Main Memory Database Recovery

2021 ◽  
Vol 54 (2) ◽  
pp. 1-36
Author(s):  
Arlino Magalhaes ◽  
Jose Maria Monteiro ◽  
Angelo Brayner
Keyword(s):  
Data Storage ◽  
Concurrency Control ◽  
Database Systems ◽  
Good Alternative ◽  
Main Memory ◽  
Time Data ◽  
Recovery Techniques ◽  
Main Memory Database ◽  
Database Recovery ◽  
Real Time Data Processing

Many of today’s applications need massive real-time data processing. In-memory database systems have become a good alternative for these requirements. 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 survey aims to provide a thorough review of in-memory database recovery techniques. To achieve this goal, we reviewed 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.

A Novel Crash Recovery Scheme for Distributed Real-Time Databases

2009 ◽  
pp. 769-787
Author(s):  
Yingyuan Xiao
Keyword(s):  
Real Time ◽  
Agile Manufacturing ◽  
Database System ◽  
Main Memory ◽  
Information Support ◽  
Timing Constraints ◽  
Time Data ◽  
Main Memory Database ◽  
Distributed Database System ◽  
Hard Real Time

Recently, the demand for real-time data services has been increasing (Aslinger & Son, 2005). Many applications such as online stock trading, agile manufacturing, traffic control, target tracking, network management, and so forth, require the support of a distributed real-time database system (DRTDBS). Typically, these applications need predictable response time, and they often have to process various kinds of queries in a timely fashion. A DRTDBS is defined as a distributed database system within which transactions and data have timing characteristics or explicit timing constraints and system correctness that depend not only on the logic results but also on the time at which the logic results are produced. Similar to conventional real-time systems, transactions in DRTDBSs are usually associated with timing constraints. On the other hand, a DRTDBS must maintain databases for useful information, support the manipulation of the databases, and process transactions. Timing constraints of transactions in a DRTDBS are typically specified in the form of deadlines that require a transaction to be completed by a specified time. For soft realtime transactions, failure to meet a deadline can cause the results to lose their value, and for firm or hard real-time transactions, a result produced too late may be useless or harmful. DRTDBSs often process both temporal data that lose validity after their period of validity and persistent data that remain valid regardless of time. In order to meet the timing constraints of transactions and data, DRTDBSs usually adopt main memory database (MMDB) as their ground support. In an MMDB, “working copy” of a database is placed in the main memory, and a “secondary copy” of the database on disks serves as backup. Data I/O can be eliminated during a transaction execution by adopting an MMDB so that a substantial performance improvement can be achieved. We define a DRTDBS integrating MMDB as a distributed real-time main memory database system (DRTMMDBS).

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