Principles of transaction-oriented database recovery
ACM Computing Surveys (CSUR)
A Crash Recovery Scheme for a Memory-Resident Database System
IEEE Transactions on Computers
Incremental Recovery in Main Memory Database Systems
IEEE Transactions on Knowledge and Data Engineering
Checkpointing Memory-Resident Databases
Proceedings of the Fifth International Conference on Data Engineering
Proceedings of the 17th International Conference on Data Engineering
Oracle Real Application Clusters
Oracle Real Application Clusters
An integrated approach to recovery and high availability in an updatable, distributed data warehouse
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
The end of an architectural era: (it's time for a complete rewrite)
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Transaction processing performance council (TPC): state of the council 2010
TPCTC'10 Proceedings of the Second TPC technology conference on Performance evaluation, measurement and characterization of complex systems
Fast checkpoint recovery algorithms for frequently consistent applications
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
HyPer: A hybrid OLTP&OLAP main memory database system based on virtual memory snapshots
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Optimizing write performance for read optimized databases
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
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The introduction of a 64 bit address space in commodity operating systems and the constant drop in hardware prices made large capacities of main memory in the order of terabytes technically feasible and economically viable. Especially column-oriented in-memory databases are a promising platform to improve data management for enterprise applications. As in-memory databases hold the primary persistence in volatile memory, some form of recovery mechanism is required to prevent potential data loss in case of failures. Two desirable characteristics of any recovery mechanism are (1) that it has a minimal impact on the running system, and (2) that the system recovers quickly and without any data loss after a failure. This paper introduces an efficient logging mechanism for dictionary-compressed column structures that addresses these two characteristics by (1) reducing the overall log size by writing dictionary-compressed values and (2) allowing for parallel writing and reading of log files. We demonstrate the efficiency of our logging approach by comparing the resulting log-file size with traditional logical logging on a workload produced by a productive enterprise system.