CAREER: Intermittent Query Processing

职业：间歇查询处理

• 批准号: 2048088
• 负责人: Aaron Elmore
• 金额: $ 58.04万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2048088&HistoricalAwards=false
• 关键词: CAREER; Intermittent; Query; Processing

Database systems that facilitate the storage and analysis of data are the backbone of modern data-intensive applications, from machine learning to the Internet of Things, and represent a significant market force with an estimated worldwide market value of over 50 billion USD. Many new database applications demand fast complex analytics over changing data. However, the slowdown in the growth of hardware performance, coupled with the continued increase of data and data-intensive applications creates a crisis, as existing database systems are not well suited for applications that want to trade-off performance and resource efficiency. To address this gap of queries over fresh data with controlled resource utilization, a new database system paradigm is needed.This award investigates a new query execution paradigm, Intermittent Query Processing, to answer the question: how should database systems provide low latency for ongoing queries with bursty data arrival or periodic queries while minimizing resource utilization? This framework allows a query to initially execute as a batch, with subsequent intermittent processing to update the result while minimizing the resources required to update the query result by selectively persisting artifacts from the initial query execution and by exploiting knowledge about expected data arrivals and models for how amenable a query is for incremental computation. This project will investigate methods for predicting query and data arrivals, scheduling and placement decisions for workloads where queries partially overlap, opportunities for state sharing, resource allocation, and new query optimization techniques.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

促进数据存储和分析的数据库系统是现代数据密集型应用（从机器学习到物联网）的支柱，代表着一股重要的市场力量，估计全球市场价值超过500亿美元。许多新的数据库应用程序需要对变化的数据进行快速复杂的分析。然而，硬件性能增长的放缓，加上数据和数据密集型应用程序的持续增长，造成了一种危机，因为现有的数据库系统不太适合希望权衡性能和资源效率的应用程序。为了在控制资源利用率的情况下解决对新数据查询的这种差距，需要一种新的数据库系统范例。该奖项研究了一种新的查询执行范式，间歇查询处理，以回答以下问题：数据库系统应该如何为具有突发数据到达或周期性查询的正在进行的查询提供低延迟，同时最大限度地减少资源利用率？该框架允许查询最初作为批处理执行，随后进行间歇处理以更新结果，同时通过有选择地持久化初始查询执行中的工件，并利用有关预期数据到达的知识和模型来了解查询对增量计算的适应性，从而最大限度地减少更新查询结果所需的资源。该项目将研究预测查询和数据到达的方法、查询部分重叠的工作负载的调度和放置决策、状态共享的机会、资源分配和新的查询优化技术。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。