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14 Commits
5f83ecd7ee ... view_manag

Author SHA1 Message Date
Jonas Linter
a9f6323715 unified_insights sein iatz stabiler 2025-11-26 11:30:04 +00:00
Jonas Linter
c0f24715f4 Added geotargets from fixed google download 2025-11-26 10:13:12 +00:00
Jonas Linter
7fe9dfe6f6 Before adding even more views 2025-11-14 19:04:46 +00:00
Jonas Linter
112755224f Create device specific version of account_insights 2025-11-13 19:35:14 +00:00
Jonas Linter
4778d2043b Added name to adset_insights 2025-11-13 08:13:59 +00:00
Jonas Linter
10d5bdc518 Removed the dumb extension stuf 2025-11-12 21:40:35 +00:00
Jonas Linter
5ecf58ba6a Once again missed something 2025-11-12 21:37:45 +00:00
Jonas Linter
5002528eae More views and no longer dropping schema 2025-11-12 21:36:00 +00:00
Jonas Linter
4826c4a744 More new views + timescaledb stuff 2025-11-12 21:19:55 +00:00
Jonas Linter
56e1edb0db Updated views in a few key ways 2025-11-12 14:58:09 +00:00
Jonas Linter
45bc817428 Updated meta_api_grabber to view manager 2025-11-12 13:42:17 +00:00
Jonas Linter
a1d9a19d04 Updated view queries 2025-11-12 12:47:31 +00:00
Jonas Linter
c5fa92c4ec Fixed stuff 2025-11-10 13:07:08 +01:00
Jonas Linter
a92c5b699f But no account_currency 2025-11-10 11:49:00 +01:00
38 changed files with 230942 additions and 5581 deletions
Expand all files Collapse all files

2
CLAUDE.md
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@@ -1,2 +1,4 @@
Uv managed python project that grabs data from the meta api and saves it in a timescaledb database.
Always use uv run to execute python related stuff

22
Dockerfile
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@@ -1,5 +1,5 @@
# Dockerfile for Meta API Grabber
# Production-ready container for scheduled data collection
# Dockerfile for View Manager
# Production-ready container for database view management
FROM python:3.13-slim
@@ -15,26 +15,20 @@ RUN apt-get update && apt-get install -y \
RUN pip install --no-cache-dir uv
# Copy project files
COPY pyproject.toml uv.lock README.md ./
COPY pyproject.toml README.md ./
COPY src/ ./src/
COPY metadata.yaml ./metadata.yaml
# Install Python dependencies using uv
RUN uv pip install --system -e .
# Copy environment file template (will be overridden by volume mount)
# This is just for documentation - actual .env should be mounted
COPY .env.example .env.example
# Create directory for token metadata
RUN mkdir -p /app/data
# Set environment variables
ENV PYTHONUNBUFFERED=1
ENV PYTHONPATH=/app
# Health check - verify the script can at least import
# Health check - verify database connection
HEALTHCHECK --interval=30s --timeout=10s --start-period=40s --retries=3 \
CMD python -c "from src.meta_api_grabber.scheduled_grabber import ScheduledInsightsGrabber; print('OK')" || exit 1
CMD python -c "import asyncio; from meta_api_grabber.database import TimescaleDBClient; asyncio.run(TimescaleDBClient().connect())" || exit 1
# Run the scheduled grabber
CMD ["python", "-m", "src.meta_api_grabber.scheduled_grabber"]
# Run the view manager setup
CMD ["uv", "run", "view-manager-setup"]

210
RATE_LIMITER_ENHANCEMENTS.md
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@@ -1,210 +0,0 @@
# Meta API Rate Limiter Enhancements
## Summary
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).
## New Headers Monitored
### 1. **X-App-Usage** (Platform Rate Limits)
Tracks application-level rate limits across all users.
**Fields:**
- `call_count`: Percentage of calls made (0-100)
- `total_cputime`: Percentage of CPU time used (0-100)
- `total_time`: Percentage of total time used (0-100)
**Example:**
```json
{
"call_count": 28,
"total_time": 25,
"total_cputime": 25
}
```
### 2. **X-Ad-Account-Usage** (Ad Account Specific)
Tracks rate limits for specific ad accounts. **Stored per account ID** to support multiple accounts.
**Fields:**
- `acc_id_util_pct`: Percentage of ad account usage (0-100)
- `reset_time_duration`: Time in seconds until rate limit resets
- `ads_api_access_tier`: Access tier (e.g., "standard_access", "development_access")
**Example:**
```json
{
"acc_id_util_pct": 9.67,
"reset_time_duration": 100,
"ads_api_access_tier": "standard_access"
}
```
**Note:** Metrics are tracked separately for each ad account in a dictionary keyed by account ID (e.g., `act_123456789`).
### 3. **X-Business-Use-Case-Usage** (Business Use Case Limits)
Tracks rate limits per business use case (ads_insights, ads_management, etc.).
**Fields:**
- `business_id`: Business object ID
- `type`: Type of BUC (ads_insights, ads_management, custom_audience, etc.)
- `call_count`: Percentage of calls made (0-100)
- `total_cputime`: Percentage of CPU time (0-100)
- `total_time`: Percentage of total time (0-100)
- `estimated_time_to_regain_access`: Time in minutes until access is restored
- `ads_api_access_tier`: Access tier
**Example:**
```json
{
"66782684": [{
"type": "ads_management",
"call_count": 95,
"total_cputime": 20,
"total_time": 20,
"estimated_time_to_regain_access": 0,
"ads_api_access_tier": "development_access"
}]
}
```
### 4. **x-fb-ads-insights-throttle** (Legacy)
Original header still supported for backward compatibility.
**Fields:**
- `app_id_util_pct`: App usage percentage
- `acc_id_util_pct`: Account usage percentage
## Key Enhancements
### 1. Intelligent Throttling
The rate limiter now uses `estimated_time_to_regain_access` and `reset_time_duration` to calculate optimal delays:
```python
# If we have estimated_time_to_regain_access from BUC header
if self.estimated_time_to_regain_access > 0:
delay = self.estimated_time_to_regain_access * 60 # Convert minutes to seconds
# If we have reset_time_duration from Ad Account header
elif self.reset_time_duration > 0:
delay = self.reset_time_duration * 0.5 # Use fraction as safety margin
```
### 2. Comprehensive Error Code Detection
Expanded error code detection to include all Meta rate limit error codes:
- **4**: App rate limit
- **17**: User rate limit
- **32**: Pages rate limit
- **613**: Custom rate limit
- **80000-80014**: Business Use Case rate limits (Ads Insights, Ads Management, Custom Audience, Instagram, LeadGen, Messenger, Pages, WhatsApp, Catalog)
### 3. Debug Logging
All headers are now logged in DEBUG mode with detailed parsing information:
```python
logger.debug(f"X-App-Usage header: {header_value}")
logger.debug(f"Parsed X-App-Usage: {result}")
```
### 4. Enhanced Statistics
The `get_stats()` and `print_stats()` methods now display comprehensive metrics from all headers:
```
======================================================================
RATE LIMITER STATISTICS
======================================================================
Total Requests: 0
Throttled Requests: 0
Rate Limit Errors: 0
X-App-Usage (Platform Rate Limits):
Call Count: 95.0%
Total CPU Time: 90.0%
Total Time: 88.0%
X-Ad-Account-Usage:
Account Usage: 97.5%
Reset Time Duration: 300s
API Access Tier: standard_access
X-Business-Use-Case-Usage:
Type: ads_insights
Call Count: 98.0%
Total CPU Time: 95.0%
Total Time: 92.0%
Est. Time to Regain: 15 min
Legacy (x-fb-ads-insights-throttle):
App Usage: 93.0%
Account Usage: 96.0%
Max Usage Across All Metrics: 98.0%
Currently Throttled: True
======================================================================
```
## Usage
### Enable Debug Logging
To see all header parsing in debug mode:
```python
import logging
logging.basicConfig(
level=logging.DEBUG,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
```
### Updating Usage with Account ID
When calling `update_usage()`, you can optionally provide an account ID to track per-account metrics:
```python
# Option 1: Provide account_id explicitly
limiter.update_usage(response, account_id='act_123456789')
# Option 2: Let the limiter try to extract it from the response
limiter.update_usage(response) # Will attempt to extract account_id
```
### Access New Metrics
All metrics are available through the `get_stats()` method:
```python
stats = limiter.get_stats()
print(f"App call count: {stats['app_call_count']}%")
print(f"Regain access in: {stats['estimated_time_to_regain_access']} min")
# Per-account metrics
for account_id, usage in stats['ad_account_usage'].items():
print(f"Account {account_id}:")
print(f" Usage: {usage['acc_id_util_pct']}%")
print(f" Reset in: {usage['reset_time_duration']}s")
print(f" API tier: {usage['ads_api_access_tier']}")
# Business use case details
for buc in stats['buc_usage']:
print(f"BUC {buc['type']}: {buc['call_count']}%")
```
## Testing
Run the test script to see the rate limiter in action:
```bash
uv run python test_rate_limiter.py
```
This will demonstrate:
- Parsing all four header types
- Intelligent throttling based on usage
- Comprehensive statistics display
- Debug logging output
## References
- [Meta Graph API Rate Limiting](https://developers.facebook.com/docs/graph-api/overview/rate-limiting)
- [Meta Marketing API Best Practices](https://developers.facebook.com/docs/marketing-api/insights/best-practices/)

88
VIEWS_SUMMARY.md Normal file
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# Database Views Summary
## Overview
Updated the public schema with new views to make advertising analytics data from Meta, Google, and combined sources more accessible.
## Changes Made
### 1. Adapted `explore_schemas.py` Script
- Modified to save schema exploration output to `schema_info.txt`
- Provides persistent reference for table structures across meta, google, and alpinebits schemas
- Usage: `uv run explore_schemas.py`
### 2. New Views Added
#### **account_insights_by_country** ✓
- Aggregates Meta campaign insights by country to account level
- **Columns**: time, account_id, country, impressions, clicks, spend, link_click, landing_page_view, lead
- **Purpose**: Analyze Meta advertising performance by geographic region
- **Source**: meta.custom_campaign_country
#### **g_campaign_insights** ✓
- Google campaign-level insights with calculated performance metrics
- **Columns**: time, campaign_id, campaign_name, clicks, impressions, interactions, cost_micros, cost, conversions, all_conversions, conversions_value, ctr, cpm, cpc, cost_per_conversion
- **Purpose**: Unified Google campaign performance view with key metrics (CTR, CPM, CPC, Cost per Conversion)
- **Source**: google.campaign_metrics
#### **unified_account_insights_by_device** ✓
- Combines Meta and Google account insights broken down by device type
- **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
- **Purpose**: Compare Meta vs Google performance by device (DESKTOP, MOBILE, TABLET)
- **Requires**: account_metadata table for linking Meta and Google accounts
#### **unified_account_insights_by_gender** ✓
- Meta audience insights broken down by gender
- **Columns**: time, meta_account_id, gender, impressions, clicks, spend, link_clicks, leads
- **Purpose**: Analyze Meta advertising performance by audience gender demographics
- **Source**: meta account_insights_by_gender
### 3. Existing Views (Not Modified)
The following views were already present and working correctly:
- **campaign_insights** - Base Meta campaign insights
- **campaign_insights_by_gender/age/device/country** - Meta campaign breakdowns
- **account_insights** - Aggregated Meta account insights
- **account_insights_by_gender/age/device/gender_and_age** - Meta account breakdowns
- **ads_insights** (materialized) - Individual ad performance
- **adset_insights** - Ad set level insights
- **g_account_insights** - Google account-level insights
- **g_account_insights_device** - Google account insights by device
- **unified_account_insights** - Combined Meta+Google account-level view
## Testing
All views have been tested and verified to:
- Execute without errors
- Return valid data with correct column structures
- Support LIMIT queries for performance verification
## Usage Examples
```sql
-- View Meta performance by country
SELECT time, country, SUM(impressions), SUM(spend), SUM(lead)
FROM account_insights_by_country
WHERE account_id = '1416908162571377'
GROUP BY time, country;
-- View Google campaign metrics
SELECT time, campaign_name, clicks, impressions, cpc, cpm
FROM g_campaign_insights
WHERE ctr > 2.0
ORDER BY time DESC
LIMIT 10;
-- Compare device performance across Meta and Google
SELECT time, device, total_impressions, total_clicks, total_spend
FROM unified_account_insights_by_device
ORDER BY time DESC;
-- Analyze Meta audience by gender
SELECT time, gender, impressions, clicks, spend, leads
FROM unified_account_insights_by_gender
WHERE time > CURRENT_DATE - INTERVAL '30 days'
ORDER BY time, gender;
```
## Next Steps
- Set up automated refreshes for materialized views if needed
- Create additional unified views for campaign-level comparisons
- Consider indexing frequently queried views for performance optimization

16
docker-compose.yml
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@@ -19,22 +19,22 @@ services:
retries: 5
restart: unless-stopped
# Meta API Grabber - Scheduled data collection
meta-grabber:
# View Manager - Setup database schema and views
view-manager:
build:
context: .
dockerfile: Dockerfile
container_name: meta_api_grabber
container_name: view_manager
environment:
# Database connection (connects to timescaledb service)
DATABASE_URL: postgresql://meta_user:meta_password@timescaledb:5432/meta_insights
env_file:
- .env # Must contain META_ACCESS_TOKEN, META_APP_ID, META_APP_SECRET
- .env # Optional: for any additional configuration
volumes:
# Mount .env for token updates (auto-refresh will update the file)
- ./.env:/app/.env
# Mount token metadata file (preserves token refresh state across restarts)
- ./.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:
condition: service_healthy

110
explore_schemas.py Normal file
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@@ -0,0 +1,110 @@
#!/usr/bin/env python3
"""
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():
"""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
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@@ -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"

17
pyproject.toml
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@@ -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
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@@ -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

291
src/meta_api_grabber/auth.py
View File

@@ -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())

67
src/meta_api_grabber/database.py
View File

@@ -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)")
@@ -310,7 +333,6 @@ class TimescaleDBClient:
account_id: str,
data: Dict[str, Any],
date_preset: str = "today",
cache_metadata: bool = True,
):
"""
Insert campaign-level insights data.
@@ -321,10 +343,9 @@ class TimescaleDBClient:
account_id: Ad account ID
data: Insights data dictionary from Meta API
date_preset: Date preset used
cache_metadata: If True, automatically cache campaign metadata from insights data
"""
# Cache campaign metadata if requested and available in the insights data
if cache_metadata and data.get("campaign_name"):
# Auto-cache campaign metadata if available in the insights data
if data.get("campaign_name"):
await self.upsert_campaign(
campaign_id=campaign_id,
account_id=account_id,
@@ -389,7 +410,6 @@ class TimescaleDBClient:
account_id: str,
data: Dict[str, Any],
date_preset: str = "today",
cache_metadata: bool = True,
):
"""
Insert ad set level insights data.
@@ -401,21 +421,18 @@ class TimescaleDBClient:
account_id: Ad account ID
data: Insights data dictionary from Meta API
date_preset: Date preset used
cache_metadata: If True, automatically cache adset/campaign metadata from insights data
"""
# Cache metadata if requested and available in the insights data
if cache_metadata:
# Cache adset metadata if available
# Note: Campaign should already exist from cache_campaigns_metadata or grab_campaign_insights
# If it doesn't exist, the foreign key constraint will fail with a clear error
# This is intentional - we should never silently create campaigns with 'Unknown' names
if data.get("adset_name"):
await self.upsert_adset(
adset_id=adset_id,
campaign_id=campaign_id,
adset_name=data["adset_name"],
status=None, # Not available in insights response
)
# Auto-cache adset metadata if available in the insights data
# Note: Campaign should already exist from cache_campaigns_metadata or grab_campaign_insights
# If it doesn't exist, the foreign key constraint will fail with a clear error
# This is intentional - we should never silently create campaigns with 'Unknown' names
if data.get("adset_name"):
await self.upsert_adset(
adset_id=adset_id,
campaign_id=campaign_id,
adset_name=data["adset_name"],
status=None, # Not available in insights response
)
query = """
INSERT INTO adset_insights (

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src/meta_api_grabber/discover_accounts.py
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"""
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())

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src/meta_api_grabber/geotargets-2025-10-29.csv Normal file
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src/meta_api_grabber/insights_grabber.py
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"""
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)

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src/meta_api_grabber/main.py
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def main():
print("Hello from meta-api-grabber!")
if __name__ == "__main__":
main()

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src/meta_api_grabber/rate_limiter.py
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"""
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))

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src/meta_api_grabber/scheduled_grabber.py
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"""
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()

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src/meta_api_grabber/test_ad_accounts.py
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"""
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()

91
src/meta_api_grabber/test_campaign_insights.py
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@@ -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!")

118
src/meta_api_grabber/test_campaigns.py
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@@ -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!")

125
src/meta_api_grabber/test_date_fields.py
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@@ -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()))

132
src/meta_api_grabber/test_google_ads_accounts.py
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@@ -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()

171
src/meta_api_grabber/test_page_leads.py
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@@ -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()

108
src/meta_api_grabber/test_rate_limiter_issue.py
View File

@@ -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())

87
src/meta_api_grabber/test_today_preset.py
View File

@@ -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())

307
src/meta_api_grabber/token_manager.py
View File

@@ -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())

116
src/meta_api_grabber/view_manager.py
View File

@@ -1,116 +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",
]
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()

20
src/meta_api_grabber/views/account_insights_by_device.sql Normal file
View File

@@ -0,0 +1,20 @@
--- account insights by 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;

53
src/meta_api_grabber/views/account_insights_by_gender.sql Normal file
View File

@@ -0,0 +1,53 @@
DROP VIEW IF EXISTS account_insights_by_gender CASCADE;
CREATE VIEW account_insights_by_gender AS
SELECT
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_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;

55
src/meta_api_grabber/views/account_insights_google.sql Normal file
View File

@@ -0,0 +1,55 @@
DROP VIEW IF EXISTS g_account_insights CASCADE;
CREATE VIEW g_account_insights AS
SELECT
time,
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 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 account_id, time, customer_currency_code, account_name
) base;

20
src/meta_api_grabber/views/adset_insights.sql
View File

@@ -35,4 +35,22 @@ 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;
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

33
src/meta_api_grabber/views/campaign_insights.sql
View File

@@ -3,10 +3,9 @@
DROP MATERIALIZED VIEW IF EXISTS campaign_insights_flattened CASCADE;
CREATE MATERIALIZED VIEW campaign_insights_flattened AS
SELECT
time,
account_id,
campaign_id,
SELECT date_start AS "time",
concat('act_', account_id) AS account_id,
campaign_id,
impressions,
clicks,
spend,
@@ -14,25 +13,21 @@ SELECT
ctr,
cpc,
cpm,
date_preset,
date_start,
date_stop,
fetched_at,
(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 campaign_insights;
( 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;
CREATE INDEX idx_campaign_insights_flat_date ON campaign_insights_flattened(date_start, date_stop);
CREATE UNIQUE INDEX idx_campaign_insights_flat_unique ON campaign_insights_flattened(time, campaign_id);
REFRESH MATERIALIZED VIEW CONCURRENTLY campaign_insights_flattened;
REFRESH MATERIALIZED VIEW CONCURRENTLY campaign_insights_flattened;

10
src/meta_api_grabber/views/campaign_insights_by_country.sql
View File

@@ -3,22 +3,20 @@
DROP MATERIALIZED VIEW IF EXISTS campaign_insights_by_country_flattened CASCADE;
CREATE MATERIALIZED VIEW campaign_insights_by_country_flattened AS
SELECT
time,
account_id,
SELECT date_start AS "time",
concat('act_', account_id) AS account_id,
campaign_id,
country,
impressions,
clicks,
spend,
reach,
frequency,
ctr,
cpc,
cpm,
date_preset,
date_start,
date_stop,
fetched_at,
(SELECT (value->>'value')::numeric
FROM jsonb_array_elements(actions)
WHERE value->>'action_type' = 'link_click') AS link_click,
@@ -29,7 +27,7 @@ SELECT
FROM jsonb_array_elements(actions)
WHERE value->>'action_type' = 'lead') AS lead
FROM campaign_insights_by_country;
FROM meta.custom_campaign_country;
CREATE INDEX idx_campaign_insights_by_country_flat_date ON campaign_insights_by_country_flattened(date_start, date_stop);

32
src/meta_api_grabber/views/campaign_insights_by_device.sql Normal file
View File

@@ -0,0 +1,32 @@
--- campaign insights by device
DROP MATERIALIZED VIEW IF EXISTS campaign_insights_by_device_flattened CASCADE;
CREATE MATERIALIZED VIEW campaign_insights_by_device_flattened AS
SELECT date_start AS "time",
concat('act_', account_id) AS account_id,
campaign_id,
device_platform,
impressions,
clicks,
spend,
reach,
frequency,
date_start,
date_stop,
(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;
CREATE INDEX idx_campaign_insights_by_device_flat_date ON campaign_insights_by_device_flattened(date_start, date_stop);
CREATE UNIQUE INDEX idx_campaign_insights_by_device_flat_unique ON campaign_insights_by_device_flattened(time, campaign_id, device_platform);
REFRESH MATERIALIZED VIEW CONCURRENTLY campaign_insights_by_device_flattened;

71
src/meta_api_grabber/views/campaign_insights_by_gender.sql Normal file
View File

@@ -0,0 +1,71 @@
--- campaign insights by country
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",
concat('act_', account_id) AS account_id,
campaign_id,
gender,
age,
impressions,
clicks,
spend,
reach,
frequency,
ctr,
cpc,
cpm,
date_start,
date_stop,
(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 INDEX idx_campaign_insights_by_gender_and_age_date ON campaign_insights_by_gender_and_age(date_start, date_stop);
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,
account_id
from campaign_insights_by_gender_and_age
group by time, gender, account_id, campaign_id, date_start, date_stop;
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,
account_id
from campaign_insights_by_gender_and_age
group by time, age, account_id, campaign_id, date_start, date_stop;

893
uv.lock generated
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