Large-Scale Intelligent Microservices

TL;DR

This paper presents a scalable microservice framework based on Apache Spark that integrates machine learning and web services into databases, enabling efficient, large-scale intelligent applications like search and analytics.

Contribution

It introduces a novel Spark-based orchestration system extending database operations with web service primitives for large-scale intelligent service deployment.

Findings

Framework supports orchestration across hundreds of machines.

Achieves low-latency communication with containerized architecture.

Demonstrates competitive performance on various benchmarks.

Abstract

Deploying Machine Learning (ML) algorithms within databases is a challenge due to the varied computational footprints of modern ML algorithms and the myriad of database technologies each with its own restrictive syntax. We introduce an Apache Spark-based micro-service orchestration framework that extends database operations to include web service primitives. Our system can orchestrate web services across hundreds of machines and takes full advantage of cluster, thread, and asynchronous parallelism. Using this framework, we provide large scale clients for intelligent services such as speech, vision, search, anomaly detection, and text analysis. This allows users to integrate ready-to-use intelligence into any datastore with an Apache Spark connector. To eliminate the majority of overhead from network communication, we also introduce a low-latency containerized version of our architecture. Finally, we demonstrate that the services we investigate are competitive on a variety of benchmarks, and present two applications of this framework to create intelligent search engines, and real-time auto race analytics systems.