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IBM Research

Computer Science

Autonomic Databases


As part of the SMART initiative, IBM Researchers are automating the process of data partitioning. Given a workload of SQL statements, scientists are trying to determine automatically how to partition data across multiple nodes to achieve optimal performance. The traditional approach to this problem has been to use heuristic rules, but this method does not consider all aspects of a query performance. IBM Researchers are taking a slightly different approach to automating and improving data partitioning by using the query optimizer to recommend possible partitions for each table that will have a positive impact on each query workload. Additionally, IBM data management experts have compared a rank-based enumeration method with a random-based on and experiment results have demonstrated that the former is more effective.

Additional Information:
Link to content in pdf format Automating Physical Database Design in a Parallel Database
 (179KB PDF file)
Partitioning Advisor
Partitioning Advisor[click on image for larger view]

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