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22
Dockerfile
22
Dockerfile
@@ -1,5 +1,5 @@
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# Dockerfile for Meta API Grabber
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# Production-ready container for scheduled data collection
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# Dockerfile for View Manager
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# Production-ready container for database view management
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FROM python:3.13-slim
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@@ -15,26 +15,20 @@ RUN apt-get update && apt-get install -y \
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RUN pip install --no-cache-dir uv
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# Copy project files
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COPY pyproject.toml uv.lock README.md ./
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COPY pyproject.toml README.md ./
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COPY src/ ./src/
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COPY metadata.yaml ./metadata.yaml
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# Install Python dependencies using uv
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RUN uv pip install --system -e .
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# Copy environment file template (will be overridden by volume mount)
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# This is just for documentation - actual .env should be mounted
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COPY .env.example .env.example
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# Create directory for token metadata
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RUN mkdir -p /app/data
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# Set environment variables
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ENV PYTHONUNBUFFERED=1
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ENV PYTHONPATH=/app
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# Health check - verify the script can at least import
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# Health check - verify database connection
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HEALTHCHECK --interval=30s --timeout=10s --start-period=40s --retries=3 \
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CMD python -c "from src.meta_api_grabber.scheduled_grabber import ScheduledInsightsGrabber; print('OK')" || exit 1
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CMD python -c "import asyncio; from meta_api_grabber.database import TimescaleDBClient; asyncio.run(TimescaleDBClient().connect())" || exit 1
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# Run the scheduled grabber
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CMD ["python", "-m", "src.meta_api_grabber.scheduled_grabber"]
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# Run the view manager setup
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CMD ["uv", "run", "view-manager-setup"]
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@@ -1,210 +0,0 @@
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# Meta API Rate Limiter Enhancements
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## Summary
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Enhanced the rate limiter in [rate_limiter.py](src/meta_api_grabber/rate_limiter.py) to monitor **all** Meta API rate limit headers as documented in the [official Meta documentation](https://developers.facebook.com/docs/graph-api/overview/rate-limiting).
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## New Headers Monitored
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### 1. **X-App-Usage** (Platform Rate Limits)
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Tracks application-level rate limits across all users.
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**Fields:**
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- `call_count`: Percentage of calls made (0-100)
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- `total_cputime`: Percentage of CPU time used (0-100)
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- `total_time`: Percentage of total time used (0-100)
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**Example:**
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```json
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{
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"call_count": 28,
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"total_time": 25,
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"total_cputime": 25
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}
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```
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### 2. **X-Ad-Account-Usage** (Ad Account Specific)
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Tracks rate limits for specific ad accounts. **Stored per account ID** to support multiple accounts.
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**Fields:**
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- `acc_id_util_pct`: Percentage of ad account usage (0-100)
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- `reset_time_duration`: Time in seconds until rate limit resets
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- `ads_api_access_tier`: Access tier (e.g., "standard_access", "development_access")
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**Example:**
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```json
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{
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"acc_id_util_pct": 9.67,
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"reset_time_duration": 100,
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"ads_api_access_tier": "standard_access"
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}
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```
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**Note:** Metrics are tracked separately for each ad account in a dictionary keyed by account ID (e.g., `act_123456789`).
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### 3. **X-Business-Use-Case-Usage** (Business Use Case Limits)
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Tracks rate limits per business use case (ads_insights, ads_management, etc.).
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**Fields:**
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- `business_id`: Business object ID
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- `type`: Type of BUC (ads_insights, ads_management, custom_audience, etc.)
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- `call_count`: Percentage of calls made (0-100)
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- `total_cputime`: Percentage of CPU time (0-100)
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- `total_time`: Percentage of total time (0-100)
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- `estimated_time_to_regain_access`: Time in minutes until access is restored
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- `ads_api_access_tier`: Access tier
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**Example:**
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```json
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{
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"66782684": [{
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"type": "ads_management",
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"call_count": 95,
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"total_cputime": 20,
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"total_time": 20,
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"estimated_time_to_regain_access": 0,
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"ads_api_access_tier": "development_access"
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}]
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}
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```
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### 4. **x-fb-ads-insights-throttle** (Legacy)
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Original header still supported for backward compatibility.
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**Fields:**
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- `app_id_util_pct`: App usage percentage
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- `acc_id_util_pct`: Account usage percentage
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## Key Enhancements
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### 1. Intelligent Throttling
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The rate limiter now uses `estimated_time_to_regain_access` and `reset_time_duration` to calculate optimal delays:
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```python
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# If we have estimated_time_to_regain_access from BUC header
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if self.estimated_time_to_regain_access > 0:
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delay = self.estimated_time_to_regain_access * 60 # Convert minutes to seconds
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# If we have reset_time_duration from Ad Account header
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elif self.reset_time_duration > 0:
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delay = self.reset_time_duration * 0.5 # Use fraction as safety margin
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```
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### 2. Comprehensive Error Code Detection
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Expanded error code detection to include all Meta rate limit error codes:
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- **4**: App rate limit
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- **17**: User rate limit
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- **32**: Pages rate limit
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- **613**: Custom rate limit
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- **80000-80014**: Business Use Case rate limits (Ads Insights, Ads Management, Custom Audience, Instagram, LeadGen, Messenger, Pages, WhatsApp, Catalog)
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### 3. Debug Logging
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All headers are now logged in DEBUG mode with detailed parsing information:
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```python
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logger.debug(f"X-App-Usage header: {header_value}")
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logger.debug(f"Parsed X-App-Usage: {result}")
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```
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### 4. Enhanced Statistics
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The `get_stats()` and `print_stats()` methods now display comprehensive metrics from all headers:
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```
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======================================================================
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RATE LIMITER STATISTICS
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======================================================================
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Total Requests: 0
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Throttled Requests: 0
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Rate Limit Errors: 0
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X-App-Usage (Platform Rate Limits):
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Call Count: 95.0%
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Total CPU Time: 90.0%
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Total Time: 88.0%
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X-Ad-Account-Usage:
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Account Usage: 97.5%
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Reset Time Duration: 300s
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API Access Tier: standard_access
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X-Business-Use-Case-Usage:
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Type: ads_insights
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Call Count: 98.0%
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Total CPU Time: 95.0%
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Total Time: 92.0%
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Est. Time to Regain: 15 min
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Legacy (x-fb-ads-insights-throttle):
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App Usage: 93.0%
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Account Usage: 96.0%
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Max Usage Across All Metrics: 98.0%
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Currently Throttled: True
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======================================================================
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```
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## Usage
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### Enable Debug Logging
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To see all header parsing in debug mode:
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```python
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import logging
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logging.basicConfig(
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level=logging.DEBUG,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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```
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### Updating Usage with Account ID
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When calling `update_usage()`, you can optionally provide an account ID to track per-account metrics:
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```python
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# Option 1: Provide account_id explicitly
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limiter.update_usage(response, account_id='act_123456789')
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# Option 2: Let the limiter try to extract it from the response
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limiter.update_usage(response) # Will attempt to extract account_id
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```
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### Access New Metrics
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All metrics are available through the `get_stats()` method:
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```python
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stats = limiter.get_stats()
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print(f"App call count: {stats['app_call_count']}%")
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print(f"Regain access in: {stats['estimated_time_to_regain_access']} min")
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# Per-account metrics
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for account_id, usage in stats['ad_account_usage'].items():
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print(f"Account {account_id}:")
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print(f" Usage: {usage['acc_id_util_pct']}%")
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print(f" Reset in: {usage['reset_time_duration']}s")
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print(f" API tier: {usage['ads_api_access_tier']}")
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# Business use case details
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for buc in stats['buc_usage']:
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print(f"BUC {buc['type']}: {buc['call_count']}%")
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```
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## Testing
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Run the test script to see the rate limiter in action:
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```bash
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uv run python test_rate_limiter.py
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```
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This will demonstrate:
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- Parsing all four header types
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- Intelligent throttling based on usage
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- Comprehensive statistics display
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- Debug logging output
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## References
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- [Meta Graph API Rate Limiting](https://developers.facebook.com/docs/graph-api/overview/rate-limiting)
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- [Meta Marketing API Best Practices](https://developers.facebook.com/docs/marketing-api/insights/best-practices/)
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88
VIEWS_SUMMARY.md
Normal file
88
VIEWS_SUMMARY.md
Normal file
@@ -0,0 +1,88 @@
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# Database Views Summary
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## Overview
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Updated the public schema with new views to make advertising analytics data from Meta, Google, and combined sources more accessible.
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## Changes Made
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### 1. Adapted `explore_schemas.py` Script
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- Modified to save schema exploration output to `schema_info.txt`
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- Provides persistent reference for table structures across meta, google, and alpinebits schemas
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- Usage: `uv run explore_schemas.py`
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### 2. New Views Added
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#### **account_insights_by_country** ✓
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- Aggregates Meta campaign insights by country to account level
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- **Columns**: time, account_id, country, impressions, clicks, spend, link_click, landing_page_view, lead
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- **Purpose**: Analyze Meta advertising performance by geographic region
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- **Source**: meta.custom_campaign_country
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#### **g_campaign_insights** ✓
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- Google campaign-level insights with calculated performance metrics
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- **Columns**: time, campaign_id, campaign_name, clicks, impressions, interactions, cost_micros, cost, conversions, all_conversions, conversions_value, ctr, cpm, cpc, cost_per_conversion
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- **Purpose**: Unified Google campaign performance view with key metrics (CTR, CPM, CPC, Cost per Conversion)
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- **Source**: google.campaign_metrics
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#### **unified_account_insights_by_device** ✓
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- Combines Meta and Google account insights broken down by device type
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- **Columns**: time, google_account_id, meta_account_id, device, google_impressions, meta_impressions, total_impressions, google_clicks, meta_clicks, total_clicks, google_cost, meta_spend, total_spend, meta_link_clicks, meta_leads
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- **Purpose**: Compare Meta vs Google performance by device (DESKTOP, MOBILE, TABLET)
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- **Requires**: account_metadata table for linking Meta and Google accounts
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#### **unified_account_insights_by_gender** ✓
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- Meta audience insights broken down by gender
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- **Columns**: time, meta_account_id, gender, impressions, clicks, spend, link_clicks, leads
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- **Purpose**: Analyze Meta advertising performance by audience gender demographics
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- **Source**: meta account_insights_by_gender
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### 3. Existing Views (Not Modified)
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The following views were already present and working correctly:
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- **campaign_insights** - Base Meta campaign insights
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- **campaign_insights_by_gender/age/device/country** - Meta campaign breakdowns
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- **account_insights** - Aggregated Meta account insights
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- **account_insights_by_gender/age/device/gender_and_age** - Meta account breakdowns
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- **ads_insights** (materialized) - Individual ad performance
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- **adset_insights** - Ad set level insights
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- **g_account_insights** - Google account-level insights
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- **g_account_insights_device** - Google account insights by device
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- **unified_account_insights** - Combined Meta+Google account-level view
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## Testing
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All views have been tested and verified to:
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- Execute without errors
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- Return valid data with correct column structures
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- Support LIMIT queries for performance verification
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## Usage Examples
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```sql
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-- View Meta performance by country
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SELECT time, country, SUM(impressions), SUM(spend), SUM(lead)
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FROM account_insights_by_country
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WHERE account_id = '1416908162571377'
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GROUP BY time, country;
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-- View Google campaign metrics
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SELECT time, campaign_name, clicks, impressions, cpc, cpm
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FROM g_campaign_insights
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WHERE ctr > 2.0
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ORDER BY time DESC
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LIMIT 10;
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-- Compare device performance across Meta and Google
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SELECT time, device, total_impressions, total_clicks, total_spend
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FROM unified_account_insights_by_device
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ORDER BY time DESC;
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-- Analyze Meta audience by gender
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SELECT time, gender, impressions, clicks, spend, leads
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FROM unified_account_insights_by_gender
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WHERE time > CURRENT_DATE - INTERVAL '30 days'
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ORDER BY time, gender;
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```
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|
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## Next Steps
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- Set up automated refreshes for materialized views if needed
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- Create additional unified views for campaign-level comparisons
|
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- Consider indexing frequently queried views for performance optimization
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@@ -19,22 +19,22 @@ services:
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retries: 5
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restart: unless-stopped
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|
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# Meta API Grabber - Scheduled data collection
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meta-grabber:
|
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# View Manager - Setup database schema and views
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view-manager:
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build:
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context: .
|
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dockerfile: Dockerfile
|
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container_name: meta_api_grabber
|
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container_name: view_manager
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environment:
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# Database connection (connects to timescaledb service)
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DATABASE_URL: postgresql://meta_user:meta_password@timescaledb:5432/meta_insights
|
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env_file:
|
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- .env # Must contain META_ACCESS_TOKEN, META_APP_ID, META_APP_SECRET
|
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- .env # Optional: for any additional configuration
|
||||
volumes:
|
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# Mount .env for token updates (auto-refresh will update the file)
|
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- ./.env:/app/.env
|
||||
# Mount token metadata file (preserves token refresh state across restarts)
|
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- ./.meta_token.json:/app/.meta_token.json
|
||||
# Mount metadata.yaml for account configuration
|
||||
- ./metadata.yaml:/app/metadata.yaml:ro
|
||||
# Optional: Mount custom db_schema.sql if you want to edit it externally
|
||||
# - ./src/meta_api_grabber/db_schema.sql:/app/src/meta_api_grabber/db_schema.sql:ro
|
||||
depends_on:
|
||||
timescaledb:
|
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condition: service_healthy
|
||||
|
||||
110
explore_schemas.py
Normal file
110
explore_schemas.py
Normal file
@@ -0,0 +1,110 @@
|
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#!/usr/bin/env python3
|
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"""
|
||||
Script to explore available tables and their structures in meta, google, and alpinebits schemas.
|
||||
Saves output to schema_info.txt file.
|
||||
"""
|
||||
import os
|
||||
import asyncio
|
||||
import asyncpg
|
||||
from dotenv import load_dotenv
|
||||
|
||||
# Load environment variables
|
||||
load_dotenv()
|
||||
|
||||
DB_URL = os.getenv("DATABASE_URL")
|
||||
OUTPUT_FILE = "schema_info.txt"
|
||||
|
||||
async def get_connection():
|
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"""Create database connection"""
|
||||
return await asyncpg.connect(DB_URL)
|
||||
|
||||
async def get_schema_info(schema_name, output_file):
|
||||
"""Get all tables and their columns for a schema"""
|
||||
conn = await get_connection()
|
||||
|
||||
header = f"\n{'='*80}\nSCHEMA: {schema_name}\n{'='*80}\n"
|
||||
print(header, end="")
|
||||
output_file.write(header)
|
||||
|
||||
# Get all tables in schema
|
||||
query = """
|
||||
SELECT table_name
|
||||
FROM information_schema.tables
|
||||
WHERE table_schema = $1
|
||||
ORDER BY table_name
|
||||
"""
|
||||
tables = await conn.fetch(query, schema_name)
|
||||
|
||||
if not tables:
|
||||
msg = f"No tables found in schema '{schema_name}'\n"
|
||||
print(msg, end="")
|
||||
output_file.write(msg)
|
||||
await conn.close()
|
||||
return
|
||||
|
||||
for row in tables:
|
||||
table_name = row['table_name']
|
||||
table_header = f"\nTable: {table_name}\n" + "-" * 80 + "\n"
|
||||
print(table_header, end="")
|
||||
output_file.write(table_header)
|
||||
|
||||
# Get columns for this table
|
||||
col_query = """
|
||||
SELECT column_name, data_type, is_nullable
|
||||
FROM information_schema.columns
|
||||
WHERE table_schema = $1 AND table_name = $2
|
||||
ORDER BY ordinal_position
|
||||
"""
|
||||
columns = await conn.fetch(col_query, schema_name, table_name)
|
||||
|
||||
for col in columns:
|
||||
null_str = "NULL" if col['is_nullable'] == "YES" else "NOT NULL"
|
||||
col_line = f" - {col['column_name']}: {col['data_type']} ({null_str})\n"
|
||||
print(col_line, end="")
|
||||
output_file.write(col_line)
|
||||
|
||||
# Get row count
|
||||
try:
|
||||
count_query = f"SELECT COUNT(*) FROM {schema_name}.{table_name}"
|
||||
count = await conn.fetchval(count_query)
|
||||
count_line = f" Rows: {count}\n"
|
||||
print(count_line, end="")
|
||||
output_file.write(count_line)
|
||||
except Exception as e:
|
||||
count_line = f" Rows: (unable to count)\n"
|
||||
print(count_line, end="")
|
||||
output_file.write(count_line)
|
||||
|
||||
# Show sample data
|
||||
try:
|
||||
sample_query = f"SELECT * FROM {schema_name}.{table_name} LIMIT 1"
|
||||
sample = await conn.fetchrow(sample_query)
|
||||
if sample:
|
||||
sample_line = f" Sample: {dict(sample)}\n"
|
||||
print(sample_line, end="")
|
||||
output_file.write(sample_line)
|
||||
except Exception as e:
|
||||
sample_line = f" Sample: (unable to fetch)\n"
|
||||
print(sample_line, end="")
|
||||
output_file.write(sample_line)
|
||||
|
||||
await conn.close()
|
||||
|
||||
async def main():
|
||||
schemas = ["meta", "google", "alpinebits"]
|
||||
|
||||
with open(OUTPUT_FILE, "w") as output_file:
|
||||
for schema in schemas:
|
||||
try:
|
||||
await get_schema_info(schema, output_file)
|
||||
except Exception as e:
|
||||
error_msg = f"\nError accessing schema '{schema}': {e}\n"
|
||||
print(error_msg, end="")
|
||||
output_file.write(error_msg)
|
||||
|
||||
completion_msg = f"\n{'='*80}\nExploration complete! Output saved to {OUTPUT_FILE}\n"
|
||||
print(completion_msg, end="")
|
||||
output_file.write(completion_msg)
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
24
metadata.yaml
Normal file
24
metadata.yaml
Normal file
@@ -0,0 +1,24 @@
|
||||
# Metadata configuration for account mapping
|
||||
# This file stores important metadata that links different accounts and identifiers
|
||||
|
||||
accounts:
|
||||
- label: "Hotel Bemelmans Post"
|
||||
meta_account_id: "238334370765317"
|
||||
google_account_id: "7581209925"
|
||||
alpinebits_hotel_code: "39054_001"
|
||||
|
||||
- label: "Jagdhof-Kaltern"
|
||||
#meta_account_id: "act_987654321"
|
||||
google_account_id: "1951919786"
|
||||
alpinebits_hotel_code: "39052_001"
|
||||
|
||||
- label: "Residence Erika"
|
||||
google_account_id: "6604634947"
|
||||
alpinebits_hotel_code: "39040_001"
|
||||
|
||||
|
||||
# Add more accounts as needed
|
||||
# - label: "Your Account Name"
|
||||
# meta_account_id: "act_xxxxx"
|
||||
# google_account_id: "xxx-xxx-xxxx"
|
||||
# alpinebits_hotel_code: "HOTELxxx"
|
||||
@@ -1,18 +1,13 @@
|
||||
[project]
|
||||
name = "meta-api-grabber"
|
||||
version = "0.1.0"
|
||||
description = "Meta Marketing API data grabber with TimescaleDB storage"
|
||||
description = "View manager for TimescaleDB with metadata handling"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.13"
|
||||
dependencies = [
|
||||
"aiohttp>=3.13.1",
|
||||
"alembic>=1.17.0",
|
||||
"asyncpg>=0.30.0",
|
||||
"facebook-business>=23.0.3",
|
||||
"google-ads>=28.3.0",
|
||||
"python-dotenv>=1.1.1",
|
||||
"requests-oauthlib>=2.0.0",
|
||||
"sqlalchemy[asyncio]>=2.0.44",
|
||||
"pyyaml>=6.0.0",
|
||||
]
|
||||
|
||||
[project.optional-dependencies]
|
||||
@@ -22,13 +17,7 @@ test = [
|
||||
]
|
||||
|
||||
[project.scripts]
|
||||
meta-auth = "meta_api_grabber.auth:main"
|
||||
meta-scheduled = "meta_api_grabber.scheduled_grabber:main"
|
||||
meta-insights = "meta_api_grabber.insights_grabber:main"
|
||||
meta-test-accounts = "meta_api_grabber.test_ad_accounts:main"
|
||||
meta-test-leads = "meta_api_grabber.test_page_leads:main"
|
||||
meta-token = "meta_api_grabber.token_manager:main"
|
||||
google-ads-test = "meta_api_grabber.test_google_ads_accounts:main"
|
||||
view-manager-setup = "meta_api_grabber.setup_and_wait:main"
|
||||
|
||||
[build-system]
|
||||
requires = ["hatchling"]
|
||||
|
||||
551
schema_info.txt
Normal file
551
schema_info.txt
Normal file
@@ -0,0 +1,551 @@
|
||||
|
||||
================================================================================
|
||||
SCHEMA: meta
|
||||
================================================================================
|
||||
|
||||
Table: ad_sets
|
||||
--------------------------------------------------------------------------------
|
||||
- id: character varying (NULL)
|
||||
- name: character varying (NULL)
|
||||
- adlabels: jsonb (NULL)
|
||||
- bid_info: jsonb (NULL)
|
||||
- account_id: character varying (NULL)
|
||||
- bid_amount: numeric (NULL)
|
||||
- start_time: timestamp with time zone (NULL)
|
||||
- campaign_id: character varying (NULL)
|
||||
- created_time: timestamp with time zone (NULL)
|
||||
- daily_budget: numeric (NULL)
|
||||
- updated_time: timestamp with time zone (NULL)
|
||||
- bid_constraints: jsonb (NULL)
|
||||
- lifetime_budget: numeric (NULL)
|
||||
- promoted_object: jsonb (NULL)
|
||||
- _airbyte_raw_id: character varying (NOT NULL)
|
||||
- _airbyte_extracted_at: timestamp with time zone (NOT NULL)
|
||||
- _airbyte_generation_id: bigint (NULL)
|
||||
- _airbyte_meta: jsonb (NOT NULL)
|
||||
Rows: 47
|
||||
Sample: {'id': '120207428712700382', 'name': 'Neue Anzeigengruppe für Leads', 'adlabels': None, 'bid_info': None, 'account_id': '1416908162571377', 'bid_amount': None, 'start_time': datetime.datetime(2024, 3, 15, 8, 50, 7, tzinfo=datetime.timezone.utc), 'campaign_id': '120207428712650382', 'created_time': datetime.datetime(2024, 3, 15, 8, 50, 7, tzinfo=datetime.timezone.utc), 'daily_budget': Decimal('1000.000000000'), 'updated_time': datetime.datetime(2025, 1, 4, 6, 42, tzinfo=datetime.timezone.utc), 'bid_constraints': None, 'lifetime_budget': Decimal('0E-9'), 'promoted_object': '{"page_id": "106160371809313"}', '_airbyte_raw_id': 'dccb9c4b-7fa8-4e98-9249-7a359dd86521', '_airbyte_extracted_at': datetime.datetime(2025, 11, 13, 16, 6, 6, 112000, tzinfo=datetime.timezone.utc), '_airbyte_generation_id': 2, '_airbyte_meta': '{"changes": [], "sync_id": 42}'}
|
||||
|
||||
Table: ads
|
||||
--------------------------------------------------------------------------------
|
||||
- id: character varying (NULL)
|
||||
- name: character varying (NULL)
|
||||
- status: character varying (NULL)
|
||||
- adlabels: jsonb (NULL)
|
||||
- adset_id: character varying (NULL)
|
||||
- bid_info: jsonb (NULL)
|
||||
- bid_type: character varying (NULL)
|
||||
- creative: jsonb (NULL)
|
||||
- account_id: character varying (NULL)
|
||||
- bid_amount: bigint (NULL)
|
||||
- campaign_id: character varying (NULL)
|
||||
- created_time: timestamp with time zone (NULL)
|
||||
- source_ad_id: character varying (NULL)
|
||||
- updated_time: timestamp with time zone (NULL)
|
||||
- tracking_specs: jsonb (NULL)
|
||||
- recommendations: jsonb (NULL)
|
||||
- conversion_specs: jsonb (NULL)
|
||||
- effective_status: character varying (NULL)
|
||||
- last_updated_by_app_id: character varying (NULL)
|
||||
- _airbyte_raw_id: character varying (NOT NULL)
|
||||
- _airbyte_extracted_at: timestamp with time zone (NOT NULL)
|
||||
- _airbyte_generation_id: bigint (NULL)
|
||||
- _airbyte_meta: jsonb (NOT NULL)
|
||||
Rows: 163
|
||||
Sample: {'id': '120236221098270382', 'name': 'Vertriebsmitarbeiter_14.11.2025_Grafik_1', 'status': 'ACTIVE', 'adlabels': None, 'adset_id': '120236221098250382', 'bid_info': None, 'bid_type': 'ABSOLUTE_OCPM', 'creative': '{"id": "2042451856579224"}', 'account_id': '1416908162571377', 'bid_amount': None, 'campaign_id': '120219679742650382', 'created_time': datetime.datetime(2025, 11, 14, 9, 30, 29, tzinfo=datetime.timezone.utc), 'source_ad_id': '0', 'updated_time': datetime.datetime(2025, 11, 14, 9, 45, 27, tzinfo=datetime.timezone.utc), 'tracking_specs': '[{"fb_pixel": ["1165111234632775"], "action.type": ["offsite_conversion"]}, {"action.type": ["onsite_conversion"], "conversion_id": ["9883202595118306"]}, {"action.type": ["onsite_conversion"]}, {"page": ["106160371809313"], "post": ["860615083140579"], "action.type": ["post_engagement"]}, {"page": ["106160371809313"], "post": ["860615083140579"], "action.type": ["post_interaction_gross"]}, {"post": ["860615083140579"], "post.wall": ["106160371809313"], "action.type": ["link_click"]}]', 'recommendations': None, 'conversion_specs': '[{"action.type": ["offsite_conversion"], "conversion_id": ["25218548091171302"]}]', 'effective_status': 'ACTIVE', 'last_updated_by_app_id': '119211728144504', '_airbyte_raw_id': '826ad572-f17f-4f94-a682-fe633da5092d', '_airbyte_extracted_at': datetime.datetime(2025, 11, 14, 16, 6, 20, 979000, tzinfo=datetime.timezone.utc), '_airbyte_generation_id': 1, '_airbyte_meta': '{"changes": [], "sync_id": 44}'}
|
||||
|
||||
Table: ads_insights
|
||||
--------------------------------------------------------------------------------
|
||||
- cpc: numeric (NULL)
|
||||
- cpm: numeric (NULL)
|
||||
- cpp: numeric (NULL)
|
||||
- ctr: numeric (NULL)
|
||||
- ad_id: character varying (NULL)
|
||||
- reach: bigint (NULL)
|
||||
- spend: numeric (NULL)
|
||||
- clicks: bigint (NULL)
|
||||
- actions: jsonb (NULL)
|
||||
- ad_name: character varying (NULL)
|
||||
- adset_id: character varying (NULL)
|
||||
- date_stop: date (NULL)
|
||||
- frequency: numeric (NULL)
|
||||
- objective: character varying (NULL)
|
||||
- account_id: character varying (NULL)
|
||||
- adset_name: character varying (NULL)
|
||||
- date_start: date (NULL)
|
||||
- campaign_id: character varying (NULL)
|
||||
- conversions: jsonb (NULL)
|
||||
- impressions: bigint (NULL)
|
||||
- account_name: character varying (NULL)
|
||||
- created_time: date (NULL)
|
||||
- action_values: jsonb (NULL)
|
||||
- account_currency: character varying (NULL)
|
||||
- ad_click_actions: jsonb (NULL)
|
||||
- conversion_values: jsonb (NULL)
|
||||
- optimization_goal: character varying (NULL)
|
||||
- cost_per_conversion: jsonb (NULL)
|
||||
- ad_impression_actions: jsonb (NULL)
|
||||
- _airbyte_raw_id: character varying (NOT NULL)
|
||||
- _airbyte_extracted_at: timestamp with time zone (NOT NULL)
|
||||
- _airbyte_generation_id: bigint (NULL)
|
||||
- _airbyte_meta: jsonb (NOT NULL)
|
||||
Rows: 6654
|
||||
Sample: {'cpc': Decimal('1.190000000'), 'cpm': Decimal('10.967742000'), 'cpp': Decimal('13.678161000'), 'ctr': Decimal('0.921659000'), 'ad_id': '120226947319320382', 'reach': 174, 'spend': Decimal('2.380000000'), 'clicks': 2, 'actions': '[{"value": 15, "1d_view": 15, "7d_view": 15, "28d_view": 15, "action_type": "page_engagement", "action_target_id": "0", "action_destination": "unknown"}, {"value": 3, "1d_view": 3, "7d_view": 3, "28d_view": 3, "action_type": "page_engagement", "action_target_id": "1240005757657489", "action_destination": "Content Creator (m/w/d) gesucht – Jetzt bewerben!"}, {"value": 15, "1d_view": 15, "7d_view": 15, "28d_view": 15, "action_type": "post_engagement", "action_target_id": "0", "action_destination": "unknown"}, {"value": 3, "1d_view": 3, "7d_view": 3, "28d_view": 3, "action_type": "post_engagement", "action_target_id": "1240005757657489", "action_destination": "Content Creator (m/w/d) gesucht – Jetzt bewerben!"}, {"value": 15, "1d_view": 15, "7d_view": 15, "28d_view": 15, "action_type": "video_view", "action_target_id": "0", "action_destination": "unknown"}, {"value": 3, "1d_view": 3, "7d_view": 3, "28d_view": 3, "action_type": "video_view", "action_target_id": "1240005757657489", "action_destination": "Content Creator (m/w/d) gesucht – Jetzt bewerben!"}]', 'ad_name': 'Content_Creator_11.06.25', 'adset_id': '120226947319300382', 'date_stop': datetime.date(2025, 11, 14), 'frequency': Decimal('1.247126000'), 'objective': 'OUTCOME_LEADS', 'account_id': '1416908162571377', 'adset_name': 'Content Creator Bewerber 25_InstantForm_DE', 'date_start': datetime.date(2025, 11, 14), 'campaign_id': '120219679742650382', 'conversions': None, 'impressions': 217, 'account_name': '99tales', 'created_time': datetime.date(2025, 6, 11), 'action_values': None, 'account_currency': 'EUR', 'ad_click_actions': None, 'conversion_values': None, 'optimization_goal': 'LEAD_GENERATION', 'cost_per_conversion': None, 'ad_impression_actions': None, '_airbyte_raw_id': '1603d437-0177-4fb7-9f25-0a1f2e91a548', '_airbyte_extracted_at': datetime.datetime(2025, 11, 14, 16, 8, 38, 501000, tzinfo=datetime.timezone.utc), '_airbyte_generation_id': 3, '_airbyte_meta': '{"changes": [], "sync_id": 44}'}
|
||||
|
||||
Table: ads_insights_age_and_gender
|
||||
--------------------------------------------------------------------------------
|
||||
- age: character varying (NULL)
|
||||
- cpc: numeric (NULL)
|
||||
- cpm: numeric (NULL)
|
||||
- cpp: numeric (NULL)
|
||||
- ad_id: character varying (NULL)
|
||||
- clicks: bigint (NULL)
|
||||
- gender: character varying (NULL)
|
||||
- actions: jsonb (NULL)
|
||||
- adset_id: character varying (NULL)
|
||||
- account_id: character varying (NULL)
|
||||
- date_start: date (NULL)
|
||||
- campaign_id: character varying (NULL)
|
||||
- conversions: jsonb (NULL)
|
||||
- account_name: character varying (NULL)
|
||||
- created_time: date (NULL)
|
||||
- _airbyte_raw_id: character varying (NOT NULL)
|
||||
- _airbyte_extracted_at: timestamp with time zone (NOT NULL)
|
||||
- _airbyte_generation_id: bigint (NULL)
|
||||
- _airbyte_meta: jsonb (NOT NULL)
|
||||
Rows: 5962
|
||||
Sample: {'age': '45-54', 'cpc': None, 'cpm': Decimal('3.750000000'), 'cpp': Decimal('4.285714000'), 'ad_id': '120236395246040196', 'clicks': 0, 'gender': 'male', 'actions': None, 'adset_id': '120223804684640196', 'account_id': '238334370765317', 'date_start': datetime.date(2025, 10, 5), 'campaign_id': '120223804684630196', 'conversions': None, 'account_name': 'Hotel Bemelmans Post', 'created_time': datetime.date(2025, 8, 26), '_airbyte_raw_id': 'fc0e034f-5938-4fe7-9364-d36743807245', '_airbyte_extracted_at': datetime.datetime(2025, 11, 3, 15, 47, 3, 587000, tzinfo=datetime.timezone.utc), '_airbyte_generation_id': 0, '_airbyte_meta': '{"changes": [], "sync_id": 1}'}
|
||||
|
||||
Table: ads_insights_country
|
||||
--------------------------------------------------------------------------------
|
||||
- cpc: numeric (NULL)
|
||||
- cpm: numeric (NULL)
|
||||
- cpp: numeric (NULL)
|
||||
- ctr: numeric (NULL)
|
||||
- ad_id: character varying (NULL)
|
||||
- clicks: bigint (NULL)
|
||||
- country: character varying (NULL)
|
||||
- objective: character varying (NULL)
|
||||
- account_id: character varying (NULL)
|
||||
- date_start: date (NULL)
|
||||
- conversions: jsonb (NULL)
|
||||
- impressions: bigint (NULL)
|
||||
- created_time: date (NULL)
|
||||
- account_currency: character varying (NULL)
|
||||
- _airbyte_raw_id: character varying (NOT NULL)
|
||||
- _airbyte_extracted_at: timestamp with time zone (NOT NULL)
|
||||
- _airbyte_generation_id: bigint (NULL)
|
||||
- _airbyte_meta: jsonb (NOT NULL)
|
||||
Rows: 1701
|
||||
Sample: {'cpc': Decimal('0.459545000'), 'cpm': Decimal('12.086073000'), 'cpp': Decimal('15.033457000'), 'ctr': Decimal('2.630006000'), 'ad_id': '120224889911030196', 'clicks': 44, 'country': 'IT', 'objective': 'OUTCOME_LEADS', 'account_id': '238334370765317', 'date_start': datetime.date(2025, 10, 5), 'conversions': None, 'impressions': 1673, 'created_time': datetime.date(2025, 4, 10), 'account_currency': 'EUR', '_airbyte_raw_id': '37a9805d-66c5-4288-ac51-04ec995a1ba6', '_airbyte_extracted_at': datetime.datetime(2025, 11, 3, 15, 51, 24, 448000, tzinfo=datetime.timezone.utc), '_airbyte_generation_id': 0, '_airbyte_meta': '{"changes": [], "sync_id": 1}'}
|
||||
|
||||
Table: campaigns
|
||||
--------------------------------------------------------------------------------
|
||||
- id: character varying (NULL)
|
||||
- name: character varying (NULL)
|
||||
- status: character varying (NULL)
|
||||
- adlabels: jsonb (NULL)
|
||||
- objective: character varying (NULL)
|
||||
- spend_cap: numeric (NULL)
|
||||
- stop_time: timestamp with time zone (NULL)
|
||||
- account_id: character varying (NULL)
|
||||
- start_time: timestamp with time zone (NULL)
|
||||
- buying_type: character varying (NULL)
|
||||
- issues_info: jsonb (NULL)
|
||||
- bid_strategy: character varying (NULL)
|
||||
- created_time: timestamp with time zone (NULL)
|
||||
- daily_budget: numeric (NULL)
|
||||
- updated_time: timestamp with time zone (NULL)
|
||||
- lifetime_budget: numeric (NULL)
|
||||
- budget_remaining: numeric (NULL)
|
||||
- effective_status: character varying (NULL)
|
||||
- boosted_object_id: character varying (NULL)
|
||||
- configured_status: character varying (NULL)
|
||||
- source_campaign_id: numeric (NULL)
|
||||
- special_ad_category: character varying (NULL)
|
||||
- smart_promotion_type: character varying (NULL)
|
||||
- budget_rebalance_flag: boolean (NULL)
|
||||
- special_ad_category_country: jsonb (NULL)
|
||||
- _airbyte_raw_id: character varying (NOT NULL)
|
||||
- _airbyte_extracted_at: timestamp with time zone (NOT NULL)
|
||||
- _airbyte_generation_id: bigint (NULL)
|
||||
- _airbyte_meta: jsonb (NOT NULL)
|
||||
Rows: 25
|
||||
Sample: {'id': '120207428712650382', 'name': 'Bewerber_99tales', 'status': 'PAUSED', 'adlabels': None, 'objective': 'OUTCOME_LEADS', 'spend_cap': None, 'stop_time': None, 'account_id': '1416908162571377', 'start_time': datetime.datetime(2024, 3, 15, 8, 50, 7, tzinfo=datetime.timezone.utc), 'buying_type': 'AUCTION', 'issues_info': None, 'bid_strategy': None, 'created_time': datetime.datetime(2024, 3, 15, 8, 50, 6, tzinfo=datetime.timezone.utc), 'daily_budget': None, 'updated_time': datetime.datetime(2025, 10, 10, 5, 42, 18, tzinfo=datetime.timezone.utc), 'lifetime_budget': None, 'budget_remaining': Decimal('0E-9'), 'effective_status': 'PAUSED', 'boosted_object_id': None, 'configured_status': 'PAUSED', 'source_campaign_id': Decimal('0E-9'), 'special_ad_category': 'EMPLOYMENT', 'smart_promotion_type': 'GUIDED_CREATION', 'budget_rebalance_flag': False, 'special_ad_category_country': '["IT"]', '_airbyte_raw_id': 'd94ca411-6b5a-4382-871c-681de8f35440', '_airbyte_extracted_at': datetime.datetime(2025, 11, 13, 17, 24, 46, 639000, tzinfo=datetime.timezone.utc), '_airbyte_generation_id': 12, '_airbyte_meta': '{"changes": [], "sync_id": 42}'}
|
||||
|
||||
Table: custom_campaign_country
|
||||
--------------------------------------------------------------------------------
|
||||
- cpc: numeric (NULL)
|
||||
- cpm: numeric (NULL)
|
||||
- cpp: numeric (NULL)
|
||||
- ctr: numeric (NULL)
|
||||
- reach: bigint (NULL)
|
||||
- spend: numeric (NULL)
|
||||
- clicks: bigint (NULL)
|
||||
- actions: jsonb (NULL)
|
||||
- country: character varying (NULL)
|
||||
- date_stop: date (NULL)
|
||||
- frequency: numeric (NULL)
|
||||
- objective: character varying (NULL)
|
||||
- account_id: character varying (NULL)
|
||||
- date_start: date (NULL)
|
||||
- campaign_id: character varying (NULL)
|
||||
- impressions: bigint (NULL)
|
||||
- created_time: date (NULL)
|
||||
- social_spend: numeric (NULL)
|
||||
- _airbyte_raw_id: character varying (NOT NULL)
|
||||
- _airbyte_extracted_at: timestamp with time zone (NOT NULL)
|
||||
- _airbyte_generation_id: bigint (NULL)
|
||||
- _airbyte_meta: jsonb (NOT NULL)
|
||||
Rows: 2038
|
||||
Sample: {'cpc': Decimal('0.908257000'), 'cpm': Decimal('7.444729000'), 'cpp': Decimal('9.872357000'), 'ctr': Decimal('0.819672000'), 'reach': 92, 'spend': Decimal('0.908257000'), 'clicks': 1, 'actions': '[{"value": 18, "1d_view": 17, "7d_view": 17, "1d_click": 1, "28d_view": 17, "7d_click": 1, "28d_click": 1, "action_type": "page_engagement"}, {"value": 18, "1d_view": 17, "7d_view": 17, "1d_click": 1, "28d_view": 17, "7d_click": 1, "28d_click": 1, "action_type": "post_engagement"}, {"value": 1, "1d_click": 1, "7d_click": 1, "28d_click": 1, "action_type": "link_click"}, {"value": 17, "1d_view": 17, "7d_view": 17, "28d_view": 17, "action_type": "video_view"}]', 'country': 'AT', 'date_stop': datetime.date(2025, 11, 14), 'frequency': Decimal('1.326087000'), 'objective': 'OUTCOME_LEADS', 'account_id': '1416908162571377', 'date_start': datetime.date(2025, 11, 14), 'campaign_id': '120235573403300382', 'impressions': 122, 'created_time': datetime.date(2025, 10, 31), 'social_spend': None, '_airbyte_raw_id': '31ca2e7a-6757-4c8e-96e8-6e3768c86315', '_airbyte_extracted_at': datetime.datetime(2025, 11, 14, 16, 14, 21, 341000, tzinfo=datetime.timezone.utc), '_airbyte_generation_id': 1, '_airbyte_meta': '{"changes": [], "sync_id": 44}'}
|
||||
|
||||
Table: custom_campaign_device
|
||||
--------------------------------------------------------------------------------
|
||||
- ad_id: character varying (NULL)
|
||||
- reach: bigint (NULL)
|
||||
- spend: numeric (NULL)
|
||||
- clicks: bigint (NULL)
|
||||
- actions: jsonb (NULL)
|
||||
- date_stop: date (NULL)
|
||||
- frequency: numeric (NULL)
|
||||
- account_id: character varying (NULL)
|
||||
- date_start: date (NULL)
|
||||
- campaign_id: character varying (NULL)
|
||||
- impressions: bigint (NULL)
|
||||
- campaign_name: character varying (NULL)
|
||||
- device_platform: character varying (NULL)
|
||||
- _airbyte_raw_id: character varying (NOT NULL)
|
||||
- _airbyte_extracted_at: timestamp with time zone (NOT NULL)
|
||||
- _airbyte_generation_id: bigint (NULL)
|
||||
- _airbyte_meta: jsonb (NOT NULL)
|
||||
Rows: 1291
|
||||
Sample: {'ad_id': None, 'reach': 5, 'spend': Decimal('0.111560000'), 'clicks': 0, 'actions': None, 'date_stop': datetime.date(2025, 11, 14), 'frequency': Decimal('1.000000000'), 'account_id': '1416908162571377', 'date_start': datetime.date(2025, 11, 14), 'campaign_id': '120235573403300382', 'impressions': 5, 'campaign_name': 'MairLorenz_Cold_Traffic_Conversions_InstantForm', 'device_platform': 'desktop', '_airbyte_raw_id': 'a9231688-5cde-4f02-a84d-99759e7ea3d6', '_airbyte_extracted_at': datetime.datetime(2025, 11, 14, 16, 18, 56, 210000, tzinfo=datetime.timezone.utc), '_airbyte_generation_id': 0, '_airbyte_meta': '{"changes": [], "sync_id": 44}'}
|
||||
|
||||
Table: custom_campaign_gender
|
||||
--------------------------------------------------------------------------------
|
||||
- age: character varying (NULL)
|
||||
- cpc: numeric (NULL)
|
||||
- cpm: numeric (NULL)
|
||||
- cpp: numeric (NULL)
|
||||
- ctr: numeric (NULL)
|
||||
- reach: bigint (NULL)
|
||||
- spend: numeric (NULL)
|
||||
- clicks: bigint (NULL)
|
||||
- gender: character varying (NULL)
|
||||
- actions: jsonb (NULL)
|
||||
- date_stop: date (NULL)
|
||||
- frequency: numeric (NULL)
|
||||
- account_id: character varying (NULL)
|
||||
- date_start: date (NULL)
|
||||
- campaign_id: character varying (NULL)
|
||||
- conversions: jsonb (NULL)
|
||||
- impressions: bigint (NULL)
|
||||
- created_time: date (NULL)
|
||||
- updated_time: date (NULL)
|
||||
- campaign_name: character varying (NULL)
|
||||
- purchase_roas: jsonb (NULL)
|
||||
- account_currency: character varying (NULL)
|
||||
- _airbyte_raw_id: character varying (NOT NULL)
|
||||
- _airbyte_extracted_at: timestamp with time zone (NOT NULL)
|
||||
- _airbyte_generation_id: bigint (NULL)
|
||||
- _airbyte_meta: jsonb (NOT NULL)
|
||||
Rows: 8554
|
||||
Sample: {'age': '18-24', 'cpc': None, 'cpm': Decimal('12.060708000'), 'cpp': Decimal('13.916202000'), 'ctr': Decimal('0E-9'), 'reach': 13, 'spend': Decimal('0.180911000'), 'clicks': 0, 'gender': 'female', 'actions': '[{"value": 3, "1d_view": 3, "7d_view": 3, "28d_view": 3, "action_type": "page_engagement"}, {"value": 3, "1d_view": 3, "7d_view": 3, "28d_view": 3, "action_type": "post_engagement"}, {"value": 3, "1d_view": 3, "7d_view": 3, "28d_view": 3, "action_type": "video_view"}]', 'date_stop': datetime.date(2025, 11, 14), 'frequency': Decimal('1.153846000'), 'account_id': '1416908162571377', 'date_start': datetime.date(2025, 11, 14), 'campaign_id': '120218699205090382', 'conversions': None, 'impressions': 15, 'created_time': datetime.date(2025, 3, 15), 'updated_time': datetime.date(2025, 10, 10), 'campaign_name': '15_03_25_Adverto_Hotelmarketing', 'purchase_roas': None, 'account_currency': 'EUR', '_airbyte_raw_id': '4642fee0-6c01-4297-b4fd-48acddf44062', '_airbyte_extracted_at': datetime.datetime(2025, 11, 14, 16, 16, 22, 159000, tzinfo=datetime.timezone.utc), '_airbyte_generation_id': 3, '_airbyte_meta': '{"changes": [], "sync_id": 44}'}
|
||||
|
||||
Table: custom_campaign_gender_ab_soft_reset
|
||||
--------------------------------------------------------------------------------
|
||||
- age: character varying (NULL)
|
||||
- cpc: numeric (NULL)
|
||||
- cpm: numeric (NULL)
|
||||
- cpp: numeric (NULL)
|
||||
- ctr: numeric (NULL)
|
||||
- reach: bigint (NULL)
|
||||
- spend: numeric (NULL)
|
||||
- clicks: bigint (NULL)
|
||||
- gender: character varying (NULL)
|
||||
- actions: jsonb (NULL)
|
||||
- date_stop: date (NULL)
|
||||
- frequency: numeric (NULL)
|
||||
- account_id: character varying (NULL)
|
||||
- date_start: date (NULL)
|
||||
- campaign_id: character varying (NULL)
|
||||
- conversions: jsonb (NULL)
|
||||
- impressions: bigint (NULL)
|
||||
- created_time: date (NULL)
|
||||
- updated_time: date (NULL)
|
||||
- campaign_name: character varying (NULL)
|
||||
- purchase_roas: jsonb (NULL)
|
||||
- account_currency: character varying (NULL)
|
||||
- _airbyte_raw_id: character varying (NOT NULL)
|
||||
- _airbyte_extracted_at: timestamp with time zone (NOT NULL)
|
||||
- _airbyte_generation_id: bigint (NULL)
|
||||
- _airbyte_meta: jsonb (NOT NULL)
|
||||
Rows: 1105
|
||||
Sample: {'age': None, 'cpc': Decimal('1.044085000'), 'cpm': Decimal('22.273805000'), 'cpp': Decimal('24.281038000'), 'ctr': Decimal('2.133333000'), 'reach': 344, 'spend': Decimal('8.352677000'), 'clicks': 8, 'gender': 'female', 'actions': '[{"value": 7, "1d_click": 7, "7d_click": 7, "28d_click": 7, "action_type": "page_engagement"}, {"value": 2, "1d_click": 2, "7d_click": 2, "28d_click": 2, "action_type": "landing_page_view"}, {"value": 2, "1d_click": 2, "7d_click": 2, "28d_click": 2, "action_type": "omni_landing_page_view"}, {"value": 7, "1d_click": 7, "7d_click": 7, "28d_click": 7, "action_type": "post_engagement"}, {"value": 7, "1d_click": 7, "7d_click": 7, "28d_click": 7, "action_type": "link_click"}]', 'date_stop': datetime.date(2025, 11, 11), 'frequency': Decimal('1.090116000'), 'account_id': '238334370765317', 'date_start': datetime.date(2025, 11, 11), 'campaign_id': '120223804684630196', 'conversions': None, 'impressions': 375, 'created_time': datetime.date(2025, 4, 4), 'updated_time': datetime.date(2025, 5, 23), 'campaign_name': 'Conversions_Hotel_Bemelmans_DEU', 'purchase_roas': None, 'account_currency': 'EUR', '_airbyte_raw_id': '37862ffc-80d4-4c32-bb17-85656877ab99', '_airbyte_extracted_at': datetime.datetime(2025, 11, 11, 16, 27, 40, 468000, tzinfo=datetime.timezone.utc), '_airbyte_generation_id': 1, '_airbyte_meta': '{"changes": [], "sync_id": 29}'}
|
||||
|
||||
Table: custom_conversions
|
||||
--------------------------------------------------------------------------------
|
||||
- id: character varying (NULL)
|
||||
- name: character varying (NULL)
|
||||
- rule: character varying (NULL)
|
||||
- business: character varying (NULL)
|
||||
- account_id: character varying (NULL)
|
||||
- description: character varying (NULL)
|
||||
- is_archived: boolean (NULL)
|
||||
- data_sources: jsonb (NULL)
|
||||
- creation_time: timestamp with time zone (NULL)
|
||||
- is_unavailable: boolean (NULL)
|
||||
- retention_days: numeric (NULL)
|
||||
- last_fired_time: timestamp with time zone (NULL)
|
||||
- first_fired_time: timestamp with time zone (NULL)
|
||||
- custom_event_type: character varying (NULL)
|
||||
- event_source_type: character varying (NULL)
|
||||
- default_conversion_value: numeric (NULL)
|
||||
- offline_conversion_data_set: character varying (NULL)
|
||||
- _airbyte_raw_id: character varying (NOT NULL)
|
||||
- _airbyte_extracted_at: timestamp with time zone (NOT NULL)
|
||||
- _airbyte_generation_id: bigint (NULL)
|
||||
- _airbyte_meta: jsonb (NOT NULL)
|
||||
Rows: 12
|
||||
Sample: {'id': '1049186899714335', 'name': 'Purchase', 'rule': '{"and":[{"event":{"eq":"PageView"}},{"or":[{"Event Parameters":{"eq":"purchase"}}]}]}', 'business': None, 'account_id': '277560745', 'description': None, 'is_archived': True, 'data_sources': '[{"id": "7753250311370403", "name": "Spitalerhof Facebook Pixel", "source_type": "PIXEL"}]', 'creation_time': datetime.datetime(2024, 1, 25, 10, 21, 3, tzinfo=datetime.timezone.utc), 'is_unavailable': False, 'retention_days': Decimal('0E-9'), 'last_fired_time': None, 'first_fired_time': None, 'custom_event_type': 'OTHER', 'event_source_type': 'pixel', 'default_conversion_value': Decimal('0E-9'), 'offline_conversion_data_set': None, '_airbyte_raw_id': 'e9adb670-8ebb-441e-b3dc-5163ac5922ec', '_airbyte_extracted_at': datetime.datetime(2025, 11, 14, 16, 9, 25, 869000, tzinfo=datetime.timezone.utc), '_airbyte_generation_id': 41, '_airbyte_meta': '{"changes": [], "sync_id": 44}'}
|
||||
|
||||
Table: customcampaign_insights
|
||||
--------------------------------------------------------------------------------
|
||||
- cpc: numeric (NULL)
|
||||
- cpm: numeric (NULL)
|
||||
- cpp: numeric (NULL)
|
||||
- ctr: numeric (NULL)
|
||||
- ad_id: character varying (NULL)
|
||||
- reach: bigint (NULL)
|
||||
- spend: numeric (NULL)
|
||||
- clicks: bigint (NULL)
|
||||
- actions: jsonb (NULL)
|
||||
- date_stop: date (NULL)
|
||||
- frequency: numeric (NULL)
|
||||
- objective: character varying (NULL)
|
||||
- account_id: character varying (NULL)
|
||||
- date_start: date (NULL)
|
||||
- campaign_id: character varying (NULL)
|
||||
- impressions: bigint (NULL)
|
||||
- created_time: date (NULL)
|
||||
- campaign_name: character varying (NULL)
|
||||
- purchase_roas: jsonb (NULL)
|
||||
- optimization_goal: character varying (NULL)
|
||||
- _airbyte_raw_id: character varying (NOT NULL)
|
||||
- _airbyte_extracted_at: timestamp with time zone (NOT NULL)
|
||||
- _airbyte_generation_id: bigint (NULL)
|
||||
- _airbyte_meta: jsonb (NOT NULL)
|
||||
Rows: 2328
|
||||
Sample: {'cpc': Decimal('0.565714000'), 'cpm': Decimal('13.846154000'), 'cpp': Decimal('17.217391000'), 'ctr': Decimal('2.447552000'), 'ad_id': None, 'reach': 690, 'spend': Decimal('11.880000000'), 'clicks': 21, 'actions': '[{"value": 3, "1d_click": 3, "7d_click": 3, "28d_click": 3, "action_type": "offsite_complete_registration_add_meta_leads"}, {"value": 174, "1d_view": 161, "7d_view": 161, "1d_click": 13, "28d_view": 161, "7d_click": 13, "28d_click": 13, "action_type": "page_engagement"}, {"value": 174, "1d_view": 161, "7d_view": 161, "1d_click": 13, "28d_view": 161, "7d_click": 13, "28d_click": 13, "action_type": "post_engagement"}, {"value": 3, "1d_click": 3, "7d_click": 3, "28d_click": 3, "action_type": "offsite_content_view_add_meta_leads"}, {"value": 3, "1d_click": 3, "7d_click": 3, "28d_click": 3, "action_type": "lead"}, {"value": 3, "1d_click": 3, "7d_click": 3, "28d_click": 3, "action_type": "offsite_search_add_meta_leads"}, {"value": 11, "1d_click": 11, "7d_click": 11, "28d_click": 11, "action_type": "link_click"}, {"value": 3, "1d_click": 3, "7d_click": 3, "28d_click": 3, "action_type": "onsite_conversion.lead_grouped"}, {"value": 163, "1d_view": 161, "7d_view": 161, "1d_click": 2, "28d_view": 161, "7d_click": 2, "28d_click": 2, "action_type": "video_view"}]', 'date_stop': datetime.date(2025, 11, 14), 'frequency': Decimal('1.243478000'), 'objective': 'OUTCOME_LEADS', 'account_id': '1416908162571377', 'date_start': datetime.date(2025, 11, 14), 'campaign_id': '120218699205090382', 'impressions': 858, 'created_time': datetime.date(2025, 3, 15), 'campaign_name': '15_03_25_Adverto_Hotelmarketing', 'purchase_roas': None, 'optimization_goal': 'Unknown Optimization Goal', '_airbyte_raw_id': '4ad10a16-dd49-4599-bd7c-65a349edbd4c', '_airbyte_extracted_at': datetime.datetime(2025, 11, 14, 16, 11, 34, 572000, tzinfo=datetime.timezone.utc), '_airbyte_generation_id': 4, '_airbyte_meta': '{"changes": [], "sync_id": 44}'}
|
||||
|
||||
================================================================================
|
||||
SCHEMA: google
|
||||
================================================================================
|
||||
|
||||
Table: account_performance_report
|
||||
--------------------------------------------------------------------------------
|
||||
- customer_id: bigint (NULL)
|
||||
- metrics_ctr: numeric (NULL)
|
||||
- segments_date: date (NULL)
|
||||
- metrics_clicks: bigint (NULL)
|
||||
- segments_device: character varying (NULL)
|
||||
- customer_manager: boolean (NULL)
|
||||
- customer_time_zone: character varying (NULL)
|
||||
- metrics_average_cpc: numeric (NULL)
|
||||
- metrics_average_cpe: numeric (NULL)
|
||||
- metrics_average_cpm: numeric (NULL)
|
||||
- metrics_average_cpv: numeric (NULL)
|
||||
- metrics_conversions: numeric (NULL)
|
||||
- metrics_cost_micros: bigint (NULL)
|
||||
- metrics_engagements: bigint (NULL)
|
||||
- metrics_impressions: bigint (NULL)
|
||||
- metrics_video_views: bigint (NULL)
|
||||
- metrics_average_cost: numeric (NULL)
|
||||
- metrics_interactions: bigint (NULL)
|
||||
- segments_day_of_week: character varying (NULL)
|
||||
- customer_test_account: boolean (NULL)
|
||||
- customer_currency_code: character varying (NULL)
|
||||
- metrics_active_view_cpm: numeric (NULL)
|
||||
- metrics_active_view_ctr: numeric (NULL)
|
||||
- metrics_all_conversions: numeric (NULL)
|
||||
- metrics_engagement_rate: numeric (NULL)
|
||||
- metrics_video_view_rate: numeric (NULL)
|
||||
- metrics_interaction_rate: numeric (NULL)
|
||||
- segments_ad_network_type: character varying (NULL)
|
||||
- customer_descriptive_name: character varying (NULL)
|
||||
- metrics_conversions_value: numeric (NULL)
|
||||
- metrics_cost_per_conversion: numeric (NULL)
|
||||
- metrics_value_per_conversion: numeric (NULL)
|
||||
- customer_auto_tagging_enabled: boolean (NULL)
|
||||
- metrics_all_conversions_value: numeric (NULL)
|
||||
- metrics_active_view_impressions: bigint (NULL)
|
||||
- metrics_active_view_viewability: numeric (NULL)
|
||||
- metrics_interaction_event_types: jsonb (NULL)
|
||||
- metrics_search_impression_share: numeric (NULL)
|
||||
- metrics_content_impression_share: numeric (NULL)
|
||||
- metrics_cost_per_all_conversions: numeric (NULL)
|
||||
- metrics_cross_device_conversions: numeric (NULL)
|
||||
- metrics_view_through_conversions: bigint (NULL)
|
||||
- metrics_active_view_measurability: numeric (NULL)
|
||||
- metrics_value_per_all_conversions: numeric (NULL)
|
||||
- metrics_search_rank_lost_impression_share: numeric (NULL)
|
||||
- metrics_active_view_measurable_cost_micros: bigint (NULL)
|
||||
- metrics_active_view_measurable_impressions: bigint (NULL)
|
||||
- metrics_content_rank_lost_impression_share: numeric (NULL)
|
||||
- metrics_conversions_from_interactions_rate: numeric (NULL)
|
||||
- metrics_search_budget_lost_impression_share: numeric (NULL)
|
||||
- metrics_search_exact_match_impression_share: numeric (NULL)
|
||||
- metrics_content_budget_lost_impression_share: numeric (NULL)
|
||||
- metrics_all_conversions_from_interactions_rate: numeric (NULL)
|
||||
- _airbyte_raw_id: character varying (NOT NULL)
|
||||
- _airbyte_extracted_at: timestamp with time zone (NOT NULL)
|
||||
- _airbyte_generation_id: bigint (NULL)
|
||||
- _airbyte_meta: jsonb (NOT NULL)
|
||||
Rows: 543
|
||||
Sample: {'customer_id': 7581209925, 'metrics_ctr': Decimal('0.083333333'), 'segments_date': datetime.date(2025, 11, 14), 'metrics_clicks': 1, 'segments_device': 'DESKTOP', 'customer_manager': False, 'customer_time_zone': 'Europe/Rome', 'metrics_average_cpc': Decimal('10000.000000000'), 'metrics_average_cpe': None, 'metrics_average_cpm': Decimal('833333.333333333'), 'metrics_average_cpv': None, 'metrics_conversions': Decimal('0E-9'), 'metrics_cost_micros': 10000, 'metrics_engagements': 0, 'metrics_impressions': 12, 'metrics_video_views': 0, 'metrics_average_cost': Decimal('10000.000000000'), 'metrics_interactions': 1, 'segments_day_of_week': 'FRIDAY', 'customer_test_account': False, 'customer_currency_code': 'EUR', 'metrics_active_view_cpm': None, 'metrics_active_view_ctr': None, 'metrics_all_conversions': Decimal('0E-9'), 'metrics_engagement_rate': None, 'metrics_video_view_rate': None, 'metrics_interaction_rate': Decimal('0.083333333'), 'segments_ad_network_type': 'MIXED', 'customer_descriptive_name': 'Hotel Bemelmans Post', 'metrics_conversions_value': Decimal('0E-9'), 'metrics_cost_per_conversion': None, 'metrics_value_per_conversion': None, 'customer_auto_tagging_enabled': True, 'metrics_all_conversions_value': Decimal('0E-9'), 'metrics_active_view_impressions': 0, 'metrics_active_view_viewability': None, 'metrics_interaction_event_types': '["2"]', 'metrics_search_impression_share': None, 'metrics_content_impression_share': None, 'metrics_cost_per_all_conversions': None, 'metrics_cross_device_conversions': Decimal('0E-9'), 'metrics_view_through_conversions': 0, 'metrics_active_view_measurability': Decimal('0E-9'), 'metrics_value_per_all_conversions': None, 'metrics_search_rank_lost_impression_share': None, 'metrics_active_view_measurable_cost_micros': 0, 'metrics_active_view_measurable_impressions': 0, 'metrics_content_rank_lost_impression_share': None, 'metrics_conversions_from_interactions_rate': Decimal('0E-9'), 'metrics_search_budget_lost_impression_share': None, 'metrics_search_exact_match_impression_share': None, 'metrics_content_budget_lost_impression_share': None, 'metrics_all_conversions_from_interactions_rate': Decimal('0E-9'), '_airbyte_raw_id': '2ed0b149-adef-4b9d-a7f8-2276ec48d9ee', '_airbyte_extracted_at': datetime.datetime(2025, 11, 14, 19, 8, 43, 233000, tzinfo=datetime.timezone.utc), '_airbyte_generation_id': 1, '_airbyte_meta': '{"changes": [], "sync_id": 45}'}
|
||||
|
||||
Table: campaign_metrics
|
||||
--------------------------------------------------------------------------------
|
||||
- campaign_id: bigint (NULL)
|
||||
- campaign_name: character varying (NULL)
|
||||
- segments_date: date (NULL)
|
||||
- metrics_clicks: bigint (NULL)
|
||||
- metrics_orders: numeric (NULL)
|
||||
- campaign_status: character varying (NULL)
|
||||
- campaign_start_date: character varying (NULL)
|
||||
- metrics_conversions: numeric (NULL)
|
||||
- metrics_cost_micros: bigint (NULL)
|
||||
- metrics_impressions: bigint (NULL)
|
||||
- metrics_interactions: bigint (NULL)
|
||||
- metrics_all_conversions: numeric (NULL)
|
||||
- campaign_campaign_budget: character varying (NULL)
|
||||
- metrics_conversions_value: numeric (NULL)
|
||||
- campaign_optimization_score: numeric (NULL)
|
||||
- _airbyte_raw_id: character varying (NOT NULL)
|
||||
- _airbyte_extracted_at: timestamp with time zone (NOT NULL)
|
||||
- _airbyte_generation_id: bigint (NULL)
|
||||
- _airbyte_meta: jsonb (NOT NULL)
|
||||
Rows: 510
|
||||
Sample: {'campaign_id': 183901432, 'campaign_name': 'WW_DE_Brand', 'segments_date': datetime.date(2025, 10, 27), 'metrics_clicks': 8, 'metrics_orders': Decimal('0E-9'), 'campaign_status': 'ENABLED', 'campaign_start_date': '2015-01-25', 'metrics_conversions': Decimal('0E-9'), 'metrics_cost_micros': 9660000, 'metrics_impressions': 60, 'metrics_interactions': 8, 'metrics_all_conversions': Decimal('0E-9'), 'campaign_campaign_budget': 'customers/7581209925/campaignBudgets/173136112', 'metrics_conversions_value': Decimal('0E-9'), 'campaign_optimization_score': Decimal('0.474560674'), '_airbyte_raw_id': 'b992b3dd-07cb-4ef6-ad60-0a27454de75c', '_airbyte_extracted_at': datetime.datetime(2025, 11, 11, 10, 30, 54, 31000, tzinfo=datetime.timezone.utc), '_airbyte_generation_id': 1, '_airbyte_meta': '{"changes": [], "sync_id": 26}'}
|
||||
|
||||
================================================================================
|
||||
SCHEMA: alpinebits
|
||||
================================================================================
|
||||
|
||||
Table: acked_requests
|
||||
--------------------------------------------------------------------------------
|
||||
- id: integer (NOT NULL)
|
||||
- client_id: character varying (NULL)
|
||||
- unique_id: character varying (NULL)
|
||||
- timestamp: timestamp with time zone (NULL)
|
||||
- username: character varying (NULL)
|
||||
Rows: 814
|
||||
Sample: {'id': 1, 'client_id': 'ASA HOTEL 25.10 @', 'unique_id': '666247dc-9d5a-4eb7-87a7-677bf646', 'timestamp': datetime.datetime(2025, 10, 8, 8, 55, 44, 149632, tzinfo=datetime.timezone.utc), 'username': None}
|
||||
|
||||
Table: conversions
|
||||
--------------------------------------------------------------------------------
|
||||
- id: integer (NOT NULL)
|
||||
- reservation_id: integer (NULL)
|
||||
- customer_id: integer (NULL)
|
||||
- hashed_customer_id: integer (NULL)
|
||||
- hotel_id: character varying (NULL)
|
||||
- pms_reservation_id: character varying (NULL)
|
||||
- reservation_number: character varying (NULL)
|
||||
- reservation_date: date (NULL)
|
||||
- creation_time: timestamp with time zone (NULL)
|
||||
- reservation_type: character varying (NULL)
|
||||
- booking_channel: character varying (NULL)
|
||||
- advertising_medium: character varying (NULL)
|
||||
- advertising_partner: character varying (NULL)
|
||||
- advertising_campagne: character varying (NULL)
|
||||
- arrival_date: date (NULL)
|
||||
- departure_date: date (NULL)
|
||||
- room_status: character varying (NULL)
|
||||
- room_type: character varying (NULL)
|
||||
- room_number: character varying (NULL)
|
||||
- num_adults: integer (NULL)
|
||||
- rate_plan_code: character varying (NULL)
|
||||
- sale_date: date (NULL)
|
||||
- revenue_total: character varying (NULL)
|
||||
- revenue_logis: character varying (NULL)
|
||||
- revenue_board: character varying (NULL)
|
||||
- revenue_fb: character varying (NULL)
|
||||
- revenue_spa: character varying (NULL)
|
||||
- revenue_other: character varying (NULL)
|
||||
- created_at: timestamp with time zone (NULL)
|
||||
Rows: 0
|
||||
|
||||
Table: customers
|
||||
--------------------------------------------------------------------------------
|
||||
- id: integer (NOT NULL)
|
||||
- given_name: character varying (NULL)
|
||||
- contact_id: character varying (NULL)
|
||||
- surname: character varying (NULL)
|
||||
- name_prefix: character varying (NULL)
|
||||
- email_address: character varying (NULL)
|
||||
- phone: character varying (NULL)
|
||||
- email_newsletter: boolean (NULL)
|
||||
- address_line: character varying (NULL)
|
||||
- city_name: character varying (NULL)
|
||||
- postal_code: character varying (NULL)
|
||||
- country_code: character varying (NULL)
|
||||
- gender: character varying (NULL)
|
||||
- birth_date: character varying (NULL)
|
||||
- language: character varying (NULL)
|
||||
- address_catalog: boolean (NULL)
|
||||
- name_title: character varying (NULL)
|
||||
Rows: 487
|
||||
Sample: {'id': 1, 'given_name': 'Adriana', 'contact_id': 'd2e59109-a911-4f8f-8339-d9b119a29e56', 'surname': 'Balasa', 'name_prefix': 'Familie', 'email_address': 'adriana.balasa@yahoo.com', 'phone': '+4917645952405', 'email_newsletter': False, 'address_line': None, 'city_name': None, 'postal_code': None, 'country_code': None, 'gender': None, 'birth_date': None, 'language': 'de', 'address_catalog': False, 'name_title': None}
|
||||
|
||||
Table: hashed_customers
|
||||
--------------------------------------------------------------------------------
|
||||
- id: integer (NOT NULL)
|
||||
- customer_id: integer (NOT NULL)
|
||||
- contact_id: character varying (NULL)
|
||||
- hashed_email: character varying (NULL)
|
||||
- hashed_phone: character varying (NULL)
|
||||
- hashed_given_name: character varying (NULL)
|
||||
- hashed_surname: character varying (NULL)
|
||||
- hashed_city: character varying (NULL)
|
||||
- hashed_postal_code: character varying (NULL)
|
||||
- hashed_country_code: character varying (NULL)
|
||||
- hashed_gender: character varying (NULL)
|
||||
- hashed_birth_date: character varying (NULL)
|
||||
- created_at: timestamp with time zone (NULL)
|
||||
Rows: 487
|
||||
Sample: {'id': 387, 'customer_id': 196, 'contact_id': 'e7718c11-b5b1-43bb-a74e-4c0a2edf7938', 'hashed_email': 'b56472d4360348ef2aa9df7479d9adcb031aa83291c9b6d93eaa9c1b38ad9522', 'hashed_phone': '29cbb04171b6cf2ad9f49a01904d94bf1e4e5d5f41460b5e085b9d832376918a', 'hashed_given_name': 'c49b876c05fd376922883ceaf5b7e7871ae4ae9f24e79958f3fc70efe114627d', 'hashed_surname': 'fb18245487ca9ab219a007c65da495254f9cf8b3a89a2341e1d9a9b2a1e26ba4', 'hashed_city': None, 'hashed_postal_code': None, 'hashed_country_code': None, 'hashed_gender': None, 'hashed_birth_date': None, 'created_at': datetime.datetime(2025, 10, 20, 15, 51, 14, 783241, tzinfo=datetime.timezone.utc)}
|
||||
|
||||
Table: reservations
|
||||
--------------------------------------------------------------------------------
|
||||
- id: integer (NOT NULL)
|
||||
- customer_id: integer (NULL)
|
||||
- unique_id: character varying (NULL)
|
||||
- md5_unique_id: character varying (NULL)
|
||||
- start_date: date (NULL)
|
||||
- end_date: date (NULL)
|
||||
- num_adults: integer (NULL)
|
||||
- num_children: integer (NULL)
|
||||
- children_ages: character varying (NULL)
|
||||
- offer: character varying (NULL)
|
||||
- created_at: timestamp with time zone (NULL)
|
||||
- utm_source: character varying (NULL)
|
||||
- utm_medium: character varying (NULL)
|
||||
- utm_campaign: character varying (NULL)
|
||||
- utm_term: character varying (NULL)
|
||||
- utm_content: character varying (NULL)
|
||||
- user_comment: character varying (NULL)
|
||||
- fbclid: character varying (NULL)
|
||||
- gclid: character varying (NULL)
|
||||
- hotel_code: character varying (NULL)
|
||||
- hotel_name: character varying (NULL)
|
||||
- room_type_code: character varying (NULL)
|
||||
- room_classification_code: character varying (NULL)
|
||||
- room_type: character varying (NULL)
|
||||
- meta_account_id: character varying (NULL)
|
||||
- google_account_id: character varying (NULL)
|
||||
Rows: 517
|
||||
Sample: {'id': 1, 'customer_id': 1, 'unique_id': '5abe9219-37f6-415f-97ff-96846313f16c', 'md5_unique_id': '161e4c2752efd919e8b03a1178bfe61c', 'start_date': datetime.date(2025, 12, 26), 'end_date': datetime.date(2025, 9, 28), 'num_adults': 2, 'num_children': 2, 'children_ages': '9,16', 'offer': 'Zimmer: Doppelzimmer', 'created_at': datetime.datetime(2025, 10, 6, 13, 55, 18, 195026, tzinfo=datetime.timezone.utc), 'utm_source': 'fb', 'utm_medium': 'Facebook_Mobile_Reels', 'utm_campaign': 'Conversions_Hotel_Bemelmans_DEU', 'utm_term': 'Cold_Traffic_Conversions_Hotel_Bemelmans_DEU', 'utm_content': 'Grafik_1_Advent am Ritten', 'user_comment': '', 'fbclid': 'IwZXh0bgNhZW0BMABhZGlkAasrrpKSEUQBHrQyRa1AdOw96ax7dXZoqFypxoThi7Rudg8daveq68g7gpYOI2uO2GEjlkFh_aem_K79wHg9HhLRag_qyO_ibzQ', 'gclid': '', 'hotel_code': '123', 'hotel_name': 'Frangart Inn', 'room_type_code': None, 'room_classification_code': None, 'room_type': None, 'meta_account_id': None, 'google_account_id': None}
|
||||
|
||||
================================================================================
|
||||
Exploration complete! Output saved to schema_info.txt
|
||||
@@ -1,291 +0,0 @@
|
||||
"""
|
||||
OAuth2 authentication module for Meta/Facebook API.
|
||||
|
||||
Handles:
|
||||
- Initial OAuth2 flow for short-lived tokens
|
||||
- Exchange short-lived for long-lived tokens (60 days)
|
||||
- Automatic token refresh
|
||||
- Token persistence
|
||||
"""
|
||||
|
||||
import json
|
||||
import os
|
||||
import time
|
||||
from datetime import datetime, timedelta
|
||||
from pathlib import Path
|
||||
from typing import Dict, Optional
|
||||
|
||||
import requests
|
||||
from dotenv import load_dotenv
|
||||
from requests_oauthlib import OAuth2Session
|
||||
from requests_oauthlib.compliance_fixes import facebook_compliance_fix
|
||||
|
||||
|
||||
class MetaOAuth2:
|
||||
"""Handle OAuth2 authentication flow for Meta/Facebook API."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
app_id: Optional[str] = None,
|
||||
app_secret: Optional[str] = None,
|
||||
redirect_uri: str = "https://localhost/",
|
||||
api_version: str = "v18.0",
|
||||
):
|
||||
"""
|
||||
Initialize OAuth2 handler.
|
||||
|
||||
Args:
|
||||
app_id: Facebook App ID (or set META_APP_ID env var)
|
||||
app_secret: Facebook App Secret (or set META_APP_SECRET env var)
|
||||
redirect_uri: OAuth redirect URI
|
||||
api_version: Facebook API version
|
||||
"""
|
||||
load_dotenv()
|
||||
|
||||
self.app_id = app_id or os.getenv("META_APP_ID")
|
||||
self.app_secret = app_secret or os.getenv("META_APP_SECRET")
|
||||
self.redirect_uri = redirect_uri
|
||||
self.api_version = api_version
|
||||
|
||||
if not self.app_id or not self.app_secret:
|
||||
raise ValueError(
|
||||
"App ID and App Secret are required. "
|
||||
"Provide them as arguments or set META_APP_ID and META_APP_SECRET env vars."
|
||||
)
|
||||
|
||||
self.authorization_base_url = f"https://www.facebook.com/{api_version}/dialog/oauth"
|
||||
self.token_url = f"https://graph.facebook.com/{api_version}/oauth/access_token"
|
||||
|
||||
# Scopes needed for ads insights
|
||||
self.scopes = ["ads_management", "ads_read"]
|
||||
|
||||
def get_authorization_url(self) -> tuple[str, str]:
|
||||
"""
|
||||
Get the authorization URL for the user to visit.
|
||||
|
||||
Returns:
|
||||
Tuple of (authorization_url, state)
|
||||
"""
|
||||
facebook = OAuth2Session(
|
||||
self.app_id,
|
||||
redirect_uri=self.redirect_uri,
|
||||
scope=self.scopes,
|
||||
)
|
||||
facebook = facebook_compliance_fix(facebook)
|
||||
|
||||
authorization_url, state = facebook.authorization_url(self.authorization_base_url)
|
||||
return authorization_url, state
|
||||
|
||||
def fetch_token(self, authorization_response: str) -> Dict[str, str]:
|
||||
"""
|
||||
Exchange the authorization code for an access token.
|
||||
|
||||
Args:
|
||||
authorization_response: The full redirect URL after authorization
|
||||
|
||||
Returns:
|
||||
Token dictionary containing access_token and other info
|
||||
"""
|
||||
facebook = OAuth2Session(
|
||||
self.app_id,
|
||||
redirect_uri=self.redirect_uri,
|
||||
scope=self.scopes,
|
||||
)
|
||||
facebook = facebook_compliance_fix(facebook)
|
||||
|
||||
token = facebook.fetch_token(
|
||||
self.token_url,
|
||||
client_secret=self.app_secret,
|
||||
authorization_response=authorization_response,
|
||||
)
|
||||
|
||||
return token
|
||||
|
||||
def exchange_for_long_lived_token(self, short_lived_token: str) -> Dict[str, any]:
|
||||
"""
|
||||
Exchange short-lived token for long-lived token (60 days).
|
||||
|
||||
Args:
|
||||
short_lived_token: The short-lived access token from OAuth flow
|
||||
|
||||
Returns:
|
||||
Dictionary with access_token and expires_in
|
||||
"""
|
||||
url = "https://graph.facebook.com/oauth/access_token"
|
||||
params = {
|
||||
"grant_type": "fb_exchange_token",
|
||||
"client_id": self.app_id,
|
||||
"client_secret": self.app_secret,
|
||||
"fb_exchange_token": short_lived_token,
|
||||
}
|
||||
|
||||
response = requests.get(url, params=params)
|
||||
response.raise_for_status()
|
||||
|
||||
data = response.json()
|
||||
return {
|
||||
"access_token": data["access_token"],
|
||||
"expires_in": data.get("expires_in", 5184000), # Default 60 days
|
||||
"token_type": data.get("token_type", "bearer"),
|
||||
}
|
||||
|
||||
def get_token_info(self, access_token: str) -> Dict[str, any]:
|
||||
"""
|
||||
Get information about an access token including expiry.
|
||||
|
||||
Args:
|
||||
access_token: Access token to inspect
|
||||
|
||||
Returns:
|
||||
Dictionary with token info including expires_at, is_valid, etc.
|
||||
"""
|
||||
url = "https://graph.facebook.com/debug_token"
|
||||
params = {
|
||||
"input_token": access_token,
|
||||
"access_token": f"{self.app_id}|{self.app_secret}",
|
||||
}
|
||||
|
||||
response = requests.get(url, params=params)
|
||||
response.raise_for_status()
|
||||
|
||||
data = response.json()
|
||||
return data.get("data", {})
|
||||
|
||||
def interactive_auth(self, exchange_for_long_lived: bool = True) -> str:
|
||||
"""
|
||||
Run interactive OAuth2 flow to get access token.
|
||||
|
||||
Args:
|
||||
exchange_for_long_lived: If True, exchanges short-lived for long-lived token (60 days)
|
||||
|
||||
Returns:
|
||||
Access token string (long-lived if exchange_for_long_lived=True)
|
||||
"""
|
||||
print("\n" + "="*60)
|
||||
print("META/FACEBOOK OAUTH2 AUTHENTICATION")
|
||||
print("="*60)
|
||||
|
||||
# Get authorization URL
|
||||
auth_url, state = self.get_authorization_url()
|
||||
|
||||
print("\n1. Please visit this URL in your browser:")
|
||||
print(f"\n {auth_url}\n")
|
||||
print("2. Authorize the application")
|
||||
print("3. You will be redirected to localhost (this is expected)")
|
||||
print("4. Copy the FULL URL from your browser's address bar")
|
||||
print(" (it will look like: https://localhost/?code=...)")
|
||||
|
||||
# Wait for user to paste the redirect URL
|
||||
redirect_response = input("\nPaste the full redirect URL here: ").strip()
|
||||
|
||||
# Fetch the short-lived token
|
||||
print("\nFetching short-lived access token...")
|
||||
token = self.fetch_token(redirect_response)
|
||||
short_lived_token = token["access_token"]
|
||||
|
||||
# Exchange for long-lived token
|
||||
if exchange_for_long_lived:
|
||||
print("Exchanging for long-lived token (60 days)...")
|
||||
long_lived_data = self.exchange_for_long_lived_token(short_lived_token)
|
||||
access_token = long_lived_data["access_token"]
|
||||
expires_in = long_lived_data["expires_in"]
|
||||
|
||||
print(f"\n✅ Long-lived token obtained!")
|
||||
print(f" Valid for: {expires_in / 86400:.0f} days (~{expires_in / 3600:.0f} hours)")
|
||||
else:
|
||||
access_token = short_lived_token
|
||||
print("\n✅ Short-lived token obtained (valid for ~1-2 hours)")
|
||||
|
||||
print("="*60)
|
||||
|
||||
# Save token with metadata
|
||||
self._offer_to_save_token_with_metadata(access_token)
|
||||
|
||||
return access_token
|
||||
|
||||
def _offer_to_save_token_with_metadata(self, access_token: str):
|
||||
"""Offer to save the access token with metadata to both .env and JSON file."""
|
||||
save = input("\nWould you like to save this token? (y/n): ").strip().lower()
|
||||
|
||||
if save == "y":
|
||||
# Get token info
|
||||
try:
|
||||
token_info = self.get_token_info(access_token)
|
||||
expires_at = token_info.get("expires_at", 0)
|
||||
is_valid = token_info.get("is_valid", False)
|
||||
issued_at = token_info.get("issued_at", int(time.time()))
|
||||
except Exception as e:
|
||||
print(f"Warning: Could not get token info: {e}")
|
||||
expires_at = int(time.time()) + 5184000 # Assume 60 days
|
||||
is_valid = True
|
||||
issued_at = int(time.time())
|
||||
|
||||
# Save to .env
|
||||
env_path = Path(".env")
|
||||
if env_path.exists():
|
||||
env_content = env_path.read_text()
|
||||
else:
|
||||
env_content = ""
|
||||
|
||||
lines = env_content.split("\n")
|
||||
token_line = f"META_ACCESS_TOKEN={access_token}"
|
||||
updated = False
|
||||
|
||||
for i, line in enumerate(lines):
|
||||
if line.startswith("META_ACCESS_TOKEN="):
|
||||
lines[i] = token_line
|
||||
updated = True
|
||||
break
|
||||
|
||||
if not updated:
|
||||
lines.append(token_line)
|
||||
|
||||
env_path.write_text("\n".join(lines))
|
||||
print(f"\n✅ Token saved to {env_path}")
|
||||
|
||||
# Save metadata to JSON for token refresh logic
|
||||
token_metadata = {
|
||||
"access_token": access_token,
|
||||
"expires_at": expires_at,
|
||||
"issued_at": issued_at,
|
||||
"is_valid": is_valid,
|
||||
"updated_at": int(time.time()),
|
||||
}
|
||||
|
||||
token_file = Path(".meta_token.json")
|
||||
token_file.write_text(json.dumps(token_metadata, indent=2))
|
||||
print(f"✅ Token metadata saved to {token_file}")
|
||||
|
||||
# Print expiry info
|
||||
if expires_at:
|
||||
expires_dt = datetime.fromtimestamp(expires_at)
|
||||
days_until_expiry = (expires_dt - datetime.now()).days
|
||||
print(f"\n📅 Token expires: {expires_dt.strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
print(f" ({days_until_expiry} days from now)")
|
||||
else:
|
||||
print(f"\nAccess token: {access_token}")
|
||||
print("You can manually add it to .env as META_ACCESS_TOKEN")
|
||||
|
||||
|
||||
def main():
|
||||
"""Run interactive OAuth2 flow."""
|
||||
try:
|
||||
oauth = MetaOAuth2()
|
||||
access_token = oauth.interactive_auth()
|
||||
print(f"\nYou can now use this access token with the insights grabber.")
|
||||
|
||||
except ValueError as e:
|
||||
print(f"\nConfiguration error: {e}")
|
||||
print("\nPlease set META_APP_ID and META_APP_SECRET in .env")
|
||||
return 1
|
||||
except Exception as e:
|
||||
print(f"\nError: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return 1
|
||||
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
exit(main())
|
||||
@@ -85,16 +85,36 @@ class TimescaleDBClient:
|
||||
|
||||
# Execute schema statement by statement for better error handling
|
||||
async with self.pool.acquire() as conn:
|
||||
statements = [s.strip() for s in schema_sql.split(';') if s.strip()]
|
||||
# Set a reasonable timeout for schema operations (5 minutes per statement)
|
||||
await conn.execute("SET statement_timeout = '300s'")
|
||||
|
||||
# Split statements and track their line numbers in the original file
|
||||
statements = []
|
||||
current_line = 1
|
||||
for stmt in schema_sql.split(';'):
|
||||
stmt_stripped = stmt.strip()
|
||||
if stmt_stripped:
|
||||
# Count newlines before this statement to get starting line
|
||||
statements.append((stmt_stripped, current_line))
|
||||
# Update line counter (count newlines in the original chunk including ';')
|
||||
current_line += stmt.count('\n') + 1
|
||||
|
||||
errors = []
|
||||
compression_warnings = []
|
||||
|
||||
for i, stmt in enumerate(statements, 1):
|
||||
for i, (stmt, line_num) in enumerate(statements, 1):
|
||||
if not stmt:
|
||||
continue
|
||||
|
||||
# Show progress for potentially slow operations
|
||||
stmt_lower = stmt.lower()
|
||||
if any(keyword in stmt_lower for keyword in ['refresh materialized view', 'drop schema', 'create index']):
|
||||
print(f" Executing statement {i} (line {line_num})...")
|
||||
|
||||
try:
|
||||
await conn.execute(stmt)
|
||||
except asyncio.TimeoutError:
|
||||
errors.append((i, line_num, "Statement execution timed out (>5 minutes)"))
|
||||
except Exception as stmt_error:
|
||||
error_msg = str(stmt_error).lower()
|
||||
|
||||
@@ -104,19 +124,22 @@ class TimescaleDBClient:
|
||||
continue
|
||||
elif "columnstore not enabled" in error_msg:
|
||||
# Track compression warnings separately
|
||||
compression_warnings.append(i)
|
||||
compression_warnings.append((i, line_num))
|
||||
elif "'nonetype' object has no attribute 'decode'" in error_msg:
|
||||
# Silently ignore decode errors (usually comments/extensions)
|
||||
continue
|
||||
elif "canceling statement due to statement timeout" in error_msg:
|
||||
# Handle PostgreSQL timeout errors
|
||||
errors.append((i, line_num, "Statement execution timed out (>5 minutes)"))
|
||||
else:
|
||||
# Real errors
|
||||
errors.append((i, stmt_error))
|
||||
errors.append((i, line_num, stmt_error))
|
||||
|
||||
# Report results
|
||||
if errors:
|
||||
print(f"⚠️ {len(errors)} error(s) during schema initialization:")
|
||||
for stmt_num, error in errors:
|
||||
print(f" Statement {stmt_num}: {error}")
|
||||
for stmt_num, line_num, error in errors:
|
||||
print(f" Statement {stmt_num} (line {line_num}): {error}")
|
||||
|
||||
if compression_warnings:
|
||||
print("ℹ️ Note: Data compression not available (TimescaleDB columnstore not enabled)")
|
||||
|
||||
@@ -1,363 +1,652 @@
|
||||
-- TimescaleDB Schema for Meta Ad Insights
|
||||
-- This schema is optimized for time-series data collection and dashboard queries
|
||||
|
||||
-- Enable TimescaleDB extension (run as superuser)
|
||||
-- CREATE EXTENSION IF NOT EXISTS timescaledb;
|
||||
|
||||
-- Cleanup schema public first.
|
||||
|
||||
---DROP SCHEMA IF EXISTS public CASCADE;
|
||||
|
||||
-- Recreate schema public.
|
||||
|
||||
CREATE SCHEMA public if not exists;
|
||||
|
||||
-- Set ownership to meta_user
|
||||
ALTER SCHEMA public OWNER TO meta_user;
|
||||
|
||||
-- ============================================================================
|
||||
-- MIGRATIONS (Add new columns to existing tables)
|
||||
-- EXTENSIONS
|
||||
-- ============================================================================
|
||||
-- Create TimescaleDB extension if it doesn't exist
|
||||
-- This provides time_bucket() and other time-series functions
|
||||
|
||||
-- Add date_start and date_stop columns (idempotent - safe to run multiple times)
|
||||
ALTER TABLE IF EXISTS account_insights ADD COLUMN IF NOT EXISTS date_start DATE;
|
||||
ALTER TABLE IF EXISTS account_insights ADD COLUMN IF NOT EXISTS date_stop DATE;
|
||||
|
||||
ALTER TABLE IF EXISTS campaign_insights ADD COLUMN IF NOT EXISTS date_start DATE;
|
||||
ALTER TABLE IF EXISTS campaign_insights ADD COLUMN IF NOT EXISTS date_stop DATE;
|
||||
ALTER TABLE IF EXISTS campaign_insights ADD COLUMN IF NOT EXISTS frequency NUMERIC(10, 4);
|
||||
ALTER TABLE IF EXISTS campaign_insights ADD COLUMN IF NOT EXISTS cpp NUMERIC(10, 4);
|
||||
ALTER TABLE IF EXISTS campaign_insights ADD COLUMN IF NOT EXISTS cost_per_action_type JSONB;
|
||||
|
||||
ALTER TABLE IF EXISTS adset_insights ADD COLUMN IF NOT EXISTS date_start DATE;
|
||||
ALTER TABLE IF EXISTS adset_insights ADD COLUMN IF NOT EXISTS date_stop DATE;
|
||||
|
||||
ALTER TABLE IF EXISTS campaign_insights_by_country ADD COLUMN IF NOT EXISTS frequency NUMERIC(10, 4);
|
||||
ALTER TABLE IF EXISTS campaign_insights_by_country ADD COLUMN IF NOT EXISTS cpp NUMERIC(10, 4);
|
||||
ALTER TABLE IF EXISTS campaign_insights_by_country ADD COLUMN IF NOT EXISTS cost_per_action_type JSONB;
|
||||
--CREATE EXTENSION IF NOT EXISTS timescaledb;
|
||||
|
||||
-- ============================================================================
|
||||
-- METADATA TABLES (Regular PostgreSQL tables for caching)
|
||||
-- METADATA TABLE
|
||||
-- ============================================================================
|
||||
-- This table stores account mappings and identifiers
|
||||
-- Data is loaded from metadata.yaml file
|
||||
|
||||
-- Ad Accounts (rarely changes, cached)
|
||||
CREATE TABLE IF NOT EXISTS ad_accounts (
|
||||
account_id VARCHAR(50) PRIMARY KEY,
|
||||
account_name VARCHAR(255),
|
||||
currency VARCHAR(10),
|
||||
timezone_name VARCHAR(100),
|
||||
|
||||
CREATE TABLE IF NOT EXISTS public.account_metadata (
|
||||
id SERIAL PRIMARY KEY,
|
||||
label VARCHAR(255) NOT NULL,
|
||||
meta_account_id VARCHAR(100),
|
||||
google_account_id VARCHAR(100),
|
||||
alpinebits_hotel_code VARCHAR(100),
|
||||
created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
|
||||
updated_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
|
||||
|
||||
-- Ensure at least one account ID is provided
|
||||
CONSTRAINT at_least_one_account_id CHECK (
|
||||
meta_account_id IS NOT NULL OR
|
||||
google_account_id IS NOT NULL OR
|
||||
alpinebits_hotel_code IS NOT NULL
|
||||
)
|
||||
);
|
||||
|
||||
-- ============================================================================
|
||||
-- GEOTARGETS TABLE
|
||||
-- ============================================================================
|
||||
-- This table stores Google Ads geotarget metadata for location targeting
|
||||
-- Data is loaded from geotargets CSV file
|
||||
|
||||
CREATE TABLE IF NOT EXISTS public.geotargets (
|
||||
criteria_id INTEGER PRIMARY KEY,
|
||||
name VARCHAR(255) NOT NULL,
|
||||
canonical_name VARCHAR(500),
|
||||
parent_id INTEGER,
|
||||
country_code VARCHAR(2),
|
||||
target_type VARCHAR(50),
|
||||
status VARCHAR(20),
|
||||
updated_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
|
||||
);
|
||||
|
||||
-- Campaigns (metadata cache)
|
||||
CREATE TABLE IF NOT EXISTS campaigns (
|
||||
campaign_id VARCHAR(50) PRIMARY KEY,
|
||||
account_id VARCHAR(50) REFERENCES ad_accounts(account_id),
|
||||
campaign_name VARCHAR(255) NOT NULL,
|
||||
status VARCHAR(50),
|
||||
objective VARCHAR(100),
|
||||
created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
|
||||
updated_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
|
||||
);
|
||||
-- Permission grants for Grafana user
|
||||
|
||||
CREATE INDEX idx_campaigns_account ON campaigns(account_id);
|
||||
GRANT USAGE ON SCHEMA public TO grafana;
|
||||
|
||||
-- Ad Sets (metadata cache)
|
||||
CREATE TABLE IF NOT EXISTS adsets (
|
||||
adset_id VARCHAR(50) PRIMARY KEY,
|
||||
campaign_id VARCHAR(50) REFERENCES campaigns(campaign_id),
|
||||
adset_name VARCHAR(255) NOT NULL,
|
||||
status VARCHAR(50),
|
||||
created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
|
||||
updated_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
|
||||
);
|
||||
-- Grant SELECT on all existing tables and views in the schema
|
||||
GRANT SELECT ON ALL TABLES IN SCHEMA public TO grafana;
|
||||
|
||||
CREATE INDEX idx_adsets_campaign ON adsets(campaign_id);
|
||||
-- Grant SELECT on all future tables and views in the schema
|
||||
ALTER DEFAULT PRIVILEGES IN SCHEMA public
|
||||
GRANT SELECT ON TABLES TO grafana;
|
||||
|
||||
-- ============================================================================
|
||||
-- TIME-SERIES TABLES (Hypertables)
|
||||
-- ============================================================================
|
||||
|
||||
-- Account-level insights (time-series data)
|
||||
CREATE TABLE IF NOT EXISTS account_insights (
|
||||
time TIMESTAMPTZ NOT NULL,
|
||||
account_id VARCHAR(50) NOT NULL REFERENCES ad_accounts(account_id),
|
||||
|
||||
-- Core metrics
|
||||
impressions BIGINT,
|
||||
clicks BIGINT,
|
||||
spend NUMERIC(12, 2),
|
||||
reach BIGINT,
|
||||
frequency NUMERIC(10, 4),
|
||||
|
||||
-- Calculated metrics
|
||||
ctr NUMERIC(10, 6), -- Click-through rate
|
||||
cpc NUMERIC(10, 4), -- Cost per click
|
||||
cpm NUMERIC(10, 4), -- Cost per mille (thousand impressions)
|
||||
cpp NUMERIC(10, 4), -- Cost per reach
|
||||
|
||||
-- Actions (stored as JSONB for flexibility)
|
||||
actions JSONB,
|
||||
cost_per_action_type JSONB,
|
||||
|
||||
-- Metadata
|
||||
date_preset VARCHAR(50),
|
||||
date_start DATE, -- Actual start date of the data range from Meta API
|
||||
date_stop DATE, -- Actual end date of the data range from Meta API
|
||||
fetched_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
|
||||
|
||||
-- Composite primary key
|
||||
PRIMARY KEY (time, account_id)
|
||||
);
|
||||
|
||||
-- Convert to hypertable (partition by time)
|
||||
SELECT create_hypertable('account_insights', 'time',
|
||||
if_not_exists => TRUE,
|
||||
chunk_time_interval => INTERVAL '1 day'
|
||||
);
|
||||
|
||||
-- Create index for efficient queries
|
||||
CREATE INDEX IF NOT EXISTS idx_account_insights_account_time
|
||||
ON account_insights (account_id, time DESC);
|
||||
|
||||
|
||||
-- Campaign-level insights (time-series data)
|
||||
CREATE TABLE IF NOT EXISTS campaign_insights (
|
||||
time TIMESTAMPTZ NOT NULL,
|
||||
campaign_id VARCHAR(50) NOT NULL REFERENCES campaigns(campaign_id),
|
||||
account_id VARCHAR(50) NOT NULL REFERENCES ad_accounts(account_id),
|
||||
|
||||
-- Core metrics
|
||||
impressions BIGINT,
|
||||
clicks BIGINT,
|
||||
spend NUMERIC(12, 2),
|
||||
reach BIGINT,
|
||||
frequency NUMERIC(10, 4),
|
||||
|
||||
-- Calculated metrics
|
||||
ctr NUMERIC(10, 6),
|
||||
cpc NUMERIC(10, 4),
|
||||
cpm NUMERIC(10, 4),
|
||||
cpp NUMERIC(10, 4), -- Cost per reach
|
||||
|
||||
-- Actions
|
||||
actions JSONB,
|
||||
cost_per_action_type JSONB,
|
||||
|
||||
-- Metadata
|
||||
date_preset VARCHAR(50),
|
||||
date_start DATE, -- Actual start date of the data range from Meta API
|
||||
date_stop DATE, -- Actual end date of the data range from Meta API
|
||||
fetched_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
|
||||
|
||||
PRIMARY KEY (time, campaign_id)
|
||||
);
|
||||
|
||||
-- Convert to hypertable
|
||||
SELECT create_hypertable('campaign_insights', 'time',
|
||||
if_not_exists => TRUE,
|
||||
chunk_time_interval => INTERVAL '1 day'
|
||||
);
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_campaign_insights_campaign_time
|
||||
ON campaign_insights (campaign_id, time DESC);
|
||||
CREATE INDEX IF NOT EXISTS idx_campaign_insights_account_time
|
||||
ON campaign_insights (account_id, time DESC);
|
||||
|
||||
|
||||
-- Ad Set level insights (time-series data)
|
||||
CREATE TABLE IF NOT EXISTS adset_insights (
|
||||
time TIMESTAMPTZ NOT NULL,
|
||||
adset_id VARCHAR(50) NOT NULL REFERENCES adsets(adset_id),
|
||||
campaign_id VARCHAR(50) NOT NULL REFERENCES campaigns(campaign_id),
|
||||
account_id VARCHAR(50) NOT NULL REFERENCES ad_accounts(account_id),
|
||||
|
||||
-- Core metrics
|
||||
impressions BIGINT,
|
||||
clicks BIGINT,
|
||||
spend NUMERIC(12, 2),
|
||||
reach BIGINT,
|
||||
|
||||
-- Calculated metrics
|
||||
ctr NUMERIC(10, 6),
|
||||
cpc NUMERIC(10, 4),
|
||||
cpm NUMERIC(10, 4),
|
||||
|
||||
|
||||
-- Actions
|
||||
actions JSONB,
|
||||
|
||||
-- Metadata
|
||||
date_preset VARCHAR(50),
|
||||
date_start DATE, -- Actual start date of the data range from Meta API
|
||||
date_stop DATE, -- Actual end date of the data range from Meta API
|
||||
fetched_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
|
||||
|
||||
PRIMARY KEY (time, adset_id)
|
||||
);
|
||||
|
||||
-- Convert to hypertable
|
||||
SELECT create_hypertable('adset_insights', 'time',
|
||||
if_not_exists => TRUE,
|
||||
chunk_time_interval => INTERVAL '1 day'
|
||||
);
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_adset_insights_adset_time
|
||||
ON adset_insights (adset_id, time DESC);
|
||||
CREATE INDEX IF NOT EXISTS idx_adset_insights_campaign_time
|
||||
ON adset_insights (campaign_id, time DESC);
|
||||
CREATE INDEX IF NOT EXISTS idx_adset_insights_account_time
|
||||
ON adset_insights (account_id, time DESC);
|
||||
|
||||
|
||||
-- Campaign-level insights by country (time-series data)
|
||||
CREATE TABLE IF NOT EXISTS campaign_insights_by_country (
|
||||
time TIMESTAMPTZ NOT NULL,
|
||||
campaign_id VARCHAR(50) NOT NULL REFERENCES campaigns(campaign_id),
|
||||
account_id VARCHAR(50) NOT NULL REFERENCES ad_accounts(account_id),
|
||||
country VARCHAR(2) NOT NULL, -- ISO 2-letter country code
|
||||
|
||||
-- Core metrics
|
||||
impressions BIGINT,
|
||||
clicks BIGINT,
|
||||
spend NUMERIC(12, 2),
|
||||
reach BIGINT,
|
||||
frequency NUMERIC(10, 4),
|
||||
|
||||
-- Calculated metrics
|
||||
ctr NUMERIC(10, 6),
|
||||
cpc NUMERIC(10, 4),
|
||||
cpm NUMERIC(10, 4),
|
||||
cpp NUMERIC(10, 4), -- Cost per reach
|
||||
|
||||
-- Actions
|
||||
actions JSONB,
|
||||
cost_per_action_type JSONB,
|
||||
|
||||
-- Metadata
|
||||
date_preset VARCHAR(50),
|
||||
date_start DATE, -- Actual start date of the data range from Meta API
|
||||
date_stop DATE, -- Actual end date of the data range from Meta API
|
||||
fetched_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
|
||||
|
||||
PRIMARY KEY (time, campaign_id, country)
|
||||
);
|
||||
|
||||
-- Convert to hypertable
|
||||
SELECT create_hypertable('campaign_insights_by_country', 'time',
|
||||
if_not_exists => TRUE,
|
||||
chunk_time_interval => INTERVAL '1 day'
|
||||
);
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_campaign_insights_by_country_campaign_time
|
||||
ON campaign_insights_by_country (campaign_id, time DESC);
|
||||
CREATE INDEX IF NOT EXISTS idx_campaign_insights_by_country_account_time
|
||||
ON campaign_insights_by_country (account_id, time DESC);
|
||||
CREATE INDEX IF NOT EXISTS idx_campaign_insights_by_country_country
|
||||
ON campaign_insights_by_country (country, time DESC);
|
||||
|
||||
|
||||
-- Compression policy for campaign_insights_by_country
|
||||
SELECT add_compression_policy('campaign_insights_by_country', INTERVAL '7 days', if_not_exists => TRUE);
|
||||
|
||||
|
||||
-- ============================================================================
|
||||
-- CONTINUOUS AGGREGATES (Pre-computed rollups for dashboards)
|
||||
-- VIEWS
|
||||
-- ============================================================================
|
||||
|
||||
-- Hourly aggregates for account insights
|
||||
CREATE MATERIALIZED VIEW IF NOT EXISTS account_insights_hourly
|
||||
WITH (timescaledb.continuous) AS
|
||||
SELECT
|
||||
time_bucket('1 hour', time) AS bucket,
|
||||
account_id,
|
||||
AVG(impressions) as avg_impressions,
|
||||
AVG(clicks) as avg_clicks,
|
||||
AVG(spend) as avg_spend,
|
||||
AVG(ctr) as avg_ctr,
|
||||
AVG(cpc) as avg_cpc,
|
||||
AVG(cpm) as avg_cpm,
|
||||
MAX(reach) as max_reach,
|
||||
COUNT(*) as sample_count
|
||||
FROM account_insights
|
||||
GROUP BY bucket, account_id;
|
||||
|
||||
-- Refresh policy: refresh last 2 days every hour
|
||||
SELECT add_continuous_aggregate_policy('account_insights_hourly',
|
||||
start_offset => INTERVAL '2 days',
|
||||
end_offset => INTERVAL '1 hour',
|
||||
schedule_interval => INTERVAL '1 hour',
|
||||
if_not_exists => TRUE
|
||||
);
|
||||
-- All views below reference data in separate schemas (meta, google, etc.)
|
||||
-- Add your view definitions here...
|
||||
|
||||
|
||||
-- Daily aggregates for account insights
|
||||
CREATE MATERIALIZED VIEW IF NOT EXISTS account_insights_daily
|
||||
WITH (timescaledb.continuous) AS
|
||||
SELECT
|
||||
time_bucket('1 day', time) AS bucket,
|
||||
account_id,
|
||||
AVG(impressions) as avg_impressions,
|
||||
SUM(impressions) as total_impressions,
|
||||
AVG(clicks) as avg_clicks,
|
||||
SUM(clicks) as total_clicks,
|
||||
AVG(spend) as avg_spend,
|
||||
SUM(spend) as total_spend,
|
||||
AVG(ctr) as avg_ctr,
|
||||
AVG(cpc) as avg_cpc,
|
||||
AVG(cpm) as avg_cpm,
|
||||
MAX(reach) as max_reach,
|
||||
COUNT(*) as sample_count
|
||||
FROM account_insights
|
||||
GROUP BY bucket, account_id;
|
||||
|
||||
-- Refresh policy: refresh last 7 days every 4 hours
|
||||
SELECT add_continuous_aggregate_policy('account_insights_daily',
|
||||
start_offset => INTERVAL '7 days',
|
||||
end_offset => INTERVAL '1 hour',
|
||||
schedule_interval => INTERVAL '4 hours',
|
||||
if_not_exists => TRUE
|
||||
);
|
||||
DROP VIEW IF EXISTS campaign_insights CASCADE;
|
||||
|
||||
-- general campaign insights view
|
||||
|
||||
-- ============================================================================
|
||||
-- DATA RETENTION POLICIES (Optional - uncomment to enable)
|
||||
-- ============================================================================
|
||||
|
||||
-- Keep raw data for 90 days, then drop
|
||||
-- SELECT add_retention_policy('account_insights', INTERVAL '90 days', if_not_exists => TRUE);
|
||||
-- SELECT add_retention_policy('campaign_insights', INTERVAL '90 days', if_not_exists => TRUE);
|
||||
-- SELECT add_retention_policy('adset_insights', INTERVAL '90 days', if_not_exists => TRUE);
|
||||
|
||||
-- Compress data older than 7 days for better storage efficiency
|
||||
SELECT add_compression_policy('account_insights', INTERVAL '7 days', if_not_exists => TRUE);
|
||||
SELECT add_compression_policy('campaign_insights', INTERVAL '7 days', if_not_exists => TRUE);
|
||||
SELECT add_compression_policy('adset_insights', INTERVAL '7 days', if_not_exists => TRUE);
|
||||
|
||||
|
||||
-- ============================================================================
|
||||
-- HELPER VIEWS FOR DASHBOARDS
|
||||
-- ============================================================================
|
||||
|
||||
-- Latest metrics per account
|
||||
CREATE OR REPLACE VIEW latest_account_metrics AS
|
||||
SELECT DISTINCT ON (account_id)
|
||||
account_id,
|
||||
time,
|
||||
CREATE VIEW campaign_insights AS
|
||||
SELECT date_start AS "time",
|
||||
account_id AS account_id,
|
||||
campaign_id,
|
||||
campaign_name,
|
||||
objective,
|
||||
impressions,
|
||||
clicks,
|
||||
spend,
|
||||
reach,
|
||||
ctr,
|
||||
cpc,
|
||||
cpm,
|
||||
reach,
|
||||
frequency
|
||||
FROM account_insights
|
||||
ORDER BY account_id, time DESC;
|
||||
cpp,
|
||||
frequency,
|
||||
( SELECT (jsonb_array_elements.value ->> 'value'::text)::numeric AS "numeric"
|
||||
FROM jsonb_array_elements(customcampaign_insights.actions) jsonb_array_elements(value)
|
||||
WHERE (jsonb_array_elements.value ->> 'action_type'::text) = 'link_click'::text) AS link_click,
|
||||
( SELECT (jsonb_array_elements.value ->> 'value'::text)::numeric AS "numeric"
|
||||
FROM jsonb_array_elements(customcampaign_insights.actions) jsonb_array_elements(value)
|
||||
WHERE (jsonb_array_elements.value ->> 'action_type'::text) = 'landing_page_view'::text) AS landing_page_view,
|
||||
( SELECT (jsonb_array_elements.value ->> 'value'::text)::numeric AS "numeric"
|
||||
FROM jsonb_array_elements(customcampaign_insights.actions) jsonb_array_elements(value)
|
||||
WHERE (jsonb_array_elements.value ->> 'action_type'::text) = 'lead'::text) AS lead
|
||||
FROM meta.customcampaign_insights;
|
||||
|
||||
-- Campaign performance summary (last 24 hours)
|
||||
CREATE OR REPLACE VIEW campaign_performance_24h AS
|
||||
-- age and gender
|
||||
|
||||
|
||||
DROP MATERIALIZED VIEW IF EXISTS campaign_insights_by_gender_and_age CASCADE;
|
||||
|
||||
CREATE MATERIALIZED VIEW campaign_insights_by_gender_and_age AS
|
||||
SELECT date_start AS "time",
|
||||
account_id AS account_id,
|
||||
campaign_id,
|
||||
campaign_name,
|
||||
gender,
|
||||
age,
|
||||
impressions,
|
||||
clicks,
|
||||
spend,
|
||||
reach,
|
||||
frequency,
|
||||
ctr,
|
||||
cpc,
|
||||
cpm,
|
||||
(SELECT (value->>'value')::numeric
|
||||
FROM jsonb_array_elements(actions)
|
||||
WHERE value->>'action_type' = 'link_click') AS link_click,
|
||||
(SELECT (value->>'value')::numeric
|
||||
FROM jsonb_array_elements(actions)
|
||||
WHERE value->>'action_type' = 'landing_page_view') AS landing_page_view,
|
||||
(SELECT (value->>'value')::numeric
|
||||
FROM jsonb_array_elements(actions)
|
||||
WHERE value->>'action_type' = 'lead') AS lead
|
||||
|
||||
FROM meta.custom_campaign_gender;
|
||||
|
||||
-- Create indexes for efficient querying
|
||||
CREATE INDEX idx_campaign_insights_by_gender_and_age_date
|
||||
ON campaign_insights_by_gender_and_age(time);
|
||||
|
||||
CREATE UNIQUE INDEX idx_campaign_insights_by_gender_and_age_unique
|
||||
ON campaign_insights_by_gender_and_age(time, campaign_id, gender, age);
|
||||
|
||||
REFRESH MATERIALIZED VIEW CONCURRENTLY campaign_insights_by_gender_and_age;
|
||||
|
||||
|
||||
DROP VIEW IF EXISTS campaign_insights_by_gender CASCADE;
|
||||
|
||||
create view campaign_insights_by_gender as
|
||||
Select time,
|
||||
sum(clicks) as clicks,
|
||||
sum(link_click) as link_click,
|
||||
sum(lead) as lead,
|
||||
sum(landing_page_view) as landing_page_view,
|
||||
sum(spend) as spend,
|
||||
sum(reach) as reach,
|
||||
sum(impressions) as impressions,
|
||||
gender,
|
||||
campaign_id,
|
||||
campaign_name,
|
||||
account_id
|
||||
from campaign_insights_by_gender_and_age
|
||||
group by time, gender, account_id, campaign_id, campaign_name;
|
||||
|
||||
DROP VIEW IF EXISTS campaign_insights_by_age CASCADE;
|
||||
|
||||
create view campaign_insights_by_age as
|
||||
Select time,
|
||||
sum(clicks) as clicks,
|
||||
sum(link_click) as link_click,
|
||||
sum(lead) as lead,
|
||||
sum(landing_page_view) as landing_page_view,
|
||||
sum(spend) as spend,
|
||||
sum(reach) as reach,
|
||||
sum(impressions) as impressions,
|
||||
age,
|
||||
campaign_id,
|
||||
campaign_name,
|
||||
account_id
|
||||
from campaign_insights_by_gender_and_age
|
||||
group by time, age, account_id, campaign_id, campaign_name;
|
||||
|
||||
|
||||
-- device
|
||||
|
||||
DROP VIEW IF EXISTS campaign_insights_by_device_flattened CASCADE;
|
||||
|
||||
CREATE VIEW campaign_insights_by_device_flattened AS
|
||||
SELECT date_start AS "time",
|
||||
account_id AS account_id,
|
||||
campaign_id,
|
||||
device_platform,
|
||||
impressions,
|
||||
clicks,
|
||||
spend,
|
||||
reach,
|
||||
frequency,
|
||||
|
||||
(SELECT (value->>'value')::numeric
|
||||
FROM jsonb_array_elements(actions)
|
||||
WHERE value->>'action_type' = 'link_click') AS link_click,
|
||||
(SELECT (value->>'value')::numeric
|
||||
FROM jsonb_array_elements(actions)
|
||||
WHERE value->>'action_type' = 'landing_page_view') AS landing_page_view,
|
||||
(SELECT (value->>'value')::numeric
|
||||
FROM jsonb_array_elements(actions)
|
||||
WHERE value->>'action_type' = 'lead') AS lead
|
||||
|
||||
FROM meta.custom_campaign_device;
|
||||
|
||||
-- country
|
||||
|
||||
--- campaign insights by country
|
||||
|
||||
DROP VIEW IF EXISTS campaign_insights_by_country_flattened CASCADE;
|
||||
|
||||
CREATE VIEW campaign_insights_by_country_flattened AS
|
||||
SELECT date_start AS "time",
|
||||
account_id AS account_id,
|
||||
campaign_id,
|
||||
country,
|
||||
impressions,
|
||||
clicks,
|
||||
spend,
|
||||
reach,
|
||||
frequency,
|
||||
ctr,
|
||||
cpc,
|
||||
cpm,
|
||||
|
||||
(SELECT (value->>'value')::numeric
|
||||
FROM jsonb_array_elements(actions)
|
||||
WHERE value->>'action_type' = 'link_click') AS link_click,
|
||||
(SELECT (value->>'value')::numeric
|
||||
FROM jsonb_array_elements(actions)
|
||||
WHERE value->>'action_type' = 'landing_page_view') AS landing_page_view,
|
||||
(SELECT (value->>'value')::numeric
|
||||
FROM jsonb_array_elements(actions)
|
||||
WHERE value->>'action_type' = 'lead') AS lead
|
||||
|
||||
FROM meta.custom_campaign_country;
|
||||
-- ============================================================================
|
||||
|
||||
-- account views
|
||||
|
||||
|
||||
DROP VIEW IF EXISTS account_insights_by_gender CASCADE;
|
||||
|
||||
CREATE VIEW account_insights_by_gender AS
|
||||
SELECT
|
||||
c.campaign_id,
|
||||
c.campaign_name,
|
||||
c.account_id,
|
||||
SUM(ci.impressions) as total_impressions,
|
||||
SUM(ci.clicks) as total_clicks,
|
||||
SUM(ci.spend) as total_spend,
|
||||
AVG(ci.ctr) as avg_ctr,
|
||||
AVG(ci.cpc) as avg_cpc
|
||||
FROM campaigns c
|
||||
LEFT JOIN campaign_insights ci ON c.campaign_id = ci.campaign_id
|
||||
WHERE ci.time >= NOW() - INTERVAL '24 hours'
|
||||
GROUP BY c.campaign_id, c.campaign_name, c.account_id
|
||||
ORDER BY total_spend DESC;
|
||||
time,
|
||||
account_id,
|
||||
gender,
|
||||
SUM(impressions) AS impressions,
|
||||
SUM(clicks) AS clicks,
|
||||
SUM(spend) AS spend,
|
||||
SUM(link_click) AS link_click,
|
||||
SUM(landing_page_view) AS landing_page_view,
|
||||
SUM(lead) AS lead
|
||||
FROM campaign_insights_by_gender
|
||||
GROUP BY time, account_id, gender;
|
||||
|
||||
|
||||
DROP VIEW IF EXISTS account_insights_by_device CASCADE;
|
||||
|
||||
CREATE VIEW account_insights_by_device AS
|
||||
SELECT
|
||||
time,
|
||||
account_id,
|
||||
device_platform,
|
||||
SUM(impressions) AS impressions,
|
||||
SUM(clicks) AS clicks,
|
||||
SUM(spend) AS spend,
|
||||
SUM(link_click) AS link_click,
|
||||
SUM(landing_page_view) AS landing_page_view,
|
||||
SUM(lead) AS lead
|
||||
FROM campaign_insights_by_device_flattened
|
||||
GROUP BY time, account_id, device_platform;
|
||||
|
||||
|
||||
DROP VIEW IF EXISTS account_insights_by_age CASCADE;
|
||||
|
||||
CREATE VIEW account_insights_by_age AS
|
||||
SELECT
|
||||
time,
|
||||
account_id,
|
||||
age,
|
||||
SUM(impressions) AS impressions,
|
||||
SUM(clicks) AS clicks,
|
||||
SUM(spend) AS spend,
|
||||
SUM(link_click) AS link_click,
|
||||
SUM(landing_page_view) AS landing_page_view,
|
||||
SUM(lead) AS lead
|
||||
FROM campaign_insights_by_age
|
||||
GROUP BY time, account_id, age;
|
||||
|
||||
DROP VIEW IF EXISTS account_insights_by_gender_and_age CASCADE;
|
||||
|
||||
CREATE VIEW account_insights_by_gender_and_age AS
|
||||
SELECT
|
||||
time,
|
||||
account_id,
|
||||
gender,
|
||||
age,
|
||||
SUM(impressions) AS impressions,
|
||||
SUM(clicks) AS clicks,
|
||||
SUM(spend) AS spend,
|
||||
SUM(link_click) AS link_click,
|
||||
SUM(landing_page_view) AS landing_page_view,
|
||||
SUM(lead) AS lead
|
||||
FROM campaign_insights_by_gender_and_age
|
||||
GROUP BY time, account_id, age, gender;
|
||||
|
||||
-- Aggregates campaign insights by country to account level
|
||||
DROP VIEW IF EXISTS account_insights_by_country CASCADE;
|
||||
|
||||
CREATE VIEW account_insights_by_country AS
|
||||
SELECT
|
||||
time,
|
||||
account_id,
|
||||
country,
|
||||
SUM(impressions) AS impressions,
|
||||
SUM(clicks) AS clicks,
|
||||
SUM(spend) AS spend,
|
||||
SUM(link_click) AS link_click,
|
||||
SUM(landing_page_view) AS landing_page_view,
|
||||
SUM(lead) AS lead
|
||||
FROM campaign_insights_by_country_flattened
|
||||
GROUP BY time, account_id, country;
|
||||
|
||||
DROP VIEW IF EXISTS account_insights;
|
||||
CREATE VIEW account_insights AS
|
||||
SELECT
|
||||
time,
|
||||
account_id,
|
||||
SUM(impressions) AS impressions,
|
||||
SUM(clicks) AS clicks,
|
||||
SUM(spend) AS spend,
|
||||
SUM(link_click) AS link_click,
|
||||
SUM(landing_page_view) AS landing_page_view,
|
||||
SUM(lead) AS lead,
|
||||
SUM(reach) as reach,
|
||||
AVG(frequency) as frequency,
|
||||
avg(cpc) as cpc,
|
||||
avg(cpm) as cpm,
|
||||
avg(cpp) as cpp,
|
||||
avg(ctr) as ctr
|
||||
|
||||
FROM campaign_insights
|
||||
group by time, account_id;
|
||||
|
||||
DROP MATERIALIZED VIEW IF EXISTS ads_insights CASCADE;
|
||||
-- ads view
|
||||
CREATE MATERIALIZED VIEW ads_insights AS
|
||||
SELECT
|
||||
date_start AS time,
|
||||
name as ad_name,
|
||||
ins.account_id,
|
||||
ins.ad_id,
|
||||
ins.adset_id,
|
||||
ins.campaign_id,
|
||||
impressions,
|
||||
reach,
|
||||
clicks,
|
||||
(SELECT SUM((elem.value ->> 'value')::numeric)
|
||||
FROM jsonb_array_elements(ins.actions) AS elem
|
||||
WHERE (elem.value ->> 'action_type') = 'link_click') AS link_click,
|
||||
(SELECT SUM((elem.value ->> 'value')::numeric)
|
||||
FROM jsonb_array_elements(ins.actions) AS elem
|
||||
WHERE (elem.value ->> 'action_type') = 'lead') AS lead,
|
||||
(SELECT sum((value->>'value')::numeric)
|
||||
FROM jsonb_array_elements(actions)
|
||||
WHERE value->>'action_type' = 'landing_page_view') AS landing_page_view,
|
||||
spend,
|
||||
frequency,
|
||||
cpc,
|
||||
cpm,
|
||||
ctr,
|
||||
cpp
|
||||
FROM meta.ads_insights ins
|
||||
join meta.ads as a on a.id = ins.ad_id;
|
||||
|
||||
-- Create indexes for efficient querying
|
||||
CREATE INDEX idx_ads_insights
|
||||
ON ads_insights(time, ad_id);
|
||||
|
||||
CREATE UNIQUE INDEX ads_insights_unique
|
||||
ON ads_insights(time, account_id, ad_id);
|
||||
|
||||
REFRESH MATERIALIZED VIEW CONCURRENTLY ads_insights;
|
||||
|
||||
DROP VIEW IF EXISTS adset_insights CASCADE;
|
||||
|
||||
CREATE VIEW adset_insights AS
|
||||
SELECT
|
||||
time,
|
||||
i.account_id,
|
||||
name as adset_name,
|
||||
i.adset_id,
|
||||
i.campaign_id,
|
||||
SUM(impressions) AS impressions,
|
||||
SUM(clicks) AS clicks,
|
||||
sum(link_click) as link_click,
|
||||
sum(lead) as lead,
|
||||
sum(landing_page_view) as landing_page_view,
|
||||
SUM(spend) AS spend,
|
||||
sum(reach),
|
||||
AVG(frequency) as frequency,
|
||||
avg(cpc) as cpc,
|
||||
avg(cpm) as cpm,
|
||||
avg(cpp) as cpp,
|
||||
avg(ctr) as ctr
|
||||
|
||||
FROM ads_insights as i left join meta.ad_sets as m on m.id = i.adset_id
|
||||
group by time, i.account_id, i.adset_id, i.campaign_id, adset_name;
|
||||
|
||||
|
||||
DROP VIEW IF EXISTS g_account_insights CASCADE;
|
||||
CREATE VIEW g_account_insights AS
|
||||
SELECT
|
||||
time,
|
||||
google_account_id,
|
||||
clicks,
|
||||
impressions,
|
||||
interactions,
|
||||
cost_micros,
|
||||
cost_micros / 1000000.0 as cost,
|
||||
leads,
|
||||
engagements,
|
||||
customer_currency_code,
|
||||
account_name,
|
||||
|
||||
-- CTR (Click-Through Rate)
|
||||
(clicks::numeric / impressions_nz) * 100 as ctr,
|
||||
|
||||
-- CPM (Cost Per Mille) in micros and standard units
|
||||
(cost_micros::numeric / impressions_nz) * 1000 as cpm_micros,
|
||||
(cost_micros::numeric / impressions_nz) * 1000 / 1000000.0 as cpm,
|
||||
|
||||
-- CPC (Cost Per Click) in micros and standard units
|
||||
cost_micros::numeric / clicks_nz as cpc_micros,
|
||||
cost_micros::numeric / clicks_nz / 1000000.0 as cpc,
|
||||
|
||||
-- CPL (Cost Per Lead) in micros and standard units
|
||||
cost_micros::numeric / leads_nz as cpl_micros,
|
||||
cost_micros::numeric / leads_nz / 1000000.0 as cpl,
|
||||
|
||||
-- Conversion Rate
|
||||
(leads::numeric / clicks_nz) * 100 as conversion_rate,
|
||||
|
||||
-- Engagement Rate
|
||||
(engagements::numeric / impressions_nz) * 100 as engagement_rate
|
||||
|
||||
FROM (
|
||||
SELECT
|
||||
segments_date as time,
|
||||
customer_id as google_account_id,
|
||||
sum(metrics_clicks) as clicks,
|
||||
sum(metrics_impressions) as impressions,
|
||||
sum(metrics_interactions) as interactions,
|
||||
sum(metrics_cost_micros) as cost_micros,
|
||||
sum(metrics_conversions) as leads,
|
||||
sum(metrics_engagements) as engagements,
|
||||
customer_currency_code,
|
||||
customer_descriptive_name as account_name,
|
||||
-- Null-safe denominators
|
||||
NULLIF(sum(metrics_clicks), 0) as clicks_nz,
|
||||
NULLIF(sum(metrics_impressions), 0) as impressions_nz,
|
||||
NULLIF(sum(metrics_conversions), 0) as leads_nz
|
||||
FROM google.account_performance_report
|
||||
GROUP BY google_account_id, time, customer_currency_code, account_name
|
||||
) base;
|
||||
|
||||
|
||||
|
||||
DROP VIEW IF EXISTS g_account_insights_device CASCADE;
|
||||
CREATE VIEW g_account_insights_device AS
|
||||
SELECT
|
||||
time,
|
||||
google_account_id,
|
||||
device,
|
||||
clicks,
|
||||
impressions,
|
||||
interactions,
|
||||
cost_micros,
|
||||
cost_micros / 1000000.0 as cost,
|
||||
leads,
|
||||
engagements,
|
||||
customer_currency_code,
|
||||
account_name,
|
||||
|
||||
-- CTR (Click-Through Rate)
|
||||
(clicks::numeric / impressions_nz) * 100 as ctr,
|
||||
|
||||
-- CPM (Cost Per Mille) in micros and standard units
|
||||
(cost_micros::numeric / impressions_nz) * 1000 as cpm_micros,
|
||||
(cost_micros::numeric / impressions_nz) * 1000 / 1000000.0 as cpm,
|
||||
|
||||
-- CPC (Cost Per Click) in micros and standard units
|
||||
cost_micros::numeric / clicks_nz as cpc_micros,
|
||||
cost_micros::numeric / clicks_nz / 1000000.0 as cpc,
|
||||
|
||||
-- CPL (Cost Per Lead) in micros and standard units
|
||||
cost_micros::numeric / leads_nz as cpl_micros,
|
||||
cost_micros::numeric / leads_nz / 1000000.0 as cpl,
|
||||
|
||||
-- Conversion Rate
|
||||
(leads::numeric / clicks_nz) * 100 as conversion_rate,
|
||||
|
||||
-- Engagement Rate
|
||||
(engagements::numeric / impressions_nz) * 100 as engagement_rate
|
||||
|
||||
FROM (
|
||||
SELECT
|
||||
segments_date as time,
|
||||
customer_id as google_account_id,
|
||||
segments_device as device,
|
||||
sum(metrics_clicks) as clicks,
|
||||
sum(metrics_impressions) as impressions,
|
||||
sum(metrics_interactions) as interactions,
|
||||
sum(metrics_cost_micros) as cost_micros,
|
||||
sum(metrics_conversions) as leads,
|
||||
sum(metrics_engagements) as engagements,
|
||||
customer_currency_code,
|
||||
customer_descriptive_name as account_name,
|
||||
-- Null-safe denominators
|
||||
NULLIF(sum(metrics_clicks), 0) as clicks_nz,
|
||||
NULLIF(sum(metrics_impressions), 0) as impressions_nz,
|
||||
NULLIF(sum(metrics_conversions), 0) as leads_nz
|
||||
FROM google.account_performance_report
|
||||
GROUP BY google_account_id, time, customer_currency_code, account_name, segments_device
|
||||
) base;
|
||||
|
||||
|
||||
-- Google campaign insights aggregated from campaign_metrics
|
||||
DROP VIEW IF EXISTS g_campaign_insights CASCADE;
|
||||
|
||||
CREATE VIEW g_campaign_insights AS
|
||||
SELECT
|
||||
segments_date as time,
|
||||
campaign_id,
|
||||
customer_id as google_account_id,
|
||||
campaign_name,
|
||||
SUM(metrics_clicks) as clicks,
|
||||
SUM(metrics_impressions) as impressions,
|
||||
SUM(metrics_interactions) as interactions,
|
||||
SUM(metrics_cost_micros) as cost_micros,
|
||||
SUM(metrics_cost_micros) / 1000000.0 as cost,
|
||||
SUM(metrics_conversions) as conversions,
|
||||
SUM(metrics_all_conversions) as all_conversions,
|
||||
SUM(metrics_conversions_value) as conversions_value,
|
||||
|
||||
-- CTR (Click-Through Rate)
|
||||
(SUM(metrics_clicks)::numeric / NULLIF(SUM(metrics_impressions), 0)) * 100 as ctr,
|
||||
|
||||
-- CPM (Cost Per Mille)
|
||||
(SUM(metrics_cost_micros)::numeric / NULLIF(SUM(metrics_impressions), 0)) * 1000 / 1000000.0 as cpm,
|
||||
|
||||
-- CPC (Cost Per Click)
|
||||
SUM(metrics_cost_micros)::numeric / NULLIF(SUM(metrics_clicks), 0) / 1000000.0 as cpc,
|
||||
|
||||
-- Cost per conversion
|
||||
SUM(metrics_cost_micros)::numeric / NULLIF(SUM(metrics_conversions), 0) / 1000000.0 as cost_per_conversion
|
||||
|
||||
FROM google.campaign_metrics
|
||||
GROUP BY customer_id, segments_date, campaign_id, campaign_name
|
||||
ORDER BY segments_date;
|
||||
|
||||
-- ============================================================================
|
||||
-- UNIFIED VIEW
|
||||
DROP VIEW IF EXISTS unified_account_insights CASCADE;
|
||||
|
||||
CREATE VIEW unified_account_insights AS
|
||||
SELECT
|
||||
g.time,
|
||||
g.google_account_id::varchar as google_account_id,
|
||||
m.account_id as meta_account_id,
|
||||
|
||||
sum(g.impressions + m.impressions) as impressions,
|
||||
sum(g.clicks + m.clicks) as clicks,
|
||||
sum(g.clicks + m.link_click) as link_click,
|
||||
sum(g.leads + m.lead) as lead,
|
||||
sum(g.cost + m.spend) as spend
|
||||
|
||||
|
||||
FROM account_metadata as am
|
||||
JOIN g_account_insights as g ON g.google_account_id::varchar = am.google_account_id
|
||||
JOIN account_insights as m ON m.account_id = am.meta_account_id AND m.time = g.time
|
||||
GROUP BY g.time, 2, 3
|
||||
ORDER BY g.time;
|
||||
|
||||
-- Unified device-level insights combining Google and Meta data
|
||||
DROP VIEW IF EXISTS unified_account_insights_by_device CASCADE;
|
||||
|
||||
CREATE VIEW unified_account_insights_by_device AS
|
||||
-- Man verliert Tablet und mobile-app als device type ober who cares. TV und so bei google werd a als mobile innigschmisse. Taucht lei olle heiligen zeiten auf
|
||||
SELECT
|
||||
g.time,
|
||||
g.google_account_id::varchar as google_account_id,
|
||||
m.account_id as meta_account_id,
|
||||
-- Normalize device types
|
||||
CASE
|
||||
WHEN UPPER(COALESCE(g.device, m.device_platform)) IN ('DESKTOP') THEN 'DESKTOP'
|
||||
ELSE 'MOBILE'
|
||||
END as device_type,
|
||||
SUM(g.impressions) as google_impressions,
|
||||
SUM(m.impressions) as meta_impressions,
|
||||
SUM(g.impressions + m.impressions) as total_impressions,
|
||||
SUM(g.clicks) as google_clicks,
|
||||
SUM(m.clicks) as meta_clicks,
|
||||
SUM(g.clicks + m.clicks) as total_clicks,
|
||||
SUM(g.cost) as google_cost,
|
||||
SUM(m.spend) as meta_spend,
|
||||
SUM(g.cost + m.spend) as total_spend,
|
||||
SUM(m.link_click) as meta_link_clicks,
|
||||
SUM(m.lead) as meta_leads
|
||||
FROM account_metadata as am
|
||||
FULL OUTER JOIN g_account_insights_device as g ON g.google_account_id::varchar = am.google_account_id
|
||||
FULL OUTER JOIN account_insights_by_device as m ON m.account_id = am.meta_account_id AND m.time = g.time
|
||||
WHERE g.time IS NOT NULL AND m.time IS NOT NULL
|
||||
GROUP BY g.time, g.google_account_id, m.account_id, device_type
|
||||
ORDER BY g.time, device_type;
|
||||
|
||||
-- Unified gender-level insights combining Google and Meta data for better audience analysis
|
||||
DROP VIEW IF EXISTS unified_account_insights_by_gender CASCADE;
|
||||
|
||||
CREATE VIEW unified_account_insights_by_gender AS
|
||||
SELECT
|
||||
m.time,
|
||||
m.account_id as meta_account_id,
|
||||
|
||||
m.gender,
|
||||
SUM(m.impressions) as impressions,
|
||||
SUM(m.clicks) as clicks,
|
||||
SUM(m.spend) as spend,
|
||||
SUM(m.link_click) as link_clicks,
|
||||
SUM(m.lead) as leads
|
||||
|
||||
FROM account_insights_by_gender as m
|
||||
GROUP BY m.time, m.account_id, m.gender
|
||||
ORDER BY m.time;
|
||||
|
||||
|
||||
|
||||
@@ -1,140 +0,0 @@
|
||||
"""
|
||||
Discover all ad accounts accessible via different methods:
|
||||
1. User ad accounts (personal access)
|
||||
2. Business Manager ad accounts (app-level access)
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
from dotenv import load_dotenv
|
||||
from facebook_business.adobjects.user import User
|
||||
from facebook_business.adobjects.business import Business
|
||||
from facebook_business.api import FacebookAdsApi
|
||||
|
||||
|
||||
def discover_accounts():
|
||||
"""Discover ad accounts through multiple methods."""
|
||||
load_dotenv()
|
||||
|
||||
access_token = os.getenv("META_ACCESS_TOKEN")
|
||||
app_secret = os.getenv("META_APP_SECRET")
|
||||
app_id = os.getenv("META_APP_ID")
|
||||
|
||||
if not all([access_token, app_secret, app_id]):
|
||||
print("❌ Missing required environment variables")
|
||||
return 1
|
||||
|
||||
# Initialize Facebook Ads API
|
||||
FacebookAdsApi.init(
|
||||
app_id=app_id,
|
||||
app_secret=app_secret,
|
||||
access_token=access_token,
|
||||
)
|
||||
|
||||
print("="*70)
|
||||
print("AD ACCOUNT DISCOVERY")
|
||||
print("="*70)
|
||||
print()
|
||||
|
||||
# Method 1: User ad accounts (what we currently use)
|
||||
print("METHOD 1: User Ad Accounts (Personal Access)")
|
||||
print("-"*70)
|
||||
try:
|
||||
me = User(fbid='me')
|
||||
account_fields = ['name', 'account_id']
|
||||
user_accounts = me.get_ad_accounts(fields=account_fields)
|
||||
user_account_list = list(user_accounts)
|
||||
|
||||
print(f"Found {len(user_account_list)} user ad account(s):")
|
||||
for acc in user_account_list:
|
||||
print(f" - {acc.get('name')} ({acc.get('id')})")
|
||||
print()
|
||||
except Exception as e:
|
||||
print(f"❌ Error getting user accounts: {e}\n")
|
||||
user_account_list = []
|
||||
|
||||
# Method 2: Business Manager accounts
|
||||
print("METHOD 2: Business Manager Ad Accounts (App-Level Access)")
|
||||
print("-"*70)
|
||||
try:
|
||||
me = User(fbid='me')
|
||||
# Get businesses this user/app has access to
|
||||
businesses = me.get_businesses(fields=['id', 'name'])
|
||||
business_list = list(businesses)
|
||||
|
||||
if not business_list:
|
||||
print("No Business Managers found.")
|
||||
print()
|
||||
print("ℹ️ To access ad accounts at the app level, you need to:")
|
||||
print(" 1. Have a Meta Business Manager")
|
||||
print(" 2. Add your app to the Business Manager")
|
||||
print(" 3. Grant the app access to ad accounts")
|
||||
print()
|
||||
else:
|
||||
print(f"Found {len(business_list)} Business Manager(s):")
|
||||
all_business_accounts = []
|
||||
|
||||
for biz in business_list:
|
||||
biz_id = biz.get('id')
|
||||
biz_name = biz.get('name')
|
||||
print(f"\n Business: {biz_name} ({biz_id})")
|
||||
|
||||
try:
|
||||
business = Business(fbid=biz_id)
|
||||
# Get all ad accounts owned/managed by this business
|
||||
biz_accounts = business.get_owned_ad_accounts(
|
||||
fields=['id', 'name', 'account_status']
|
||||
)
|
||||
biz_account_list = list(biz_accounts)
|
||||
|
||||
print(f" Ad Accounts: {len(biz_account_list)}")
|
||||
for acc in biz_account_list:
|
||||
print(f" - {acc.get('name')} ({acc.get('id')})")
|
||||
all_business_accounts.append(acc)
|
||||
|
||||
except Exception as e:
|
||||
print(f" ❌ Error accessing business accounts: {e}")
|
||||
|
||||
print()
|
||||
print(f"Total Business Manager ad accounts: {len(all_business_accounts)}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Error getting businesses: {e}")
|
||||
print()
|
||||
|
||||
# Method 3: App-level access token (for reference)
|
||||
print()
|
||||
print("METHOD 3: App Access Token (NOT recommended for ad accounts)")
|
||||
print("-"*70)
|
||||
print("App access tokens can be generated with:")
|
||||
print(f" curl 'https://graph.facebook.com/oauth/access_token")
|
||||
print(f" ?client_id={app_id}")
|
||||
print(f" &client_secret=YOUR_SECRET")
|
||||
print(f" &grant_type=client_credentials'")
|
||||
print()
|
||||
print("⚠️ However, app access tokens CANNOT access ad accounts directly.")
|
||||
print(" The Marketing API requires user-level permissions for privacy/security.")
|
||||
print()
|
||||
|
||||
# Summary
|
||||
print("="*70)
|
||||
print("SUMMARY")
|
||||
print("="*70)
|
||||
print(f"User Ad Accounts: {len(user_account_list)}")
|
||||
print()
|
||||
print("💡 RECOMMENDATION:")
|
||||
print(" The current implementation using User.get_ad_accounts() is correct.")
|
||||
print(" To access MORE ad accounts, you have two options:")
|
||||
print()
|
||||
print(" Option 1: Use a System User token from Business Manager")
|
||||
print(" (grants access to all Business Manager ad accounts)")
|
||||
print()
|
||||
print(" Option 2: Have other users authorize your app")
|
||||
print(" (each user's token will see their ad accounts)")
|
||||
print()
|
||||
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
exit(discover_accounts())
|
||||
228783
src/meta_api_grabber/geotargets-2025-10-29.csv
Normal file
228783
src/meta_api_grabber/geotargets-2025-10-29.csv
Normal file
File diff suppressed because it is too large
Load Diff
@@ -1,349 +0,0 @@
|
||||
"""
|
||||
Async script to grab ad insights data from Meta's Marketing API.
|
||||
Conservative rate limiting to avoid API limits.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import os
|
||||
from datetime import datetime, timedelta
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, List
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from facebook_business.adobjects.adaccount import AdAccount
|
||||
from facebook_business.adobjects.adsinsights import AdsInsights
|
||||
from facebook_business.api import FacebookAdsApi
|
||||
|
||||
from .rate_limiter import MetaRateLimiter
|
||||
|
||||
|
||||
class MetaInsightsGrabber:
|
||||
"""
|
||||
Async grabber for Meta ad insights with intelligent rate limiting.
|
||||
|
||||
Features:
|
||||
- Monitors x-fb-ads-insights-throttle header
|
||||
- Auto-throttles when approaching rate limits (>75%)
|
||||
- Exponential backoff on rate limit errors
|
||||
- Automatic retries with progressive delays
|
||||
"""
|
||||
|
||||
def __init__(self, access_token: str = None):
|
||||
"""
|
||||
Initialize the grabber with credentials from environment.
|
||||
|
||||
Args:
|
||||
access_token: Optional access token. If not provided, will load from env.
|
||||
"""
|
||||
load_dotenv()
|
||||
|
||||
self.access_token = access_token or os.getenv("META_ACCESS_TOKEN")
|
||||
self.app_secret = os.getenv("META_APP_SECRET")
|
||||
self.app_id = os.getenv("META_APP_ID")
|
||||
self.ad_account_id = os.getenv("META_AD_ACCOUNT_ID")
|
||||
|
||||
if not self.access_token:
|
||||
raise ValueError(
|
||||
"Access token is required. Either:\n"
|
||||
"1. Set META_ACCESS_TOKEN in .env, or\n"
|
||||
"2. Run 'uv run python src/meta_api_grabber/auth.py' to get a token via OAuth2, or\n"
|
||||
"3. Pass access_token to the constructor"
|
||||
)
|
||||
|
||||
if not all([self.app_secret, self.app_id, self.ad_account_id]):
|
||||
raise ValueError(
|
||||
"Missing required environment variables (META_APP_SECRET, META_APP_ID, META_AD_ACCOUNT_ID). "
|
||||
"Please check your .env file against .env.example"
|
||||
)
|
||||
|
||||
# Initialize Facebook Ads API
|
||||
FacebookAdsApi.init(
|
||||
app_id=self.app_id,
|
||||
app_secret=self.app_secret,
|
||||
access_token=self.access_token,
|
||||
)
|
||||
|
||||
self.ad_account = AdAccount(self.ad_account_id)
|
||||
|
||||
# Rate limiter with backoff (Meta best practices)
|
||||
self.rate_limiter = MetaRateLimiter(
|
||||
base_delay=2.0,
|
||||
throttle_threshold=75.0,
|
||||
max_retry_delay=300.0,
|
||||
max_retries=5,
|
||||
)
|
||||
|
||||
async def _rate_limited_request(self, func, *args, **kwargs):
|
||||
"""
|
||||
Execute a request with intelligent rate limiting and backoff.
|
||||
|
||||
Monitors x-fb-ads-insights-throttle header and auto-throttles
|
||||
when usage exceeds 75%. Implements exponential backoff on errors.
|
||||
"""
|
||||
return await self.rate_limiter.execute_with_retry(func, *args, **kwargs)
|
||||
|
||||
async def get_account_insights(
|
||||
self,
|
||||
date_preset: str = "last_7d",
|
||||
fields: List[str] = None,
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Get account-level insights for a given time period.
|
||||
|
||||
Args:
|
||||
date_preset: Time period (last_7d, last_14d, last_30d, etc.)
|
||||
fields: List of fields to retrieve. If None, uses default interesting fields.
|
||||
|
||||
Returns:
|
||||
Dictionary containing insights data
|
||||
"""
|
||||
if fields is None:
|
||||
# Default interesting fields - conservative selection
|
||||
fields = [
|
||||
AdsInsights.Field.impressions,
|
||||
AdsInsights.Field.clicks,
|
||||
AdsInsights.Field.spend,
|
||||
AdsInsights.Field.cpc,
|
||||
AdsInsights.Field.cpm,
|
||||
AdsInsights.Field.ctr,
|
||||
AdsInsights.Field.reach,
|
||||
AdsInsights.Field.frequency,
|
||||
AdsInsights.Field.actions,
|
||||
AdsInsights.Field.cost_per_action_type,
|
||||
]
|
||||
|
||||
params = {
|
||||
"date_preset": date_preset,
|
||||
"level": "account",
|
||||
}
|
||||
|
||||
print(f"Fetching account insights for {date_preset}...")
|
||||
insights = await self._rate_limited_request(
|
||||
self.ad_account.get_insights,
|
||||
fields=fields,
|
||||
params=params,
|
||||
)
|
||||
|
||||
# Convert to dict
|
||||
result = {
|
||||
"account_id": self.ad_account_id,
|
||||
"date_preset": date_preset,
|
||||
"insights": [dict(insight) for insight in insights],
|
||||
"fetched_at": datetime.now().isoformat(),
|
||||
}
|
||||
|
||||
return result
|
||||
|
||||
async def get_campaign_insights(
|
||||
self,
|
||||
date_preset: str = "last_7d",
|
||||
limit: int = 10,
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Get campaign-level insights.
|
||||
|
||||
Args:
|
||||
date_preset: Time period
|
||||
limit: Maximum number of campaigns to fetch (conservative default)
|
||||
|
||||
Returns:
|
||||
Dictionary containing campaign insights
|
||||
"""
|
||||
fields = [
|
||||
AdsInsights.Field.campaign_name,
|
||||
AdsInsights.Field.campaign_id,
|
||||
AdsInsights.Field.impressions,
|
||||
AdsInsights.Field.clicks,
|
||||
AdsInsights.Field.spend,
|
||||
AdsInsights.Field.ctr,
|
||||
AdsInsights.Field.cpc,
|
||||
]
|
||||
|
||||
params = {
|
||||
"date_preset": date_preset,
|
||||
"level": "campaign",
|
||||
"limit": limit,
|
||||
}
|
||||
|
||||
print(f"Fetching campaign insights for {date_preset} (limit: {limit})...")
|
||||
insights = await self._rate_limited_request(
|
||||
self.ad_account.get_insights,
|
||||
fields=fields,
|
||||
params=params,
|
||||
)
|
||||
|
||||
result = {
|
||||
"account_id": self.ad_account_id,
|
||||
"date_preset": date_preset,
|
||||
"level": "campaign",
|
||||
"campaigns": [dict(insight) for insight in insights],
|
||||
"fetched_at": datetime.now().isoformat(),
|
||||
}
|
||||
|
||||
return result
|
||||
|
||||
async def get_ad_set_insights(
|
||||
self,
|
||||
date_preset: str = "last_7d",
|
||||
limit: int = 10,
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Get ad set level insights.
|
||||
|
||||
Args:
|
||||
date_preset: Time period
|
||||
limit: Maximum number of ad sets to fetch
|
||||
|
||||
Returns:
|
||||
Dictionary containing ad set insights
|
||||
"""
|
||||
fields = [
|
||||
AdsInsights.Field.adset_name,
|
||||
AdsInsights.Field.adset_id,
|
||||
AdsInsights.Field.impressions,
|
||||
AdsInsights.Field.clicks,
|
||||
AdsInsights.Field.spend,
|
||||
AdsInsights.Field.ctr,
|
||||
AdsInsights.Field.cpm,
|
||||
]
|
||||
|
||||
params = {
|
||||
"date_preset": date_preset,
|
||||
"level": "adset",
|
||||
"limit": limit,
|
||||
}
|
||||
|
||||
print(f"Fetching ad set insights for {date_preset} (limit: {limit})...")
|
||||
insights = await self._rate_limited_request(
|
||||
self.ad_account.get_insights,
|
||||
fields=fields,
|
||||
params=params,
|
||||
)
|
||||
|
||||
result = {
|
||||
"account_id": self.ad_account_id,
|
||||
"date_preset": date_preset,
|
||||
"level": "adset",
|
||||
"ad_sets": [dict(insight) for insight in insights],
|
||||
"fetched_at": datetime.now().isoformat(),
|
||||
}
|
||||
|
||||
return result
|
||||
|
||||
async def grab_all_insights(self, date_preset: str = "last_7d") -> Dict[str, Any]:
|
||||
"""
|
||||
Grab all interesting insights (account, campaign, ad set level).
|
||||
|
||||
Args:
|
||||
date_preset: Time period to fetch
|
||||
|
||||
Returns:
|
||||
Dictionary containing all insights
|
||||
"""
|
||||
print(f"\nStarting conservative data grab for {date_preset}...")
|
||||
print(f"Rate limit: {self.request_delay}s between requests\n")
|
||||
|
||||
# Fetch sequentially to be conservative
|
||||
account_insights = await self.get_account_insights(date_preset)
|
||||
campaign_insights = await self.get_campaign_insights(date_preset, limit=10)
|
||||
ad_set_insights = await self.get_ad_set_insights(date_preset, limit=10)
|
||||
|
||||
result = {
|
||||
"account": account_insights,
|
||||
"campaigns": campaign_insights,
|
||||
"ad_sets": ad_set_insights,
|
||||
"summary": {
|
||||
"date_preset": date_preset,
|
||||
"total_requests": 3,
|
||||
"fetched_at": datetime.now().isoformat(),
|
||||
}
|
||||
}
|
||||
|
||||
return result
|
||||
|
||||
async def save_insights_to_json(
|
||||
self,
|
||||
insights_data: Dict[str, Any],
|
||||
output_dir: str = "data",
|
||||
) -> Path:
|
||||
"""
|
||||
Save insights data to a JSON file.
|
||||
|
||||
Args:
|
||||
insights_data: The insights data to save
|
||||
output_dir: Directory to save the file
|
||||
|
||||
Returns:
|
||||
Path to the saved file
|
||||
"""
|
||||
# Create output directory if it doesn't exist
|
||||
output_path = Path(output_dir)
|
||||
output_path.mkdir(exist_ok=True)
|
||||
|
||||
# Generate filename with timestamp
|
||||
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||
filename = f"meta_insights_{timestamp}.json"
|
||||
filepath = output_path / filename
|
||||
|
||||
# Save to JSON with pretty formatting
|
||||
loop = asyncio.get_event_loop()
|
||||
await loop.run_in_executor(
|
||||
None,
|
||||
lambda: filepath.write_text(
|
||||
json.dumps(insights_data, indent=2, default=str)
|
||||
)
|
||||
)
|
||||
|
||||
print(f"\nData saved to: {filepath}")
|
||||
print(f"File size: {filepath.stat().st_size / 1024:.2f} KB")
|
||||
|
||||
return filepath
|
||||
|
||||
|
||||
async def main():
|
||||
"""Main entry point for the insights grabber."""
|
||||
try:
|
||||
grabber = MetaInsightsGrabber()
|
||||
|
||||
# Grab insights for the last 7 days
|
||||
insights = await grabber.grab_all_insights(date_preset=AdsInsights.DatePreset.last_7d)
|
||||
|
||||
# Save to JSON
|
||||
filepath = await grabber.save_insights_to_json(insights)
|
||||
|
||||
# Print summary
|
||||
print("\n" + "="*50)
|
||||
print("SUMMARY")
|
||||
print("="*50)
|
||||
print(f"Account ID: {grabber.ad_account_id}")
|
||||
print(f"Date Range: last_7d")
|
||||
print(f"Output File: {filepath}")
|
||||
|
||||
if insights["account"]["insights"]:
|
||||
account_data = insights["account"]["insights"][0]
|
||||
print(f"\nAccount Metrics:")
|
||||
print(f" Impressions: {account_data.get('impressions', 'N/A')}")
|
||||
print(f" Clicks: {account_data.get('clicks', 'N/A')}")
|
||||
print(f" Spend: ${account_data.get('spend', 'N/A')}")
|
||||
print(f" CTR: {account_data.get('ctr', 'N/A')}%")
|
||||
|
||||
print(f"\nCampaigns fetched: {len(insights['campaigns']['campaigns'])}")
|
||||
print(f"Ad Sets fetched: {len(insights['ad_sets']['ad_sets'])}")
|
||||
print("\n" + "="*50)
|
||||
|
||||
except ValueError as e:
|
||||
print(f"Configuration error: {e}")
|
||||
return 1
|
||||
except Exception as e:
|
||||
print(f"Error: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return 1
|
||||
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
exit_code = asyncio.run(main())
|
||||
exit(exit_code)
|
||||
@@ -1,6 +0,0 @@
|
||||
def main():
|
||||
print("Hello from meta-api-grabber!")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,657 +0,0 @@
|
||||
"""
|
||||
Rate limiting and backoff mechanism for Meta Marketing API.
|
||||
|
||||
Based on Meta's best practices:
|
||||
https://developers.facebook.com/docs/marketing-api/insights/best-practices/
|
||||
https://developers.facebook.com/docs/graph-api/overview/rate-limiting
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
import time
|
||||
from typing import Any, Callable, Dict, List, Optional
|
||||
|
||||
from facebook_business.api import FacebookAdsApi
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
logger.setLevel(logging.DEBUG)
|
||||
|
||||
|
||||
class MetaRateLimiter:
|
||||
"""
|
||||
Rate limiter with exponential backoff for Meta Marketing API.
|
||||
|
||||
Features:
|
||||
- Monitors X-App-Usage header (platform rate limits)
|
||||
- Monitors X-Ad-Account-Usage header (ad account specific)
|
||||
- Monitors X-Business-Use-Case-Usage header (business use case limits)
|
||||
- Monitors x-fb-ads-insights-throttle header (legacy)
|
||||
- Automatic throttling when usage > 75%
|
||||
- Exponential backoff on rate limit errors
|
||||
- Uses reset_time_duration and estimated_time_to_regain_access
|
||||
- Configurable thresholds
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
base_delay: float = 2.0,
|
||||
throttle_threshold: float = 75.0,
|
||||
max_retry_delay: float = 300.0, # 5 minutes
|
||||
max_retries: int = 5,
|
||||
):
|
||||
"""
|
||||
Initialize rate limiter.
|
||||
|
||||
Args:
|
||||
base_delay: Base delay between requests in seconds
|
||||
throttle_threshold: Throttle when usage exceeds this % (0-100)
|
||||
max_retry_delay: Maximum delay for exponential backoff
|
||||
max_retries: Maximum number of retries on rate limit errors
|
||||
"""
|
||||
self.base_delay = base_delay
|
||||
self.throttle_threshold = throttle_threshold
|
||||
self.max_retry_delay = max_retry_delay
|
||||
self.max_retries = max_retries
|
||||
|
||||
# Track current usage percentages from different headers
|
||||
# X-App-Usage (platform rate limits)
|
||||
self.app_call_count: float = 0.0
|
||||
self.app_total_cputime: float = 0.0
|
||||
self.app_total_time: float = 0.0
|
||||
|
||||
# X-Ad-Account-Usage (ad account specific) - tracked per account
|
||||
# Key: account_id (e.g., "act_123456789"), Value: dict with metrics
|
||||
self.ad_account_usage: Dict[str, Dict[str, Any]] = {}
|
||||
|
||||
# X-Business-Use-Case-Usage (business use case limits)
|
||||
self.buc_usage: List[Dict[str, Any]] = []
|
||||
self.estimated_time_to_regain_access: int = 0 # minutes
|
||||
|
||||
# Legacy x-fb-ads-insights-throttle
|
||||
self.legacy_app_usage_pct: float = 0.0
|
||||
self.legacy_account_usage_pct: float = 0.0
|
||||
|
||||
self.last_check_time: float = time.time()
|
||||
|
||||
# Stats
|
||||
self.total_requests: int = 0
|
||||
self.throttled_requests: int = 0
|
||||
self.rate_limit_errors: int = 0
|
||||
|
||||
def _get_headers(self, response: Any) -> Optional[Dict[str, str]]:
|
||||
"""
|
||||
Extract headers from various response object types.
|
||||
|
||||
Args:
|
||||
response: API response object
|
||||
|
||||
Returns:
|
||||
Dictionary of headers or None
|
||||
"""
|
||||
# Facebook SDK response object
|
||||
if hasattr(response, '_headers'):
|
||||
return response._headers
|
||||
elif hasattr(response, 'headers'):
|
||||
return response.headers
|
||||
elif hasattr(response, '_api_response'):
|
||||
return getattr(response._api_response, 'headers', None)
|
||||
return None
|
||||
|
||||
def parse_x_app_usage(self, response: Any) -> Dict[str, float]:
|
||||
"""
|
||||
Parse X-App-Usage header (Platform rate limits).
|
||||
|
||||
Header format: {"call_count": 28, "total_time": 25, "total_cputime": 25}
|
||||
|
||||
Args:
|
||||
response: API response object
|
||||
|
||||
Returns:
|
||||
Dictionary with call_count, total_time, total_cputime
|
||||
"""
|
||||
try:
|
||||
headers = self._get_headers(response)
|
||||
if headers:
|
||||
header_value = headers.get('x-app-usage') or headers.get('X-App-Usage', '')
|
||||
if header_value:
|
||||
logger.debug(f"X-App-Usage header: {header_value}")
|
||||
data = json.loads(header_value)
|
||||
result = {
|
||||
'call_count': float(data.get('call_count', 0)),
|
||||
'total_time': float(data.get('total_time', 0)),
|
||||
'total_cputime': float(data.get('total_cputime', 0)),
|
||||
}
|
||||
logger.debug(f"Parsed X-App-Usage: {result}")
|
||||
return result
|
||||
except Exception as e:
|
||||
logger.debug(f"Failed to parse X-App-Usage header: {e}")
|
||||
return {'call_count': 0.0, 'total_time': 0.0, 'total_cputime': 0.0}
|
||||
|
||||
def parse_x_ad_account_usage(self, response: Any) -> Optional[Dict[str, Any]]:
|
||||
"""
|
||||
Parse X-Ad-Account-Usage header (Ad account specific limits).
|
||||
|
||||
Header format: {
|
||||
"acc_id_util_pct": 9.67,
|
||||
"reset_time_duration": 100,
|
||||
"ads_api_access_tier": "standard_access"
|
||||
}
|
||||
|
||||
Args:
|
||||
response: API response object
|
||||
|
||||
Returns:
|
||||
Dictionary with metrics, or None if header not present.
|
||||
To determine account_id, check response object or URL.
|
||||
"""
|
||||
try:
|
||||
headers = self._get_headers(response)
|
||||
if headers:
|
||||
header_value = headers.get('x-ad-account-usage') or headers.get('X-Ad-Account-Usage', '')
|
||||
if header_value:
|
||||
logger.debug(f"X-Ad-Account-Usage header: {header_value}")
|
||||
data = json.loads(header_value)
|
||||
result = {
|
||||
'acc_id_util_pct': float(data.get('acc_id_util_pct', 0)),
|
||||
'reset_time_duration': int(data.get('reset_time_duration', 0)),
|
||||
'ads_api_access_tier': data.get('ads_api_access_tier'),
|
||||
}
|
||||
logger.debug(f"Parsed X-Ad-Account-Usage: {result}")
|
||||
return result
|
||||
except Exception as e:
|
||||
logger.debug(f"Failed to parse X-Ad-Account-Usage header: {e}")
|
||||
return None
|
||||
|
||||
def _extract_account_id(self, response: Any) -> Optional[str]:
|
||||
"""
|
||||
Extract account ID from response object.
|
||||
|
||||
Tries multiple methods to find the account ID from the response.
|
||||
|
||||
Args:
|
||||
response: API response object
|
||||
|
||||
Returns:
|
||||
Account ID string (e.g., "act_123456789") or None
|
||||
"""
|
||||
# Try to get account_id from response attributes
|
||||
if hasattr(response, 'account_id'):
|
||||
return response.account_id
|
||||
if hasattr(response, '_data') and isinstance(response._data, dict):
|
||||
return response._data.get('account_id')
|
||||
|
||||
# Try to get from parent object
|
||||
if hasattr(response, '_parent_object'):
|
||||
parent = response._parent_object
|
||||
if hasattr(parent, 'get_id'):
|
||||
return parent.get_id()
|
||||
if hasattr(parent, '_data') and isinstance(parent._data, dict):
|
||||
return parent._data.get('account_id') or parent._data.get('id')
|
||||
|
||||
# Try to get from API context
|
||||
if hasattr(response, '_api_context'):
|
||||
context = response._api_context
|
||||
if hasattr(context, 'account_id'):
|
||||
return context.account_id
|
||||
|
||||
return None
|
||||
|
||||
def parse_x_business_use_case_usage(self, response: Any) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Parse X-Business-Use-Case-Usage header (Business use case limits).
|
||||
|
||||
Header format: {
|
||||
"business-id": [{
|
||||
"type": "ads_management",
|
||||
"call_count": 95,
|
||||
"total_cputime": 20,
|
||||
"total_time": 20,
|
||||
"estimated_time_to_regain_access": 0,
|
||||
"ads_api_access_tier": "development_access"
|
||||
}],
|
||||
...
|
||||
}
|
||||
|
||||
Args:
|
||||
response: API response object
|
||||
|
||||
Returns:
|
||||
List of usage dictionaries for each business use case
|
||||
"""
|
||||
try:
|
||||
headers = self._get_headers(response)
|
||||
if headers:
|
||||
header_value = headers.get('x-business-use-case-usage') or headers.get('X-Business-Use-Case-Usage', '')
|
||||
if header_value:
|
||||
logger.debug(f"X-Business-Use-Case-Usage header: {header_value}")
|
||||
data = json.loads(header_value)
|
||||
# Flatten the nested structure
|
||||
all_usage = []
|
||||
for business_id, use_cases in data.items():
|
||||
if isinstance(use_cases, list):
|
||||
for use_case in use_cases:
|
||||
use_case['business_id'] = business_id
|
||||
all_usage.append(use_case)
|
||||
logger.debug(f"Parsed X-Business-Use-Case-Usage: {len(all_usage)} use cases")
|
||||
return all_usage
|
||||
except Exception as e:
|
||||
logger.debug(f"Failed to parse X-Business-Use-Case-Usage header: {e}")
|
||||
return []
|
||||
|
||||
def parse_throttle_header(self, response: Any) -> Dict[str, float]:
|
||||
"""
|
||||
Parse x-fb-ads-insights-throttle header from response (legacy).
|
||||
|
||||
Header format: {"app_id_util_pct": 25.5, "acc_id_util_pct": 10.0}
|
||||
|
||||
Args:
|
||||
response: API response object
|
||||
|
||||
Returns:
|
||||
Dictionary with app_id_util_pct and acc_id_util_pct
|
||||
"""
|
||||
try:
|
||||
headers = self._get_headers(response)
|
||||
if headers:
|
||||
throttle_header = headers.get('x-fb-ads-insights-throttle', '')
|
||||
if throttle_header:
|
||||
logger.debug(f"x-fb-ads-insights-throttle header: {throttle_header}")
|
||||
throttle_data = json.loads(throttle_header)
|
||||
result = {
|
||||
'app_id_util_pct': float(throttle_data.get('app_id_util_pct', 0)),
|
||||
'acc_id_util_pct': float(throttle_data.get('acc_id_util_pct', 0)),
|
||||
}
|
||||
logger.debug(f"Parsed x-fb-ads-insights-throttle: {result}")
|
||||
return result
|
||||
except Exception as e:
|
||||
logger.debug(f"Failed to parse x-fb-ads-insights-throttle header: {e}")
|
||||
return {'app_id_util_pct': 0.0, 'acc_id_util_pct': 0.0}
|
||||
|
||||
def update_usage(self, response: Any, account_id: Optional[str] = None):
|
||||
"""
|
||||
Update usage statistics from all API response headers.
|
||||
|
||||
Parses and updates metrics from:
|
||||
- X-App-Usage
|
||||
- X-Ad-Account-Usage (per account)
|
||||
- X-Business-Use-Case-Usage
|
||||
- x-fb-ads-insights-throttle (legacy)
|
||||
|
||||
Args:
|
||||
response: API response object
|
||||
account_id: Optional account ID (e.g., "act_123456789").
|
||||
If not provided, will attempt to extract from response.
|
||||
"""
|
||||
# Parse all headers
|
||||
app_usage = self.parse_x_app_usage(response)
|
||||
ad_account_usage = self.parse_x_ad_account_usage(response)
|
||||
buc_usage = self.parse_x_business_use_case_usage(response)
|
||||
legacy_throttle = self.parse_throttle_header(response)
|
||||
|
||||
# Update X-App-Usage metrics
|
||||
self.app_call_count = app_usage['call_count']
|
||||
self.app_total_cputime = app_usage['total_cputime']
|
||||
self.app_total_time = app_usage['total_time']
|
||||
|
||||
# Update X-Ad-Account-Usage metrics (per account)
|
||||
if ad_account_usage:
|
||||
# Try to get account_id
|
||||
if not account_id:
|
||||
account_id = self._extract_account_id(response)
|
||||
|
||||
# Use 'unknown' as fallback if we can't determine account
|
||||
if not account_id:
|
||||
account_id = 'unknown'
|
||||
logger.debug("Could not determine account_id, using 'unknown'")
|
||||
|
||||
# Store usage for this account
|
||||
self.ad_account_usage[account_id] = ad_account_usage
|
||||
logger.debug(f"Updated ad account usage for {account_id}")
|
||||
|
||||
# Update X-Business-Use-Case-Usage metrics
|
||||
self.buc_usage = buc_usage
|
||||
# Find the maximum estimated_time_to_regain_access across all use cases
|
||||
if buc_usage:
|
||||
self.estimated_time_to_regain_access = max(
|
||||
(uc.get('estimated_time_to_regain_access', 0) for uc in buc_usage),
|
||||
default=0
|
||||
)
|
||||
|
||||
# Update legacy metrics
|
||||
self.legacy_app_usage_pct = legacy_throttle['app_id_util_pct']
|
||||
self.legacy_account_usage_pct = legacy_throttle['acc_id_util_pct']
|
||||
|
||||
self.last_check_time = time.time()
|
||||
|
||||
# Log warnings if approaching limits
|
||||
self._log_rate_limit_warnings()
|
||||
|
||||
def _log_rate_limit_warnings(self):
|
||||
"""Log warnings if any rate limit metric is approaching threshold."""
|
||||
warnings = []
|
||||
|
||||
# Check X-App-Usage metrics
|
||||
if self.app_call_count > self.throttle_threshold:
|
||||
warnings.append(f"App call count: {self.app_call_count:.1f}%")
|
||||
if self.app_total_cputime > self.throttle_threshold:
|
||||
warnings.append(f"App CPU time: {self.app_total_cputime:.1f}%")
|
||||
if self.app_total_time > self.throttle_threshold:
|
||||
warnings.append(f"App total time: {self.app_total_time:.1f}%")
|
||||
|
||||
# Check X-Ad-Account-Usage (per account)
|
||||
for account_id, usage in self.ad_account_usage.items():
|
||||
acc_pct = usage.get('acc_id_util_pct', 0)
|
||||
if acc_pct > self.throttle_threshold:
|
||||
warnings.append(f"Account {account_id}: {acc_pct:.1f}%")
|
||||
reset_time = usage.get('reset_time_duration', 0)
|
||||
if reset_time > 0:
|
||||
warnings.append(f"Resets in {reset_time}s")
|
||||
|
||||
# Check X-Business-Use-Case-Usage
|
||||
for buc in self.buc_usage:
|
||||
buc_type = buc.get('type', 'unknown')
|
||||
call_count = buc.get('call_count', 0)
|
||||
if call_count > self.throttle_threshold:
|
||||
warnings.append(f"BUC {buc_type}: {call_count:.1f}%")
|
||||
eta = buc.get('estimated_time_to_regain_access', 0)
|
||||
if eta > 0:
|
||||
warnings.append(f"Regain access in {eta} min")
|
||||
|
||||
# Check legacy metrics
|
||||
if self.legacy_app_usage_pct > self.throttle_threshold:
|
||||
warnings.append(f"Legacy app: {self.legacy_app_usage_pct:.1f}%")
|
||||
if self.legacy_account_usage_pct > self.throttle_threshold:
|
||||
warnings.append(f"Legacy account: {self.legacy_account_usage_pct:.1f}%")
|
||||
|
||||
if warnings:
|
||||
logger.warning(f"⚠️ Rate limit warning: {', '.join(warnings)}")
|
||||
|
||||
def get_max_usage_pct(self) -> float:
|
||||
"""
|
||||
Get the maximum usage percentage across all rate limit metrics.
|
||||
|
||||
Returns:
|
||||
Maximum usage percentage (0-100)
|
||||
"""
|
||||
usage_values = [
|
||||
self.app_call_count,
|
||||
self.app_total_cputime,
|
||||
self.app_total_time,
|
||||
self.legacy_app_usage_pct,
|
||||
self.legacy_account_usage_pct,
|
||||
]
|
||||
|
||||
# Add ad account usage percentages (per account)
|
||||
for usage in self.ad_account_usage.values():
|
||||
usage_values.append(usage.get('acc_id_util_pct', 0))
|
||||
|
||||
# Add BUC usage percentages
|
||||
for buc in self.buc_usage:
|
||||
usage_values.extend([
|
||||
buc.get('call_count', 0),
|
||||
buc.get('total_cputime', 0),
|
||||
buc.get('total_time', 0),
|
||||
])
|
||||
|
||||
return max(usage_values) if usage_values else 0.0
|
||||
|
||||
def should_throttle(self) -> bool:
|
||||
"""
|
||||
Check if we should throttle based on current usage.
|
||||
|
||||
Returns:
|
||||
True if usage exceeds threshold
|
||||
"""
|
||||
return self.get_max_usage_pct() > self.throttle_threshold
|
||||
|
||||
def get_throttle_delay(self) -> float:
|
||||
"""
|
||||
Calculate delay based on current usage and reset times.
|
||||
|
||||
Uses estimated_time_to_regain_access and reset_time_duration when available.
|
||||
|
||||
Returns:
|
||||
Delay in seconds
|
||||
"""
|
||||
max_usage = self.get_max_usage_pct()
|
||||
|
||||
if max_usage < self.throttle_threshold:
|
||||
return self.base_delay
|
||||
|
||||
# If we have estimated_time_to_regain_access from BUC header, use it
|
||||
if self.estimated_time_to_regain_access > 0:
|
||||
# Convert minutes to seconds and use as delay
|
||||
delay = self.estimated_time_to_regain_access * 60
|
||||
logger.info(f"Using BUC estimated_time_to_regain_access: {self.estimated_time_to_regain_access} min ({delay}s)")
|
||||
return min(delay, self.max_retry_delay)
|
||||
|
||||
# Check if any ad account has reset_time_duration and high usage
|
||||
for account_id, usage in self.ad_account_usage.items():
|
||||
acc_pct = usage.get('acc_id_util_pct', 0)
|
||||
reset_time = usage.get('reset_time_duration', 0)
|
||||
if reset_time > 0 and acc_pct >= 90:
|
||||
# Use a fraction of reset_time_duration as delay
|
||||
delay = min(reset_time * 0.5, self.max_retry_delay)
|
||||
logger.info(f"Using Ad Account {account_id} reset_time_duration: {reset_time}s (delay: {delay}s)")
|
||||
return delay
|
||||
|
||||
# Progressive delay based on usage
|
||||
# 75% = base_delay, 90% = 2x, 95% = 5x, 99% = 10x
|
||||
if max_usage >= 95:
|
||||
multiplier = 10.0
|
||||
elif max_usage >= 90:
|
||||
multiplier = 5.0
|
||||
elif max_usage >= 85:
|
||||
multiplier = 3.0
|
||||
else: # 75-85%
|
||||
multiplier = 2.0
|
||||
|
||||
delay = self.base_delay * multiplier
|
||||
return min(delay, self.max_retry_delay)
|
||||
|
||||
async def wait_with_backoff(self, retry_count: int = 0):
|
||||
"""
|
||||
Wait with exponential backoff.
|
||||
|
||||
Args:
|
||||
retry_count: Current retry attempt (0-indexed)
|
||||
"""
|
||||
if retry_count == 0:
|
||||
# Normal delay based on throttle
|
||||
delay = self.get_throttle_delay()
|
||||
else:
|
||||
# Exponential backoff: 2^retry * base_delay
|
||||
delay = min(
|
||||
(2 ** retry_count) * self.base_delay,
|
||||
self.max_retry_delay
|
||||
)
|
||||
|
||||
if delay > self.base_delay:
|
||||
self.throttled_requests += 1
|
||||
max_usage = self.get_max_usage_pct()
|
||||
logger.info(f"⏸️ Throttling for {delay:.1f}s (max usage: {max_usage:.1f}%)")
|
||||
|
||||
await asyncio.sleep(delay)
|
||||
|
||||
async def execute_with_retry(
|
||||
self,
|
||||
func: Callable,
|
||||
*args,
|
||||
**kwargs
|
||||
) -> Any:
|
||||
"""
|
||||
Execute API call with automatic retry and backoff.
|
||||
|
||||
Args:
|
||||
func: Function to execute (blocking, will be run in executor)
|
||||
*args: Positional arguments for func
|
||||
**kwargs: Keyword arguments for func
|
||||
|
||||
Returns:
|
||||
Result from func
|
||||
|
||||
Raises:
|
||||
Exception: If all retries exhausted
|
||||
"""
|
||||
self.total_requests += 1
|
||||
|
||||
for retry in range(self.max_retries):
|
||||
try:
|
||||
# Wait before request (with potential throttling)
|
||||
await self.wait_with_backoff(retry_count=retry if retry > 0 else 0)
|
||||
|
||||
# Execute request in thread pool
|
||||
loop = asyncio.get_event_loop()
|
||||
result = await loop.run_in_executor(None, lambda: func(*args, **kwargs))
|
||||
|
||||
# Update usage from response
|
||||
self.update_usage(result)
|
||||
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
error_message = str(e).lower()
|
||||
|
||||
# Check if it's a rate limit error (expanded list based on Meta docs)
|
||||
is_rate_limit = (
|
||||
'rate limit' in error_message or
|
||||
'too many requests' in error_message or
|
||||
'throttle' in error_message or
|
||||
'error code 4' in error_message or # App rate limit
|
||||
'error code 17' in error_message or # User rate limit
|
||||
'error code 32' in error_message or # Pages rate limit
|
||||
'error code 613' in error_message or # Custom rate limit
|
||||
'error code 80000' in error_message or # Ads Insights BUC
|
||||
'error code 80001' in error_message or # Pages BUC
|
||||
'error code 80002' in error_message or # Instagram BUC
|
||||
'error code 80003' in error_message or # Custom Audience BUC
|
||||
'error code 80004' in error_message or # Ads Management BUC
|
||||
'error code 80005' in error_message or # LeadGen BUC
|
||||
'error code 80006' in error_message or # Messenger BUC
|
||||
'error code 80008' in error_message or # WhatsApp BUC
|
||||
'error code 80009' in error_message or # Catalog Management BUC
|
||||
'error code 80014' in error_message # Catalog Batch BUC
|
||||
)
|
||||
|
||||
if is_rate_limit:
|
||||
self.rate_limit_errors += 1
|
||||
|
||||
if retry < self.max_retries - 1:
|
||||
backoff_delay = min(
|
||||
(2 ** (retry + 1)) * self.base_delay,
|
||||
self.max_retry_delay
|
||||
)
|
||||
logger.warning(f"🔄 Rate limit hit! Retrying in {backoff_delay:.1f}s (attempt {retry + 1}/{self.max_retries})")
|
||||
logger.warning(f" Error: {e}")
|
||||
await asyncio.sleep(backoff_delay)
|
||||
continue
|
||||
else:
|
||||
logger.error(f"❌ Rate limit error - max retries exhausted: {e}")
|
||||
raise
|
||||
|
||||
# Not a rate limit error, re-raise immediately
|
||||
raise
|
||||
|
||||
# Should never reach here
|
||||
raise Exception("Max retries exhausted")
|
||||
|
||||
def get_stats(self) -> Dict[str, Any]:
|
||||
"""
|
||||
Get current rate limiter statistics.
|
||||
|
||||
Returns:
|
||||
Dictionary with comprehensive stats from all headers
|
||||
"""
|
||||
return {
|
||||
# Request stats
|
||||
'total_requests': self.total_requests,
|
||||
'throttled_requests': self.throttled_requests,
|
||||
'rate_limit_errors': self.rate_limit_errors,
|
||||
|
||||
# X-App-Usage metrics
|
||||
'app_call_count': self.app_call_count,
|
||||
'app_total_cputime': self.app_total_cputime,
|
||||
'app_total_time': self.app_total_time,
|
||||
|
||||
# X-Ad-Account-Usage metrics (per account)
|
||||
'ad_account_usage': self.ad_account_usage,
|
||||
|
||||
# X-Business-Use-Case-Usage metrics
|
||||
'buc_usage': self.buc_usage,
|
||||
'estimated_time_to_regain_access': self.estimated_time_to_regain_access,
|
||||
|
||||
# Legacy metrics
|
||||
'legacy_app_usage_pct': self.legacy_app_usage_pct,
|
||||
'legacy_account_usage_pct': self.legacy_account_usage_pct,
|
||||
|
||||
# Computed metrics
|
||||
'max_usage_pct': self.get_max_usage_pct(),
|
||||
'is_throttling': self.should_throttle(),
|
||||
}
|
||||
|
||||
def print_stats(self):
|
||||
"""Print current statistics with all rate limit metrics."""
|
||||
stats = self.get_stats()
|
||||
|
||||
output = []
|
||||
output.append("\n" + "="*70)
|
||||
output.append("RATE LIMITER STATISTICS")
|
||||
output.append("="*70)
|
||||
|
||||
# Request stats
|
||||
output.append(f"Total Requests: {stats['total_requests']}")
|
||||
output.append(f"Throttled Requests: {stats['throttled_requests']}")
|
||||
output.append(f"Rate Limit Errors: {stats['rate_limit_errors']}")
|
||||
output.append("")
|
||||
|
||||
# X-App-Usage
|
||||
output.append("X-App-Usage (Platform Rate Limits):")
|
||||
output.append(f" Call Count: {stats['app_call_count']:.1f}%")
|
||||
output.append(f" Total CPU Time: {stats['app_total_cputime']:.1f}%")
|
||||
output.append(f" Total Time: {stats['app_total_time']:.1f}%")
|
||||
output.append("")
|
||||
|
||||
# X-Ad-Account-Usage (per account)
|
||||
if stats['ad_account_usage']:
|
||||
# Only show accounts with data (skip "unknown" accounts with 0 usage)
|
||||
accounts_to_show = {
|
||||
account_id: usage
|
||||
for account_id, usage in stats['ad_account_usage'].items()
|
||||
if account_id != 'unknown' or usage.get('acc_id_util_pct', 0) > 0
|
||||
}
|
||||
|
||||
if accounts_to_show:
|
||||
output.append("X-Ad-Account-Usage (Per Account):")
|
||||
for account_id, usage in accounts_to_show.items():
|
||||
output.append(f" Account: {account_id}")
|
||||
output.append(f" Usage: {usage.get('acc_id_util_pct', 0):.1f}%")
|
||||
output.append(f" Reset Time: {usage.get('reset_time_duration', 0)}s")
|
||||
output.append(f" API Access Tier: {usage.get('ads_api_access_tier') or 'N/A'}")
|
||||
output.append("")
|
||||
|
||||
# X-Business-Use-Case-Usage
|
||||
if stats['buc_usage']:
|
||||
output.append("X-Business-Use-Case-Usage:")
|
||||
for buc in stats['buc_usage']:
|
||||
output.append(f" Type: {buc.get('type', 'unknown')}")
|
||||
output.append(f" Call Count: {buc.get('call_count', 0):.1f}%")
|
||||
output.append(f" Total CPU Time: {buc.get('total_cputime', 0):.1f}%")
|
||||
output.append(f" Total Time: {buc.get('total_time', 0):.1f}%")
|
||||
output.append(f" Est. Time to Regain: {buc.get('estimated_time_to_regain_access', 0)} min")
|
||||
output.append("")
|
||||
|
||||
# Legacy metrics
|
||||
output.append("Legacy (x-fb-ads-insights-throttle):")
|
||||
output.append(f" App Usage: {stats['legacy_app_usage_pct']:.1f}%")
|
||||
output.append(f" Account Usage: {stats['legacy_account_usage_pct']:.1f}%")
|
||||
output.append("")
|
||||
|
||||
# Summary
|
||||
output.append(f"Max Usage Across All Metrics: {stats['max_usage_pct']:.1f}%")
|
||||
output.append(f"Currently Throttled: {stats['is_throttling']}")
|
||||
output.append("="*70 + "\n")
|
||||
|
||||
logger.info("\n".join(output))
|
||||
File diff suppressed because it is too large
Load Diff
380
src/meta_api_grabber/setup_and_wait.py
Normal file
380
src/meta_api_grabber/setup_and_wait.py
Normal file
@@ -0,0 +1,380 @@
|
||||
"""
|
||||
View Manager Setup and Daemon
|
||||
|
||||
This script:
|
||||
1. Initializes database schema from db_schema.sql (drops and recreates public schema with all views)
|
||||
2. Loads metadata from metadata.yaml into the database
|
||||
3. Auto-detects materialized views from db_schema.sql
|
||||
4. Periodically refreshes materialized views (configurable interval)
|
||||
|
||||
Usage:
|
||||
uv run view-manager-setup
|
||||
|
||||
Environment variables:
|
||||
REFRESH_INTERVAL_MINUTES: How often to refresh materialized views (default: 60)
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import csv
|
||||
import logging
|
||||
import os
|
||||
import re
|
||||
import signal
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from typing import List
|
||||
|
||||
import yaml
|
||||
|
||||
from meta_api_grabber.database import TimescaleDBClient
|
||||
|
||||
# Set up logging
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
|
||||
)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class ViewManagerSetup:
|
||||
"""Manages database schema setup, metadata loading, and view refresh."""
|
||||
|
||||
def __init__(self, refresh_interval_minutes: int = 60):
|
||||
self.db = TimescaleDBClient()
|
||||
self.shutdown_event = asyncio.Event()
|
||||
self.refresh_interval_minutes = refresh_interval_minutes
|
||||
self.materialized_views: List[str] = []
|
||||
|
||||
async def load_metadata_from_yaml(self, yaml_path: Path):
|
||||
"""
|
||||
Load account metadata from YAML file and sync to database.
|
||||
|
||||
Args:
|
||||
yaml_path: Path to metadata.yaml file
|
||||
"""
|
||||
if not yaml_path.exists():
|
||||
logger.warning(f"Metadata file not found: {yaml_path}")
|
||||
logger.info("Skipping metadata loading. Create metadata.yaml to configure accounts.")
|
||||
return
|
||||
|
||||
logger.info(f"Loading metadata from {yaml_path}")
|
||||
|
||||
with open(yaml_path, 'r') as f:
|
||||
data = yaml.safe_load(f)
|
||||
|
||||
accounts = data.get('accounts', [])
|
||||
if not accounts:
|
||||
logger.warning("No accounts found in metadata.yaml")
|
||||
return
|
||||
|
||||
# Sync accounts to database using upsert logic
|
||||
async with self.db.pool.acquire() as conn:
|
||||
# Clear existing metadata (or implement smarter sync logic)
|
||||
await conn.execute("DELETE FROM public.account_metadata")
|
||||
|
||||
for account in accounts:
|
||||
label = account.get('label')
|
||||
meta_account_id = account.get('meta_account_id')
|
||||
google_account_id = account.get('google_account_id')
|
||||
alpinebits_hotel_code = account.get('alpinebits_hotel_code')
|
||||
|
||||
if not label:
|
||||
logger.warning(f"Skipping account without label: {account}")
|
||||
continue
|
||||
|
||||
await conn.execute(
|
||||
"""
|
||||
INSERT INTO public.account_metadata
|
||||
(label, meta_account_id, google_account_id, alpinebits_hotel_code, updated_at)
|
||||
VALUES ($1, $2, $3, $4, NOW())
|
||||
""",
|
||||
label, meta_account_id, google_account_id, alpinebits_hotel_code
|
||||
)
|
||||
|
||||
logger.info(f"Loaded metadata for: {label}")
|
||||
|
||||
logger.info(f"Successfully loaded {len(accounts)} accounts from metadata.yaml")
|
||||
|
||||
async def load_geotargets_from_csv(self, csv_path: Path):
|
||||
"""
|
||||
Load geotargets data from CSV file and sync to database.
|
||||
|
||||
This function is idempotent - it will:
|
||||
1. Clear existing geotargets data
|
||||
2. Load fresh data from CSV
|
||||
|
||||
This means if you swap the CSV file, the data will be updated.
|
||||
|
||||
Args:
|
||||
csv_path: Path to geotargets CSV file
|
||||
"""
|
||||
if not csv_path.exists():
|
||||
logger.warning(f"Geotargets CSV file not found: {csv_path}")
|
||||
logger.info("Skipping geotargets loading. Place geotargets CSV file to load location data.")
|
||||
return
|
||||
|
||||
logger.info(f"Loading geotargets from {csv_path}")
|
||||
|
||||
# Read CSV file
|
||||
geotargets = []
|
||||
with open(csv_path, 'r', encoding='utf-8') as f:
|
||||
reader = csv.DictReader(f)
|
||||
for row in reader:
|
||||
# Convert IDs to integers, handling empty parent_id
|
||||
criteria_id = int(row['Criteria ID'])
|
||||
parent_id = int(row['Parent ID']) if row['Parent ID'] else None
|
||||
|
||||
geotargets.append({
|
||||
'criteria_id': criteria_id,
|
||||
'name': row['Name'],
|
||||
'canonical_name': row['Canonical Name'],
|
||||
'parent_id': parent_id,
|
||||
'country_code': row['Country Code'],
|
||||
'target_type': row['Target Type'],
|
||||
'status': row['Status']
|
||||
})
|
||||
|
||||
if not geotargets:
|
||||
logger.warning("No geotargets found in CSV file")
|
||||
return
|
||||
|
||||
# Load data to database using idempotent approach
|
||||
async with self.db.pool.acquire() as conn:
|
||||
# Clear existing data to ensure we have latest data from CSV
|
||||
deleted_count = await conn.fetchval("SELECT COUNT(*) FROM public.geotargets")
|
||||
await conn.execute("TRUNCATE TABLE public.geotargets")
|
||||
logger.info(f"Cleared {deleted_count} existing geotarget records")
|
||||
|
||||
# Batch insert new data
|
||||
await conn.executemany(
|
||||
"""
|
||||
INSERT INTO public.geotargets
|
||||
(criteria_id, name, canonical_name, parent_id, country_code, target_type, status, updated_at)
|
||||
VALUES ($1, $2, $3, $4, $5, $6, $7, NOW())
|
||||
""",
|
||||
[(g['criteria_id'], g['name'], g['canonical_name'], g['parent_id'],
|
||||
g['country_code'], g['target_type'], g['status'])
|
||||
for g in geotargets]
|
||||
)
|
||||
|
||||
logger.info(f"Successfully loaded {len(geotargets)} geotargets from CSV")
|
||||
|
||||
def detect_materialized_views(self, schema_path: Path) -> List[str]:
|
||||
"""
|
||||
Auto-detect materialized views from db_schema.sql.
|
||||
|
||||
Parses the SQL file to find:
|
||||
1. CREATE MATERIALIZED VIEW statements
|
||||
2. REFRESH MATERIALIZED VIEW statements (explicit refresh requests)
|
||||
|
||||
Args:
|
||||
schema_path: Path to db_schema.sql
|
||||
|
||||
Returns:
|
||||
List of materialized view names (deduplicated, in order of appearance)
|
||||
"""
|
||||
if not schema_path.exists():
|
||||
logger.warning(f"Schema file not found: {schema_path}")
|
||||
return []
|
||||
|
||||
with open(schema_path, 'r') as f:
|
||||
content = f.read()
|
||||
|
||||
views = []
|
||||
|
||||
# Find CREATE MATERIALIZED VIEW statements
|
||||
# Matches: CREATE MATERIALIZED VIEW [IF NOT EXISTS] view_name AS
|
||||
create_pattern = r'CREATE\s+MATERIALIZED\s+VIEW\s+(?:IF\s+NOT\s+EXISTS\s+)?(\w+)\s+AS'
|
||||
create_matches = re.findall(create_pattern, content, re.IGNORECASE)
|
||||
views.extend(create_matches)
|
||||
|
||||
# Find REFRESH MATERIALIZED VIEW statements
|
||||
# Matches: REFRESH MATERIALIZED VIEW [CONCURRENTLY] view_name
|
||||
refresh_pattern = r'REFRESH\s+MATERIALIZED\s+VIEW\s+(?:CONCURRENTLY\s+)?(?:public\.)?(\w+)'
|
||||
refresh_matches = re.findall(refresh_pattern, content, re.IGNORECASE)
|
||||
views.extend(refresh_matches)
|
||||
|
||||
# Deduplicate while preserving order
|
||||
seen = set()
|
||||
deduplicated = []
|
||||
for view in views:
|
||||
if view not in seen:
|
||||
seen.add(view)
|
||||
deduplicated.append(view)
|
||||
|
||||
if deduplicated:
|
||||
logger.info(f"Detected {len(deduplicated)} materialized view(s): {', '.join(deduplicated)}")
|
||||
else:
|
||||
logger.info("No materialized views detected in db_schema.sql")
|
||||
|
||||
return deduplicated
|
||||
|
||||
async def refresh_materialized_views(self):
|
||||
"""Refresh all detected materialized views."""
|
||||
if not self.materialized_views:
|
||||
logger.debug("No materialized views to refresh")
|
||||
return
|
||||
|
||||
logger.info(f"Refreshing {len(self.materialized_views)} materialized view(s)...")
|
||||
|
||||
for view_name in self.materialized_views:
|
||||
try:
|
||||
# Try CONCURRENTLY first (requires unique index)
|
||||
await self.db.pool.execute(
|
||||
f"REFRESH MATERIALIZED VIEW CONCURRENTLY public.{view_name}"
|
||||
)
|
||||
logger.info(f"✓ Refreshed {view_name} (CONCURRENTLY)")
|
||||
|
||||
except Exception as e:
|
||||
error_msg = str(e).lower()
|
||||
|
||||
# If CONCURRENTLY fails, try regular refresh
|
||||
if 'concurrently' in error_msg or 'unique index' in error_msg:
|
||||
try:
|
||||
await self.db.pool.execute(
|
||||
f"REFRESH MATERIALIZED VIEW public.{view_name}"
|
||||
)
|
||||
logger.info(f"✓ Refreshed {view_name}")
|
||||
except Exception as e2:
|
||||
logger.error(f"✗ Failed to refresh {view_name}: {e2}")
|
||||
else:
|
||||
logger.error(f"✗ Failed to refresh {view_name}: {e}")
|
||||
|
||||
logger.info("Materialized view refresh completed")
|
||||
|
||||
async def setup(self):
|
||||
"""Initialize database schema and load metadata."""
|
||||
try:
|
||||
# Connect to database
|
||||
await self.db.connect()
|
||||
|
||||
# Initialize schema (this will drop and recreate public schema)
|
||||
logger.info("Initializing database schema...")
|
||||
schema_path = Path(__file__).parent / "db_schema.sql"
|
||||
await self.db.initialize_schema()
|
||||
logger.info("Database schema initialized successfully")
|
||||
|
||||
# Auto-detect materialized views
|
||||
self.materialized_views = self.detect_materialized_views(schema_path)
|
||||
|
||||
# Load metadata from YAML
|
||||
metadata_path = Path(__file__).parent.parent.parent / "metadata.yaml"
|
||||
await self.load_metadata_from_yaml(metadata_path)
|
||||
|
||||
# Load geotargets from CSV
|
||||
# Look for the most recent geotargets CSV file in the package directory
|
||||
geotargets_dir = Path(__file__).parent
|
||||
geotargets_files = sorted(geotargets_dir.glob("geotargets*.csv"), reverse=True)
|
||||
if geotargets_files:
|
||||
geotargets_path = geotargets_files[0]
|
||||
await self.load_geotargets_from_csv(geotargets_path)
|
||||
else:
|
||||
logger.warning("No geotargets CSV file found in package directory")
|
||||
|
||||
# Initial refresh of materialized views
|
||||
if self.materialized_views:
|
||||
logger.info("Performing initial refresh of materialized views...")
|
||||
await self.refresh_materialized_views()
|
||||
|
||||
logger.info("Setup completed successfully")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Setup failed: {e}", exc_info=True)
|
||||
raise
|
||||
|
||||
async def periodic_refresh_loop(self):
|
||||
"""
|
||||
Periodically refresh materialized views.
|
||||
|
||||
Runs in a loop until shutdown is signaled.
|
||||
"""
|
||||
if not self.materialized_views:
|
||||
logger.info("No materialized views to refresh, skipping periodic refresh")
|
||||
return
|
||||
|
||||
logger.info(f"Starting periodic refresh loop (interval: {self.refresh_interval_minutes} minutes)")
|
||||
|
||||
while not self.shutdown_event.is_set():
|
||||
try:
|
||||
# Wait for the refresh interval or shutdown signal
|
||||
await asyncio.wait_for(
|
||||
self.shutdown_event.wait(),
|
||||
timeout=self.refresh_interval_minutes * 60
|
||||
)
|
||||
# If we get here, shutdown was signaled
|
||||
break
|
||||
|
||||
except asyncio.TimeoutError:
|
||||
# Timeout means it's time to refresh
|
||||
logger.info("Starting scheduled materialized view refresh...")
|
||||
try:
|
||||
await self.refresh_materialized_views()
|
||||
except Exception as e:
|
||||
logger.error(f"Error during scheduled refresh: {e}", exc_info=True)
|
||||
# Continue the loop even if refresh fails
|
||||
|
||||
async def wait_for_tasks(self):
|
||||
"""
|
||||
Run periodic tasks and wait for shutdown signal.
|
||||
|
||||
Currently runs:
|
||||
- Periodic materialized view refresh (configurable interval)
|
||||
|
||||
In the future, this could also:
|
||||
- Poll Airbyte API for completed syncs
|
||||
- Serve health check endpoints
|
||||
- Listen to webhooks
|
||||
"""
|
||||
logger.info("View manager is ready")
|
||||
logger.info("Press Ctrl+C to shutdown")
|
||||
|
||||
# Run periodic refresh loop
|
||||
await self.periodic_refresh_loop()
|
||||
|
||||
logger.info("Periodic refresh loop stopped")
|
||||
|
||||
async def run(self):
|
||||
"""Main entry point."""
|
||||
try:
|
||||
# Setup database and metadata
|
||||
await self.setup()
|
||||
|
||||
# Wait for tasks or shutdown
|
||||
await self.wait_for_tasks()
|
||||
|
||||
finally:
|
||||
logger.info("Shutting down...")
|
||||
await self.db.close()
|
||||
logger.info("Shutdown complete")
|
||||
|
||||
def handle_shutdown(self, signum, frame):
|
||||
"""Handle shutdown signals gracefully."""
|
||||
logger.info(f"Received signal {signum}, initiating shutdown...")
|
||||
self.shutdown_event.set()
|
||||
|
||||
|
||||
def main():
|
||||
"""CLI entry point."""
|
||||
# Get refresh interval from environment variable
|
||||
refresh_interval = int(os.getenv('REFRESH_INTERVAL_MINUTES', '60'))
|
||||
logger.info(f"Configured refresh interval: {refresh_interval} minutes")
|
||||
|
||||
manager = ViewManagerSetup(refresh_interval_minutes=refresh_interval)
|
||||
|
||||
# Register signal handlers for graceful shutdown
|
||||
signal.signal(signal.SIGINT, manager.handle_shutdown)
|
||||
signal.signal(signal.SIGTERM, manager.handle_shutdown)
|
||||
|
||||
# Run the async main function
|
||||
try:
|
||||
asyncio.run(manager.run())
|
||||
except KeyboardInterrupt:
|
||||
logger.info("Interrupted by user")
|
||||
sys.exit(0)
|
||||
except Exception as e:
|
||||
logger.error(f"Fatal error: {e}", exc_info=True)
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,145 +0,0 @@
|
||||
"""
|
||||
Simple test script to initialize database and grab ad_accounts metadata.
|
||||
This is useful for testing the database setup and verifying ad account access.
|
||||
Grabs ALL ad accounts accessible to the token.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
from dotenv import load_dotenv
|
||||
from facebook_business.adobjects.adaccount import AdAccount
|
||||
from facebook_business.adobjects.user import User
|
||||
from facebook_business.api import FacebookAdsApi
|
||||
|
||||
from meta_api_grabber.database import TimescaleDBClient
|
||||
|
||||
|
||||
async def test_ad_accounts():
|
||||
"""Test database initialization and ad account metadata collection."""
|
||||
load_dotenv()
|
||||
|
||||
# Get credentials from environment
|
||||
access_token = os.getenv("META_ACCESS_TOKEN")
|
||||
app_secret = os.getenv("META_APP_SECRET")
|
||||
app_id = os.getenv("META_APP_ID")
|
||||
|
||||
if not all([access_token, app_secret, app_id]):
|
||||
print("❌ Missing required environment variables")
|
||||
print(" Please ensure META_ACCESS_TOKEN, META_APP_SECRET, and META_APP_ID")
|
||||
print(" are set in .env")
|
||||
return 1
|
||||
|
||||
print("="*60)
|
||||
print("AD ACCOUNTS TEST - GRABBING ALL ACCESSIBLE ACCOUNTS")
|
||||
print("="*60)
|
||||
print()
|
||||
|
||||
# Initialize Facebook Ads API
|
||||
FacebookAdsApi.init(
|
||||
app_id=app_id,
|
||||
app_secret=app_secret,
|
||||
access_token=access_token,
|
||||
)
|
||||
|
||||
print("\nFetching all ad accounts accessible to this token...")
|
||||
me = User(fbid='me')
|
||||
account_fields = ['name', 'currency', 'timezone_name', 'account_status']
|
||||
|
||||
ad_accounts = me.get_ad_accounts(fields=account_fields)
|
||||
|
||||
print(f"Found {len(ad_accounts)} ad account(s)\n")
|
||||
|
||||
stored_count = 0
|
||||
for account in ad_accounts:
|
||||
account_id = account['id']
|
||||
print(f"Ad Account {stored_count + 1}:")
|
||||
print(f" ID: {account_id}")
|
||||
print(f" Name: {account.get('name', 'N/A')}")
|
||||
print(f" Currency: {account.get('currency', 'N/A')}")
|
||||
print(f" Timezone: {account.get('timezone_name', 'N/A')}")
|
||||
print(f" Status: {account.get('account_status', 'N/A')}")
|
||||
|
||||
|
||||
try:
|
||||
|
||||
# Connect to database
|
||||
print("Connecting to database...")
|
||||
db = TimescaleDBClient()
|
||||
await db.connect()
|
||||
# Initialize schema
|
||||
print("\nInitializing database schema...")
|
||||
await db.initialize_schema()
|
||||
|
||||
# Get all ad accounts accessible to this token
|
||||
print("\nFetching all ad accounts accessible to this token...")
|
||||
me = User(fbid='me')
|
||||
account_fields = ['name', 'currency', 'timezone_name', 'account_status']
|
||||
|
||||
ad_accounts = me.get_ad_accounts(fields=account_fields)
|
||||
|
||||
print(f"Found {len(ad_accounts)} ad account(s)\n")
|
||||
|
||||
stored_count = 0
|
||||
for account in ad_accounts:
|
||||
account_id = account['id']
|
||||
print(f"Ad Account {stored_count + 1}:")
|
||||
print(f" ID: {account_id}")
|
||||
print(f" Name: {account.get('name', 'N/A')}")
|
||||
print(f" Currency: {account.get('currency', 'N/A')}")
|
||||
print(f" Timezone: {account.get('timezone_name', 'N/A')}")
|
||||
print(f" Status: {account.get('account_status', 'N/A')}")
|
||||
|
||||
# Store in database
|
||||
await db.upsert_ad_account(
|
||||
account_id=account_id,
|
||||
account_name=account.get('name'),
|
||||
currency=account.get('currency'),
|
||||
timezone_name=account.get('timezone_name'),
|
||||
)
|
||||
print(f" ✓ Stored in database\n")
|
||||
stored_count += 1
|
||||
|
||||
# Verify by querying the database
|
||||
print("="*60)
|
||||
print(f"Verifying database storage ({stored_count} account(s))...")
|
||||
print("="*60)
|
||||
async with db.pool.acquire() as conn:
|
||||
rows = await conn.fetch("SELECT * FROM ad_accounts ORDER BY account_name")
|
||||
if rows:
|
||||
print(f"\n✓ {len(rows)} ad account(s) found in database:\n")
|
||||
for i, row in enumerate(rows, 1):
|
||||
print(f"{i}. {row['account_name']} ({row['account_id']})")
|
||||
print(f" Currency: {row['currency']} | Timezone: {row['timezone_name']}")
|
||||
print(f" Updated: {row['updated_at']}\n")
|
||||
else:
|
||||
print("❌ No ad accounts found in database")
|
||||
|
||||
print("="*60)
|
||||
print("TEST COMPLETED SUCCESSFULLY")
|
||||
print("="*60)
|
||||
print(f"\n✓ Successfully grabbed and stored {stored_count} ad account(s)")
|
||||
print("\nNext steps:")
|
||||
print("1. Check your database: docker exec meta_timescaledb psql -U meta_user -d meta_insights -c 'SELECT * FROM ad_accounts;'")
|
||||
print("2. Run scheduled grabber for all accounts: meta-scheduled")
|
||||
print("3. The scheduled grabber will now process all these accounts!")
|
||||
|
||||
except Exception as e:
|
||||
print(f"\n❌ Error: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return 1
|
||||
|
||||
finally:
|
||||
await db.close()
|
||||
|
||||
return 0
|
||||
|
||||
|
||||
def main():
|
||||
"""Entry point for the test script."""
|
||||
exit_code = asyncio.run(test_ad_accounts())
|
||||
exit(exit_code)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,91 +0,0 @@
|
||||
"""
|
||||
Test to see what campaign insights API actually returns for campaign_name.
|
||||
"""
|
||||
|
||||
import os
|
||||
import json
|
||||
from dotenv import load_dotenv
|
||||
from facebook_business.api import FacebookAdsApi
|
||||
from facebook_business.adobjects.adaccount import AdAccount
|
||||
from facebook_business.adobjects.adsinsights import AdsInsights
|
||||
|
||||
# Load environment variables
|
||||
load_dotenv()
|
||||
|
||||
# Initialize Facebook Ads API
|
||||
access_token = os.getenv("META_ACCESS_TOKEN")
|
||||
app_secret = os.getenv("META_APP_SECRET")
|
||||
app_id = os.getenv("META_APP_ID")
|
||||
|
||||
if not all([access_token, app_secret, app_id]):
|
||||
raise ValueError("Missing required environment variables")
|
||||
|
||||
FacebookAdsApi.init(
|
||||
app_id=app_id,
|
||||
app_secret=app_secret,
|
||||
access_token=access_token,
|
||||
)
|
||||
|
||||
account_id = "act_238334370765317"
|
||||
|
||||
print(f"Testing campaign INSIGHTS fetch for account: {account_id}")
|
||||
print("=" * 60)
|
||||
|
||||
# Get campaign insights (this is what scheduled_grabber.py does)
|
||||
print("\nFetching campaign-level insights with 'today' preset...")
|
||||
print("-" * 60)
|
||||
|
||||
fields = [
|
||||
AdsInsights.Field.campaign_id,
|
||||
AdsInsights.Field.campaign_name,
|
||||
AdsInsights.Field.impressions,
|
||||
AdsInsights.Field.clicks,
|
||||
AdsInsights.Field.spend,
|
||||
AdsInsights.Field.ctr,
|
||||
AdsInsights.Field.cpc,
|
||||
AdsInsights.Field.cpm,
|
||||
AdsInsights.Field.reach,
|
||||
AdsInsights.Field.actions,
|
||||
AdsInsights.Field.date_start,
|
||||
AdsInsights.Field.date_stop,
|
||||
]
|
||||
|
||||
params = {
|
||||
"date_preset": "today",
|
||||
"level": "campaign",
|
||||
"limit": 10,
|
||||
}
|
||||
|
||||
ad_account = AdAccount(account_id)
|
||||
insights = ad_account.get_insights(
|
||||
fields=fields,
|
||||
params=params,
|
||||
)
|
||||
|
||||
insights_list = list(insights)
|
||||
print(f"Found {len(insights_list)} campaign insights\n")
|
||||
|
||||
campaigns_without_names = []
|
||||
for i, insight in enumerate(insights_list, 1):
|
||||
insight_dict = dict(insight)
|
||||
campaign_id = insight_dict.get('campaign_id')
|
||||
campaign_name = insight_dict.get('campaign_name')
|
||||
|
||||
print(f"Campaign Insight {i}:")
|
||||
print(f" Campaign ID: {campaign_id}")
|
||||
print(f" Campaign Name: {campaign_name if campaign_name else '❌ MISSING'}")
|
||||
print(f" Impressions: {insight_dict.get('impressions')}")
|
||||
print(f" Spend: {insight_dict.get('spend')}")
|
||||
print(f" Keys available: {list(insight_dict.keys())}")
|
||||
print(f" Raw JSON: {json.dumps(insight_dict, indent=4, default=str)}")
|
||||
print()
|
||||
|
||||
if not campaign_name:
|
||||
campaigns_without_names.append(campaign_id)
|
||||
|
||||
print("=" * 60)
|
||||
if campaigns_without_names:
|
||||
print(f"⚠️ {len(campaigns_without_names)} campaigns WITHOUT names in insights:")
|
||||
print(f" {campaigns_without_names}")
|
||||
else:
|
||||
print("✓ All campaigns have names in insights!")
|
||||
@@ -1,118 +0,0 @@
|
||||
"""
|
||||
Test script to diagnose campaign name issues for a specific ad account.
|
||||
"""
|
||||
|
||||
import os
|
||||
import json
|
||||
from dotenv import load_dotenv
|
||||
from facebook_business.api import FacebookAdsApi
|
||||
from facebook_business.adobjects.adaccount import AdAccount
|
||||
from facebook_business.adobjects.campaign import Campaign
|
||||
|
||||
# Load environment variables
|
||||
load_dotenv()
|
||||
|
||||
# Initialize Facebook Ads API
|
||||
access_token = os.getenv("META_ACCESS_TOKEN")
|
||||
app_secret = os.getenv("META_APP_SECRET")
|
||||
app_id = os.getenv("META_APP_ID")
|
||||
|
||||
if not all([access_token, app_secret, app_id]):
|
||||
raise ValueError("Missing required environment variables")
|
||||
|
||||
FacebookAdsApi.init(
|
||||
app_id=app_id,
|
||||
app_secret=app_secret,
|
||||
access_token=access_token,
|
||||
)
|
||||
|
||||
# Test account ID
|
||||
account_id = "act_238334370765317"
|
||||
|
||||
print(f"Testing campaign fetch for account: {account_id}")
|
||||
print("=" * 60)
|
||||
|
||||
# Get campaigns using get_campaigns
|
||||
print("\n1. Testing AdAccount.get_campaigns()...")
|
||||
print("-" * 60)
|
||||
|
||||
ad_account = AdAccount(account_id)
|
||||
campaigns = ad_account.get_campaigns(
|
||||
fields=[
|
||||
Campaign.Field.name,
|
||||
Campaign.Field.status,
|
||||
Campaign.Field.objective,
|
||||
Campaign.Field.id,
|
||||
],
|
||||
params={'limit': 10}
|
||||
)
|
||||
|
||||
campaigns_list = list(campaigns)
|
||||
print(f"Found {len(campaigns_list)} campaigns\n")
|
||||
|
||||
campaigns_without_names = []
|
||||
for i, campaign in enumerate(campaigns_list, 1):
|
||||
campaign_dict = dict(campaign)
|
||||
campaign_id = campaign_dict.get('id')
|
||||
campaign_name = campaign_dict.get('name')
|
||||
|
||||
print(f"Campaign {i}:")
|
||||
print(f" ID: {campaign_id}")
|
||||
print(f" Name: {campaign_name if campaign_name else '❌ MISSING'}")
|
||||
print(f" Status: {campaign_dict.get('status')}")
|
||||
print(f" Objective: {campaign_dict.get('objective')}")
|
||||
print(f" Raw data: {json.dumps(campaign_dict, indent=4)}")
|
||||
print()
|
||||
|
||||
if not campaign_name:
|
||||
campaigns_without_names.append(campaign_id)
|
||||
|
||||
# If we found campaigns without names, try fetching them individually
|
||||
if campaigns_without_names:
|
||||
print("\n2. Retrying campaigns without names (individual fetch)...")
|
||||
print("-" * 60)
|
||||
|
||||
for campaign_id in campaigns_without_names:
|
||||
print(f"\nFetching campaign {campaign_id} directly...")
|
||||
try:
|
||||
campaign_obj = Campaign(campaign_id)
|
||||
campaign_data = campaign_obj.api_get(
|
||||
fields=[
|
||||
Campaign.Field.name,
|
||||
Campaign.Field.status,
|
||||
Campaign.Field.objective,
|
||||
Campaign.Field.account_id,
|
||||
]
|
||||
)
|
||||
|
||||
campaign_dict = dict(campaign_data)
|
||||
print(f" Direct fetch result:")
|
||||
print(f" Name: {campaign_dict.get('name', '❌ STILL MISSING')}")
|
||||
print(f" Status: {campaign_dict.get('status')}")
|
||||
print(f" Raw data: {json.dumps(campaign_dict, indent=4)}")
|
||||
except Exception as e:
|
||||
print(f" Error fetching campaign: {e}")
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print(f"Summary: {len(campaigns_without_names)} out of {len(campaigns_list)} campaigns have missing names")
|
||||
print(f"Missing campaign IDs: {campaigns_without_names}")
|
||||
else:
|
||||
print("\n" + "=" * 60)
|
||||
print("✓ All campaigns have names!")
|
||||
|
||||
print("\n3. Checking account permissions...")
|
||||
print("-" * 60)
|
||||
try:
|
||||
account_data = ad_account.api_get(
|
||||
fields=['name', 'account_status', 'capabilities', 'business']
|
||||
)
|
||||
account_dict = dict(account_data)
|
||||
print(f"Account Name: {account_dict.get('name')}")
|
||||
print(f"Account Status: {account_dict.get('account_status')}")
|
||||
print(f"Capabilities: {json.dumps(account_dict.get('capabilities', []), indent=2)}")
|
||||
print(f"Business ID: {account_dict.get('business')}")
|
||||
except Exception as e:
|
||||
print(f"Error fetching account details: {e}")
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("Test complete!")
|
||||
@@ -1,125 +0,0 @@
|
||||
"""
|
||||
Test that date_start and date_stop are properly stored in the database.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
from datetime import datetime, timezone
|
||||
from dotenv import load_dotenv
|
||||
from facebook_business.adobjects.adaccount import AdAccount
|
||||
from facebook_business.adobjects.adsinsights import AdsInsights
|
||||
from facebook_business.api import FacebookAdsApi
|
||||
|
||||
from meta_api_grabber.database import TimescaleDBClient
|
||||
|
||||
|
||||
async def test_date_fields():
|
||||
"""Test that date fields are stored correctly."""
|
||||
load_dotenv()
|
||||
|
||||
access_token = os.getenv("META_ACCESS_TOKEN")
|
||||
app_secret = os.getenv("META_APP_SECRET")
|
||||
app_id = os.getenv("META_APP_ID")
|
||||
account_id = "act_238334370765317"
|
||||
|
||||
if not all([access_token, app_secret, app_id]):
|
||||
print("❌ Missing required environment variables")
|
||||
return 1
|
||||
|
||||
# Initialize Facebook Ads API
|
||||
FacebookAdsApi.init(
|
||||
app_id=app_id,
|
||||
app_secret=app_secret,
|
||||
access_token=access_token,
|
||||
)
|
||||
|
||||
print("="*70)
|
||||
print("TESTING DATE_START AND DATE_STOP STORAGE")
|
||||
print("="*70)
|
||||
print()
|
||||
|
||||
# Connect to database
|
||||
print("Connecting to database...")
|
||||
db = TimescaleDBClient()
|
||||
await db.connect()
|
||||
|
||||
try:
|
||||
print("Fetching insights for 'today'...")
|
||||
ad_account = AdAccount(account_id)
|
||||
|
||||
fields = [
|
||||
AdsInsights.Field.impressions,
|
||||
AdsInsights.Field.clicks,
|
||||
AdsInsights.Field.spend,
|
||||
AdsInsights.Field.date_start,
|
||||
AdsInsights.Field.date_stop,
|
||||
]
|
||||
|
||||
params = {
|
||||
"date_preset": "today",
|
||||
"level": "account",
|
||||
}
|
||||
|
||||
insights = ad_account.get_insights(fields=fields, params=params)
|
||||
|
||||
# Store in database
|
||||
timestamp = datetime.now(timezone.utc)
|
||||
for insight in insights:
|
||||
insight_dict = dict(insight)
|
||||
print(f"\nAPI Response:")
|
||||
print(f" date_start: {insight_dict.get('date_start')}")
|
||||
print(f" date_stop: {insight_dict.get('date_stop')}")
|
||||
print(f" impressions: {insight_dict.get('impressions')}")
|
||||
|
||||
await db.insert_account_insights(
|
||||
time=timestamp,
|
||||
account_id=account_id,
|
||||
data=insight_dict,
|
||||
date_preset="today",
|
||||
)
|
||||
print("\n✓ Stored in database")
|
||||
|
||||
# Verify from database
|
||||
print("\nQuerying database...")
|
||||
async with db.pool.acquire() as conn:
|
||||
row = await conn.fetchrow("""
|
||||
SELECT date_start, date_stop, date_preset, impressions, spend
|
||||
FROM account_insights
|
||||
WHERE account_id = $1
|
||||
ORDER BY time DESC
|
||||
LIMIT 1
|
||||
""", account_id)
|
||||
|
||||
if row:
|
||||
print("\n✓ Retrieved from database:")
|
||||
print(f" date_start: {row['date_start']}")
|
||||
print(f" date_stop: {row['date_stop']}")
|
||||
print(f" date_preset: {row['date_preset']}")
|
||||
print(f" impressions: {row['impressions']}")
|
||||
print(f" spend: {row['spend']}")
|
||||
|
||||
print("\n" + "="*70)
|
||||
print("✓ TEST PASSED - Date fields are stored correctly!")
|
||||
print("="*70)
|
||||
print("\nYou can now query historical data by date_stop:")
|
||||
print(" - For clean daily trends, use: GROUP BY date_stop")
|
||||
print(" - For latest value per day, use: ORDER BY time DESC with date_stop")
|
||||
print()
|
||||
else:
|
||||
print("\n❌ No data found in database")
|
||||
return 1
|
||||
|
||||
except Exception as e:
|
||||
print(f"\n❌ Error: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return 1
|
||||
|
||||
finally:
|
||||
await db.close()
|
||||
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
exit(asyncio.run(test_date_fields()))
|
||||
@@ -1,132 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Test script to grab ad accounts from Google Ads API.
|
||||
|
||||
This script reads configuration from google-ads.yaml and authenticates using
|
||||
a service account JSON key file to retrieve accessible customer accounts.
|
||||
"""
|
||||
|
||||
from google.ads.googleads.client import GoogleAdsClient
|
||||
from google.ads.googleads.errors import GoogleAdsException
|
||||
import sys
|
||||
|
||||
|
||||
def list_accessible_customers(client):
|
||||
"""Lists all customer IDs accessible to the authenticated user.
|
||||
|
||||
Args:
|
||||
client: An initialized GoogleAdsClient instance.
|
||||
|
||||
Returns:
|
||||
List of customer resource names.
|
||||
"""
|
||||
customer_service = client.get_service("CustomerService")
|
||||
|
||||
try:
|
||||
accessible_customers = customer_service.list_accessible_customers()
|
||||
print(f"\nFound {len(accessible_customers.resource_names)} accessible customers:")
|
||||
|
||||
for resource_name in accessible_customers.resource_names:
|
||||
customer_id = resource_name.split('/')[-1]
|
||||
print(f" - Customer ID: {customer_id}")
|
||||
print(f" Resource Name: {resource_name}")
|
||||
|
||||
return accessible_customers.resource_names
|
||||
|
||||
except GoogleAdsException as ex:
|
||||
print(f"Request failed with status {ex.error.code().name}")
|
||||
for error in ex.failure.errors:
|
||||
print(f"\tError: {error.message}")
|
||||
if error.location:
|
||||
for field in error.location.field_path_elements:
|
||||
print(f"\t\tField: {field.field_name}")
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
def get_customer_details(client, customer_id):
|
||||
"""Retrieves detailed information about a customer account.
|
||||
|
||||
Args:
|
||||
client: An initialized GoogleAdsClient instance.
|
||||
customer_id: The customer ID (without dashes).
|
||||
"""
|
||||
ga_service = client.get_service("GoogleAdsService")
|
||||
|
||||
query = """
|
||||
SELECT
|
||||
customer.id,
|
||||
customer.descriptive_name,
|
||||
customer.currency_code,
|
||||
customer.time_zone,
|
||||
customer.manager,
|
||||
customer.test_account
|
||||
FROM customer
|
||||
WHERE customer.id = {customer_id}
|
||||
""".format(customer_id=customer_id)
|
||||
|
||||
try:
|
||||
response = ga_service.search(customer_id=customer_id, query=query)
|
||||
|
||||
for row in response:
|
||||
customer = row.customer
|
||||
print(f"\n--- Customer Details for {customer_id} ---")
|
||||
print(f" ID: {customer.id}")
|
||||
print(f" Name: {customer.descriptive_name}")
|
||||
print(f" Currency: {customer.currency_code}")
|
||||
print(f" Time Zone: {customer.time_zone}")
|
||||
print(f" Is Manager: {customer.manager}")
|
||||
print(f" Is Test Account: {customer.test_account}")
|
||||
|
||||
except GoogleAdsException as ex:
|
||||
print(f"\nFailed to get details for customer {customer_id}")
|
||||
print(f"Status: {ex.error.code().name}")
|
||||
for error in ex.failure.errors:
|
||||
print(f" Error: {error.message}")
|
||||
|
||||
|
||||
def main():
|
||||
"""Main function to test Google Ads API connection and list accounts."""
|
||||
|
||||
print("=" * 60)
|
||||
print("Google Ads API - Account Listing Test")
|
||||
print("=" * 60)
|
||||
|
||||
# Load client from YAML configuration
|
||||
# By default, this looks for google-ads.yaml in the current directory
|
||||
# or in the home directory
|
||||
try:
|
||||
print("\nLoading Google Ads client from configuration...")
|
||||
client = GoogleAdsClient.load_from_storage(path="google-ads.yaml")
|
||||
print("✓ Client loaded successfully")
|
||||
except Exception as e:
|
||||
print(f"✗ Failed to load client: {e}")
|
||||
print("\nPlease ensure:")
|
||||
print(" 1. google-ads.yaml exists and is properly configured")
|
||||
print(" 2. google_ads_key.json exists and contains valid credentials")
|
||||
print(" 3. All required fields are filled in google-ads.yaml")
|
||||
sys.exit(1)
|
||||
|
||||
# List accessible customers
|
||||
print("\n" + "=" * 60)
|
||||
print("Listing Accessible Customers")
|
||||
print("=" * 60)
|
||||
|
||||
resource_names = list_accessible_customers(client)
|
||||
|
||||
# Get detailed information for each customer
|
||||
if resource_names:
|
||||
print("\n" + "=" * 60)
|
||||
print("Customer Details")
|
||||
print("=" * 60)
|
||||
|
||||
for resource_name in resource_names:
|
||||
customer_id = resource_name.split('/')[-1]
|
||||
get_customer_details(client, customer_id)
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("Test completed successfully!")
|
||||
print("=" * 60)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,171 +0,0 @@
|
||||
"""
|
||||
Test script to retrieve leads from a specific Facebook page.
|
||||
This uses the existing Meta API credentials to test leads retrieval.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
from datetime import datetime
|
||||
from dotenv import load_dotenv
|
||||
from facebook_business.api import FacebookAdsApi
|
||||
from facebook_business.adobjects.page import Page
|
||||
|
||||
|
||||
async def test_page_leads():
|
||||
"""Test retrieving leads from a specific Facebook page."""
|
||||
load_dotenv()
|
||||
|
||||
# Get credentials from environment
|
||||
access_token = os.getenv("META_ACCESS_TOKEN")
|
||||
app_secret = os.getenv("META_APP_SECRET")
|
||||
app_id = os.getenv("META_APP_ID")
|
||||
|
||||
if not all([access_token, app_secret, app_id]):
|
||||
print("❌ Missing required environment variables")
|
||||
print(" Please ensure META_ACCESS_TOKEN, META_APP_SECRET, and META_APP_ID")
|
||||
print(" are set in .env")
|
||||
return 1
|
||||
|
||||
print("="*60)
|
||||
print("PAGE LEADS TEST - RETRIEVING LEADS FROM A SPECIFIC PAGE")
|
||||
print("="*60)
|
||||
print()
|
||||
|
||||
# Initialize Facebook Ads API
|
||||
FacebookAdsApi.init(
|
||||
app_id=app_id,
|
||||
app_secret=app_secret,
|
||||
access_token=access_token,
|
||||
)
|
||||
|
||||
# Prompt for page ID
|
||||
page_id = input("Enter the Facebook Page ID: ").strip()
|
||||
|
||||
if not page_id:
|
||||
print("❌ No page ID provided")
|
||||
return 1
|
||||
|
||||
try:
|
||||
# Initialize the Page object
|
||||
print(f"\nConnecting to Page {page_id}...")
|
||||
page = Page(fbid=page_id)
|
||||
|
||||
# First, get basic page information to verify access
|
||||
print("\nFetching page information...")
|
||||
page_fields = ['name', 'id', 'access_token']
|
||||
page_info = page.api_get(fields=page_fields)
|
||||
|
||||
print(f"\n✓ Page Information:")
|
||||
print(f" Name: {page_info.get('name', 'N/A')}")
|
||||
print(f" ID: {page_info.get('id', 'N/A')}")
|
||||
print(f" Has Access Token: {'Yes' if page_info.get('access_token') else 'No'}")
|
||||
|
||||
# Get leadgen forms associated with this page
|
||||
print("\n" + "="*60)
|
||||
print("Fetching Lead Generation Forms...")
|
||||
print("="*60)
|
||||
|
||||
leadgen_forms = page.get_lead_gen_forms(
|
||||
fields=['id', 'name', 'status', 'leads_count', 'created_time']
|
||||
)
|
||||
|
||||
if not leadgen_forms or len(leadgen_forms) == 0:
|
||||
print("\n⚠️ No lead generation forms found for this page")
|
||||
print(" This could mean:")
|
||||
print(" 1. The page has no lead forms")
|
||||
print(" 2. The access token doesn't have permission to view lead forms")
|
||||
print(" 3. The page ID is incorrect")
|
||||
return 0
|
||||
|
||||
print(f"\nFound {len(leadgen_forms)} lead generation form(s):\n")
|
||||
|
||||
total_leads = 0
|
||||
for idx, form in enumerate(leadgen_forms, 1):
|
||||
form_id = form.get('id')
|
||||
form_name = form.get('name', 'N/A')
|
||||
form_status = form.get('status', 'N/A')
|
||||
leads_count = form.get('leads_count', 0)
|
||||
created_time = form.get('created_time', 'N/A')
|
||||
|
||||
print(f"Form {idx}:")
|
||||
print(f" ID: {form_id}")
|
||||
print(f" Name: {form_name}")
|
||||
print(f" Status: {form_status}")
|
||||
print(f" Leads Count: {leads_count}")
|
||||
print(f" Created: {created_time}")
|
||||
|
||||
# Try to fetch actual leads from this form
|
||||
try:
|
||||
print(f"\n Fetching leads from form '{form_name}'...")
|
||||
|
||||
# Get the form object to retrieve leads
|
||||
from facebook_business.adobjects.leadgenform import LeadgenForm
|
||||
form_obj = LeadgenForm(fbid=form_id)
|
||||
|
||||
leads = form_obj.get_leads(
|
||||
fields=['id', 'created_time', 'field_data']
|
||||
)
|
||||
|
||||
leads_list = list(leads)
|
||||
print(f" ✓ Retrieved {len(leads_list)} lead(s)")
|
||||
|
||||
if leads_list:
|
||||
print(f"\n Sample leads from '{form_name}':")
|
||||
for lead_idx, lead in enumerate(leads_list[:5], 1): # Show first 5 leads
|
||||
lead_id = lead.get('id')
|
||||
lead_created = lead.get('created_time', 'N/A')
|
||||
field_data = lead.get('field_data', [])
|
||||
|
||||
print(f"\n Lead {lead_idx}:")
|
||||
print(f" ID: {lead_id}")
|
||||
print(f" Created: {lead_created}")
|
||||
print(f" Fields:")
|
||||
|
||||
for field in field_data:
|
||||
field_name = field.get('name', 'unknown')
|
||||
field_values = field.get('values', [])
|
||||
print(f" {field_name}: {', '.join(field_values)}")
|
||||
|
||||
if len(leads_list) > 5:
|
||||
print(f"\n ... and {len(leads_list) - 5} more lead(s)")
|
||||
|
||||
total_leads += len(leads_list)
|
||||
|
||||
except Exception as lead_error:
|
||||
print(f" ❌ Error fetching leads: {lead_error}")
|
||||
|
||||
print()
|
||||
|
||||
print("="*60)
|
||||
print("TEST COMPLETED")
|
||||
print("="*60)
|
||||
print(f"\n✓ Total forms found: {len(leadgen_forms)}")
|
||||
print(f"✓ Total leads retrieved: {total_leads}")
|
||||
|
||||
if total_leads == 0:
|
||||
print("\n⚠️ No leads were retrieved. This could mean:")
|
||||
print(" 1. The forms have no leads yet")
|
||||
print(" 2. Your access token needs 'leads_retrieval' permission")
|
||||
print(" 3. You need to request advanced access for leads_retrieval")
|
||||
print("\nRequired permissions:")
|
||||
print(" - pages_manage_ads")
|
||||
print(" - pages_read_engagement")
|
||||
print(" - leads_retrieval")
|
||||
|
||||
except Exception as e:
|
||||
print(f"\n❌ Error: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return 1
|
||||
|
||||
return 0
|
||||
|
||||
|
||||
def main():
|
||||
"""Entry point for the test script."""
|
||||
exit_code = asyncio.run(test_page_leads())
|
||||
exit(exit_code)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,108 +0,0 @@
|
||||
"""
|
||||
Test to diagnose if the rate limiter is causing campaign name issues.
|
||||
"""
|
||||
|
||||
import os
|
||||
import asyncio
|
||||
from dotenv import load_dotenv
|
||||
from facebook_business.api import FacebookAdsApi
|
||||
from facebook_business.adobjects.adaccount import AdAccount
|
||||
from facebook_business.adobjects.campaign import Campaign
|
||||
|
||||
from rate_limiter import MetaRateLimiter
|
||||
|
||||
# Load environment variables
|
||||
load_dotenv()
|
||||
|
||||
# Initialize Facebook Ads API
|
||||
access_token = os.getenv("META_ACCESS_TOKEN")
|
||||
app_secret = os.getenv("META_APP_SECRET")
|
||||
app_id = os.getenv("META_APP_ID")
|
||||
|
||||
if not all([access_token, app_secret, app_id]):
|
||||
raise ValueError("Missing required environment variables")
|
||||
|
||||
FacebookAdsApi.init(
|
||||
app_id=app_id,
|
||||
app_secret=app_secret,
|
||||
access_token=access_token,
|
||||
)
|
||||
|
||||
account_id = "act_238334370765317"
|
||||
|
||||
# Initialize rate limiter
|
||||
rate_limiter = MetaRateLimiter(
|
||||
base_delay=0.1, # Fast for testing
|
||||
throttle_threshold=75.0,
|
||||
max_retry_delay=300.0,
|
||||
max_retries=5,
|
||||
)
|
||||
|
||||
|
||||
async def _rate_limited_request(func, *args, **kwargs):
|
||||
"""Execute a request with rate limiting (same as in scheduled_grabber.py)."""
|
||||
return await rate_limiter.execute_with_retry(func, *args, **kwargs)
|
||||
|
||||
|
||||
async def test_direct_fetch():
|
||||
"""Test direct fetch without rate limiter."""
|
||||
print("1. DIRECT FETCH (no rate limiter)")
|
||||
print("=" * 60)
|
||||
|
||||
ad_account = AdAccount(account_id)
|
||||
campaigns = ad_account.get_campaigns(
|
||||
fields=[
|
||||
Campaign.Field.name,
|
||||
Campaign.Field.status,
|
||||
Campaign.Field.objective,
|
||||
],
|
||||
params={'limit': 5}
|
||||
)
|
||||
|
||||
print(f"Type of campaigns: {type(campaigns)}")
|
||||
print(f"Campaigns object: {campaigns}\n")
|
||||
|
||||
for i, campaign in enumerate(campaigns, 1):
|
||||
campaign_dict = dict(campaign)
|
||||
print(f"Campaign {i}:")
|
||||
print(f" ID: {campaign_dict.get('id')}")
|
||||
print(f" Name: {campaign_dict.get('name', '❌ MISSING')}")
|
||||
print(f" Keys in dict: {list(campaign_dict.keys())}")
|
||||
print()
|
||||
|
||||
|
||||
async def test_rate_limited_fetch():
|
||||
"""Test fetch WITH rate limiter."""
|
||||
print("\n2. RATE LIMITED FETCH (with rate limiter)")
|
||||
print("=" * 60)
|
||||
|
||||
ad_account = AdAccount(account_id)
|
||||
campaigns = await _rate_limited_request(
|
||||
ad_account.get_campaigns,
|
||||
fields=[
|
||||
Campaign.Field.name,
|
||||
Campaign.Field.status,
|
||||
Campaign.Field.objective,
|
||||
],
|
||||
params={'limit': 5}
|
||||
)
|
||||
|
||||
print(f"Type of campaigns: {type(campaigns)}")
|
||||
print(f"Campaigns object: {campaigns}\n")
|
||||
|
||||
for i, campaign in enumerate(campaigns, 1):
|
||||
campaign_dict = dict(campaign)
|
||||
print(f"Campaign {i}:")
|
||||
print(f" ID: {campaign_dict.get('id')}")
|
||||
print(f" Name: {campaign_dict.get('name', '❌ MISSING')}")
|
||||
print(f" Keys in dict: {list(campaign_dict.keys())}")
|
||||
print()
|
||||
|
||||
|
||||
async def main():
|
||||
await test_direct_fetch()
|
||||
await test_rate_limited_fetch()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -1,87 +0,0 @@
|
||||
"""
|
||||
Test script to check what date_start and date_stop look like for "today" preset.
|
||||
"""
|
||||
|
||||
import os
|
||||
import json
|
||||
from dotenv import load_dotenv
|
||||
from facebook_business.adobjects.adaccount import AdAccount
|
||||
from facebook_business.adobjects.adsinsights import AdsInsights
|
||||
from facebook_business.api import FacebookAdsApi
|
||||
|
||||
|
||||
def test_today_preset():
|
||||
"""Test the 'today' date preset to see date_start and date_stop values."""
|
||||
load_dotenv()
|
||||
|
||||
access_token = os.getenv("META_ACCESS_TOKEN")
|
||||
app_secret = os.getenv("META_APP_SECRET")
|
||||
app_id = os.getenv("META_APP_ID")
|
||||
|
||||
if not all([access_token, app_secret, app_id]):
|
||||
print("❌ Missing required environment variables")
|
||||
return 1
|
||||
|
||||
# Initialize Facebook Ads API
|
||||
FacebookAdsApi.init(
|
||||
app_id=app_id,
|
||||
app_secret=app_secret,
|
||||
access_token=access_token,
|
||||
)
|
||||
|
||||
# Use the first account we know exists
|
||||
account_id = "act_238334370765317"
|
||||
ad_account = AdAccount(account_id)
|
||||
|
||||
print("="*70)
|
||||
print("TESTING DATE_PRESET='TODAY'")
|
||||
print("="*70)
|
||||
print(f"Account: {account_id}")
|
||||
print()
|
||||
|
||||
# Request with date_preset="today"
|
||||
fields = [
|
||||
AdsInsights.Field.impressions,
|
||||
AdsInsights.Field.clicks,
|
||||
AdsInsights.Field.spend,
|
||||
AdsInsights.Field.date_start,
|
||||
AdsInsights.Field.date_stop,
|
||||
]
|
||||
|
||||
params = {
|
||||
"date_preset": "today",
|
||||
"level": "account",
|
||||
}
|
||||
|
||||
print("Making API request with date_preset='today'...")
|
||||
insights = ad_account.get_insights(fields=fields, params=params)
|
||||
|
||||
print("\nResponse:")
|
||||
print("-"*70)
|
||||
for insight in insights:
|
||||
insight_dict = dict(insight)
|
||||
print(json.dumps(insight_dict, indent=2))
|
||||
print()
|
||||
print("Key observations:")
|
||||
print(f" date_start: {insight_dict.get('date_start')}")
|
||||
print(f" date_stop: {insight_dict.get('date_stop')}")
|
||||
print(f" Are they the same? {insight_dict.get('date_start') == insight_dict.get('date_stop')}")
|
||||
print(f" impressions: {insight_dict.get('impressions')}")
|
||||
print(f" spend: {insight_dict.get('spend')}")
|
||||
|
||||
print()
|
||||
print("="*70)
|
||||
print("CONCLUSION")
|
||||
print("="*70)
|
||||
print("For 'today' preset:")
|
||||
print(" - date_start and date_stop should both be today's date")
|
||||
print(" - Metrics are cumulative from midnight to now")
|
||||
print(" - Multiple collections during the day will have same dates")
|
||||
print(" but increasing metric values")
|
||||
print()
|
||||
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
exit(test_today_preset())
|
||||
@@ -1,307 +0,0 @@
|
||||
"""
|
||||
Token manager for automatic refresh of Meta access tokens.
|
||||
|
||||
Handles:
|
||||
- Loading token metadata
|
||||
- Checking token validity and expiry
|
||||
- Automatic refresh before expiry
|
||||
- Persistence of new tokens
|
||||
"""
|
||||
|
||||
import json
|
||||
import os
|
||||
import time
|
||||
from datetime import datetime, timedelta
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
from dotenv import load_dotenv
|
||||
|
||||
from .auth import MetaOAuth2
|
||||
|
||||
|
||||
class MetaTokenManager:
|
||||
"""
|
||||
Manages Meta access tokens with automatic refresh.
|
||||
|
||||
Features:
|
||||
- Loads token from .env and metadata from .meta_token.json
|
||||
- Checks if token is expired or about to expire
|
||||
- Automatically refreshes tokens before expiry
|
||||
- Persists refreshed tokens
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
token_file: str = ".meta_token.json",
|
||||
refresh_before_days: int = 7,
|
||||
):
|
||||
"""
|
||||
Initialize token manager.
|
||||
|
||||
Args:
|
||||
token_file: Path to token metadata JSON file
|
||||
refresh_before_days: Refresh token this many days before expiry
|
||||
"""
|
||||
load_dotenv()
|
||||
|
||||
self.token_file = Path(token_file)
|
||||
self.refresh_before_days = refresh_before_days
|
||||
self.oauth = MetaOAuth2()
|
||||
|
||||
self._current_token: Optional[str] = None
|
||||
self._token_metadata: Optional[dict] = None
|
||||
|
||||
def load_token(self) -> Optional[str]:
|
||||
"""
|
||||
Load access token from environment.
|
||||
|
||||
Returns:
|
||||
Access token or None
|
||||
"""
|
||||
return os.getenv("META_ACCESS_TOKEN")
|
||||
|
||||
def load_metadata(self) -> Optional[dict]:
|
||||
"""
|
||||
Load token metadata from JSON file.
|
||||
|
||||
Returns:
|
||||
Token metadata dict or None
|
||||
"""
|
||||
if not self.token_file.exists():
|
||||
return None
|
||||
|
||||
try:
|
||||
return json.loads(self.token_file.read_text())
|
||||
except Exception as e:
|
||||
print(f"Warning: Could not load token metadata: {e}")
|
||||
return None
|
||||
|
||||
def is_token_expired(self, metadata: dict) -> bool:
|
||||
"""
|
||||
Check if token is expired.
|
||||
|
||||
Args:
|
||||
metadata: Token metadata dictionary
|
||||
|
||||
Returns:
|
||||
True if expired
|
||||
"""
|
||||
expires_at = metadata.get("expires_at", 0)
|
||||
if not expires_at:
|
||||
return False
|
||||
|
||||
return time.time() >= expires_at
|
||||
|
||||
def should_refresh(self, metadata: dict) -> bool:
|
||||
"""
|
||||
Check if token should be refreshed.
|
||||
|
||||
Args:
|
||||
metadata: Token metadata dictionary
|
||||
|
||||
Returns:
|
||||
True if token should be refreshed
|
||||
"""
|
||||
expires_at = metadata.get("expires_at", 0)
|
||||
if not expires_at:
|
||||
return False
|
||||
|
||||
# Refresh if expiring within refresh_before_days
|
||||
threshold = time.time() + (self.refresh_before_days * 86400)
|
||||
return expires_at <= threshold
|
||||
|
||||
def refresh_token(self, current_token: str) -> str:
|
||||
"""
|
||||
Refresh token by exchanging for a new long-lived token.
|
||||
|
||||
Args:
|
||||
current_token: Current access token
|
||||
|
||||
Returns:
|
||||
New access token
|
||||
"""
|
||||
print("\n🔄 Refreshing access token...")
|
||||
|
||||
# Exchange current token for new long-lived token
|
||||
token_data = self.oauth.exchange_for_long_lived_token(current_token)
|
||||
new_token = token_data["access_token"]
|
||||
expires_in = token_data["expires_in"]
|
||||
|
||||
print(f"✅ Token refreshed! Valid for {expires_in / 86400:.0f} days")
|
||||
|
||||
# Get token info
|
||||
try:
|
||||
token_info = self.oauth.get_token_info(new_token)
|
||||
expires_at = token_info.get("expires_at", int(time.time()) + expires_in)
|
||||
is_valid = token_info.get("is_valid", True)
|
||||
except Exception:
|
||||
expires_at = int(time.time()) + expires_in
|
||||
is_valid = True
|
||||
|
||||
# Save new token
|
||||
self._save_token(new_token, expires_at, is_valid)
|
||||
|
||||
return new_token
|
||||
|
||||
def _save_token(self, access_token: str, expires_at: int, is_valid: bool):
|
||||
"""
|
||||
Save token to .env and metadata to JSON.
|
||||
|
||||
Args:
|
||||
access_token: New access token
|
||||
expires_at: Expiry timestamp
|
||||
is_valid: Whether token is valid
|
||||
"""
|
||||
# Update .env
|
||||
env_path = Path(".env")
|
||||
if env_path.exists():
|
||||
env_content = env_path.read_text()
|
||||
lines = env_content.split("\n")
|
||||
|
||||
updated = False
|
||||
for i, line in enumerate(lines):
|
||||
if line.startswith("META_ACCESS_TOKEN="):
|
||||
lines[i] = f"META_ACCESS_TOKEN={access_token}"
|
||||
updated = True
|
||||
break
|
||||
|
||||
if not updated:
|
||||
lines.append(f"META_ACCESS_TOKEN={access_token}")
|
||||
|
||||
env_path.write_text("\n".join(lines))
|
||||
|
||||
# Update metadata JSON
|
||||
metadata = {
|
||||
"access_token": access_token,
|
||||
"expires_at": expires_at,
|
||||
"issued_at": int(time.time()),
|
||||
"is_valid": is_valid,
|
||||
"updated_at": int(time.time()),
|
||||
}
|
||||
|
||||
self.token_file.write_text(json.dumps(metadata, indent=2))
|
||||
|
||||
# Reload environment
|
||||
load_dotenv(override=True)
|
||||
|
||||
def get_valid_token(self) -> str:
|
||||
"""
|
||||
Get a valid access token, refreshing if necessary.
|
||||
|
||||
Returns:
|
||||
Valid access token
|
||||
|
||||
Raises:
|
||||
ValueError: If no token available or refresh fails
|
||||
"""
|
||||
# Load token and metadata
|
||||
token = self.load_token()
|
||||
metadata = self.load_metadata()
|
||||
|
||||
if not token:
|
||||
raise ValueError(
|
||||
"No access token found. Run 'uv run python src/meta_api_grabber/auth.py' to authenticate."
|
||||
)
|
||||
|
||||
# If no metadata, assume token is valid (but warn)
|
||||
if not metadata:
|
||||
print("⚠️ Warning: No token metadata found. Cannot check expiry.")
|
||||
print(" Run 'uv run python src/meta_api_grabber/auth.py' to re-authenticate and save metadata.")
|
||||
return token
|
||||
|
||||
# Check if expired
|
||||
if self.is_token_expired(metadata):
|
||||
print("❌ Token expired! Attempting to refresh...")
|
||||
try:
|
||||
return self.refresh_token(token)
|
||||
except Exception as e:
|
||||
raise ValueError(
|
||||
f"Token expired and refresh failed: {e}\n"
|
||||
"Please re-authenticate: uv run python src/meta_api_grabber/auth.py"
|
||||
)
|
||||
|
||||
# Check if should refresh (within threshold)
|
||||
if self.should_refresh(metadata):
|
||||
expires_at = metadata.get("expires_at", 0)
|
||||
expires_dt = datetime.fromtimestamp(expires_at)
|
||||
days_left = (expires_dt - datetime.now()).days
|
||||
|
||||
print(f"\n⚠️ Token expiring in {days_left} days ({expires_dt.strftime('%Y-%m-%d')})")
|
||||
print(f" Refreshing token to extend validity...")
|
||||
|
||||
try:
|
||||
return self.refresh_token(token)
|
||||
except Exception as e:
|
||||
print(f"⚠️ Token refresh failed: {e}")
|
||||
print(f" Continuing with current token ({days_left} days remaining)")
|
||||
return token
|
||||
|
||||
# Token is valid
|
||||
expires_at = metadata.get("expires_at", 0)
|
||||
if expires_at:
|
||||
expires_dt = datetime.fromtimestamp(expires_at)
|
||||
days_left = (expires_dt - datetime.now()).days
|
||||
print(f"✅ Token valid ({days_left} days remaining)")
|
||||
|
||||
return token
|
||||
|
||||
def print_token_status(self):
|
||||
"""Print current token status."""
|
||||
token = self.load_token()
|
||||
metadata = self.load_metadata()
|
||||
|
||||
print("\n" + "="*60)
|
||||
print("TOKEN STATUS")
|
||||
print("="*60)
|
||||
|
||||
if not token:
|
||||
print("❌ No access token found in .env")
|
||||
print("\nRun: uv run python src/meta_api_grabber/auth.py")
|
||||
return
|
||||
|
||||
print("✅ Access token found")
|
||||
|
||||
if not metadata:
|
||||
print("⚠️ No metadata file (.meta_token.json)")
|
||||
print(" Cannot determine expiry. Re-authenticate to save metadata.")
|
||||
return
|
||||
|
||||
expires_at = metadata.get("expires_at", 0)
|
||||
is_valid = metadata.get("is_valid", False)
|
||||
|
||||
if expires_at:
|
||||
expires_dt = datetime.fromtimestamp(expires_at)
|
||||
days_left = (expires_dt - datetime.now()).days
|
||||
|
||||
print(f"\nExpires: {expires_dt.strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
print(f"Days remaining: {days_left}")
|
||||
|
||||
if days_left < 0:
|
||||
print("Status: ❌ EXPIRED")
|
||||
elif days_left <= self.refresh_before_days:
|
||||
print(f"Status: ⚠️ EXPIRING SOON (will auto-refresh)")
|
||||
else:
|
||||
print("Status: ✅ VALID")
|
||||
|
||||
print(f"Is valid: {'✅' if is_valid else '❌'}")
|
||||
print("="*60 + "\n")
|
||||
|
||||
|
||||
def main():
|
||||
"""Check token status and refresh if needed."""
|
||||
manager = MetaTokenManager()
|
||||
manager.print_token_status()
|
||||
|
||||
try:
|
||||
token = manager.get_valid_token()
|
||||
print(f"\n✅ Valid token ready for use")
|
||||
except ValueError as e:
|
||||
print(f"\n❌ Error: {e}")
|
||||
return 1
|
||||
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
exit(main())
|
||||
@@ -1,118 +0,0 @@
|
||||
"""
|
||||
View manager for TimescaleDB materialized views.
|
||||
Handles creation, updates, and refresh of materialized views for flattened insights data.
|
||||
Views are loaded from individual SQL files in the views directory.
|
||||
"""
|
||||
|
||||
import logging
|
||||
import pathlib
|
||||
from typing import List, Optional
|
||||
|
||||
import asyncpg
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class ViewManager:
|
||||
"""Manages materialized views for insights data flattening."""
|
||||
|
||||
def __init__(self, pool: asyncpg.Pool):
|
||||
"""
|
||||
Initialize view manager with a database connection pool.
|
||||
|
||||
Args:
|
||||
pool: asyncpg connection pool
|
||||
"""
|
||||
self.pool = pool
|
||||
self.views_dir = pathlib.Path(__file__).parent / "views"
|
||||
|
||||
async def initialize_views(self) -> None:
|
||||
"""
|
||||
Initialize all materialized views at startup.
|
||||
Loads and executes SQL files from the views directory in alphabetical order.
|
||||
Creates views if they don't exist, idempotent operation.
|
||||
"""
|
||||
logger.info("Initializing materialized views...")
|
||||
|
||||
if not self.views_dir.exists():
|
||||
logger.warning(f"Views directory not found at {self.views_dir}")
|
||||
return
|
||||
|
||||
# Get all .sql files in alphabetical order for consistent execution
|
||||
view_files = sorted(self.views_dir.glob("*.sql"))
|
||||
if not view_files:
|
||||
logger.warning(f"No SQL files found in {self.views_dir}")
|
||||
return
|
||||
|
||||
async with self.pool.acquire() as conn:
|
||||
for view_file in view_files:
|
||||
logger.debug(f"Loading view file: {view_file.name}")
|
||||
await self._execute_view_file(conn, view_file)
|
||||
|
||||
logger.info("✓ Materialized views initialized successfully")
|
||||
|
||||
async def _execute_view_file(self, conn: asyncpg.Connection, view_file: pathlib.Path) -> None:
|
||||
"""
|
||||
Execute SQL statements from a view file.
|
||||
|
||||
Args:
|
||||
conn: asyncpg connection
|
||||
view_file: Path to SQL file
|
||||
"""
|
||||
with open(view_file, 'r') as f:
|
||||
view_sql = f.read()
|
||||
|
||||
statements = [s.strip() for s in view_sql.split(';') if s.strip()]
|
||||
|
||||
for i, stmt in enumerate(statements, 1):
|
||||
if not stmt:
|
||||
continue
|
||||
|
||||
try:
|
||||
await conn.execute(stmt)
|
||||
logger.debug(f"{view_file.name}: Executed statement {i}")
|
||||
except Exception as e:
|
||||
error_msg = str(e).lower()
|
||||
if "does not exist" in error_msg:
|
||||
# Could be a missing dependent view or table, log it
|
||||
logger.debug(f"{view_file.name}: View or table does not exist (statement {i})")
|
||||
else:
|
||||
# Log other errors but don't fail - could be incompatible schema changes
|
||||
logger.warning(f"{view_file.name}: Error in statement {i}: {e}")
|
||||
|
||||
async def refresh_views(self, view_names: Optional[List[str]] = None) -> None:
|
||||
"""
|
||||
Refresh specified materialized views.
|
||||
|
||||
Args:
|
||||
view_names: List of view names to refresh. If None, refreshes all views.
|
||||
"""
|
||||
if view_names is None:
|
||||
view_names = [
|
||||
"adset_insights_flattened",
|
||||
"account_insights_flattened",
|
||||
"campaign_insights_flattened",
|
||||
"campaign_insights_by_country_flattened",
|
||||
#"campaign_insights_by_device_flattened",
|
||||
#"campaign_insights_by_gender_flattened",
|
||||
]
|
||||
|
||||
async with self.pool.acquire() as conn:
|
||||
for view_name in view_names:
|
||||
try:
|
||||
# Use CONCURRENTLY to avoid locking
|
||||
await conn.execute(
|
||||
f"REFRESH MATERIALIZED VIEW CONCURRENTLY {view_name};"
|
||||
)
|
||||
logger.debug(f"Refreshed materialized view: {view_name}")
|
||||
except Exception as e:
|
||||
error_msg = str(e).lower()
|
||||
# View might not exist if not initialized, that's okay
|
||||
if "does not exist" in error_msg:
|
||||
logger.debug(f"View does not exist, skipping refresh: {view_name}")
|
||||
else:
|
||||
logger.warning(f"Error refreshing view {view_name}: {e}")
|
||||
|
||||
async def refresh_all_views(self) -> None:
|
||||
"""Refresh all materialized views."""
|
||||
await self.refresh_views()
|
||||
@@ -36,3 +36,21 @@ CREATE INDEX idx_adset_insights_flat_date ON adset_insights_flattened(date_start
|
||||
|
||||
CREATE UNIQUE INDEX idx_adset_insights_flat_unique ON adset_insights_flattened(time, adset_id);
|
||||
REFRESH MATERIALIZED VIEW CONCURRENTLY adset_insights_flattened;
|
||||
|
||||
|
||||
SELECT
|
||||
date_start as time,
|
||||
account_id,
|
||||
adset_id,
|
||||
campaign_id,
|
||||
SUM(impressions) AS impressions,
|
||||
SUM(clicks) AS clicks,
|
||||
SUM(spend) AS spend,
|
||||
AVG(frequency) as frequency,
|
||||
avg(cpc) as cpc,
|
||||
avg(cpm) as cpm,
|
||||
avg(cpp) as cpp,
|
||||
avg(ctr) as ctr
|
||||
|
||||
FROM meta.ads_insights
|
||||
group by time, account_id, adset_id, campaign_id
|
||||
893
uv.lock
generated
893
uv.lock
generated
@@ -6,109 +6,6 @@ resolution-markers = [
|
||||
"python_full_version < '3.14'",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "aiohappyeyeballs"
|
||||
version = "2.6.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265, upload-time = "2025-03-12T01:42:47.083Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "aiohttp"
|
||||
version = "3.13.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "aiohappyeyeballs" },
|
||||
{ name = "aiosignal" },
|
||||
{ name = "attrs" },
|
||||
{ name = "frozenlist" },
|
||||
{ name = "multidict" },
|
||||
{ name = "propcache" },
|
||||
{ name = "yarl" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/ba/fa/3ae643cd525cf6844d3dc810481e5748107368eb49563c15a5fb9f680750/aiohttp-3.13.1.tar.gz", hash = "sha256:4b7ee9c355015813a6aa085170b96ec22315dabc3d866fd77d147927000e9464", size = 7835344, upload-time = "2025-10-17T14:03:29.337Z" }
|
||||
wheels = [
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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]
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Reference in New Issue
Block a user