Cyclic reference counting for combinator machines
Proc. of a conference on Functional programming languages and computer architecture
Cyclic reference counting with local mark-scan
Information Processing Letters
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Partition selection policies in object database garbage collection
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Storage Reclamation and Reorganization in Client-Server Persistent Object Stores
Proceedings of the Tenth International Conference on Data Engineering
Efficient Incremental Garbage Collection for Client-Server Object Database Systems
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Garbage Collection in Object Oriented Databases Using Transactional Cyclic Reference Counting
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
A reference-counting garbage collection algorithmfor cyclical functional programming
Proceedings of the 7th international symposium on Memory management
Online reorganization of databases
ACM Computing Surveys (CSUR)
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Garbage collection is important in object-oriented databases to free the programmer from explicitly deallocating memory. In this paper, we present a garbage collection algorithm, called Transactional Cyclic Reference Counting (TCRC), for object-oriented databases. The algorithm is based on a variant of a reference-counting algorithm proposed for functional programming languages The algorithm keeps track of auxiliary reference count information to detect and collect cyclic garbage. The algorithm works correctly in the presence of concurrently running transactions, and system failures. It does not obtain any long-term locks, thereby minimizing interference with transaction processing. It uses recovery subsystem logs to detect pointer updates; thus, existing code need not be rewritten. Finally, it exploits schema information, if available, to reduce costs. We have implemented the TCRC algorithm and present results of a performance study of the implementation.