Updated data collection system

2025-10-21 11:55:14 +02:00
parent 0d754846ce
commit 6ba8a0dba2
9 changed files with 1418 additions and 57 deletions
--- a/.env.example
+++ b/.env.example
@@ -13,3 +13,10 @@ META_AD_ACCOUNT_ID=act_your_account_id_here
 #   Run: uv run python src/meta_api_grabber/auth.py
 # Option 2: Manually provide a token from https://developers.facebook.com/tools/explorer/
 META_ACCESS_TOKEN=your_access_token_here
+
+# Database Configuration (TimescaleDB)
+# For local development using docker-compose, use these defaults:
+DATABASE_URL=postgresql://meta_user:meta_password@localhost:5432/meta_insights
+
+# For production, update with your actual database credentials:
+# DATABASE_URL=postgresql://username:password@host:port/database
--- a/README.md
+++ b/README.md
@@ -1,70 +1,94 @@
 # Meta API Grabber

-Async script to grab ad insights data from Meta's Marketing API with conservative rate limiting.
+Async data collection system for Meta's Marketing API with TimescaleDB time-series storage and dashboard support.

 ## Features

 - **OAuth2 Authentication** - Automated token generation flow
+- **TimescaleDB Integration** - Optimized time-series database for ad metrics
+- **Scheduled Collection** - Periodic data grabbing (every 2 hours recommended)
+- **Metadata Caching** - Smart caching of accounts, campaigns, and ad sets
 - **Async/await architecture** for efficient API calls
 - **Conservative rate limiting** (2s between requests, 1 concurrent request)
 - **Multi-level insights** - Account, campaign, and ad set data
- **JSON output** with timestamps
- **Configurable date ranges**
+- **Dashboard Ready** - Includes Grafana setup for visualization
+- **Continuous Aggregates** - Pre-computed hourly/daily rollups
+- **Data Compression** - Automatic compression of older data

-## Setup
+## Quick Start

-1. Install dependencies using uv:
+### 1. Install Dependencies
 ```bash
 uv sync
 ```

-2. Configure your Meta API credentials:
+### 2. Start TimescaleDB
+```bash
+docker-compose up -d
+```
+
+This starts:
+- **TimescaleDB** on port 5432 (PostgreSQL-compatible)
+- **Grafana** on port 3000 (for dashboards)
+
+### 3. Configure Credentials
 ```bash
 cp .env.example .env
 ```

-3. Edit `.env` and add your App ID, App Secret, and Ad Account ID:
-   - Get your App credentials from [Meta for Developers](https://developers.facebook.com/)
-   - Find your ad account ID in Meta Ads Manager (format: `act_1234567890`)
+Edit `.env` and add:
+- **META_APP_ID** and **META_APP_SECRET** from [Meta for Developers](https://developers.facebook.com/)
+- **META_AD_ACCOUNT_ID** from Meta Ads Manager (format: `act_1234567890`)
+- **DATABASE_URL** is pre-configured for local Docker setup

-4. Get an access token (choose one method):
+### 4. Get Access Token

 **Option A: OAuth2 Flow (Recommended)**
 ```bash
 uv run python src/meta_api_grabber/auth.py
 ```
-   This will:
-   - Generate an authorization URL
-   - Walk you through the OAuth2 flow
-   - Offer to save the access token to `.env` automatically
+Follow the prompts to authorize and save your token.

 **Option B: Manual Token**
 - Get a token from [Graph API Explorer](https://developers.facebook.com/tools/explorer/)
-   - Add it manually to `.env` as `META_ACCESS_TOKEN`
+- Add it to `.env` as `META_ACCESS_TOKEN`

-## Usage
-
-Run the insights grabber:
+### 5. Start Scheduled Collection
 ```bash
-uv run python src/meta_api_grabber/insights_grabber.py
+uv run python src/meta_api_grabber/scheduled_grabber.py
 ```

 This will:
- Fetch insights for the last 7 days
- Grab account-level, campaign-level (top 10), and ad set-level (top 10) data
- Save results to `data/meta_insights_TIMESTAMP.json`
+- Collect data every 2 hours using the `today` date preset (recommended by Meta)
+- Cache metadata (accounts, campaigns, ad sets) twice daily
+- Store time-series data in TimescaleDB
+- Use upsert strategy to handle updates

-### Authentication Scripts
+## Usage Modes

-**Get OAuth2 Access Token:**
+### 1. Scheduled Collection (Recommended for Dashboards)
 ```bash
-uv run python src/meta_api_grabber/auth.py
+uv run python src/meta_api_grabber/scheduled_grabber.py
 ```
+- Runs continuously, collecting data every 2 hours
+- Stores data in TimescaleDB for dashboard visualization
+- Uses `today` date preset (recommended by Meta)
+- Caches metadata to reduce API calls

-**Grab Insights Data:**
+### 2. One-Time Data Export (JSON)
 ```bash
 uv run python src/meta_api_grabber/insights_grabber.py
 ```
+- Fetches insights for the last 7 days
+- Saves to `data/meta_insights_TIMESTAMP.json`
+- Good for ad-hoc analysis or testing
+
+### 3. OAuth2 Authentication
+```bash
+uv run python src/meta_api_grabber/auth.py
+```
+- Interactive flow to get access token
+- Saves token to `.env` automatically

 ## Data Collected

@@ -84,23 +108,68 @@ uv run python src/meta_api_grabber/insights_grabber.py
 - Impressions, clicks, spend
 - CTR, CPM

+## Database Schema
+
+### Time-Series Tables (Hypertables)
+- **account_insights** - Account-level metrics over time
+- **campaign_insights** - Campaign-level metrics over time
+- **adset_insights** - Ad set level metrics over time
+
+### Metadata Tables (Cached)
+- **ad_accounts** - Account metadata
+- **campaigns** - Campaign metadata
+- **adsets** - Ad set metadata
+
+### Continuous Aggregates
+- **account_insights_hourly** - Hourly rollups
+- **account_insights_daily** - Daily rollups
+
+### Features
+- **Automatic partitioning** by day (chunk_time_interval = 1 day)
+- **Compression** for data older than 7 days
+- **Indexes** on account_id, campaign_id, adset_id + time
+- **Upsert strategy** to handle duplicate/updated data
+
+## Dashboard Setup
+
+### Access Grafana
+1. Open http://localhost:3000
+2. Login with `admin` / `admin`
+3. Add TimescaleDB as data source:
+   - Type: PostgreSQL
+   - Host: `timescaledb:5432`
+   - Database: `meta_insights`
+   - User: `meta_user`
+   - Password: `meta_password`
+   - TLS/SSL Mode: disable
+
+### Example Queries
+
+**Latest Account Metrics:**
+```sql
+SELECT * FROM latest_account_metrics WHERE account_id = 'act_your_id';
+```
+
+**Campaign Performance (Last 24h):**
+```sql
+SELECT * FROM campaign_performance_24h ORDER BY total_spend DESC;
+```
+
+**Hourly Trend:**
+```sql
+SELECT bucket, avg_impressions, avg_clicks, avg_spend
+FROM account_insights_hourly
+WHERE account_id = 'act_your_id'
+  AND bucket >= NOW() - INTERVAL '7 days'
+ORDER BY bucket;
+```
+
 ## Rate Limiting

-The script is configured to be very conservative:
- 2 seconds delay between API requests
- Only 1 concurrent request at a time
- Limited to top 10 campaigns and ad sets
+The system is configured to be very conservative:
+- **2 seconds delay** between API requests
+- **Only 1 concurrent request** at a time
+- **Top 50 campaigns/adsets** per collection
+- **2 hour intervals** between collections

-You can adjust these settings in the `MetaInsightsGrabber` class if needed.
-
-## Output
-
-Data is saved to `data/meta_insights_TIMESTAMP.json` with the following structure:
-```json
-{
-  "account": { ... },
-  "campaigns": { ... },
-  "ad_sets": { ... },
-  "summary": { ... }
-}
-```
+This ensures you stay well within Meta's API rate limits.
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -0,0 +1,42 @@
+version: '3.8'
+
+services:
+  timescaledb:
+    image: timescale/timescaledb:latest-pg16
+    container_name: meta_timescaledb
+    ports:
+      - "5432:5432"
+    environment:
+      POSTGRES_DB: meta_insights
+      POSTGRES_USER: meta_user
+      POSTGRES_PASSWORD: meta_password
+    volumes:
+      - timescale_data:/var/lib/postgresql/data
+      - ./src/meta_api_grabber/db_schema.sql:/docker-entrypoint-initdb.d/01-schema.sql
+    healthcheck:
+      test: ["CMD-SHELL", "pg_isready -U meta_user -d meta_insights"]
+      interval: 10s
+      timeout: 5s
+      retries: 5
+    restart: unless-stopped
+
+  # Optional: Grafana for visualization
+  grafana:
+    image: grafana/grafana:latest
+    container_name: meta_grafana
+    ports:
+      - "3000:3000"
+    environment:
+      GF_SECURITY_ADMIN_USER: admin
+      GF_SECURITY_ADMIN_PASSWORD: admin
+      GF_INSTALL_PLUGINS: grafana-clock-panel
+    volumes:
+      - grafana_data:/var/lib/grafana
+    depends_on:
+      timescaledb:
+        condition: service_healthy
+    restart: unless-stopped
+
+volumes:
+  timescale_data:
+  grafana_data:
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,7 +6,10 @@ 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",
    "python-dotenv>=1.1.1",
    "requests-oauthlib>=2.0.0",
+    "sqlalchemy[asyncio]>=2.0.44",
 ]
--- a/src/meta_api_grabber/database.py
+++ b/src/meta_api_grabber/database.py
@@ -0,0 +1,376 @@
+"""
+Database module for storing Meta ad insights in TimescaleDB.
+Handles time-series data with metadata caching.
+"""
+
+import asyncio
+import os
+from datetime import datetime
+from typing import Any, Dict, List, Optional
+
+import asyncpg
+from dotenv import load_dotenv
+
+
+class TimescaleDBClient:
+    """Async client for TimescaleDB operations with metadata caching."""
+
+    def __init__(self, connection_string: Optional[str] = None):
+        """
+        Initialize the database client.
+
+        Args:
+            connection_string: PostgreSQL connection string.
+                If not provided, will load from DATABASE_URL env var.
+        """
+        load_dotenv()
+
+        self.connection_string = connection_string or os.getenv("DATABASE_URL")
+        if not self.connection_string:
+            raise ValueError(
+                "Database connection string required. "
+                "Set DATABASE_URL in .env or pass connection_string parameter."
+            )
+
+        self.pool: Optional[asyncpg.Pool] = None
+
+    async def connect(self):
+        """Create connection pool."""
+        if not self.pool:
+            self.pool = await asyncpg.create_pool(
+                self.connection_string,
+                min_size=2,
+                max_size=10,
+                command_timeout=60,
+            )
+            print("Connected to TimescaleDB")
+
+    async def close(self):
+        """Close connection pool."""
+        if self.pool:
+            await self.pool.close()
+            self.pool = None
+            print("Disconnected from TimescaleDB")
+
+    async def __aenter__(self):
+        """Context manager entry."""
+        await self.connect()
+        return self
+
+    async def __aexit__(self, exc_type, exc_val, exc_tb):
+        """Context manager exit."""
+        await self.close()
+
+    # ========================================================================
+    # METADATA CACHING (Ad Accounts, Campaigns, Ad Sets)
+    # ========================================================================
+
+    async def upsert_ad_account(
+        self,
+        account_id: str,
+        account_name: Optional[str] = None,
+        currency: Optional[str] = None,
+        timezone_name: Optional[str] = None,
+    ):
+        """
+        Insert or update ad account metadata.
+
+        Args:
+            account_id: Meta ad account ID
+            account_name: Account name
+            currency: Account currency
+            timezone_name: Account timezone
+        """
+        query = """
+            INSERT INTO ad_accounts (account_id, account_name, currency, timezone_name, updated_at)
+            VALUES ($1, $2, $3, $4, NOW())
+            ON CONFLICT (account_id)
+            DO UPDATE SET
+                account_name = COALESCE(EXCLUDED.account_name, ad_accounts.account_name),
+                currency = COALESCE(EXCLUDED.currency, ad_accounts.currency),
+                timezone_name = COALESCE(EXCLUDED.timezone_name, ad_accounts.timezone_name),
+                updated_at = NOW()
+        """
+        async with self.pool.acquire() as conn:
+            await conn.execute(query, account_id, account_name, currency, timezone_name)
+
+    async def upsert_campaign(
+        self,
+        campaign_id: str,
+        account_id: str,
+        campaign_name: str,
+        status: Optional[str] = None,
+        objective: Optional[str] = None,
+    ):
+        """
+        Insert or update campaign metadata.
+
+        Args:
+            campaign_id: Meta campaign ID
+            account_id: Parent ad account ID
+            campaign_name: Campaign name
+            status: Campaign status
+            objective: Campaign objective
+        """
+        query = """
+            INSERT INTO campaigns (campaign_id, account_id, campaign_name, status, objective, updated_at)
+            VALUES ($1, $2, $3, $4, $5, NOW())
+            ON CONFLICT (campaign_id)
+            DO UPDATE SET
+                campaign_name = EXCLUDED.campaign_name,
+                status = COALESCE(EXCLUDED.status, campaigns.status),
+                objective = COALESCE(EXCLUDED.objective, campaigns.objective),
+                updated_at = NOW()
+        """
+        async with self.pool.acquire() as conn:
+            await conn.execute(query, campaign_id, account_id, campaign_name, status, objective)
+
+    async def upsert_adset(
+        self,
+        adset_id: str,
+        campaign_id: str,
+        adset_name: str,
+        status: Optional[str] = None,
+    ):
+        """
+        Insert or update ad set metadata.
+
+        Args:
+            adset_id: Meta ad set ID
+            campaign_id: Parent campaign ID
+            adset_name: Ad set name
+            status: Ad set status
+        """
+        query = """
+            INSERT INTO adsets (adset_id, campaign_id, adset_name, status, updated_at)
+            VALUES ($1, $2, $3, $4, NOW())
+            ON CONFLICT (adset_id)
+            DO UPDATE SET
+                adset_name = EXCLUDED.adset_name,
+                status = COALESCE(EXCLUDED.status, adsets.status),
+                updated_at = NOW()
+        """
+        async with self.pool.acquire() as conn:
+            await conn.execute(query, adset_id, campaign_id, adset_name, status)
+
+    # ========================================================================
+    # TIME-SERIES DATA INSERTION
+    # ========================================================================
+
+    async def insert_account_insights(
+        self,
+        time: datetime,
+        account_id: str,
+        data: Dict[str, Any],
+        date_preset: str = "today",
+    ):
+        """
+        Insert account-level insights data.
+
+        Args:
+            time: Timestamp for the data point
+            account_id: Ad account ID
+            data: Insights data dictionary from Meta API
+            date_preset: Date preset used (e.g., 'today', 'yesterday')
+        """
+        query = """
+            INSERT INTO account_insights (
+                time, account_id, impressions, clicks, spend, reach, frequency,
+                ctr, cpc, cpm, cpp, actions, cost_per_action_type, date_preset, fetched_at
+            ) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, $11, $12, $13, $14, NOW())
+            ON CONFLICT (time, account_id)
+            DO UPDATE SET
+                impressions = EXCLUDED.impressions,
+                clicks = EXCLUDED.clicks,
+                spend = EXCLUDED.spend,
+                reach = EXCLUDED.reach,
+                frequency = EXCLUDED.frequency,
+                ctr = EXCLUDED.ctr,
+                cpc = EXCLUDED.cpc,
+                cpm = EXCLUDED.cpm,
+                cpp = EXCLUDED.cpp,
+                actions = EXCLUDED.actions,
+                cost_per_action_type = EXCLUDED.cost_per_action_type,
+                fetched_at = NOW()
+        """
+
+        # Extract metrics with safe defaults
+        impressions = int(data.get("impressions", 0)) if data.get("impressions") else None
+        clicks = int(data.get("clicks", 0)) if data.get("clicks") else None
+        spend = float(data.get("spend", 0)) if data.get("spend") else None
+        reach = int(data.get("reach", 0)) if data.get("reach") else None
+        frequency = float(data.get("frequency", 0)) if data.get("frequency") else None
+        ctr = float(data.get("ctr", 0)) if data.get("ctr") else None
+        cpc = float(data.get("cpc", 0)) if data.get("cpc") else None
+        cpm = float(data.get("cpm", 0)) if data.get("cpm") else None
+        cpp = float(data.get("cpp", 0)) if data.get("cpp") else None
+
+        # Store actions as JSONB
+        import json
+        actions = json.dumps(data.get("actions", [])) if data.get("actions") else None
+        cost_per_action = json.dumps(data.get("cost_per_action_type", [])) if data.get("cost_per_action_type") else None
+
+        async with self.pool.acquire() as conn:
+            await conn.execute(
+                query,
+                time, account_id, impressions, clicks, spend, reach, frequency,
+                ctr, cpc, cpm, cpp, actions, cost_per_action, date_preset
+            )
+
+    async def insert_campaign_insights(
+        self,
+        time: datetime,
+        campaign_id: str,
+        account_id: str,
+        data: Dict[str, Any],
+        date_preset: str = "today",
+    ):
+        """
+        Insert campaign-level insights data.
+
+        Args:
+            time: Timestamp for the data point
+            campaign_id: Campaign ID
+            account_id: Ad account ID
+            data: Insights data dictionary from Meta API
+            date_preset: Date preset used
+        """
+        query = """
+            INSERT INTO campaign_insights (
+                time, campaign_id, account_id, impressions, clicks, spend, reach,
+                ctr, cpc, cpm, actions, date_preset, fetched_at
+            ) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, $11, $12, NOW())
+            ON CONFLICT (time, campaign_id)
+            DO UPDATE SET
+                impressions = EXCLUDED.impressions,
+                clicks = EXCLUDED.clicks,
+                spend = EXCLUDED.spend,
+                reach = EXCLUDED.reach,
+                ctr = EXCLUDED.ctr,
+                cpc = EXCLUDED.cpc,
+                cpm = EXCLUDED.cpm,
+                actions = EXCLUDED.actions,
+                fetched_at = NOW()
+        """
+
+        impressions = int(data.get("impressions", 0)) if data.get("impressions") else None
+        clicks = int(data.get("clicks", 0)) if data.get("clicks") else None
+        spend = float(data.get("spend", 0)) if data.get("spend") else None
+        reach = int(data.get("reach", 0)) if data.get("reach") else None
+        ctr = float(data.get("ctr", 0)) if data.get("ctr") else None
+        cpc = float(data.get("cpc", 0)) if data.get("cpc") else None
+        cpm = float(data.get("cpm", 0)) if data.get("cpm") else None
+
+        import json
+        actions = json.dumps(data.get("actions", [])) if data.get("actions") else None
+
+        async with self.pool.acquire() as conn:
+            await conn.execute(
+                query,
+                time, campaign_id, account_id, impressions, clicks, spend, reach,
+                ctr, cpc, cpm, actions, date_preset
+            )
+
+    async def insert_adset_insights(
+        self,
+        time: datetime,
+        adset_id: str,
+        campaign_id: str,
+        account_id: str,
+        data: Dict[str, Any],
+        date_preset: str = "today",
+    ):
+        """
+        Insert ad set level insights data.
+
+        Args:
+            time: Timestamp for the data point
+            adset_id: Ad set ID
+            campaign_id: Campaign ID
+            account_id: Ad account ID
+            data: Insights data dictionary from Meta API
+            date_preset: Date preset used
+        """
+        query = """
+            INSERT INTO adset_insights (
+                time, adset_id, campaign_id, account_id, impressions, clicks, spend, reach,
+                ctr, cpc, cpm, actions, date_preset, fetched_at
+            ) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, $11, $12, $13, NOW())
+            ON CONFLICT (time, adset_id)
+            DO UPDATE SET
+                impressions = EXCLUDED.impressions,
+                clicks = EXCLUDED.clicks,
+                spend = EXCLUDED.spend,
+                reach = EXCLUDED.reach,
+                ctr = EXCLUDED.ctr,
+                cpc = EXCLUDED.cpc,
+                cpm = EXCLUDED.cpm,
+                actions = EXCLUDED.actions,
+                fetched_at = NOW()
+        """
+
+        impressions = int(data.get("impressions", 0)) if data.get("impressions") else None
+        clicks = int(data.get("clicks", 0)) if data.get("clicks") else None
+        spend = float(data.get("spend", 0)) if data.get("spend") else None
+        reach = int(data.get("reach", 0)) if data.get("reach") else None
+        ctr = float(data.get("ctr", 0)) if data.get("ctr") else None
+        cpc = float(data.get("cpc", 0)) if data.get("cpc") else None
+        cpm = float(data.get("cpm", 0)) if data.get("cpm") else None
+
+        import json
+        actions = json.dumps(data.get("actions", [])) if data.get("actions") else None
+
+        async with self.pool.acquire() as conn:
+            await conn.execute(
+                query,
+                time, adset_id, campaign_id, account_id, impressions, clicks, spend, reach,
+                ctr, cpc, cpm, actions, date_preset
+            )
+
+    # ========================================================================
+    # QUERY HELPERS
+    # ========================================================================
+
+    async def get_latest_account_metrics(self, account_id: str) -> Optional[Dict[str, Any]]:
+        """
+        Get the most recent metrics for an account.
+
+        Args:
+            account_id: Ad account ID
+
+        Returns:
+            Dictionary of latest metrics or None
+        """
+        query = """
+            SELECT * FROM latest_account_metrics
+            WHERE account_id = $1
+        """
+        async with self.pool.acquire() as conn:
+            row = await conn.fetchrow(query, account_id)
+            return dict(row) if row else None
+
+    async def get_account_metrics_range(
+        self,
+        account_id: str,
+        start_time: datetime,
+        end_time: datetime,
+    ) -> List[Dict[str, Any]]:
+        """
+        Get account metrics for a time range.
+
+        Args:
+            account_id: Ad account ID
+            start_time: Start of time range
+            end_time: End of time range
+
+        Returns:
+            List of metric dictionaries
+        """
+        query = """
+            SELECT * FROM account_insights
+            WHERE account_id = $1 AND time BETWEEN $2 AND $3
+            ORDER BY time DESC
+        """
+        async with self.pool.acquire() as conn:
+            rows = await conn.fetch(query, account_id, start_time, end_time)
+            return [dict(row) for row in rows]
--- a/src/meta_api_grabber/db_schema.sql
+++ b/src/meta_api_grabber/db_schema.sql
@@ -0,0 +1,281 @@
+-- TimescaleDB Schema for Meta Ad Insights
+-- This schema is optimized for time-series data collection and dashboard queries
+
+-- Enable TimescaleDB extension (run as superuser)
+-- CREATE EXTENSION IF NOT EXISTS timescaledb;
+
+-- ============================================================================
+-- METADATA TABLES (Regular PostgreSQL tables for caching)
+-- ============================================================================
+
+-- Ad Accounts (rarely changes, cached)
+CREATE TABLE IF NOT EXISTS ad_accounts (
+    account_id VARCHAR(50) PRIMARY KEY,
+    account_name VARCHAR(255),
+    currency VARCHAR(10),
+    timezone_name VARCHAR(100),
+    created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
+    updated_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
+);
+
+-- Campaigns (metadata cache)
+CREATE TABLE IF NOT EXISTS campaigns (
+    campaign_id VARCHAR(50) PRIMARY KEY,
+    account_id VARCHAR(50) REFERENCES ad_accounts(account_id),
+    campaign_name VARCHAR(255) NOT NULL,
+    status VARCHAR(50),
+    objective VARCHAR(100),
+    created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
+    updated_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
+);
+
+CREATE INDEX idx_campaigns_account ON campaigns(account_id);
+
+-- Ad Sets (metadata cache)
+CREATE TABLE IF NOT EXISTS adsets (
+    adset_id VARCHAR(50) PRIMARY KEY,
+    campaign_id VARCHAR(50) REFERENCES campaigns(campaign_id),
+    adset_name VARCHAR(255) NOT NULL,
+    status VARCHAR(50),
+    created_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
+    updated_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
+);
+
+CREATE INDEX idx_adsets_campaign ON adsets(campaign_id);
+
+-- ============================================================================
+-- TIME-SERIES TABLES (Hypertables)
+-- ============================================================================
+
+-- Account-level insights (time-series data)
+CREATE TABLE IF NOT EXISTS account_insights (
+    time TIMESTAMPTZ NOT NULL,
+    account_id VARCHAR(50) NOT NULL REFERENCES ad_accounts(account_id),
+
+    -- Core metrics
+    impressions BIGINT,
+    clicks BIGINT,
+    spend NUMERIC(12, 2),
+    reach BIGINT,
+    frequency NUMERIC(10, 4),
+
+    -- Calculated metrics
+    ctr NUMERIC(10, 6),  -- Click-through rate
+    cpc NUMERIC(10, 4),  -- Cost per click
+    cpm NUMERIC(10, 4),  -- Cost per mille (thousand impressions)
+    cpp NUMERIC(10, 4),  -- Cost per reach
+
+    -- Actions (stored as JSONB for flexibility)
+    actions JSONB,
+    cost_per_action_type JSONB,
+
+    -- Metadata
+    date_preset VARCHAR(50),
+    fetched_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
+
+    -- Composite primary key
+    PRIMARY KEY (time, account_id)
+);
+
+-- Convert to hypertable (partition by time)
+SELECT create_hypertable('account_insights', 'time',
+    if_not_exists => TRUE,
+    chunk_time_interval => INTERVAL '1 day'
+);
+
+-- Create index for efficient queries
+CREATE INDEX IF NOT EXISTS idx_account_insights_account_time
+    ON account_insights (account_id, time DESC);
+
+
+-- Campaign-level insights (time-series data)
+CREATE TABLE IF NOT EXISTS campaign_insights (
+    time TIMESTAMPTZ NOT NULL,
+    campaign_id VARCHAR(50) NOT NULL REFERENCES campaigns(campaign_id),
+    account_id VARCHAR(50) NOT NULL REFERENCES ad_accounts(account_id),
+
+    -- Core metrics
+    impressions BIGINT,
+    clicks BIGINT,
+    spend NUMERIC(12, 2),
+    reach BIGINT,
+
+    -- Calculated metrics
+    ctr NUMERIC(10, 6),
+    cpc NUMERIC(10, 4),
+    cpm NUMERIC(10, 4),
+
+    -- Actions
+    actions JSONB,
+
+    -- Metadata
+    date_preset VARCHAR(50),
+    fetched_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
+
+    PRIMARY KEY (time, campaign_id)
+);
+
+-- Convert to hypertable
+SELECT create_hypertable('campaign_insights', 'time',
+    if_not_exists => TRUE,
+    chunk_time_interval => INTERVAL '1 day'
+);
+
+CREATE INDEX IF NOT EXISTS idx_campaign_insights_campaign_time
+    ON campaign_insights (campaign_id, time DESC);
+CREATE INDEX IF NOT EXISTS idx_campaign_insights_account_time
+    ON campaign_insights (account_id, time DESC);
+
+
+-- Ad Set level insights (time-series data)
+CREATE TABLE IF NOT EXISTS adset_insights (
+    time TIMESTAMPTZ NOT NULL,
+    adset_id VARCHAR(50) NOT NULL REFERENCES adsets(adset_id),
+    campaign_id VARCHAR(50) NOT NULL REFERENCES campaigns(campaign_id),
+    account_id VARCHAR(50) NOT NULL REFERENCES ad_accounts(account_id),
+
+    -- Core metrics
+    impressions BIGINT,
+    clicks BIGINT,
+    spend NUMERIC(12, 2),
+    reach BIGINT,
+
+    -- Calculated metrics
+    ctr NUMERIC(10, 6),
+    cpc NUMERIC(10, 4),
+    cpm NUMERIC(10, 4),
+
+    -- Actions
+    actions JSONB,
+
+    -- Metadata
+    date_preset VARCHAR(50),
+    fetched_at TIMESTAMPTZ NOT NULL DEFAULT NOW(),
+
+    PRIMARY KEY (time, adset_id)
+);
+
+-- Convert to hypertable
+SELECT create_hypertable('adset_insights', 'time',
+    if_not_exists => TRUE,
+    chunk_time_interval => INTERVAL '1 day'
+);
+
+CREATE INDEX IF NOT EXISTS idx_adset_insights_adset_time
+    ON adset_insights (adset_id, time DESC);
+CREATE INDEX IF NOT EXISTS idx_adset_insights_campaign_time
+    ON adset_insights (campaign_id, time DESC);
+CREATE INDEX IF NOT EXISTS idx_adset_insights_account_time
+    ON adset_insights (account_id, time DESC);
+
+
+-- ============================================================================
+-- CONTINUOUS AGGREGATES (Pre-computed rollups for dashboards)
+-- ============================================================================
+
+-- Hourly aggregates for account insights
+CREATE MATERIALIZED VIEW IF NOT EXISTS account_insights_hourly
+WITH (timescaledb.continuous) AS
+SELECT
+    time_bucket('1 hour', time) AS bucket,
+    account_id,
+    AVG(impressions) as avg_impressions,
+    AVG(clicks) as avg_clicks,
+    AVG(spend) as avg_spend,
+    AVG(ctr) as avg_ctr,
+    AVG(cpc) as avg_cpc,
+    AVG(cpm) as avg_cpm,
+    MAX(reach) as max_reach,
+    COUNT(*) as sample_count
+FROM account_insights
+GROUP BY bucket, account_id;
+
+-- Refresh policy: refresh last 2 days every hour
+SELECT add_continuous_aggregate_policy('account_insights_hourly',
+    start_offset => INTERVAL '2 days',
+    end_offset => INTERVAL '1 hour',
+    schedule_interval => INTERVAL '1 hour',
+    if_not_exists => TRUE
+);
+
+
+-- Daily aggregates for account insights
+CREATE MATERIALIZED VIEW IF NOT EXISTS account_insights_daily
+WITH (timescaledb.continuous) AS
+SELECT
+    time_bucket('1 day', time) AS bucket,
+    account_id,
+    AVG(impressions) as avg_impressions,
+    SUM(impressions) as total_impressions,
+    AVG(clicks) as avg_clicks,
+    SUM(clicks) as total_clicks,
+    AVG(spend) as avg_spend,
+    SUM(spend) as total_spend,
+    AVG(ctr) as avg_ctr,
+    AVG(cpc) as avg_cpc,
+    AVG(cpm) as avg_cpm,
+    MAX(reach) as max_reach,
+    COUNT(*) as sample_count
+FROM account_insights
+GROUP BY bucket, account_id;
+
+-- Refresh policy: refresh last 7 days every 4 hours
+SELECT add_continuous_aggregate_policy('account_insights_daily',
+    start_offset => INTERVAL '7 days',
+    end_offset => INTERVAL '1 hour',
+    schedule_interval => INTERVAL '4 hours',
+    if_not_exists => TRUE
+);
+
+
+-- ============================================================================
+-- DATA RETENTION POLICIES (Optional - uncomment to enable)
+-- ============================================================================
+
+-- Keep raw data for 90 days, then drop
+-- SELECT add_retention_policy('account_insights', INTERVAL '90 days', if_not_exists => TRUE);
+-- SELECT add_retention_policy('campaign_insights', INTERVAL '90 days', if_not_exists => TRUE);
+-- SELECT add_retention_policy('adset_insights', INTERVAL '90 days', if_not_exists => TRUE);
+
+-- Compress data older than 7 days for better storage efficiency
+SELECT add_compression_policy('account_insights', INTERVAL '7 days', if_not_exists => TRUE);
+SELECT add_compression_policy('campaign_insights', INTERVAL '7 days', if_not_exists => TRUE);
+SELECT add_compression_policy('adset_insights', INTERVAL '7 days', if_not_exists => TRUE);
+
+
+-- ============================================================================
+-- HELPER VIEWS FOR DASHBOARDS
+-- ============================================================================
+
+-- Latest metrics per account
+CREATE OR REPLACE VIEW latest_account_metrics AS
+SELECT DISTINCT ON (account_id)
+    account_id,
+    time,
+    impressions,
+    clicks,
+    spend,
+    ctr,
+    cpc,
+    cpm,
+    reach,
+    frequency
+FROM account_insights
+ORDER BY account_id, time DESC;
+
+-- Campaign performance summary (last 24 hours)
+CREATE OR REPLACE VIEW campaign_performance_24h AS
+SELECT
+    c.campaign_id,
+    c.campaign_name,
+    c.account_id,
+    SUM(ci.impressions) as total_impressions,
+    SUM(ci.clicks) as total_clicks,
+    SUM(ci.spend) as total_spend,
+    AVG(ci.ctr) as avg_ctr,
+    AVG(ci.cpc) as avg_cpc
+FROM campaigns c
+LEFT JOIN campaign_insights ci ON c.campaign_id = ci.campaign_id
+WHERE ci.time >= NOW() - INTERVAL '24 hours'
+GROUP BY c.campaign_id, c.campaign_name, c.account_id
+ORDER BY total_spend DESC;
--- a/src/meta_api_grabber/insights_grabber.py
+++ b/src/meta_api_grabber/insights_grabber.py
@@ -292,7 +292,7 @@ async def main():
        grabber = MetaInsightsGrabber()

        # Grab insights for the last 7 days
-        insights = await grabber.grab_all_insights(date_preset="last_7d")
+        insights = await grabber.grab_all_insights(date_preset=AdsInsights.DatePreset.last_7d)

        # Save to JSON
        filepath = await grabber.save_insights_to_json(insights)
--- a/src/meta_api_grabber/scheduled_grabber.py
+++ b/src/meta_api_grabber/scheduled_grabber.py
@@ -0,0 +1,424 @@
+"""
+Scheduled data grabber that collects Meta ad insights and stores in TimescaleDB.
+Runs periodically to build time-series data for dashboards.
+"""
+
+import asyncio
+import os
+from datetime import datetime, timezone
+from typing import Optional
+
+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 .database import TimescaleDBClient
+
+
+class ScheduledInsightsGrabber:
+    """
+    Scheduled grabber for Meta ad insights with TimescaleDB storage.
+
+    Optimized for periodic data collection:
+    - Uses 'today' date preset (recommended by Meta)
+    - Caches metadata (accounts, campaigns, adsets)
+    - Upserts time-series data
+    - Conservative rate limiting
+    """
+
+    def __init__(self, access_token: Optional[str] = None):
+        """
+        Initialize the scheduled grabber.
+
+        Args:
+            access_token: Optional access token. If not provided, loads 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 required. Set META_ACCESS_TOKEN in .env or "
+                "run 'uv run python src/meta_api_grabber/auth.py'"
+            )
+
+        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)"
+            )
+
+        # 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)
+
+        # Database client
+        self.db: Optional[TimescaleDBClient] = None
+
+        # Conservative rate limiting
+        self.request_delay = 2.0  # 2 seconds between API requests
+
+    async def _rate_limited_request(self, func, *args, **kwargs):
+        """Execute a request with rate limiting."""
+        await asyncio.sleep(self.request_delay)
+        loop = asyncio.get_event_loop()
+        return await loop.run_in_executor(None, lambda: func(*args, **kwargs))
+
+    async def cache_account_metadata(self):
+        """
+        Cache ad account metadata to database.
+
+        This reduces API calls by storing rarely-changing account info.
+        """
+        print("Caching account metadata...")
+
+        # Get account details
+        account_fields = ['name', 'currency', 'timezone_name']
+        account_data = await self._rate_limited_request(
+            self.ad_account.api_get,
+            fields=account_fields
+        )
+
+        # Store in database
+        await self.db.upsert_ad_account(
+            account_id=self.ad_account_id,
+            account_name=account_data.get('name'),
+            currency=account_data.get('currency'),
+            timezone_name=account_data.get('timezone_name'),
+        )
+
+        print(f"  Account cached: {account_data.get('name')}")
+
+    async def cache_campaigns_metadata(self, limit: int = 100):
+        """
+        Cache campaign metadata to database.
+
+        Args:
+            limit: Maximum number of campaigns to cache
+        """
+        print("Caching campaign metadata...")
+
+        # Get campaigns
+        from facebook_business.adobjects.campaign import Campaign
+
+        campaigns = await self._rate_limited_request(
+            self.ad_account.get_campaigns,
+            fields=[
+                Campaign.Field.name,
+                Campaign.Field.status,
+                Campaign.Field.objective,
+            ],
+            params={'limit': limit}
+        )
+
+        count = 0
+        for campaign in campaigns:
+            await self.db.upsert_campaign(
+                campaign_id=campaign['id'],
+                account_id=self.ad_account_id,
+                campaign_name=campaign.get('name', 'Unknown'),
+                status=campaign.get('status'),
+                objective=campaign.get('objective'),
+            )
+            count += 1
+
+        print(f"  {count} campaigns cached")
+
+    async def cache_adsets_metadata(self, limit: int = 100):
+        """
+        Cache ad set metadata to database.
+
+        Args:
+            limit: Maximum number of ad sets to cache
+        """
+        print("Caching ad set metadata...")
+
+        # Get ad sets
+        from facebook_business.adobjects.adset import AdSet
+
+        adsets = await self._rate_limited_request(
+            self.ad_account.get_ad_sets,
+            fields=[
+                AdSet.Field.name,
+                AdSet.Field.campaign_id,
+                AdSet.Field.status,
+            ],
+            params={'limit': limit}
+        )
+
+        count = 0
+        for adset in adsets:
+            await self.db.upsert_adset(
+                adset_id=adset['id'],
+                campaign_id=adset.get('campaign_id'),
+                adset_name=adset.get('name', 'Unknown'),
+                status=adset.get('status'),
+            )
+            count += 1
+
+        print(f"  {count} ad sets cached")
+
+    async def grab_account_insights(self, date_preset: str = "today"):
+        """
+        Grab and store account-level insights.
+
+        Args:
+            date_preset: Meta date preset (default: 'today')
+        """
+        print(f"Grabbing account insights ({date_preset})...")
+
+        fields = [
+            AdsInsights.Field.impressions,
+            AdsInsights.Field.clicks,
+            AdsInsights.Field.spend,
+            AdsInsights.Field.cpc,
+            AdsInsights.Field.cpm,
+            AdsInsights.Field.ctr,
+            AdsInsights.Field.cpp,
+            AdsInsights.Field.reach,
+            AdsInsights.Field.frequency,
+            AdsInsights.Field.actions,
+            AdsInsights.Field.cost_per_action_type,
+        ]
+
+        params = {
+            "date_preset": date_preset,
+            "level": "account",
+        }
+
+        insights = await self._rate_limited_request(
+            self.ad_account.get_insights,
+            fields=fields,
+            params=params,
+        )
+
+        # Store insights
+        timestamp = datetime.now(timezone.utc)
+        for insight in insights:
+            await self.db.insert_account_insights(
+                time=timestamp,
+                account_id=self.ad_account_id,
+                data=dict(insight),
+                date_preset=date_preset,
+            )
+
+        print(f"  Account insights stored ({len(list(insights))} records)")
+
+    async def grab_campaign_insights(self, date_preset: str = "today", limit: int = 50):
+        """
+        Grab and store campaign-level insights.
+
+        Args:
+            date_preset: Meta date preset
+            limit: Maximum number of campaigns
+        """
+        print(f"Grabbing campaign insights ({date_preset}, limit={limit})...")
+
+        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,
+        ]
+
+        params = {
+            "date_preset": date_preset,
+            "level": "campaign",
+            "limit": limit,
+        }
+
+        insights = await self._rate_limited_request(
+            self.ad_account.get_insights,
+            fields=fields,
+            params=params,
+        )
+
+        # Store insights
+        timestamp = datetime.now(timezone.utc)
+        count = 0
+        for insight in insights:
+            campaign_id = insight.get('campaign_id')
+            if campaign_id:
+                await self.db.insert_campaign_insights(
+                    time=timestamp,
+                    campaign_id=campaign_id,
+                    account_id=self.ad_account_id,
+                    data=dict(insight),
+                    date_preset=date_preset,
+                )
+                count += 1
+
+        print(f"  Campaign insights stored ({count} records)")
+
+    async def grab_adset_insights(self, date_preset: str = "today", limit: int = 50):
+        """
+        Grab and store ad set level insights.
+
+        Args:
+            date_preset: Meta date preset
+            limit: Maximum number of ad sets
+        """
+        print(f"Grabbing ad set insights ({date_preset}, limit={limit})...")
+
+        fields = [
+            AdsInsights.Field.adset_id,
+            AdsInsights.Field.adset_name,
+            AdsInsights.Field.campaign_id,
+            AdsInsights.Field.impressions,
+            AdsInsights.Field.clicks,
+            AdsInsights.Field.spend,
+            AdsInsights.Field.ctr,
+            AdsInsights.Field.cpc,
+            AdsInsights.Field.cpm,
+            AdsInsights.Field.reach,
+            AdsInsights.Field.actions,
+        ]
+
+        params = {
+            "date_preset": date_preset,
+            "level": "adset",
+            "limit": limit,
+        }
+
+        insights = await self._rate_limited_request(
+            self.ad_account.get_insights,
+            fields=fields,
+            params=params,
+        )
+
+        # Store insights
+        timestamp = datetime.now(timezone.utc)
+        count = 0
+        for insight in insights:
+            adset_id = insight.get('adset_id')
+            campaign_id = insight.get('campaign_id')
+            if adset_id and campaign_id:
+                await self.db.insert_adset_insights(
+                    time=timestamp,
+                    adset_id=adset_id,
+                    campaign_id=campaign_id,
+                    account_id=self.ad_account_id,
+                    data=dict(insight),
+                    date_preset=date_preset,
+                )
+                count += 1
+
+        print(f"  Ad set insights stored ({count} records)")
+
+    async def run_collection_cycle(self, cache_metadata: bool = True):
+        """
+        Run a single collection cycle.
+
+        Args:
+            cache_metadata: Whether to refresh metadata cache
+        """
+        print("\n" + "="*60)
+        print(f"COLLECTION CYCLE - {datetime.now().isoformat()}")
+        print("="*60)
+
+        # Refresh metadata cache if requested (do this less frequently)
+        if cache_metadata:
+            await self.cache_account_metadata()
+            await self.cache_campaigns_metadata(limit=100)
+            await self.cache_adsets_metadata(limit=100)
+
+        # Grab insights (always use 'today' for scheduled collection)
+        await self.grab_account_insights(date_preset="today")
+        await self.grab_campaign_insights(date_preset="today", limit=50)
+        await self.grab_adset_insights(date_preset="today", limit=50)
+
+        print("\n" + "="*60)
+        print("COLLECTION CYCLE COMPLETE")
+        print("="*60 + "\n")
+
+    async def run_scheduled(
+        self,
+        interval_hours: float = 2.0,
+        refresh_metadata_every_n_cycles: int = 12,
+    ):
+        """
+        Run scheduled data collection.
+
+        Args:
+            interval_hours: Hours between collection cycles (default: 2)
+            refresh_metadata_every_n_cycles: Refresh metadata every N cycles (default: 12 = once per day if interval=2h)
+        """
+        print("\n" + "="*60)
+        print("SCHEDULED INSIGHTS GRABBER STARTED")
+        print("="*60)
+        print(f"Account: {self.ad_account_id}")
+        print(f"Collection interval: {interval_hours} hours")
+        print(f"Metadata refresh: every {refresh_metadata_every_n_cycles} cycles")
+        print("="*60 + "\n")
+
+        # Connect to database
+        self.db = TimescaleDBClient()
+        await self.db.connect()
+
+        cycle_count = 0
+
+        try:
+            while True:
+                cycle_count += 1
+
+                # Refresh metadata periodically (e.g., once per day)
+                cache_metadata = (cycle_count % refresh_metadata_every_n_cycles == 1)
+
+                # Run collection
+                await self.run_collection_cycle(cache_metadata=cache_metadata)
+
+                # Wait for next cycle
+                wait_seconds = interval_hours * 3600
+                print(f"Waiting {interval_hours} hours until next cycle...")
+                print(f"Next collection: {datetime.now().astimezone().replace(microsecond=0) + timedelta(seconds=wait_seconds)}\n")
+
+                await asyncio.sleep(wait_seconds)
+
+        except KeyboardInterrupt:
+            print("\nShutting down gracefully...")
+        finally:
+            await self.db.close()
+
+
+async def main():
+    """Main entry point for scheduled grabber."""
+    try:
+        grabber = ScheduledInsightsGrabber()
+
+        # Run scheduled collection (every 2 hours)
+        await grabber.run_scheduled(
+            interval_hours=2.0,
+            refresh_metadata_every_n_cycles=12,  # Refresh metadata twice per day
+        )
+
+    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__":
+    from datetime import timedelta
+    exit_code = asyncio.run(main())
+    exit(exit_code)
--- a/uv.lock
+++ b/uv.lock
@@ -91,6 +91,36 @@ wheels = [
    { url = "https://files.pythonhosted.org/packages/fb/76/641ae371508676492379f16e2fa48f4e2c11741bd63c48be4b12a6b09cba/aiosignal-1.4.0-py3-none-any.whl", hash = "sha256:053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e", size = 7490, upload-time = "2025-07-03T22:54:42.156Z" },
 ]

+[[package]]
+name = "alembic"
+version = "1.17.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "mako" },
+    { name = "sqlalchemy" },
+    { name = "typing-extensions" },
+]
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