Indexing improves read performance by creating optimized data structures for quick lookups, but it introduces overhead for write operations. When a database index exists, every write (insert, update, or delete) must update both the data and the index. For example, inserting a new row into a table with an index on a column like email requires the database to add the new email value to the index structure (e.g., a B-tree). This additional step increases the time it takes to complete the write. The more indexes a table has, the more work is required during writes, as each index must be updated independently. This trade-off is critical in write-heavy systems, where excessive indexing can lead to noticeable slowdowns.
The performance impact depends on the type of index and the database engine. For instance, B-tree indexes, commonly used for range queries, require rebalancing when new entries are added, which can fragment the structure over time. Updating a column that is part of an index (e.g., changing a user’s username) forces the database to locate and modify the corresponding index entry, adding latency. Additionally, indexes on columns with high cardinality (e.g., unique IDs) are typically faster to update than those on low-cardinality columns (e.g., boolean flags), but all indexes still add overhead. InnoDB in MySQL, for example, uses clustered indexes where the primary key is stored with the data, making primary key updates particularly expensive because they require reorganizing the entire row’s storage.
To mitigate write performance issues, developers should carefully evaluate which indexes are necessary. For instance, if a table is primarily used for logging data with infrequent reads, fewer indexes might be preferable. In write-heavy scenarios, delaying index creation until after bulk data insertion (e.g., using CREATE INDEX AFTER LOAD in PostgreSQL) can reduce overhead. Another approach is to use partial or filtered indexes (e.g., indexing only active users) to limit their size and update frequency. Monitoring tools like execution plans can help identify underused indexes that can be safely removed. Balancing read efficiency with write performance is key—indexes should be added deliberately, tested under realistic workloads, and pruned when they no longer provide sufficient value to justify their cost.