L-Store：实时的OLTP和OLAP系统
L-Store: A Real-time OLTP and OLAP System
摘要：
可以说，数据是企业世界中新的自然资源，其扩散程度前所未有。但是，为了从数据中获得实时的【形】可操作的见解，重要的是要弥合管理高速更新的数据（即，OLTP）和分析大量数据（即，OLAP）。但是，存在一个划分，即通常部署专门的解决方案来支持OLTP或OLAP工作负载，而不是同时支持两者;因此，将分析限制在陈旧和可能不相关的数据上。在本文中，我们提出了基于血统的数据存储（L-Store），结合了实时的【形】处理事务和分析工作负载在一个单一的统一引擎，通过引入一个新的基于血统的存储架构。通过利用血统，我们开发了一个无争用和懒惰的分期列数据从写优化的形式（适用于OLTP）到读优化的形式（适用于OLAP）在事务一致的方法，也支持查询和保留当前和历史数据。我们的工作原型的L-商店展示了其优越性相比，国家的最先进的方法下，全面的实验评估。
英文摘要：
Arguably data is the new natural resource in the enterprise world with an unprecedented degree of proliferation. But to derive real-time actionable insights from the data, it is important to bridge the gap between managing the data that is being updated at a high velocity (i.e., OLTP) and analyzing a large volume of data (i.e., OLAP). However, there has been a divide where specialized solutions were often deployed to support either OLTP or OLAP workloads but not both; thus, limiting the analysis to stale and possibly irrelevant data. In this paper, we present Lineage-based Data Store (L-Store) that combines the real-time processing of transactional and analytical workloads within a single unified engine by introducing a novel lineage-based storage architecture. By exploiting the lineage, we develop a contention-free and lazy staging of columnar data from a write-optimized form (suitable for OLTP) into a read-optimized form (suitable for OLAP) in a transactionally consistent approach that also supports querying and retaining the current and historic data. Our working prototype of L-Store demonstrates its superiority compared to state-of-the-art approaches under a comprehensive experimental evaluation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
