2020/09/30 by Mark Hamilton, Nick Gonsalves, Christina Lee +9 · 1 citation
Computer Science · #Analytics #Asynchronous communication #Big data #Cloud Computing and Resource Management #Cloud computing #Computer network #Computer science #Data Stream Mining Techniques #Database #Distributed computing #Microservices #Multithreading #Operating system #Orchestration #SPARK (programming language) #Software System Performance and Reliability #Thread (computing) #Web service #World Wide Web #cs.AI #cs.DB #cs.DC #cs.LG #cs.NI
paper · pdf · doi:10.1109/bigdata50022.2020.9378270
openalex publication_date 2020/12/10 · arxiv created 2021/12/02 · arxiv updated 2022/03/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.