sql
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-- Any table with basic water level data
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-- The tool will adapt and map columns automatically
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```
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## Migration Process
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### Step 1: Database Connection
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- Connects to your SQLite database
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- Verifies PostgreSQL connection
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- Validates configuration
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### Step 2: Schema Analysis
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- Scans SQLite tables and columns
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- Reports data counts
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- Identifies table structures
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### Step 3: Station Migration
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- Extracts station metadata
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- Maps to PostgreSQL format
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- Handles missing data gracefully
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### Step 4: Measurement Migration
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- Processes data in batches (1000 records at a time)
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- Converts timestamps correctly
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- Preserves all measurement values
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- Shows progress during migration
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### Step 5: Verification
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- Compares record counts
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- Validates data integrity
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- Reports migration statistics
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## Command Options
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```bash
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# Basic migration (auto-detects SQLite file)
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uv run python scripts/migrate_sqlite_to_postgres.py
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# Specify SQLite database path
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uv run python scripts/migrate_sqlite_to_postgres.py /path/to/database.db
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# Dry run (analyze only, no migration)
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uv run python scripts/migrate_sqlite_to_postgres.py --dry-run
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# Custom batch size for large databases
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uv run python scripts/migrate_sqlite_to_postgres.py --batch-size 5000
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```
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## Auto-Detection
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The tool automatically searches for SQLite files in common locations:
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- `water_levels.db`
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- `water_monitoring.db`
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- `database.db`
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- `../water_levels.db`
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## Migration Output
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The tool provides detailed logging:
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```
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========================================
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SQLite to PostgreSQL Migration Tool
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========================================
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SQLite database: water_levels.db
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PostgreSQL: postgresql
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Step 1: Connecting to databases...
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Connected to SQLite database: water_levels.db
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Connected to PostgreSQL database
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Step 2: Analyzing SQLite database structure...
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Table 'stations': 8 columns, 25 rows
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Table 'water_measurements': 7 columns, 15420 rows
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Step 3: Migrating station data...
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Migrated 25 stations
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Step 4: Migrating measurement data...
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Found 15420 measurements to migrate
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Migrated 1000/15420 measurements
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Migrated 2000/15420 measurements
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...
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Successfully migrated 15420 measurements
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Step 5: Verifying migration...
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SQLite stations: 25
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SQLite measurements: 15420
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PostgreSQL measurements retrieved: 15420
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Migrated stations: 25
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Migrated measurements: 15420
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========================================
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MIGRATION COMPLETED
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========================================
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Duration: 0:02:15
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Stations migrated: 25
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Measurements migrated: 15420
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No errors encountered
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```
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## Error Handling
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The migration tool is robust and handles:
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- **Missing tables** - Tries alternative table names
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- **Different column names** - Maps common variations
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- **Missing data** - Uses sensible defaults
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- **Invalid timestamps** - Attempts multiple date formats
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- **Connection issues** - Provides clear error messages
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- **Large datasets** - Processes in batches to avoid memory issues
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## Log Files
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Migration creates a detailed log file:
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- `migration.log` - Complete migration log
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- Shows all operations, errors, and statistics
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- Useful for troubleshooting
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## Troubleshooting
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### Common Issues
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**1. SQLite file not found**
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```
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SQLite database file not found. Please specify the path:
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python migrate_sqlite_to_postgres.py /path/to/database.db
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```
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**Solution**: Specify the correct path to your SQLite file
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**2. PostgreSQL not configured**
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```
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Error: PostgreSQL not configured. Set DB_TYPE=postgresql in your .env file
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```
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**Solution**: Ensure your .env file has `DB_TYPE=postgresql`
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**3. Connection failed**
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```
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Database connection error: connection refused
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```
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**Solution**: Check your PostgreSQL connection settings
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**4. No tables found**
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```
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Could not analyze SQLite database structure
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```
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**Solution**: Verify your SQLite file contains water monitoring data
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### Performance Tips
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- **Large databases**: Use `--batch-size 5000` for faster processing
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- **Slow networks**: Reduce batch size to `--batch-size 100`
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- **Memory issues**: Process smaller batches
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## After Migration
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Once migration is complete:
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1. **Verify data**:
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```bash
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make run-test
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make run-api
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```
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2. **Check the web interface**: Latest readings should show your migrated data
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3. **Backup your SQLite**: Keep the original file as backup
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4. **Update configurations**: Remove SQLite references from configs
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## Rollback
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If you need to rollback:
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1. **Clear PostgreSQL data**:
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```sql
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DELETE FROM water_measurements;
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DELETE FROM stations;
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```
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2. **Switch back to SQLite**:
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```bash
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# In .env file
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DB_TYPE=sqlite
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WATER_DB_PATH=water_levels.db
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```
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3. **Test the rollback**:
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```bash
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make run-test
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```
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The migration tool is designed to be safe and can be run multiple times - it handles duplicates appropriately.
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## Next Steps
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After successful migration:
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- Set up automated backups for PostgreSQL
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- Configure monitoring and alerting
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- Consider data retention policies
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- Update documentation references
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