ClusteredLog: Optimizing Log Structures for Efficient Data Recovery and Integrity Management in Database Systems

In modern database systems, efficient log management is crucial for ensuring data integrity and facilitating swift recovery from potential data corruption or system failures. Traditional log structures, which store operations sequentially as they occur, often lead to significant delays in accessing and recovering specific data objects due to their scattered nature across the log. ClusteredLog addresses the limitations of traditional logging methods by implementing a novel logical organization of log entries. Instead of simply storing operations sequentially, it groups related operations for each data item into clusters. As a result, ClusteredLog enables faster identification and recovery of damaged data items and thus reduces the need for extensive log scanning, improving overall efficiency in database recovery processes. We introduce data structures and algorithms that facilitate the creation of these clustered logs, which also track dependencies and update operations on data items. Simulation studies demonstrate that our clustered log method significantly accelerates damage assessment and recovery times compared to traditional sequential logs, particularly as the number of transactions and data items increases. This optimization is pivotal for maintaining data integrity and operational efficiency in databases, especially in scenarios involving potential malicious modifications.