Railgun: managing large streaming windows under MAD requirements

TL;DR

Railgun is a distributed streaming system designed to efficiently handle large, long-duration sliding windows with high throughput and low latency, suitable for mission-critical applications like fraud detection.

Contribution

It introduces a fault-tolerant, elastic streaming system that accurately manages real-time sliding windows under high load and strict latency requirements, outperforming existing solutions.

Findings

Lower latency than Flink in benchmarks

Low memory usage independent of window size

Scales nearly linearly under high load

Abstract

Some mission critical systems, e.g., fraud detection, require accurate, real-time metrics over long time sliding windows on applications that demand high throughput and low latencies. As these applications need to run 'forever' and cope with large, spiky data loads, they further require to be run in a distributed setting. We are unaware of any streaming system that provides all those properties. Instead, existing systems take large simplifications, such as implementing sliding windows as a fixed set of overlapping windows, jeopardizing metric accuracy (violating regulatory 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. Further, we show that Railgun scales nearly linearly, respecting our msec-level latencies at high percentiles (<250ms @ 99.9%) even under a load of 1 million events per second.