全面高效的数据压缩
Comprehensive and Efficient Workload Compression
摘要：
这项工作研究了从给定的输入分析查询工作负载构建代表性工作负载的问题，其中前者作为后者的近似值并保证后者。我们在工作量分析和监测的背景下讨论我们的工作。作为示例，数据库系统中不断演变的系统使用模式可能导致负载不平衡和性能退化，这可以通过监视系统使用模式来控制，即，一个典型的工作量，随着时间的推移。为了以有原则的方式构造这样的工作负载，我们将工作负载{\em代表性}和{\em覆盖率}的概念形式化。这些度量捕捉到这样的直觉：压缩工作负载中的特征分布应该与目标分布相匹配，从而增加代表性，并且包括常见查询以及离群值，从而增加覆盖率。我们发现，解决这个问题的最佳方案是NP-困难的，并提出了一种新的贪心算法，提供近似保证。我们比较我们的技术，建立在这个问题空间的算法，如采样和聚类，并展示我们的技术的优势和关键权衡。
英文摘要：
This work studies the problem of constructing a representative workload from a given input analytical query workload where the former serves as an approximation with guarantees of the latter. We discuss our work in the context of workload analysis and monitoring. As an example, evolving system usage patterns in a database system can cause load imbalance and performance regressions which can be controlled by monitoring system usage patterns, i.e.,~a representative workload, over time. To construct such a workload in a principled manner, we formalize the notions of workload {\em representativity} and {\em coverage}. These metrics capture the intuition that the distribution of features in a compressed workload should match a target distribution, increasing representativity, and include common queries as well as outliers, increasing coverage. We show that solving this problem optimally is NP-hard and present a novel greedy algorithm that provides approximation guarantees. We compare our techniques to established algorithms in this problem space such as sampling and clustering, and demonstrate advantages and key trade-offs of our techniques.
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。
