Railgun: streaming windows for mission critical systems

TL;DR

Railgun is a distributed streaming system designed for mission-critical applications, offering accurate, real-time sliding windows with low latency and high throughput, outperforming existing solutions like Flink.

Contribution

It introduces a fault-tolerant, elastic streaming system supporting real-time sliding windows for high-load, low-latency scenarios, addressing limitations of existing systems.

Findings

Lower latency than Flink in benchmarks

Low memory usage independent of window size

Supports high throughput and fault tolerance

Abstract

Some mission critical systems, such as fraud detection, require accurate, real-time metrics over long time windows on applications that demand high throughputs and low latencies. As these applications need to run "forever", cope with large and spiky data loads, they further require to be run in a distributed setting. Unsurprisingly, we are unaware of any distributed streaming system that provides all those properties. Instead, existing systems take large simplifications, such as implementing sliding windows as a fixed set of partially overlapping windows, jeopardizing metric accuracy (violating financial regulator rules) or latency (breaching service agreements). In this paper, we propose Railgun, a fault-tolerant, elastic, and distributed streaming system supporting real-time sliding windows for scenarios requiring high loads and millisecond-level latencies. We benchmarked an initial prototype of Railgun using real data, showing significant lower latency than Flink, and low memory usage, independent of window size.