In my current role at Getir, working on B2B ad management platforms has provided fascinating insights into microservices architecture at scale. Here are some key lessons I’ve learned while building systems that handle high-volume advertising traffic.

The Challenge: Scale and Performance

Ad management platforms face unique challenges:

  • High throughput: Processing thousands of ad requests per second
  • Low latency: Sub-100ms response times for ad serving
  • Real-time analytics: Immediate campaign performance feedback
  • Complex business logic: Targeting, bidding, and optimization algorithms

Microservices Design Principles

1. Service Boundaries Matter

One of the most critical decisions is defining service boundaries. In ad-tech, we’ve organized services around business capabilities:

- Ad Serving Service: Handles real-time ad requests
- Campaign Management Service: CRUD operations for campaigns
- Analytics Service: Processes and aggregates performance data
- Billing Service: Handles cost calculations and invoicing
- Targeting Service: Manages audience segmentation logic

Key insight: Don’t split services too early. Start with a well-structured monolith and extract services when you have clear boundaries and scaling needs.

2. Data Consistency Strategies

With distributed data, we’ve implemented different consistency patterns:

  • Strong consistency for financial data (billing, payments)
  • Eventual consistency for analytics and reporting
  • Saga pattern for complex workflows like campaign activation

3. Communication Patterns

We use a mix of synchronous and asynchronous communication:

  • REST APIs for real-time operations (ad serving, campaign management)
  • AWS Kinesis for event streaming (analytics, audit logs)
  • Message queues for background processing (report generation, billing)

AWS Technologies in Practice

Kinesis for Real-time Processing

// Example: Streaming ad impression events
public class ImpressionEventHandler {
    private final AmazonKinesis kinesisClient;
    
    public void recordImpression(AdImpression impression) {
        PutRecordRequest request = new PutRecordRequest()
            .withStreamName("ad-impressions")
            .withData(ByteBuffer.wrap(impression.toJson().getBytes()))
            .withPartitionKey(impression.getCampaignId());
            
        kinesisClient.putRecord(request);
    }
}

ECS with Auto Scaling

For our containerized services, ECS provides excellent scaling capabilities:

  • Target tracking scaling based on CPU/memory utilization
  • Custom metrics scaling based on queue depth or request rate
  • Scheduled scaling for predictable traffic patterns

Performance Optimizations

1. Caching Strategy

Multi-level caching has been crucial:

  • Application-level: Redis for frequently accessed campaign data
  • CDN: CloudFront for static creative assets
  • Database: ElastiCache for complex query results

2. Database Design

We use different databases for different needs:

  • PostgreSQL: Transactional data (campaigns, users)
  • DynamoDB: High-volume, low-latency data (ad impressions)
  • OpenSearch: Analytics and search functionality

Monitoring and Observability

Key Metrics We Track

  1. Business Metrics
    • Ad fill rate
    • Revenue per thousand impressions (RPM)
    • Campaign performance KPIs
  2. Technical Metrics
    • Service response times
    • Error rates
    • Queue depths
    • Database connection pools
  3. Infrastructure Metrics
    • CPU/Memory utilization
    • Network I/O
    • Auto-scaling events

Distributed Tracing

Using AWS X-Ray has been invaluable for understanding request flows across services:

@Trace
public AdResponse serveAd(AdRequest request) {
    // X-Ray automatically traces this method
    Campaign campaign = campaignService.findBestMatch(request);
    CreativeAsset creative = creativeService.getCreative(campaign.getId());
    
    // Record custom annotations
    AWSXRay.getCurrentSegment().putAnnotation("campaignId", campaign.getId());
    
    return buildResponse(campaign, creative);
}

Common Pitfalls and Solutions

1. Over-Engineering Early

Problem: Starting with too many small services Solution: Begin with a modular monolith, extract services based on actual scaling needs

2. Data Synchronization Issues

Problem: Keeping related data consistent across services Solution: Implement proper event sourcing and use compensation patterns for failures

3. Testing Complexity

Problem: Integration testing becomes complex with many services Solution: Invest in contract testing and maintain good test environments

Looking Forward

Microservices aren’t a silver bullet, but when applied thoughtfully, they enable:

  • Independent scaling of components
  • Technology diversity (right tool for the job)
  • Team autonomy and faster development cycles
  • Fault isolation and better resilience

The key is balancing complexity with benefits, and always keeping the business goals in focus.

What are your experiences with microservices? I’d love to hear about the challenges and solutions you’ve encountered. Feel free to connect with me on LinkedIn!