Data cardinality refers to the uniqueness of data values in a database column. Understanding data cardinality is essential for optimizing database performance, designing efficient queries, and ensuring data integrity. It plays a crucial role in database management and impacts how data is stored, retrieved, and analyzed.

Let us delve into five aspects you should know about data cardinality. 

High cardinality

High cardinality means that a column has a large number of unique values. Examples include user IDs or email addresses. Columns with high cardinality can impact database performance because they require more storage space and can slow down query processing.

Low cardinality

Low cardinality means that a column has a small number of unique values. Examples include gender or boolean fields. Columns with low cardinality are easier to index and optimize, resulting in faster query performance and more efficient data retrieval.

Impact on indexing

Cardinality affects indexing strategies. High cardinality columns benefit from unique indexes, which improve search efficiency. Low cardinality columns can use bitmap indexes, which are more space-efficient and faster for certain types of queries.

Query optimization

Understanding cardinality helps in query optimization. Knowing the cardinality of columns allows database administrators to write more efficient queries. For example, filtering on high cardinality columns can reduce the number of rows processed, speeding up query execution.

Data integrity

Cardinality ensures data integrity by maintaining unique constraints on columns. For instance, primary keys must have high cardinality to ensure each row is uniquely identifiable. This prevents duplicate entries and maintains the accuracy of the database. 

The conclusion 

Understanding data cardinality is vital for efficient database management. By recognizing the cardinality of your data, you can design better databases, write optimized queries, and maintain high performance. This knowledge helps in making informed decisions about data storage and retrieval, ensuring a robust and efficient database system.