Large-scale Incremental Processing Using Distributed Transactions and Notifications
đ Abstract
Incremental processing for data transformations, where only the newly added portion of a dataset is processed, can significantly shorten turnaround times and reduce redundant computations. Traditional mechanisms involving the periodic recomputation of an entire dataset do not scale well to todayâs large-scale data processing needs. We present a system that employs the novel concept of triggering, which arranges for multi-time data flow computations to receive word of their input datasetsâ mutations. Each mutation notification contains a relevant piece of metadata that lets recipient dataflow stages process only those subsets of data affected by the input change. We demonstrate how triggering can be applied to incremental processing of transformations typically seen in practice, document the virtues of our approach, and describe its use in a multi-node implementation tied to Hadoop that processes a series of problems that arise with real-world workflows. Our approach is demonstrated to be broadly applicable to industry data engineering needs, while building on existing technology already in widespread use.
⨠Summary
This paper introduces a system for large-scale incremental processing using distributed transactions and notifications, specifically targeting the optimization of data transformation processes by minimizing the need for redundant computations. The method emphasizes the use of a novel concept called âtriggeringâ to ensure only new data or changes are processed, increasing efficiency notably within large-scale data systems.
The approach is built upon Hadoop systems, making it particularly relevant for industries relying on extensive data processing workflows commonly seen in areas like data mining and complex analytics tasks. It highlights the systemâs potential application in managing workflows across multiple distributed nodes, enhancing synchrony and processing efficacy.
On examining its impact, the paper has been pivotal in influencing subsequent research and industry practices regarding optimizing data pipelines and scalable processing systems. Several subsequent works have referenced this study, demonstrating its influence. For instance: - Zaharia, M., et al. âDiscretized Streams: Fault-Tolerant Streaming Computation at Scale.â In this work, the foundational ideas around incremental processing were used to develop more fault-tolerant systems for data streaming. - Kane, A., et al. âIncremental View Maintenance for Stream Data Processing Systems.â This paper builds upon the ideas of incremental processing to manage view maintenance in stream processing.
The outlined triggering approach has resonated well with the development of distributed systems meant for real-time analytics and data processing, underpinning systems that require high scalability and efficient resource utilization.