Scaling Real-Time AI Decisioning: High-Throughput Architecture for 15K+ RPS
A deep dive into engineering a distributed, event-driven system capable of sub-100ms latency at scale using Kafka, Bloom filters, and in-memory caching.
This case study highlights a significant achievement in high-throughput engineering. Below is a refined version of your content, designed to be both technically rigorous and marketable to potential clients or stakeholders.
Executive Summary
In high-stakes digital environments, the window to make a "correct" decision is measured in milliseconds. This project involved re-engineering a decisioning engine to handle massive traffic spikes while maintaining strict performance benchmarks for campaign targeting and frequency capping .
The Challenge: Speed vs. Scale
The primary hurdle was not just the volume of traffic, but the complexity of the rules being applied to it.
- Throughput: Sustain a baseline of 15,000+ requests per second.
- Latency: Ensure the entire decisioning loop—from request to response—completed in under 100ms.
- Complexity: Manage high-cardinality data and dynamic targeting rules without creating system bottlenecks.
The Intuitive Insight: "The Pre-Sorted Library"
Marketable Analogy: Imagine a library where 15,000 people walk in every second asking for a specific book. If you wait for them to arrive before you start looking for the book, the system collapses.
We solved this by "pre-sorting" the answers. By shifting heavy computation to pre-processing pipelines, we ensured that when a request arrived, the "book" was already on the counter waiting for them.
Strategic Architecture
To achieve this level of performance, the system moved away from traditional synchronous patterns toward a more resilient, event-driven model.
- Kafka-Based Streaming: Used as a backbone to decouple services, ensuring that a spike in one area didn't crash the entire system.
- Bloom Filters: Implemented for frequency capping. This allowed the system to check if a user had already seen an ad using a tiny fraction of the memory and time required by a standard database lookup.
- In-Memory Caching: Deployed distributed data stores to ensure lookups happened at RAM speeds rather than disk speeds.
- Stateless Scaling: Built services that could be "cloned" (horizontally scaled) instantly to meet traffic demands.
Key Engineering Decisions
- Latency Over Absolute Consistency: We optimized read paths to prioritize immediate responses. In a real-time campaign, a decision made in 50ms is more valuable than a "perfectly" consistent one made in 500ms.
- Event-Driven Orchestration: Swapped slow, "wait-and-see" synchronous calls for a reactive architecture that processes data as it flows.
Impact & Business Value
The resulting architecture transformed a potential bottleneck into a competitive advantage:
- Reliable Performance: Successfully sustained 15K+ RPS while maintaining stable, predictable latency.
- Lightning Fast: Achieved a consistent sub-100ms response time, meeting the most demanding platform requirements.
- Future-Ready: Established a high-performance foundation capable of supporting future AI-driven decisioning and real-time optimization.