Materialized views are a core construct in database systems, used to accelerate analytical queries and optimize batch pipelines for extract-transform-load (ETL) workflows. Maintaining view consistency as underlying data evolves is a fundamental challenge, especially in high-throughput and real-time settings. Incremental view maintenance (IVM) has been studied for decades and continues to attract significant investment from major database vendors. However, most industrial systems either offer limited SQL-operator coverage or require users to hand-tune refresh strategies. This paper presents Enzyme, an IVM engine developed at Databricks to power Spark Declarative Pipelines. It provides a built-in, end-to-end approach to incremental pipelines, utilizing materialized views as first-class building blocks. By automating refresh planning, Enzyme reduces total cost of ownership and lets users focus on business logic rather than MV mechanics. Validation across thousands of large-scale production pipelines spanning diverse application domains has demonstrated substantial computational efficiency gains, yielding a cumulative daily compute reduction of billions of CPU seconds. Built atop Apache Spark primitives, Enzyme adds a cost-based optimization layer that selects refresh strategies for collections of materialized views organized into pipelines. Enzyme's modular architecture is designed to generalize across data sources and query engines. We present key design decisions for incremental refresh planning and execution, including optimizations that exploit batching opportunities across materialized view sources. Experimental results on standard benchmarks demonstrate significant performance improvements at scale.

翻译：摘要：物化视图是数据库系统中的核心构建，用于加速分析查询并优化提取-转换-加载（ETL）工作流的批处理管道。随着底层数据的演变，保持视图一致性是一项基本挑战，尤其是在高吞吐量和实时场景中。增量视图维护（IVM）已研究数十年，并持续吸引着各大数据库厂商的重大投入。然而，大多数工业系统要么提供有限的SQL操作覆盖范围，要么要求用户手动调优刷新策略。本文介绍了Enzyme，一个在Databricks开发的IVM引擎，用于驱动Spark声明式管道。它提供了一种内置的端到端方法来实现增量管道，将物化视图作为第一类构建块。通过自动化刷新规划，Enzyme降低了总拥有成本，使用户能够专注于业务逻辑而非MV机制。在涵盖不同应用领域的数千个大规模生产管道上的验证表明，其计算效率显著提升，每日累计减少数十亿CPU秒的计算量。基于Apache Spark原语构建，Enzyme添加了一个基于成本的优化层，为组织成管道的物化视图集合选择刷新策略。Enzyme的模块化架构设计旨在跨数据源和查询引擎进行泛化。我们介绍了增量刷新规划和执行的关键设计决策，包括利用物化视图源之间批处理机会的优化。在标准基准上的实验结果表明，在大规模场景下性能显著提升。