System requirements

Functional:

Advertiser Management

User registration and authentication for advertisers.
Campaign creation and management.
Ad creative upload and management.
Budget management and allocation.

Publisher Management

User registration and authentication for publishers.
Inventory management (ad spaces).
Revenue tracking and reporting.

Ad Serving

Real-time ad serving based on targeting criteria.
Ad rotation and frequency capping.
Support for various ad formats (banner, video, native).

Ad Bidding and Targeting

Real-time bidding (RTB) system.
Audience targeting based on demographics, behavior, and context.
Geo-targeting and device targeting.

Performance Tracking

Click tracking and conversion tracking.
Real-time analytics and reporting.
ROI calculation for advertisers.

Fraud Detection

Detection and prevention of click fraud and impression fraud.
Anomaly detection in traffic patterns.

Non-Functional:

Scalability

The system should handle up to 1 billion ad impressions per day.
Support for 50 million clicks per day.
Scalability to manage up to 200,000 active campaigns per month.

Reliability

99.9% uptime.
Graceful degradation in case of partial failures.

Performance

Ad serving latency should be under 100ms.
Real-time analytics with latency under 500ms.

Security

Data encryption at rest and in transit.
Secure authentication and authorization mechanisms.
Compliance with GDPR and other relevant regulations.

Maintainability

Modular architecture for ease of updates and maintenance.
Comprehensive logging and monitoring.

Usability

Intuitive user interfaces for both advertisers and publishers.
Comprehensive documentation and support.

Capacity estimation

Assumptions

Advertisers: 100,000 active advertisers.
Publishers: 50,000 active publishers.
Daily Ad Impressions: 1 billion ad impressions per day.
Daily Clicks: 50 million clicks per day.
Monthly Campaigns: 200,000 active campaigns per month.

Estimations

Storage Requirements:

Ad Impressions:

Each ad impression record: 100 bytes
Daily storage: 1 billion * 100 bytes = 100 GB
Monthly storage: 30 days * 100 GB = 3 TB

Clicks:

Each click record: 200 bytes
Daily storage: 50 million * 200 bytes = 10 GB
Monthly storage: 30 days * 10 GB = 300 GB

Campaign Data:

Each campaign record: 1 KB
Monthly storage: 200,000 * 1 KB = 200 MB

Total Monthly Storage:

Ad impressions: 3 TB
Clicks: 300 GB
Campaign data: 200 MB
Total: 3.5 TB

Throughput Requirements:

Ad Serving:

Peak ad requests per second (assuming peak hours are 10% of the day):
1 billion ad impressions/day ÷ (24 hours * 0.1) = ~115,741 requests/second

Click Handling:

Peak click requests per second (assuming peak hours are 10% of the day):
50 million clicks/day ÷ (24 hours * 0.1) = ~5,787 requests/second

Network Bandwidth:

Ad Serving:

Assuming each ad response is 5 KB:

Peak bandwidth: 115,741 requests/second * 5 KB = ~578 MB/second

Click Handling:

Assuming each click response is 1 KB:
Peak bandwidth: 5,787 requests/second * 1 KB = ~5.7 MB/second

Scalability Considerations

Database: Use a distributed database system (e.g., Cassandra, Google Cloud Spanner) to handle high write and read loads.
Caching: Implement a caching layer (e.g., Redis, Memcached) to reduce load on the database and improve response times.
Load Balancing: Deploy load balancers to distribute traffic evenly across servers.
Microservices Architecture: Break down the platform into microservices to allow independent scaling of different components (ad serving, click tracking, etc.).
Content Delivery Network (CDN): Use a CDN to serve static assets and reduce latency.

API design

Advertiser Management API

Register Advertiser

Endpoint: /api/advertisers
Method: POST

Request:
{
  "name": "Advertiser Name",
  "email": "advertiser@example.com",
  "password": "securepassword"
}

Response:
{
  "id": "advertiser_id",
  "name": "Advertiser Name",
  "email": "advertiser@example.com",
  "created_at": "2024-07-29T12:34:56Z"
}

Create Campaign

Endpoint: /api/advertisers/{advertiser_id}/campaigns
Method: POST

Request:
{
  "name": "Campaign Name",
  "budget": 1000,
  "start_date": "2024-08-01",
  "end_date": "2024-08-31",
  "targeting_criteria": {
    "age_range": [18, 35],
    "locations": ["USA", "Canada"],
    "interests": ["technology", "gaming"]
  }
}

Response:
{
  "campaign_id": "campaign_id",
  "name": "Campaign Name",
  "budget": 1000,
  "start_date": "2024-08-01",
  "end_date": "2024-08-31",
  "targeting_criteria": {
    "age_range": [18, 35],
    "locations": ["USA", "Canada"],
    "interests": ["technology", "gaming"]
  },
  "status": "created",
  "created_at": "2024-07-29T12:34:56Z"
}

Publisher Management API

Register Publisher

Endpoint: /api/publishers
Method: POST

Request:
{
  "name": "Publisher Name",
  "email": "publisher@example.com",
  "password": "securepassword"
}

Response:
{
  "id": "publisher_id",
  "name": "Publisher Name",
  "email": "publisher@example.com",
  "created_at": "2024-07-29T12:34:56Z"
}

Add Inventory

Endpoint: /api/publishers/{publisher_id}/inventory
Method: POST

Request:
{
  "name": "Ad Space Name",
  "type": "banner",
  "size": "300x250",
  "floor_price": 0.5
}

Response:
{
  "inventory_id": "inventory_id",
  "name": "Ad Space Name",
  "type": "banner",
  "size": "300x250",
  "floor_price": 0.5,
  "status": "active",
  "created_at": "2024-07-29T12:34:56Z"
}

Ad Serving API

Serve Ad

Endpoint: /api/ad_serving
Method: GET

Request:{
  "publisher_id": "publisher_id",
  "inventory_id": "inventory_id",
  "user_data": {
    "age": 25,
    "location": "USA",
    "interests": ["technology"]
  }
}

Response:
{
  "ad_id": "ad_id",
  "campaign_id": "campaign_id",
  "creative_url": "http://example.com/ad.jpg",
  "click_url": "http://example.com/click"
}

Click Tracking API

Track Click

Endpoint: /api/click_tracking
Method: POST

Request:
{
  "ad_id": "ad_id",
  "user_id": "user_id",
  "timestamp": "2024-07-29T12:34:56Z"
}

Response:
{
  "status": "success",
  "tracked_at": "2024-07-29T12:34:56Z"
}

Analytics API

Get Campaign Performance

Endpoint: /api/analytics
Method: GET

Request:
{
  "advertiser_id": "advertiser_id",
  "campaign_id": "campaign_id"
}

Response:
{
  "campaign_id": "campaign_id",
  "impressions": 1000000,
  "clicks": 50000,
  "conversions": 500,
  "spend": 900
}

Database design

erDiagram
    ADVERTISER {
        UUID advertiser_id PK
        String name
        String email
        String password_hash
        Timestamp created_at
    }


    PUBLISHER {
        UUID publisher_id PK
        String name
        String email
        String password_hash
        Timestamp created_at
    }


    CAMPAIGN {
        UUID campaign_id PK
        UUID advertiser_id FK
        String name
        Decimal budget
        Date start_date
        Date end_date
        JSON targeting_criteria
        Timestamp created_at
    }


    INVENTORY {
        UUID inventory_id PK
        UUID publisher_id FK
        String name
        String type
        String size
        Decimal floor_price
        String status
        Timestamp created_at
    }


    AD {
        UUID ad_id PK
        UUID campaign_id FK
        String creative_url
        String click_url
        Timestamp created_at
    }


    AD_IMPRESSION {
        UUID impression_id PK
        UUID ad_id FK
        Timestamp timestamp
        JSON user_data
    }


    CLICK {
        UUID click_id PK
        UUID ad_id FK
        UUID user_id
        Timestamp timestamp
    }


    CONVERSION {
        UUID conversion_id PK
        UUID click_id FK
        Timestamp timestamp
        Decimal conversion_value
    }


    ADVERTISER ||--o{ CAMPAIGN : has
    PUBLISHER ||--o{ INVENTORY : has
    CAMPAIGN ||--o{ AD : includes
    AD ||--o{ AD_IMPRESSION : generates
    AD ||--o{ CLICK : generates
    CLICK ||--o{ CONVERSION : results_in

High-level design

User Interface (UI)

Web-based dashboards for advertisers and publishers.
Mobile-friendly interfaces for campaign and inventory management.

API Gateway

Single entry point for all client requests.
Handles routing to appropriate services.

Authentication Service

Manages user registration, login, and authentication tokens.
Ensures secure access to the platform.

Advertiser Service

Manages advertiser profiles, campaigns, and ad creatives.
Interfaces with the Ad Serving Service for campaign data.

Publisher Service

Manages publisher profiles and inventory.
Interfaces with the Ad Serving Service to provide available ad spaces.

Ad Serving Service

Handles real-time ad serving requests.
Implements targeting criteria and ad rotation.
Interfaces with the Ad Bidding Service.

Ad Bidding Service

Manages real-time bidding (RTB) for ad slots.
Interfaces with advertiser campaigns to determine bids.

Analytics Service

Collects and processes data on ad impressions, clicks, and conversions.
Provides real-time analytics and reporting to advertisers and publishers.

Fraud Detection Service

Monitors for suspicious activity and prevents click and impression fraud.
Uses machine learning to detect anomalies.

Database

Stores all data related to users, campaigns, ads, impressions, clicks, and conversions.
Uses distributed databases for scalability and reliability.

Cache

Implements caching for frequently accessed data to reduce load on the database and improve performance.

Load Balancer

Distributes incoming traffic across multiple instances of services to ensure reliability and scalability.

graph TD
    UI --> |Requests| APIGateway
    APIGateway --> |Auth| AuthenticationService
    APIGateway --> |Advertiser Management| AdvertiserService
    APIGateway --> |Publisher Management| PublisherService
    APIGateway --> |Ad Serving| AdServingService
    APIGateway --> |Analytics| AnalyticsService
    APIGateway --> |Fraud Detection| FraudDetectionService
    AdvertiserService --> Database
    PublisherService --> Database
    AdServingService --> |Bidding| AdBiddingService
    AdServingService --> Database
    AdServingService --> Cache
    AdBiddingService --> Database
    AnalyticsService --> Database
    AnalyticsService --> Cache
    FraudDetectionService --> Database
    FraudDetectionService --> Cache
    Cache --> Database
    LoadBalancer --> APIGateway

Request flows

Ad Serving Flow

Ad Request from User: A user visits a publisher's website, triggering an ad request.
Request Handling by API Gateway: The ad request is received by the API Gateway.
Forward to Ad Serving Service: The API Gateway forwards the request to the Ad Serving Service.
Ad Selection and Targeting: The Ad Serving Service queries the Cache and Database for available ads that match the targeting criteria.
Bidding Process: If enabled, the Ad Bidding Service is called to determine the winning ad.
Serve Ad: The Ad Serving Service selects the best ad and responds with the ad creative URL.
Log Impression: The Ad Serving Service logs the ad impression to the Database.
Display Ad to User: The user's browser receives the ad creative and displays it.

Click Tracking Flow

User Clicks Ad: A user clicks on an ad.
Click Tracking Request: The click event is sent to the API Gateway.
Forward to Click Tracking Service: The API Gateway forwards the request to the Click Tracking Service.
Log Click: The Click Tracking Service logs the click event to the Database.
Redirect to Click URL: The user is redirected to the advertiser's landing page.

sequenceDiagram
    participant User
    participant PublisherSite
    participant APIGateway
    participant AdServingService
    participant AdBiddingService
    participant Database
    participant Cache


    User ->> PublisherSite: Visit Page
    PublisherSite ->> APIGateway: Ad Request
    APIGateway ->> AdServingService: Forward Ad Request
    AdServingService ->> Cache: Query for Ads
    Cache -->> AdServingService: Return Cached Ads
    AdServingService ->> Database: Query for Ads
    Database -->> AdServingService: Return Ads
    AdServingService ->> AdBiddingService: Start Bidding
    AdBiddingService -->> AdServingService: Return Winning Ad
    AdServingService ->> APIGateway: Respond with Ad
    APIGateway ->> PublisherSite: Respond with Ad
    PublisherSite ->> User: Display Ad
    AdServingService ->> Database: Log Impression


    User ->> PublisherSite: Click Ad
    PublisherSite ->> APIGateway: Click Event
    APIGateway ->> ClickTrackingService: Forward Click Event
    ClickTrackingService ->> Database: Log Click
    ClickTrackingService ->> APIGateway: Respond with Redirect URL
    APIGateway ->> PublisherSite: Redirect URL
    PublisherSite ->> User: Redirect to Advertiser

Detailed component design

Ad Serving Service

Role: The Ad Serving Service is responsible for selecting and delivering ads in response to requests from users visiting publisher sites. It ensures ads are targeted correctly and logs impressions for analytics.

Components:

Ad Selection: Queries available ads from the cache and database based on targeting criteria.
Ad Rotation: Implements frequency capping and ad rotation to ensure fair distribution of ad impressions.
Integration with Bidding: Interfaces with the Ad Bidding Service to select the highest-bid ad.

Scalability:

Horizontal Scaling: Multiple instances of the Ad Serving Service can run behind a load balancer.
Caching: Use Redis or Memcached to cache frequently accessed ad data.

Algorithm: Ad Selection

def select_ad(user_data, available_ads):
    # Filter ads based on targeting criteria
    filtered_ads = [ad for ad in available_ads if matches_targeting(ad, user_data)]
    # Apply frequency capping
    filtered_ads = apply_frequency_capping(filtered_ads, user_data)
    # If bidding is enabled, select the highest bid
    if is_bidding_enabled():
        return get_highest_bid(filtered_ads)
    # Otherwise, return a random ad from the filtered list
    return random.choice(filtered_ads)

Ad Bidding Service

Role: The Ad Bidding Service manages the real-time bidding process, allowing advertisers to bid for ad slots. It ensures that the highest-bid ad is selected for each impression.

Components:

Bid Processing: Receives bids from advertisers and stores them in a bidding queue.
Winner Selection: Selects the highest bid for each ad request.
Integration with Ad Serving: Provides the winning bid to the Ad Serving Service for ad delivery.

Scalability:

Message Queue: Use a message queue like RabbitMQ or Kafka to handle bid processing.
Distributed Processing: Implement distributed bid processing to handle high volumes of bids.

Algorithm: Bid Selection

def get_highest_bid(ads):
    highest_bid = None
    for ad in ads:
        if highest_bid is None or ad['bid'] > highest_bid['bid']:
            highest_bid = ad
    return highest_bid

Fraud Detection Service

Role: The Fraud Detection Service monitors and prevents click and impression fraud by analyzing traffic patterns and detecting anomalies.

Components:

Traffic Analysis: Continuously monitors traffic to detect unusual patterns.
Anomaly Detection: Uses machine learning models to identify potential fraud.
Action Mechanism: Takes action (e.g., blocking suspicious IPs) when fraud is detected.

Scalability:

Stream Processing: Use stream processing tools like Apache Flink or Spark Streaming for real-time traffic analysis.
Machine Learning Models: Deploy scalable machine learning models using frameworks like TensorFlow or PyTorch.

Algorithm: Anomaly Detection (Simplified)

def detect_anomalies(traffic_data):
    # Example rule-based detection
    anomalies = []
    for data_point in traffic_data:
        if is_anomalous(data_point):
            anomalies.append(data_point)
    return anomalies


def is_anomalous(data_point):
    # Check if data point deviates significantly from expected pattern
    return data_point['click_rate'] > threshold

Trade offs/Tech choices

SQL vs. NoSQL Databases

Choice: NoSQL (e.g., Cassandra)
Reason: NoSQL databases are chosen for their ability to handle high write and read throughput, horizontal scalability, and flexible schema design, which is suitable for storing large volumes of ad impressions, clicks, and user data.
Trade-off: Potential challenges with complex queries and transactions compared to SQL databases.

Synchronous vs. Asynchronous Processing

Choice: Asynchronous Processing
Reason: Asynchronous processing (using message queues like RabbitMQ or Kafka) is used for handling bid processing, click tracking, and fraud detection to ensure that the system remains responsive and can handle high throughput without bottlenecks.
Trade-off: Increased complexity in ensuring message delivery guarantees and handling eventual consistency.

Custom Machine Learning Models vs. Third-Party Fraud Detection

Choice: Custom Machine Learning Models
Reason: Building custom machine learning models allows for tailored fraud detection algorithms specific to the platform's traffic patterns and potential fraud vectors.
Trade-off: Requires significant expertise and resources to develop, train, and maintain the models.

Failure scenarios/bottlenecks

API Gateway Failure

Scenario: The API Gateway becomes a single point of failure.
Mitigation:
High Availability: Deploy the API Gateway in a highly available configuration across multiple regions.
Auto-scaling: Enable auto-scaling to handle sudden spikes in traffic.
Failover Mechanisms: Implement failover mechanisms to redirect traffic to a secondary gateway if the primary gateway fails.

Database Bottleneck

Scenario: High read/write load on the database leads to performance degradation.
Mitigation:
Distributed Databases: Use distributed databases (e.g., Cassandra, Google Cloud Spanner) to handle high throughput.
Read Replicas: Implement read replicas to distribute read traffic.
Caching: Use caching (e.g., Redis, Memcached) to reduce database load.

Ad Serving Latency

Scenario: High latency in ad serving affects user experience.
Mitigation:
Edge Servers: Deploy edge servers or use a CDN to serve ads closer to users.
Caching: Cache frequently served ads in memory.
Load Balancing: Use load balancers to distribute traffic evenly across ad serving instances.

Fraud Detection Accuracy

Scenario: Inaccurate fraud detection leads to either missed fraud or false positives.
Mitigation:
Machine Learning Models: Continuously train and update machine learning models to improve accuracy.
Hybrid Approach: Combine rule-based and machine learning approaches for better detection.
Monitoring and Alerts: Implement monitoring and alerting to quickly identify and address inaccuracies.

Network Bandwidth Limitations

Scenario: Limited network bandwidth affects the performance of ad serving and click tracking.
Mitigation:
CDN: Use a Content Delivery Network to serve static content and reduce bandwidth usage.
Compression: Compress ad creatives and other large files to reduce bandwidth.
Efficient Protocols: Use efficient data transfer protocols (e.g., HTTP/2, gRPC).

Scaling Issues

Scenario: Difficulty in scaling the system to handle increased load.
Mitigation:
Microservices Architecture: Use a microservices architecture to allow independent scaling of components.
Auto-scaling: Implement auto-scaling policies for all services.
Capacity Planning: Regularly conduct capacity planning and stress testing.

System Downtime

Scenario: Unexpected downtime leads to loss of service availability.
Mitigation:
Redundancy: Implement redundancy at all levels (e.g., multiple instances of services, databases).
Disaster Recovery: Develop and test disaster recovery plans.
Monitoring: Use monitoring tools to detect and respond to issues quickly.

Future improvements

Advanced Personalization and Targeting

Improvement: Implement advanced personalization algorithms to deliver more relevant ads to users based on real-time behavior and preferences.
Benefit: Increased user engagement and higher ROI for advertisers.
Mitigation of Failure Scenarios: Enhanced targeting can reduce ad serving latency by quickly selecting the most relevant ads, addressing Ad Serving Latency.

Global Content Delivery Network (CDN) Expansion

Improvement: Expand the use of CDNs globally to ensure fast and reliable ad delivery across different regions.
Benefit: Reduced latency and improved ad load times for users worldwide.
Mitigation of Failure Scenarios: A global CDN can alleviate Network Bandwidth Limitations by distributing content closer to end users.

Enhanced Security Measures

Improvement: Continuously update security protocols and implement advanced threat detection systems.
Benefit: Protects user data and maintains compliance with regulations.
Mitigation of Failure Scenarios: Enhanced security measures can reduce the risk of data breaches and ensure the system remains compliant with evolving regulations, addressing System Downtime and Security concerns.

Solutions

utopia4715 - 05/10/2024

1

nectar4678 - 07/29/2024

0

eclipse_quantum17 - 08/21/2026

1