Misty: Microservice-Based Streaming Trajectory Similarity Search

Abstract

As a fundamental operation in various LBS (Location Based Service) applications, the trajectory similarity search has long been a performance bottleneck in applications like (e.g., traffic optimization and contact tracing). When handling streaming trajectory data, the variable workload and stateful compute requirement are two crucial challenges that further complicate the problem. Distributed microservice, a mainstream industrial software design architecture, is the preferred way to address such issues. However, the trajectory instance will inevitably be split under the parallel framework. Therefore, how to distribute trajectory data among the parallel processing tasks in a real-time and lightweight manner is the crux. In this paper, we propose a Microservice-based real-time processing framework for streaming trajectory similarity search, called Misty, which effectively reduces the update cost of the secondary index and supports high scalability. Moreover, on top of Misty, we can build resilient and stateful cloud-native applications. Misty is composed of the assembler, index, coordinator, and executor. Specifically, the assembler and the index module ensure retrieval performance, while the coordinator and executor module enable the system with elastic scaling. Extensive experimental studies on real-world data demonstrate higher query throughput and lower latency over traditional approaches.