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Index selectivity is a key metric in large-scale database systems that helps determine the efficiency of an index in filtering query results. It measures the uniqueness of the values stored in a column relative to the total number of rows in a table. Understanding how to calculate index selectivity can optimize query performance and improve database design.
Understanding Index Selectivity
Index selectivity is expressed as a ratio or percentage. A high selectivity indicates that the index column contains many unique values, which is beneficial for query filtering. Conversely, low selectivity suggests many duplicate values, making the index less effective for certain queries.
Calculating Index Selectivity
The basic formula for index selectivity is:
Index Selectivity = Number of Unique Values / Total Number of Rows
For example, if a table has 10,000 rows and a column has 1,000 unique values, the selectivity is:
0.1 or 10%
Implications of Selectivity
High selectivity (close to 1) indicates that an index is likely to improve query performance significantly, especially for equality searches. Low selectivity suggests that the index may not be as effective, and alternative indexing strategies might be necessary.
Additional Considerations
Factors such as data distribution, query patterns, and database workload influence the usefulness of an index. Regularly analyzing index selectivity can guide database optimization efforts.