Architecture

This page provides a technical overview of Ring DAS architecture, including system components, data flow patterns, and integration points. This is designed for developers, solution architects, and platform operators.

Architectural Overview

Ring DAS follows a modular, domain-driven architecture where each platform component has well-defined boundaries and responsibilities. The platform is deployed as a multi-tenant SaaS solution on Amazon Web Services (AWS).

Architectural Principles

1. Domain-Driven Design: Platform organized into bounded contexts (DELIVERY, OFFERS, INVENTORY, AUDIENCE, etc.)
2. API-First: All components expose standardized APIs for integration
3. Event-Driven: Real-time data collection and processing via event streams
4. Scalable & Resilient: Designed for high throughput with multi-region redundancy
5. Security & Compliance: Built-in GDPR, DSA, and TCF v2.2 support

High-Level Architecture (C1)

The highest-level view shows Ring DAS as a cohesive platform with external integration points:

graph TB
    subgraph "External Users"
        U1[Advertisers]
        U2[Publishers]
        U3[Developers]
        U4[End Users]
    end

    subgraph "Ring DAS Platform"
        RD[Ring DAS<br/>Core Services]
    end

    subgraph "External Systems"
        E1[Google Ad Manager]
        E2[Analytics Platforms]
        E3[CRM Systems]
        E4[DMPs / CDPs]
    end

    subgraph "Client-Side"
        C1[Website / App]
        C2[Ad Tags]
        C3[Tracking Pixels]
    end

    U1 --> RD
    U2 --> RD
    U3 --> RD
    U4 --> C1
    C1 --> C2
    C2 --> RD
    C1 --> C3
    C3 --> RD
    RD --> E1
    RD --> E2
    E3 --> RD
    E4 <--> RD

    style RD fill:#e1f5ff

Key External Interfaces:

• Business Users: Access via web UI (DELIVERY, SELF SERVICE portals)
• Developers: Access via APIs (ADP API, Delivery APIs, Pixel API)
• End Users: Interact via website/app with embedded ad tags and pixels
• External Systems: Bidirectional integration with ad servers, analytics, CRMs, DMPs

Platform Architecture (C2)

The component-level view shows Ring DAS platform modules and their relationships:

graph TB
    subgraph "Client Layer"
        CL1[Web Browser]
        CL2[Mobile App]
    end

    subgraph "Delivery Layer"
        DL1[Tag Manager]
        DL2[Ad Server]
        DL3[Pixel Tracker]
    end

    subgraph "Platform Core - Ring DAS"
        subgraph "Campaign & Inventory"
            PC1[DELIVERY<br/>Deals, Line Items, Creatives]
            PC2[INVENTORY<br/>Ad Units, Placements, KVs]
            PC3[OFFERS<br/>Product Catalogs]
        end

        subgraph "Decision & Optimization"
            DC1[Bidder<br/>ML Decision Engine]
            DC2[Smart Deals<br/>Traffic Mixer]
        end

        subgraph "Data & Audience"
            DA1[DAS PIXEL<br/>Event Collection]
            DA2[AUDIENCE<br/>Segments]
            DA3[User Feature Store]
        end

        subgraph "Experience"
            EX1[SELF SERVICE<br/>Portals]
            EX2[REPORTS<br/>Analytics]
        end

        subgraph "Configuration"
            CF1[SETTINGS<br/>Network Config]
        end
    end

    subgraph "Data Layer"
        DB1[(Campaign DB)]
        DB2[(Audience DB)]
        DB3[(Events Store)]
        DB4[(Analytics DB)]
    end

    subgraph "Integration Layer"
        IL1[ADP API<br/>GraphQL]
        IL2[Delivery APIs<br/>REST]
    end

    CL1 --> DL1
    CL2 --> DL1
    DL1 --> DL2
    CL1 --> DL3
    CL2 --> DL3

    DL2 --> DC1
    DL3 --> DA1

    PC1 --> DC1
    PC2 --> DC1
    PC3 --> DC1
    DC1 --> DC2
    DA1 --> DA2
    DA2 --> DC1
    CF1 --> DC1

    PC1 --> DB1
    DA2 --> DB2
    DA1 --> DB3
    EX2 --> DB4

    PC1 --> EX1
    PC1 --> EX2
    DA1 --> EX2

    IL1 --> PC1
    IL1 --> PC2
    IL1 --> PC3
    IL1 --> DA2
    IL1 --> EX2
    IL2 --> DL2
    IL2 --> DL3

    style PC1 fill:#e1f5ff
    style DC1 fill:#ffe1f5
    style DA1 fill:#f5ffe1
    style IL1 fill:#fff5e1

Core Platform Modules

DELIVERY (Campaign Management & Ad Serving)

Purpose: Manage advertising campaigns and execute ad delivery logic.

Key Components:

• Deal Manager: Create and manage commercial agreements
• Line Item Engine: Configure targeting, budget, and delivery rules
• Creative Manager: Store and serve ad assets
• Campaign State Machine: Manage lifecycle (draft, active, paused, completed)

Data Model:

Advertiser (1) → (*) Deal (1) → (*) Line Item (1) → (*) Creative

Interfaces:

• UI: Web-based campaign management interface
• API: GraphQL mutations for CRUD operations
• Events: Publishes campaign state changes

Persistence: Campaign database (PostgreSQL)

Learn More: Campaign Management

OFFERS (Product & Catalog Management)

Purpose: Define and process advertising offers and product catalogs for retail media.

Key Components:

• Catalog Manager: Store product catalogs
• Schema Registry: Define and validate product schemas
• Sync Engine: ETL pipelines for data ingestion
• Mapper: Transform external data to internal models

Supported Sync Methods:

• API-based (REST/GraphQL)
• File-based (CSV, JSON)
• Scheduled batch imports

Limits:

• Up to 10 million products per catalog
• Up to 50,000 offer catalogs per network
• Up to 50 million offers per network

Interfaces:

• API: GraphQL for catalog operations
• File Upload: S3-based batch import
• Webhooks: Catalog update notifications

Persistence: Product database (PostgreSQL + S3)

Learn More: Offers & Product Catalogs

INVENTORY (Ad Inventory Management)

Purpose: Configure advertising inventory structure and targeting parameters.

Key Components:

• Ad Unit Registry: Define ad placements
• Key-Value Store: Custom targeting parameters
• Tag Manager: Script and pixel management
• Floor Price Engine: Dynamic pricing rules

Configuration Hierarchy:

Network → Site → Ad Unit → Placement → Tag

Limits:

• Up to 100 custom key-value keys per network
• Up to 5,000 predefined values per network
• Up to 500 active tags per network

Interfaces:

• UI: Inventory management console
• API: GraphQL for configuration
• Tag Loader: JavaScript SDK for client-side delivery

Persistence: Configuration database (PostgreSQL + Redis cache)

Learn More: Inventory Management

AUDIENCE (Segmentation & Targeting)

Purpose: Build and maintain user segments for targeting.

Key Components:

• Segment Builder: Define segment rules
• Segment Evaluator: Real-time membership computation
• Audience Database: Store user profiles and segment memberships
• Data Connectors: Integrate first-party and third-party data

Segment Types:

• Behavioral (based on DAS PIXEL events)
• Contextual (based on page context)
• Demographic (based on user attributes)
• First-party (based on customer data)

Limits:

• Up to 1,000 segment definitions per network
• Real-time segment updates

Interfaces:

• UI: Segment builder interface
• API: GraphQL for segment operations
• Streaming: Real-time segment membership updates

Persistence: Audience database (PostgreSQL + Elasticsearch)

Learn More: Audience & Data

DAS PIXEL (Activity Tracking)

Important: DAS PIXEL is a separate module from AUDIENCE. The pixel collects raw events; AUDIENCE processes them into segments.

Purpose: Collect user activity and eCommerce events.

Key Components:

• JavaScript SDK: Client-side event collection
• HTTP API: Server-side event tracking
• Session Manager: Track user sessions
• Event Router: Route events to downstream systems

Tracked Events:

• Page views
• Product views
• Cart actions (add, remove, view)
• Checkout events
• Purchase conversions
• Search queries
• Custom events

Performance Characteristics:

• Up to 50 million events per day per network
• Event ingestion latency: ≤ 60 seconds
• Event retention: minimum 30 days

Data Flow:

User Action → JavaScript SDK → Pixel API → Event Queue →
  → AUDIENCE (segment updates)
  → REPORTS (analytics aggregation)
  → ML Models (training data)

Interfaces:

• JavaScript SDK: Client-side integration
• HTTP API: Server-side tracking
• WebSocket: Real-time event streaming (optional)

Persistence: Event store (Kafka + S3 + DynamoDB)

Learn More: Activity Tracking (Pixel)

SELF SERVICE (White-Label Portals)

Purpose: Provide self-service capabilities for advertisers, brands, agencies, and affiliates.

Key Components:

• Portal Framework: White-label UI engine
• Workflow Engine: Configurable approval workflows
• Budget Manager: Self-service budget control
• Creative Studio: Upload and preview tools

Portal Types:

• Advertiser Portal: Direct advertiser access
• Brand Portal: Vendor/brand management for retail media
• Agency Portal: Multi-client account management
• Affiliate Portal: Partnership tracking

Features:

• Campaign creation and management
• Creative upload and approval
• Real-time performance dashboards
• Budget control and alerts
• White-label branding (logo, colors, domain)

Interfaces:

• Web UI: React-based SPA
• API: GraphQL for backend operations
• SSO: Support for SAML 2.0, OAuth 2.0

Persistence: Shares Campaign DB + User DB

Learn More: Self-Service Portals

REPORTS (Analytics & Reporting)

Purpose: Provide analytics, reporting, and performance insights.

Key Components:

• Query Engine: Ad-hoc report generation
• Scheduler: Automated report delivery
• Aggregator: Pre-aggregate metrics for performance
• Dashboard API: Real-time data for UI

Reporting Capabilities:

• Ad-hoc queries with custom dimensions and metrics
• Scheduled reports (email, API, S3)
• Real-time dashboards
• Data export (CSV, JSON)

Performance:

• Up to 30 concurrent reports per network
• Up to 3 concurrent reports per data source
• Data freshness: ≤ 15 minutes for standard metrics

Interfaces:

• UI: Dashboard and report builder
• API: GraphQL for report queries
• Export: S3, email, webhook

Persistence: Analytics database (ClickHouse + Redis)

Learn More: Reports & Analytics

SETTINGS (Network Configuration)

Purpose: Manage global network settings and feature flags.

Key Components:

• Network Config: Currency, timezone, language settings
• Feature Flags: Gradual rollout control
• Permission Manager: Role-based access control
• Integration Registry: Third-party connections

Configuration Scope:

• Network-level (applies to entire tenant)
• User-level (role-based permissions)
• Feature-level (feature flag overrides)

Interfaces:

• UI: Settings management console
• API: GraphQL for configuration
• Config Sync: Real-time propagation (≤ 5 minutes)

Persistence: Configuration database (PostgreSQL + Redis)

Decision & Optimization Layer

Bidder (ML Decision Engine)

Purpose: Select optimal ads for each request using machine learning.

Architecture:

graph LR
    A[Ad Request] --> B[Request Parser]
    B --> C[Candidate Fetcher]
    C --> D[Eligibility Filter]
    D --> E[ML Scoring]
    E --> F[Auction]
    F --> G[Creative Selection]
    G --> H[Response Builder]
    H --> I[Ad Response]

    subgraph "Data Sources"
        J[DELIVERY<br/>Line Items]
        K[INVENTORY<br/>Targeting]
        L[AUDIENCE<br/>Segments]
        M[ML Models]
    end

    C --> J
    D --> K
    E --> L
    E --> M

Key Algorithms:

• Candidate Selection: Fetch eligible line items based on targeting
• Eligibility Filtering: Apply targeting rules, frequency caps, budget constraints
• ML Scoring: Predict CTR, conversion probability
• Auction Logic: Calculate bid = base_price + ML_score × optimization_factor
• Creative Selection: Choose specific creative using rotation strategy

Performance:

• Decision latency: ≤ 150 ms (p95)
• Throughput: Up to 10,000 bid requests per second per network
• Auction candidates: Up to 100 line items per request

Configuration Parameters (32 total):

• Model weights and thresholds
• Optimization factors
• Fallback strategies
• Thompson sampling parameters
• Budget pacing rules

Learn More: DAS Bidder

Smart Deals / Traffic Mixer

Purpose: Optimize budget allocation across channels using ML.

Key Capabilities:

• Cross-channel optimization (on-site + off-site)
• Goal-based optimization (CPS, ROAS, CPA)
• Dynamic budget redistribution
• Real-time performance monitoring

Optimization Loop:

1. Collect performance data from all channels
2. Train ML models to predict channel efficiency
3. Calculate optimal budget allocation
4. Redistribute traffic in real-time (≤ 60 seconds)
5. Monitor and adjust continuously

Supported Channels:

• On-site inventory (Ring DAS ad units)
• Google Ads
• Facebook Ads
• Other programmatic platforms

Limits:

• Up to 1,000 concurrent Smart Deals
• Up to 10 connected channel types

Learn More: Smart Deals

Data Flow Patterns

Ad Request Flow

The complete flow from ad request to creative delivery:

sequenceDiagram
    participant User
    participant Browser
    participant TagManager as Tag Manager
    participant AdServer as Ad Server
    participant Bidder
    participant Inventory as INVENTORY
    participant Audience as AUDIENCE
    participant Delivery as DELIVERY

    User->>Browser: Visit Website
    Browser->>TagManager: Load Page
    TagManager->>AdServer: Ad Request (KVs, Context)
    AdServer->>Inventory: Get Targeting Config
    Inventory-->>AdServer: Targeting Rules
    AdServer->>Audience: Get User Segments
    Audience-->>AdServer: Segment List
    AdServer->>Delivery: Get Eligible Line Items
    Delivery-->>AdServer: Candidate Line Items
    AdServer->>Bidder: Run Auction
    Bidder-->>AdServer: Winning Line Item + Creative
    AdServer-->>Browser: Ad Markup + Tracking
    Browser->>User: Display Ad
    Browser->>AdServer: Impression Tracked

Latency Breakdown:

1. Ad Request → Ad Server: ~10 ms (network)
2. Fetch Targeting Config: ~5 ms (cached)
3. Fetch User Segments: ~10 ms (cached)
4. Fetch Candidate Line Items: ~20 ms
5. Run Auction: ~80 ms (ML scoring)
6. Creative Selection: ~10 ms
7. Response Building: ~5 ms
8. Ad Server → Browser: ~10 ms (network)

Total: ~150 ms (p95)

Event Tracking Flow

The complete flow from user action to data aggregation:

sequenceDiagram
    participant User
    participant Browser
    participant PixelSDK as Pixel SDK
    participant PixelAPI as Pixel API
    participant EventQueue as Event Queue
    participant Audience as AUDIENCE
    participant Reports as REPORTS
    participant MLModels as ML Models

    User->>Browser: Perform Action (e.g., Purchase)
    Browser->>PixelSDK: Track Event
    PixelSDK->>PixelAPI: Send Event (HTTP POST)
    PixelAPI->>EventQueue: Publish Event
    EventQueue->>Audience: Update Segments
    EventQueue->>Reports: Aggregate Metrics
    EventQueue->>MLModels: Training Data
    PixelAPI-->>PixelSDK: 200 OK
    PixelSDK-->>Browser: Event Tracked

    Note over EventQueue,MLModels: Async Processing (≤ 60s)

Event Processing Pipeline:

1. Collection: Pixel SDK → Pixel API
2. Validation: Schema validation, fraud detection
3. Enrichment: Add user context, session data
4. Routing: Publish to event queue (Kafka)
5. Processing:
   • AUDIENCE: Update segment memberships
   • REPORTS: Aggregate metrics
   • ML Models: Store as training data
6. Persistence: S3 (raw events), DynamoDB (processed)

Latency: ≤ 60 seconds from event to segment/metric availability

Campaign Creation Flow

The complete flow from campaign creation to ad delivery:

sequenceDiagram
    participant User
    participant UI
    participant ADPAPI as ADP API
    participant Delivery as DELIVERY
    participant Inventory as INVENTORY
    participant Offers as OFFERS
    participant ConfigSync as Config Sync
    participant Bidder

    User->>UI: Create Campaign
    UI->>ADPAPI: createDeal mutation
    ADPAPI->>Delivery: Save Deal
    Delivery-->>ADPAPI: Deal Created
    User->>UI: Create Line Item
    UI->>ADPAPI: createLineItem mutation
    ADPAPI->>Inventory: Validate Targeting
    Inventory-->>ADPAPI: Valid
    ADPAPI->>Delivery: Save Line Item
    Delivery-->>ADPAPI: Line Item Created
    User->>UI: Upload Creative
    UI->>ADPAPI: createCreative mutation
    ADPAPI->>Offers: Link Product (if dynamic)
    Offers-->>ADPAPI: Product Linked
    ADPAPI->>Delivery: Save Creative
    Delivery-->>ADPAPI: Creative Created
    User->>UI: Activate Campaign
    UI->>ADPAPI: updateLineItem (state=ACTIVE)
    ADPAPI->>Delivery: Update State
    Delivery->>ConfigSync: Publish Config Change
    ConfigSync->>Bidder: Sync Line Item (≤ 5 min)
    Bidder-->>ConfigSync: Config Updated
    ADPAPI-->>UI: Campaign Active

Propagation Time:

• API operation: ~500 ms average
• Config propagation to Bidder: ≤ 5 minutes
• Total time to live campaign: ≤ 6 minutes

Integration Architecture

API Layer

Ring DAS exposes two primary API surfaces:

ADP API (GraphQL)

Purpose: Campaign management, configuration, and reporting

Schema Organization:

type Query {
  # Campaign queries
  deals(networkId: String!): [Deal]
  lineItems(dealId: String!): [LineItem]
  creatives(lineItemId: String!): [Creative]

  # Audience queries
  segments(networkId: String!): [Segment]

  # Inventory queries
  adUnits(networkId: String!): [AdUnit]

  # Reporting queries
  report(query: ReportQuery!): ReportResult
}

type Mutation {
  # Campaign mutations
  createDeal(input: CreateDealInput!): Deal
  createLineItem(input: CreateLineItemInput!): LineItem
  createCreative(input: CreateCreativeInput!): Creative

  # State management
  activateLineItem(id: String!): LineItem
  pauseLineItem(id: String!): LineItem
}

Authentication: Bearer token (API key)

Rate Limits:

• 500,000 requests per day per network
• Average response time: ≤ 500 ms per month

Learn More: Management API

Delivery APIs (REST)

Purpose: Ad serving and event tracking

Endpoints:

• POST /ad-request - Fetch ads
• POST /events - Track events
• GET /click - Track clicks
• POST /impression - Track impressions

Authentication: API key (query parameter or header)

Rate Limits:

• Ad Request: 10,000 req/sec per network
• Event Tracking: 50M events/day per network

Learn More: Delivery APIs

Client-Side Integration

Tag Manager Integration:

// Load Ring DAS Tag Manager
<script async src="https://tag.ringdas.com/tm.js?id=YOUR_NETWORK_ID"></script>

// Configure ad units
<div id="ad-unit-1" data-ringdas-slot="homepage_leaderboard"></div>

// Initialize
<script>
  window.ringDAS = window.ringDAS || [];
  ringDAS.push(['init', {
    networkId: 'YOUR_NETWORK_ID',
    enableAutoRefresh: true
  }]);
</script>

Pixel Integration:

// Track purchase event
dlApi.cmd.push(function(dlApi) {
  dlApi.sendActivityEvent({
    network: 'YOUR_NETWORK_ID',
    event: 'purchased',
    actgid: 'YOUR_PIXEL_ID',
    cost: '99.99',
    ord: 'ORDER-12345',
    products: [{
      id: 'SKU-123',
      name: 'Product Name',
      price: '99.99',
      qty: '1'
    }]
  });
});

Learn More: Integration Overview

Third-Party Integrations

Google Ad Manager (GAM)

Integration Type: Server-to-server bidding

Data Flow:

1. User visits publisher website
2. GAM makes ad request
3. GAM sends server-to-server bid request to Ring DAS
4. Ring DAS Bidder responds with bid
5. GAM runs unified auction
6. Winning ad served

Benefits:

• Access to AdExchange and AdSense demand
• Unified auction with direct campaigns
• Shared revenue optimization

Analytics Platforms

Integration Type: Data export and dashboards

Supported Platforms:

• Google Analytics
• Adobe Analytics
• Tableau
• Looker

Export Methods:

• Real-time API
• Scheduled reports (CSV, JSON)
• S3 data lake export

CRM Systems

Integration Type: Bidirectional API sync

Use Cases:

• Automated campaign creation from CRM deals
• Campaign performance data back to CRM
• Advertiser billing and invoicing

Common CRMs:

• Salesforce
• HubSpot
• Custom CRM systems

Infrastructure & Deployment

Cloud Infrastructure

Provider: Amazon Web Services (AWS)

Key Services:

• Compute: ECS (Elastic Container Service) for containerized workloads
• Database: RDS (PostgreSQL), DynamoDB, ElastiCache (Redis)
• Storage: S3 for file storage, EBS for block storage
• Analytics: ClickHouse for OLAP queries
• Messaging: Kafka (MSK) for event streaming
• CDN: CloudFront for content delivery
• Load Balancing: ALB (Application Load Balancer)

Multi-Region Deployment

Regions:

• Primary: us-east-1 (US East)
• Secondary: eu-west-1 (EU Ireland)
• Tertiary: ap-southeast-1 (Asia Pacific)

Replication:

• Database: Multi-region replication with automatic failover
• Object Storage: S3 cross-region replication
• Config: Eventual consistency (≤ 5 minutes)

High Availability

SLA: ≥ 99.8% availability per month

Resilience Measures:

• Multi-AZ deployment within each region
• Auto-scaling based on traffic patterns
• Circuit breakers for downstream dependencies
• Graceful degradation (fallback to cached config)

Disaster Recovery:

• RPO (Recovery Point Objective): ≤ 1 hour
• RTO (Recovery Time Objective): ≤ 4 hours

Security Architecture

Network Security

• VPC Isolation: Ring DAS deployed in isolated VPCs
• Security Groups: Strict firewall rules per service
• WAF: Web Application Firewall for API endpoints
• DDoS Protection: AWS Shield for DDoS mitigation

Authentication & Authorization

API Authentication:

• Bearer token (API key) for programmatic access
• OAuth 2.0 / SAML 2.0 for SSO
• Per-network API keys with scope restrictions

User Authentication:

• Username/password with MFA
• SSO integration (SAML 2.0, OAuth 2.0)
• Session management with secure cookies

Authorization:

• Role-based access control (RBAC)
• Network-level isolation
• Resource-level permissions

Data Security

Encryption:

• At rest: AES-256 encryption for all data stores
• In transit: TLS 1.2+ for all communications
• Key management: AWS KMS for encryption keys

PII Protection:

• User identifiers hashed (AID = hashed email)
• Anonymization for non-production environments
• Data retention policies (GDPR compliance)

Compliance

• GDPR: Full compliance with data subject rights
• TCF v2.2: Transparent Consent Framework support
• DSA: Digital Services Act compliance
• SOC 2 Type II: Security and availability controls
• ISO 27001: Information security management

Learn More: Compliance

Performance Optimization

Caching Strategy

Cache Layers:

1. CDN: CloudFront for static assets (creatives, scripts)
2. Application Cache: Redis for frequently accessed data
   • User segments (TTL: 5 minutes)
   • Targeting config (TTL: 10 minutes)
   • Line item eligibility (TTL: 2 minutes)
3. Database Cache: PostgreSQL query cache

Cache Invalidation:

• Config changes trigger cache purge (≤ 5 minutes)
• Event-driven invalidation for real-time updates

Database Optimization

Read Replicas:

• Separate read replicas for reporting queries
• Connection pooling to reduce overhead

Indexing:

• Strategic indexes on high-cardinality fields
• Partial indexes for filtered queries
• Covering indexes for common queries

Partitioning:

• Time-based partitioning for event data
• Network-based partitioning for multi-tenancy

ML Model Optimization

Model Serving:

• Models loaded in memory for low-latency inference
• GPU acceleration for complex models (optional)
• Model versioning with A/B testing

Model Training:

• Offline batch training (daily/weekly)
• Online learning for adaptive models (experimental)
• Distributed training for large datasets

Monitoring & Observability

Metrics & Monitoring

Key Metrics:

• Ad request rate and latency (p50, p95, p99)
• Event ingestion rate and latency
• API response time and error rate
• Database query performance
• Cache hit rate

Tools:

• Metrics: CloudWatch, Prometheus
• Dashboards: Grafana
• Alerting: PagerDuty, Slack

Logging

Log Aggregation:

• Centralized logging with CloudWatch Logs
• Structured logging (JSON format)
• Log retention: 30 days (hot), 1 year (cold)

Log Types:

• Application logs (info, warn, error)
• Access logs (API, ad requests)
• Audit logs (config changes, user actions)

Distributed Tracing

Tools: AWS X-Ray, OpenTelemetry

Traced Flows:

• Ad request end-to-end
• API request end-to-end
• Event processing pipeline

Alerting

Alert Categories:

• Critical: System down, data loss, security breach
• Warning: High latency, elevated error rate, approaching limits
• Info: Deployment complete, config change, scheduled maintenance

Alert Channels:

• PagerDuty (on-call rotation)
• Slack (team channels)
• Email (digest reports)

Scaling Considerations

Horizontal Scaling

Auto-Scaling Groups:

• Ad Server: Scale based on request rate
• Bidder: Scale based on CPU utilization
• Pixel API: Scale based on event rate
• API Servers: Scale based on connection count

Scaling Policies:

• Target tracking (e.g., 70% CPU utilization)
• Step scaling for rapid traffic spikes
• Scheduled scaling for predictable patterns

Vertical Scaling

Instance Types:

• Compute-optimized (C5) for Bidder
• Memory-optimized (R5) for caching services
• General-purpose (T3) for web servers

Database Scaling

Read Scaling:

• Read replicas for reporting queries
• Connection pooling to handle concurrent connections

Write Scaling:

• Sharding by network ID for multi-tenancy
• Time-based partitioning for event data

Development & Deployment

CI/CD Pipeline

Source Control: Git (GitHub/GitLab)

Build Pipeline:

1. Code commit triggers build
2. Run unit tests and linters
3. Build Docker images
4. Push images to ECR (Elastic Container Registry)
5. Deploy to staging environment
6. Run integration tests
7. Deploy to production (blue-green deployment)

Deployment Tool: FluxCD for GitOps-based deployments

Rollout Strategy:

• Canary deployments (5% → 50% → 100%)
• Feature flags for gradual rollout
• Automated rollback on error threshold

Testing Strategy

Test Types:

• Unit tests (coverage >80%)
• Integration tests
• End-to-end tests
• Load tests (JMeter, Gatling)
• Security tests (OWASP Top 10)

Test Environments:

• Local (Docker Compose)
• Staging (mirrors production)
• Production (canary deployments)

Next Steps

This architecture overview provides a foundation for understanding Ring DAS. For deeper technical details, explore component-specific documentation:

Deep Dives:

• Decision Engine - ML-based ad selection
• DAS Bidder - Bidding algorithm details
• Activity Tracking - Event collection architecture
• Pacekeeper - Budget management system

Integration Guides:

• Integration Overview
• Ad Request Integration
• Event Tracking Setup
• API Authentication

Operational Guides:

• Rate Limits & Quotas
• Performance SLA
• Troubleshooting

Related Topics:

• Platform Overview
• Key Concepts
• API Overview