Performance efficiency
The performance efficiency pillar includes the ability to use cloud computing resources efficiently to meet system requirements and to maintain that efficiency as demand changes and technologies evolve.
The performance efficiency pillar provides an overview of design principles, best practices, and questions. You can find guidance on implementation in the Performance Efficiency Pillar whitepaper.
Design principles
The following design principles can help you achieve and maintain efficient advertising workloads in the cloud:
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Design for optimized cost: The key to cost optimization for advertising workloads is to minimize costs while you maintain a required level of performance and reliability.
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Design for handling low latency, bursty, and spiky traffic: Build a scalable architecture with automated scaling capacity to enable rapid increases and decreases of traffic gracefully. Cache application data content, when possible, to reduce response latency and the load on database clusters. Use containerized workloads and prebuilt container images for fast scaling and predictable performance. Choose server hardware optimized for memory and CPU for ultra-low latency needs.
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Design for large data volumes and transactions: Build a scalable distributed database for transactions while optimizing it for fast writes. Consider use of a distributed NoSQL database that can handle high write throughput with linear scalability. Consider compression techniques to optimize storage and an appropriate caching strategy to reduce database load for user profiles, target segments, and creatives. Use streaming services for ingestion and transportation of event data. Set up auto scaling databases to handle traffic spikes. Implement a data archive strategy to purge old ad impressions data to more cost optimized storage. Monitor database performance metrics, including latency, timeouts, and saturation, to identify and fix bottlenecks.
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Design for data volume and query processing consideration for AWS Clean Rooms collaboration: Large datasets can impact query performance. Consider data partition, aggregations, and filters to reduce result sets. Complex joins across multiple tables and number of collaborators can impact processing team. As a result, the optimal design for collaborators uses one to many collaboration channels, along with optimized pre-compiled query templates.
