Divergent physical design tuning for replicated databases
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Scalable and dynamically balanced shared-everything OLTP with physiological partitioning
The VLDB Journal — The International Journal on Very Large Data Bases
SMIX: self-managing indexes for dynamic workloads
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
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Many data-intensive websites use databases that grow much faster than the rate that users access the data. Such growing datasets lead to ever-increasing space and performance overheads for maintaining and accessing indexes. Furthermore, there is often considerable skew with popular users and recent data accessed much more frequently. These observations led us to design Shinobi, a system which uses horizontal partitioning as a mechanism for improving query performance to cluster the physical data, and increasing insert performance by only indexing data that is frequently accessed. We present database design algorithms that optimally partition tables, drop indexes from partitions that are infrequently queried, and maintain these partitions as workloads change. We show a 60脳 performance improvement over traditionally indexed tables using a real-world query workload derived from a traffic monitoring application