# Yesterday Data Collection Feature

## Overview

This implementation extends the Meta API grabber to automatically collect yesterday's data using Meta's `yesterday` date preset. The system intelligently detects when a new day starts and manages yesterday data collection with the following logic:

## Key Features

### 1. New Day Detection
- Monitors the `date_start` field from "today" preset data
- Detects when `date_start` changes to a new date
- Triggers immediate fetch of yesterday's data when a new day is detected

### 2. Yesterday Data Collection Strategy
- **First fetch**: When a new day is detected, fetch yesterday's data immediately
- **Periodic updates**: Update yesterday's data every 12 hours
- **Rationale**: Meta updates historical data, so refreshing ensures accuracy

### 3. Token Validation & Error Handling
- Validates access token on every API request
- Catches OAuth token errors (error codes 190, 102)
- Raises `ValueError` with clear error messages for invalid tokens
- Stops execution immediately if token is invalid (fail-fast approach)

## Implementation Details

### Modified Files

#### `src/meta_api_grabber/scheduled_grabber.py`

**New State Tracking:**
```python
# Track current date for detecting day changes
self.current_date: Optional[date] = None

# Track when yesterday data was last fetched
self.yesterday_last_fetched: Optional[datetime] = None
```

**New Methods:**

1. `_check_new_day(today_date_start: Optional[date]) -> bool`
   - Compares today's date_start with tracked current_date
   - Returns True if a new day has been detected

2. `_should_fetch_yesterday() -> bool`
   - Returns True if yesterday data has never been fetched
   - Returns True if 12+ hours have passed since last fetch

3. `_validate_token()`
   - Validates the access token using token_manager
   - Raises ValueError if token is invalid

**Enhanced Methods:**

- `grab_account_insights()`: Now returns the `date_start` value and handles token errors
- `grab_campaign_insights()`: Added token error handling
- `grab_adset_insights()`: Added token error handling
- `refresh_token_if_needed()`: Now raises ValueError on token validation failure
- `run_collection_cycle()`: Implements the yesterday data collection logic

### Data Flow

```
Collection Cycle Start
    ↓
Fetch "today" data for all accounts
    ↓
Extract date_start from today's data
    ↓
Check if new day detected
    ↓ (if yes)
Reset yesterday_last_fetched
    ↓
Check if should fetch yesterday
    ↓ (if yes)
Fetch "yesterday" data for all accounts
    ↓
Update yesterday_last_fetched timestamp
    ↓
Collection Cycle Complete
```

## Testing Instructions

### 1. Local Testing (Without API Calls)

Check syntax and imports:
```bash
uv run python -m py_compile src/meta_api_grabber/scheduled_grabber.py
```

### 2. Docker Container Test

Start the TimescaleDB container:
```bash
docker-compose up -d timescaledb
```

Wait for the database to be healthy:
```bash
docker-compose ps
```

### 3. Test Token Validation

Intentionally use an invalid token to verify error handling:
```bash
# Set an invalid token in .env temporarily
META_ACCESS_TOKEN=invalid_token_for_testing

# Run the grabber - it should error out immediately
uv run python src/meta_api_grabber/scheduled_grabber.py
```

Expected output: Clear error message about invalid token

### 4. Production Test Run

**Important**: Before running with real token:

1. Ensure `.env` has valid credentials:
   ```
   META_ACCESS_TOKEN=<your_valid_token>
   META_APP_ID=<your_app_id>
   META_APP_SECRET=<your_app_secret>
   DATABASE_URL=postgresql://meta_user:meta_password@localhost:5555/meta_insights
   ```

2. Run a single cycle to verify:
   ```bash
   # This will run one collection cycle and exit
   uv run python -c "
   import asyncio
   from src.meta_api_grabber.scheduled_grabber import ScheduledInsightsGrabber

   async def test():
       grabber = ScheduledInsightsGrabber(max_accounts=1)
       async with grabber.db:
           await grabber.db.connect()
           await grabber.db.initialize_schema()
           await grabber.load_ad_accounts()
           await grabber.run_collection_cycle()

   asyncio.run(test())
   "
   ```

### 5. Monitor Yesterday Data Collection

The system will:
- **First run**: Collect today's data, detect current date
- **Subsequent runs**: Continue collecting today's data every 2 hours
- **When new day starts**:
  - Log message: "📅 New day detected: YYYY-MM-DD -> YYYY-MM-DD"
  - Immediately fetch yesterday's data
- **Every 12 hours**: Update yesterday's data

Check database to verify yesterday data is being stored:
```sql
-- Connect to TimescaleDB
psql -U meta_user -d meta_insights -h localhost -p 5555

-- Check yesterday data
SELECT
    time,
    account_id,
    date_preset,
    date_start,
    date_stop,
    impressions,
    spend
FROM account_insights
WHERE date_preset = 'yesterday'
ORDER BY time DESC
LIMIT 10;
```

## Expected Behavior

### Scenario 1: Fresh Start
- Cycle 1: Fetch today's data, initialize current_date
- Cycle 2: Fetch today's data, fetch yesterday's data (first time)
- Cycle 3-6: Fetch today's data only
- Cycle 7: Fetch today's data, update yesterday's data (12h passed)

### Scenario 2: Day Change
- Cycle N: Today is 2025-10-21, fetch today's data
- Cycle N+1: Today is 2025-10-22 (new day!)
  - Log: "📅 New day detected: 2025-10-21 -> 2025-10-22"
  - Fetch today's data (2025-10-22)
  - Fetch yesterday's data (2025-10-21)

### Scenario 3: Invalid Token
- Any cycle with invalid token:
  - Error immediately with clear message
  - Stop execution (don't continue to other accounts)
  - Exit with non-zero status code

## Deployment Notes

### Docker Production Deployment

The implementation is designed to run continuously in a Docker container. If token authentication fails:

1. Container will error out and stop
2. This prevents unnecessary API calls with invalid credentials
3. You'll see clear error messages in container logs
4. Fix token issues before restarting

### Monitoring Recommendations

1. **Check logs regularly** for:
   - "📅 New day detected" messages
   - "Fetching yesterday's data" messages
   - Token validation errors

2. **Database monitoring**:
   - Verify yesterday data is being updated
   - Check for gaps in date_start/date_stop values

3. **Token expiry**:
   - System uses automatic token refresh (if enabled)
   - Monitor for token expiration warnings

## Configuration Options

In `src/meta_api_grabber/scheduled_grabber.py` `async_main()`:

```python
grabber = ScheduledInsightsGrabber(
    max_accounts=3,  # Limit number of accounts for testing
    auto_refresh_token=True  # Enable automatic token refresh
)

await grabber.run_scheduled(
    interval_hours=2.0,  # How often to collect today's data
    refresh_metadata_every_n_cycles=12,  # How often to refresh metadata cache
)
```

**Yesterday fetch interval is hardcoded to 12 hours** in the `_should_fetch_yesterday()` method.

## Troubleshooting

### Yesterday data not being fetched
- Check logs for "Fetching yesterday's data" messages
- Verify `date_start` is being extracted from today's data
- Check `self.current_date` is being initialized

### Token errors not stopping execution
- Ensure `ValueError` is being raised in grab methods
- Check that `except ValueError` blocks are re-raising the exception

### Database issues
- Verify `date_start` and `date_stop` columns exist in all insights tables
- Run schema initialization: `await self.db.initialize_schema()`

## Summary

The implementation successfully adds:
- ✅ New day detection via `date_start` monitoring
- ✅ Automatic yesterday data collection on day change
- ✅ 12-hour update cycle for yesterday data
- ✅ Token validation with fail-fast error handling
- ✅ Clear logging for debugging and monitoring

This ensures complete and accurate historical data collection with minimal API usage.