Database Index Size Calculator

Index size grows faster than you expect because databases store more than just your data. A million-row table with a simple integer index might need 12 MB of storage, but that same table with a composite index on three columns can balloon to 35 MB. The difference comes from page overhead, pointer storage, and the tree structure itself — costs that multiply with every additional column.

This calculator models B-tree indexes, the default structure in most relational databases. B-trees store data in fixed-size pages (typically 8KB), with each page containing index keys plus pointers to child pages or actual table rows. The fill factor determines how much of each page gets used before the database creates a new page, trading storage efficiency for insert performance.

Database index size estimation helps prevent storage surprises and performance bottlenecks. A 500 GB table might seem manageable until you discover its indexes consume another 200 GB. Large indexes compete with your application for RAM, and indexes that don't fit in memory turn fast queries into slow disk operations.

Calculate index sizes before building them on production tables, especially for composite indexes or tables expected to grow beyond 100,000 rows. Use these estimates to plan storage capacity and memory allocation for database servers.

This calculator is essential when evaluating index strategies for large datasets. If an index size estimate approaches your available RAM, consider alternative approaches like partial indexes, covering indexes, or table partitioning before implementation.

Run calculations during capacity planning for database migrations or when adding indexes to existing large tables. Index creation can consume significant temporary space, sometimes requiring double the final index size during the build process.

The biggest mistake is underestimating composite index overhead. Developers often assume that indexing three columns costs three times a single column, but page overhead and pointer storage create multiplicative effects. A three-column index on a large table can easily exceed the size of the underlying table data.

Another common error is ignoring fill factor impact on production systems. Setting fill factor to 100% minimizes storage but creates performance disasters when applications insert new data. Every insert triggers page splits, fragmenting the index and degrading query performance over time.

Developers frequently forget that index size directly affects memory requirements. A database might run perfectly with 16GB RAM until index sizes exceed available buffer pool space. At that point, index lookups become disk operations, and query performance drops by orders of magnitude without warning.

The core calculation starts with row size: (number of columns × average column size) + pointer overhead (typically 6 bytes per row). This gives the space needed for each index entry. Page utilization follows from the fill factor — an 8KB page with 80% fill factor provides about 6.5KB for actual data after headers.

Rows per page = floor((page_size × fill_factor) / row_size). Total pages needed = ceiling(total_rows / rows_per_page). The final index size includes both leaf pages (containing actual data) and internal pages (containing navigation pointers), though leaf pages dominate for most practical indexes.

For example: 1 million rows with 3 columns averaging 8 bytes each gives 30 bytes per row. Add 6 bytes for pointers = 36 bytes total. At 80% fill factor, each 8KB page holds floor(6553 / 36) = 182 rows. Total pages needed: ceiling(1,000,000 / 182) = 5,495 pages = 43.8 MB for leaf level, plus minimal overhead for internal pages.

The standard formula assumes uniform data distribution, but real-world skew can make indexes 30-50% larger than estimated. High-cardinality leading columns create deeper trees, while low-cardinality leading columns create wider trees with more internal pages. PostgreSQL's FILLFACTOR and SQL Server's PAD_INDEX options let you tune this trade-off per index.