Principles of transaction-oriented database recovery
ACM Computing Surveys (CSUR)
Optimistic recovery in distributed systems
ACM Transactions on Computer Systems (TOCS)
ACM Transactions on Database Systems (TODS)
Recovery mechanisms in database systems
Recovery mechanisms in database systems
The Recovery Manager of the System R Database Manager
ACM Computing Surveys (CSUR)
Transaction Processing: Concepts and Techniques
Transaction Processing: Concepts and Techniques
Persistent Applications Using Generalized Redo Recovery
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Redo Recovery after System Crashes
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Recovery guarantees in mobile systems
Proceedings of the 1st ACM international workshop on Data engineering for wireless and mobile access
High speed on-line backup when using logical log operations
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Phoenix project: fault-tolerant applications
ACM SIGMOD Record
Support for Recovery in Mobile Systems
IEEE Transactions on Computers
An Adaptable Infrastructure for Customized Persistent Object Management
EDBT '02 Proceedings of the Worshops XMLDM, MDDE, and YRWS on XML-Based Data Management and Multimedia Engineering-Revised Papers
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Recovery guarantees for Internet applications
ACM Transactions on Internet Technology (TOIT)
OpenWS-Transaction: enabling reliable web service transactions
ICSOC'05 Proceedings of the Third international conference on Service-Oriented Computing
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Recovery can be extended to new domains at reduced logging cost by exploiting “logical” log operations. During recovery, a logical log operation may read data values from any recoverable object, not solely from values on the log or from the updated object. Hence, we needn't log these values, a substantial saving. In [8], we developed a redo recovery theory that deals with general log operations and proved that the stable database remains recoverable when it is explained in terms of an installation graph. This graph was used to derived a write graph that determines a flush order for cached objects that ensures that the database remains recoverable. In this paper, we introduce a refined write graph that permits more flexible cache management that flushes smaller sets of objects. Using this write graph, we show how: (i) the cache manager can inject its own operations to break up atomic flush sets; and (ii) the recovery process can avoid redoing operations whose effects aren't needed by exploiting generalized recovery LSNs. These advances permit more cost-effective recovery for, e.g., files and applications.