Concurrency control and recovery in database systems
Concurrency control and recovery in database systems
A critique of ANSI SQL isolation levels
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Principles of distributed database systems (2nd ed.)
Principles of distributed database systems (2nd ed.)
A new approach to developing and implementing eager database replication protocols
ACM Transactions on Database Systems (TODS)
Don't Be Lazy, Be Consistent: Postgres-R, A New Way to Implement Database Replication
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Integrating Snapshot Isolation into Transactional Federation
CooplS '02 Proceedings of the 7th International Conference on Cooperative Information Systems
Non-Intrusive, Parallel Recovery of Replicated Data
SRDS '02 Proceedings of the 21st IEEE Symposium on Reliable Distributed Systems
Improving the Scalability of Fault-Tolerant Database Clusters
ICDCS '02 Proceedings of the 22 nd International Conference on Distributed Computing Systems (ICDCS'02)
Lazy Database Replication with Ordering Guarantees
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Ganymed: scalable replication for transactional web applications
Proceedings of the 5th ACM/IFIP/USENIX international conference on Middleware
Adaptive middleware for data replication
Proceedings of the 5th ACM/IFIP/USENIX international conference on Middleware
A Comparative Evaluation of Transparent Scaling Techniques for Dynamic Content Servers
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Postgres-R(SI): Combining Replica Control with Concurrency Control Based on Snapshot Isolation
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Allocating isolation levels to transactions
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Middleware based data replication providing snapshot isolation
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Making snapshot isolation serializable
ACM Transactions on Database Systems (TODS)
Fine-grained replication and scheduling with freshness and correctness guarantees
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Consistency Management for Partial Replication in a High Performance Database Cluster
ICPADS '05 Proceedings of the 11th International Conference on Parallel and Distributed Systems - Volume 01
Database Replication Using Generalized Snapshot Isolation
SRDS '05 Proceedings of the 24th IEEE Symposium on Reliable Distributed Systems
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
C-JDBC: flexible database clustering middleware
ATEC '04 Proceedings of the annual conference on USENIX Annual Technical Conference
Extending DBMSs with satellite databases
The VLDB Journal — The International Journal on Very Large Data Bases
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In many enterprise application integration scenarios, middleware has been instrumental in taking advantage of the flexibility and cost efficiency of clusters of computers. Web servers, application servers, platforms such as CORBA, J2EE or .NET, message brokers, and TP-Monitors, just to mention a few examples, are all forms of middleware that exploit and are built for distributed deployment. The one piece in the puzzle that largely remains a centralized solution is the database. There is, of course, much work done on scaling and parallelizing databases. In fact, several products support deployment on clusters. Clustered databases, however, place the emphasis on single applications and target very large databases. By contrast, the middleware platforms just mentioned use clustered deployment not only for scalability but also for efficiently supporting multiple concurrent applications. In this paper we tackle the problem of clustered deployment of a database engine for supporting multiple applications. In the database case, multiple applications imply multiple and different database instances being used concurrently. In the paper we show how to build such a system and demonstrate its ability to support up to 300 different databases without loss of performance.