Low Latency, High Throughput Trade Surveillance System Using In-Memory Data Grid

Abstract

Trade surveillance is an important concern in recent trading engines to detect and prevent fraudulent trades at earliest. In traditional trading platforms, to achieve high throughput and low latency requirements focus of developers has always been on high-performance languages such as C, C++ and FPGA based systems. These systems have limitations of scalability and fault-tolerance. With the arrival of in-memory technology these requirements can be met with Java-based frameworks like Ignite, Flink, Spark. In this paper, we propose a novel way of implementing trade surveillance architecture using Apache Ignite In-Memory Data Grid (IMDG). Paper discusses the engineering approach to tune system architecture on the single node in terms of achieving high throughput, low latency and then scaling out to multiple nodes.