When should you use a composite index in database performance and scalability? (2024)

A composite index is an index that combines two or more columns of a table into a single key. For example, if you have a table of orders with columns customer_id, order_date, and order_amount, you can create a composite index on (customer_id, order_date) to speed up queries that filter or sort by both columns. A composite index can also include expressions or functions, such as (customer_id, lower(order_status)).

A composite index works by storing the values of the indexed columns in a sorted order, along with a pointer to the corresponding row in the table. This allows the database to quickly locate and retrieve the rows that match the query conditions, without scanning the entire table. A composite index can also be used to avoid sorting the result set, if the query order matches the index order. For example, if you have a composite index on (customer_id, order_date), you can query the orders by a specific customer and sort them by date without performing an additional sort operation.

The main benefit of composite indexes is that they can improve the performance of complex queries that involve multiple columns or expressions. By using a composite index, you can reduce the number of disk I/O operations, CPU cycles, and memory usage required to execute the query. This can result in faster response times, lower resource consumption, and higher throughput. A composite index can also help to enforce uniqueness constraints on a combination of columns, such as a primary key or a foreign key.

The main drawback of composite indexes is that they increase the size and maintenance cost of the index. A composite index takes up more space than a single-column index, because it stores more values and pointers. A composite index also requires more updates and reorganizations when the data in the table changes, because it affects multiple columns. This can lead to slower insertions, deletions, and modifications, as well as more fragmentation and overhead.

When choosing columns for a composite index, it is important to consider the query patterns and data distribution of the table. Selective columns, which are used in the where clause, join condition, or order by clause of the queries, can help filter out a large number of rows. High cardinality columns, with many distinct values, can narrow down the search range more effectively than low cardinality columns. Logical order columns, which follow a natural or meaningful sequence, may help to avoid sorting the result set if they match the query order. Low correlation columns, which do not depend on each other or have a predictable relationship, can reduce redundancy and overlap of the index values. This makes the index more compact and efficient.

When using a composite index in a query, the index order and query conditions must be taken into consideration. Generally, the prefix columns (the leftmost columns) of the index should be used in the query as they determine the search range. Equality or inequality operators should be used for these columns to take advantage of the sorted order of the index. Additionally, if any additional columns (the rightmost columns) are used, they should be selective and logical to improve the performance of the query. For example, if you have a composite index on (customer_id, order_date, order_amount), you can use it to query by customer_id = 1 and order_date between '2020-01-01' and '2020-12-31' and order_amount > 100, or by customer_id = 1 and order_by order_date, order_amount. However, it cannot be used to query by customer_id > 1 or order_date > '2020-01-01', or by customer_id = 1 and order_amount < 100 or order_by order_amount, order_date.

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