CSR: Small: Efficient Techniques for Modular Past State Systems

回溯是数据存储对数据存储的一致过去状态运行特别程序的能力，就好像它们是当前状态一样。回顾使分析过去的国家变得更容易，为审计师、历史学家、经济学家、社会科学家和其他需要调查历史数据的人提供了一个宝贵的工具。对于那些想要分析过去状态以预测未来的人来说，回溯也很有价值，这是现代数据管理应用程序中越来越受欢迎的功能。目前，大多数轻量级数据存储都不支持回溯。关键原因是，为了提高性能，现有的回溯技术需要对数据存储内部结构进行侵入性的、难以采用的修改。如果不支持追溯，应用程序开发人员可能很难重建与过去感兴趣的事件相对应的一致状态。该项目将开发一种易于采用的模块化方法和一套相关技术，用于支持使用嵌入式持久一致过去状态系统的轻量级事务数据存储中的追溯。技术挑战是：1)如何在不损害数据存储性能的情况下提供一致的过去状态？过去的状态系统需要紧密集成以提高效率，但对内部数据存储组件的广泛修改是不可行的，需要在数据存储软件堆栈的低级别运行的新模块化技术。2)如何在跨越大时间间隔的过去状态下高效运行程序？过去的状态需要以增量方式创建，以避免中断数据存储，但在增量状态下运行程序可能会很慢，这需要针对增量数据优化的新集群和缓存技术。3)可以避免降低不使用回溯的程序的速度吗？为了评估任何额外的开销，该项目将在工业强度数据存储中开发一个实验原型，并进行研究，以实验和分析的方式回答这个问题。

Retrospection is the ability of a data store to run ad-hoc programs over consistent past states of a data store as if they were the current state. Retrospection makes it easier to analyze past states providing a valuable tool for auditors, historians, economists, social scientists and others with a need to investigate historical data. Retrospection is also valuable to those who want to analyze past states to predict the future, an increasingly in-demand feature in modern data management applications.Most light-weight data stores today do not support retrospection. The key reason is that existing retrospection techniques, for performance, require invasive hard-to-adopt modifications to data store internals. Without support for retrospection, it may be hard for application developers to reconstruct the consistent states corresponding to past events of interest.This project will develop an easy-to-adopt modular method and a set of associated techniques for supporting retrospection in light-weight transactional data stores using anembedded persistent consistent past-state system. The technical challenges are:1) How to provide consistent past states without harming data store performance? The past state system needs to be tightly integrated for efficiency but extensive modifications to the internal data store components are infeasible, requiring new modular techniques that operate at a low-level in the data store software stack.2) How to run programs efficiently over past state that spans large time intervals? past state needs to be created incrementally to avoid disrupting the data store but running a program over incremental state can be slow, requiring new clustering and caching techniques optimized for incremental data.3) Can one avoid slowing down programs that do not use retrospection? To evaluate any additional overhead, the project will develop an experimental prototype in an industrial strength data store, and conduct studies to answer this question experimentally and analytically.