Main Memory Database Recovery Strategies

Abstract

Most of the current application scenarios, such as trading, real-time bidding, advertising, weather forecasting, social gaming, etc., require massive real-time data processing. Main memory database systems have proved to be an efficient alternative to such applications. These systems maintain the primary copy of the database in the main memory to achieve high throughput rates and low latency. However, a database in RAM is more vulnerable to failures than in traditional disk-oriented databases because of the memory volatility. DBMSs implement recovery activities (logging, checkpoint, and restart) for recovery proposes. Although the recovery component looks similar in disk- and memory-oriented systems, these systems differ dramatically in the way they implement their architectural components, such as data storage, indexing, concurrency control, query processing, durability, and recovery. This tutorial aims to provide a thorough review of in-memory database recovery techniques. To achieve this goal, we intend to review the main concepts of database recovery and architectural choices to implement an in-memory database system. Only then, we present the techniques to recover in-memory databases and discuss the recovery strategies of a representative sample of modern in-memory databases. Besides, the tutorial presents some aspects related to challenges and future directions of research in MMDBs in order to provide guidance for other researchers.