Log-Structured Tables (LSTs), also commonly referred to as table formats, have recently emerged to bring consistency and isolation to object stores. With the separation of compute and storage, object stores have become the go-to for highly scalable and durable storage. However, this comes with its own set of challenges, such as the lack of recovery and concurrency management that traditional database management systems provide. This is where LSTs such as Delta Lake, Apache Iceberg, and Apache Hudi come into play, providing an automatic metadata layer that manages tables defined over object stores, effectively addressing these challenges. A paradigm shift in the design of these systems necessitates the updating of evaluation methodologies. In this paper, we examine the characteristics of LSTs and propose extensions to existing benchmarks, including workload patterns and metrics, to accurately capture their performance. We introduce our framework, LST-Bench, which enables users to execute benchmarks tailored for the evaluation of LSTs. Our evaluation demonstrates how these benchmarks can be utilized to evaluate the performance, efficiency, and stability of LSTs. The code for LST-Bench is open sourced and is available at https://github.com/microsoft/lst-bench/ .

翻译：日志结构化表（LSTs），通常也称为表格式，最近兴起旨在为对象存储带来一致性与隔离性。随着计算与存储的分离，对象存储已成为高度可扩展且持久存储的首选方案。然而，这带来了其特有的挑战，例如传统数据库管理系统所提供的恢复与并发管理能力的缺失。这正是Delta Lake、Apache Iceberg和Apache Hudi等LST发挥作用之处，它们提供自动化的元数据层，管理定义在对象存储之上的表，有效应对这些挑战。这类系统设计中的范式转变要求更新评估方法。本文考察了LST的特性，并提出了对现有基准测试的扩展，包括工作负载模式与指标，以准确捕捉其性能。我们介绍了框架LST-Bench，使用户能够执行专为评估LST而设计的基准测试。我们的评估展示了如何利用这些基准测试来评估LST的性能、效率与稳定性。LST-Bench的代码已开源，地址为https://github.com/microsoft/lst-bench/。