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Index selectivity is a key factor in optimizing SQL query performance. It measures how well an index filters data, influencing the speed of data retrieval. Understanding and calculating index selectivity can help database administrators improve query efficiency by choosing the most effective indexes.
What Is Index Selectivity?
Index selectivity refers to the proportion of unique values in a column relative to the total number of rows. High selectivity indicates that the column has many unique values, making indexes on it more effective. Conversely, low selectivity suggests many duplicate values, reducing the index’s usefulness.
Calculating Index Selectivity
The formula for index selectivity is straightforward:
Selectivity = Number of Unique Values / Total Number of Rows
For example, if a table has 1,000 rows and a column with 900 unique values, the selectivity is 0.9, indicating high effectiveness for indexing.
Real Data Example
Consider a table of customer data with 10,000 rows. The “Country” column contains 50 unique country names. The selectivity is:
0.005 = 50 / 10,000
This low selectivity suggests that indexing the “Country” column may not significantly improve query performance. Instead, focusing on columns with higher selectivity, like “Customer ID,” which has 10,000 unique values, would be more beneficial.
Implications for Query Optimization
Calculating index selectivity helps in deciding which columns to index. High selectivity columns are typically better candidates for indexing, leading to faster query execution. Low selectivity columns might be better suited for other optimization strategies or composite indexes.