Partitioning Retail Database for Performance Improvement

Anil Rajput, Kshmasheel Mishra, Vaibhav Khanna


Retail data analysts have a challenging task to analyze large datasets relating to retail stores. With the advent of materialized views, a database administrator creates one or more materialized views to expedite query results. This requires a proactive decision making on the right partitioning strategy during the logical design phase and the same should be implemented in the physical database schema. This in turn requires experimentation with various alternative strategies for measured performance improvements before suggesting these strategies as standard data models. This paper outlines the performance improvement techniques relating to table partitioning experiments conducted on retail sample data set. The partitioning strategies were initially applied on a representative subset of the real data (10K and 100K records). Then the experiments were conducted to measure performance improvements on the entire model data (10 lakh records). Significant performance benefits were achieved in the experiments conducted during this study using right partitioning strategy. These strategies can positively contribute towards improving the overall user satisfaction.


Keywords: Multidimensional databases, performance, partitioning, interval partitioning, range partitioning, hash partitioning, database performance experiments.

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