Data processing engines increasingly leverage distributed file systems for scalable, cost-effective storage. While the Apache Parquet columnar format has become a popular choice for data storage and retrieval, the immutability of Parquet files renders it impractical to meet the demands of frequent updates in contemporary analytical workloads. Log-Structured Tables (LSTs), such as Delta Lake, Apache Iceberg, and Apache Hudi, offer an alternative for scenarios requiring data mutability, providing a balance between efficient updates and the benefits of columnar storage. They provide features like transactions, time-travel, and schema evolution, enhancing usability and enabling access from multiple engines. Moreover, engines like Apache Spark and Trino can be configured to leverage the optimizations and controls offered by LSTs to meet specific business needs. Conventional benchmarks and tools are inadequate for evaluating the transformative changes in the storage layer resulting from these advancements, as they do not allow us to measure the impact of design and optimization choices in this new setting. In this paper, we propose a novel benchmarking approach and metrics that build upon existing benchmarks, aiming to systematically assess LSTs. We develop a framework, LST-Bench, which facilitates effective exploration and evaluation of the collaborative functioning of LSTs and data processing engines through tailored benchmark packages. A package is a mix of use patterns reflecting a target workload; LST-Bench makes it easy to define a wide range of use patterns and combine them into a package, and we include a baseline package for completeness. Our assessment demonstrates the effectiveness of our framework and benchmark packages in extracting valuable insights across diverse environments. The code for LST-Bench is open-sourced and is available at https://github.com/microsoft/lst-bench/ .

翻译：数据处理引擎日益依赖分布式文件系统以实现可扩展且成本低廉的存储。尽管Apache Parquet列式格式已成为数据存储与检索的流行选择，但其文件的不可变性使其难以满足当代分析工作负载中频繁更新的需求。日志结构化表（LST），例如Delta Lake、Apache Iceberg和Apache Hudi，为需要数据可变性的场景提供了一种替代方案，在高效更新与列式存储优势之间取得了平衡。它们提供事务、时间旅行和模式演化等特性，增强了可用性并支持多引擎访问。此外，Apache Spark和Trino等引擎可通过配置利用LST提供的优化与控制功能，以满足特定业务需求。传统基准测试和工具无法评估这些进展对存储层带来的变革性变化，因为它们无法衡量新环境下设计与优化选择的影响。本文提出了一种基于现有基准测试的新颖评估方法与指标，旨在系统评估LST。我们开发了框架LST-Bench，通过定制化基准测试包，有效探索并评估LST与数据处理引擎的协同工作机制。每个基准测试包反映目标工作负载的多种使用模式组合；LST-Bench可轻松定义各类使用模式并将其组合成包，为完整性起见，我们包含一个基础包。评估结果表明，我们的框架和基准测试包能在不同环境中有效提取有价值的见解。LST-Bench代码已开源，可从https://github.com/microsoft/lst-bench/获取。