In-Memory Database Search Optimization Using Graph Community Structure

摘要

Database searching is optimized utilizing a graph community structure. A graph is created from transaction data based upon adjacent value occurrences. This may be done by scanning transaction data from top to bottom, and creating an edge between a current index value and a previous index value. Next, algorithms identify communities in the graph to create a graph community structure. These communities comprise blocks of patterns of similar value-ids distributed in the transaction data. Scanning and transition indices may be created with an eye toward reducing memory usage and enhancing performance. Query searching is then executed in an efficient manner on a per-community basis. For example, exact queries, range queries, and/or “AND” queries may be executed more efficiently upon communities of records, skipping those not belonging to the same community. Embodiments are suited to search an in-memory database having large volumes of column-oriented data stored in RAM.