Big data systems are sufficiently stable to store and process a massive volume of rapidly changing data. However, big data systems are composed of large-scale hardware resources that make their subspecies easily fail. Fault tolerance is the main property of such systems because it maintains availability, reliability, and constant performance during faults. Achieving an efficient fault tolerance solution in a big data system is challenging because fault tolerance must meet some constraints related to the system performance and resource consumption. This study aims to provide a consistent understanding of fault tolerance in big data systems and highlights common challenges that hinder the improvement in fault tolerance efficiency. The fault tolerance solutions applied by previous studies intended to address the identified challenges are reviewed. The paper also presents a perceptive discussion of the findings derived from previous studies and proposes a list of future directions to address the fault tolerance challenges.

大数据系统足够稳定，可以存储和处理大量快速变化的数据。然而，大数据系统由大规模的硬件资源组成，这使得它们的亚种很容易失效。容错是此类系统的主要属性，因为它可以在故障期间保持可用性、可靠性和恒定性能。在大数据系统中实现高效的容错解决方案具有挑战性，因为容错必须满足与系统性能和资源消耗相关的一些约束。本研究旨在提供对大数据系统容错的一致理解，并强调阻碍容错效率提高的常见挑战。回顾了先前研究应用的容错解决方案，旨在解决已识别的挑战。