What Type of Indexing Technique Is Suitable for Low Selectivity Data?


Scott Campbell

What Type of Indexing Technique Is Suitable for Low Selectivity Data?

In the world of databases, indexing plays a crucial role in optimizing query performance. By creating an index on a particular column, we can significantly speed up searching and sorting operations.

However, not all indexing techniques are suitable for every scenario. When dealing with low selectivity data, it becomes essential to choose the right indexing technique that can efficiently handle the data distribution and improve query response time.

Understanding Low Selectivity Data

Before delving into the appropriate indexing technique for low selectivity data, let’s first understand what exactly low selectivity means. In simple terms, selectivity refers to the uniqueness or distinctness of values in a column. High selectivity means that a column has many distinct values, while low selectivity indicates a limited number of unique values.

For example, consider a column ‘Gender’ in a database table containing only two distinct values: ‘Male’ and ‘Female’. Since there are only two options, the selectivity of this column is low. On the other hand, if we have a column ‘ProductID’ with each row having a unique value, then it has high selectivity.

The Challenges of Low Selectivity Data

Low selectivity data poses specific challenges when it comes to indexing. One major issue is that traditional index structures may not be efficient for such scenarios. When there are only a few unique values in a column compared to the total number of rows in the table, using default indexing techniques may result in excessive storage requirements and slower query performance.

Let’s explore some indexing techniques that are suitable for handling low selectivity data:

B-Tree Indexing

B-Tree is one of the most commonly used indexing techniques in databases. It is designed to handle a wide range of data distributions, including low selectivity scenarios. B-Tree indexes store keys and corresponding row pointers in a balanced tree structure, allowing efficient searching and sorting operations.

Even though B-Tree indexes work well for low selectivity data, they may not be the most optimal choice if the selectivity is extremely low (e.g., binary or boolean columns). In such cases, other specialized indexing techniques might be more suitable.

Bitmap Indexing

Bitmap indexing is specifically designed for columns with low cardinality, i.e., a limited number of distinct values. It uses a bitmap for each distinct value in the column, where each bit represents whether a particular row contains that value or not.

This technique works exceptionally well for low selectivity data as it offers fast search performance and requires minimal storage space. However, bitmap indexes are typically slower when updating or inserting new rows due to the need to update multiple bitmaps simultaneously.

Hash Indexing

Hash indexing is another technique suitable for low selectivity data. It involves computing a hash value based on the indexed column’s content and storing key-value pairs accordingly. This technique provides constant-time lookup performance regardless of the number of rows in the table.

However, one limitation of hash indexing is that it only supports equality searches and does not perform well with range queries or sorting operations.


When dealing with low selectivity data, choosing an appropriate indexing technique becomes crucial for optimal query performance. B-Tree indexes are generally suitable for most scenarios, while bitmap and hash indexes offer specialized solutions for specific cases. By carefully considering the unique characteristics of your data distribution, you can implement an efficient indexing strategy that improves query response time and enhances overall database performance.

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