eBPF and Rust/Go-based Ultra-low Latency Event Stream Processing: A Deep Dive into Real-time AI Feature Engineering

Do you want to overcome the fundamental latency issues of traditional real-time data processing pipelines? This article presents an innovative architecture that combines eBPF's kernel-level event hooking capabilities with Rust/Go's userspace processing efficiency to achieve microsecond-level ultra-low latency required in real-time AI feature engineering.

1. The Challenge / Context: Latency Bottlenecks in Real-time AI

Today, AI is transforming user experiences in real-time, moving beyond the realm of mere batch processing. In domains where real-time decision-making is directly linked to business value, such as financial fraud detection, personalized recommendation systems, autonomous driving prediction models, and cyber security threat detection, the role of AI is essential. In these scenarios, inferring based on 'fresh' data—features generated within milliseconds to microseconds—is key.

However, traditional stream processing pipelines (such as Kafka, Spark Streaming, and Flink) inevitably incur tens to hundreds of milliseconds of latency due to overheads like data collection, serialization/deserialization, network transmission, and userspace processing. In particular, context switching between the kernel and userspace is a major cause of performance degradation. This latency acts as a critical bottleneck, hindering the accuracy and timeliness of real-time AI models, and there is an urgent need to resolve it to gain a competitive edge in the market.

2. Deep Dive: The Power of eBPF and the Synergy of Rust/Go

Our goal is to capture kernel-level events with minimal latency and process them immediately in userspace to generate AI features. For this, eBPF and Rust/Go combine to