🗃️ DB Row Count to RAM/Disk Calculator
Multiply row count by average row width. Now double it. That is your storage footprint before secondary indexes, MVCC bloat, and page fill factor waste have had their say. The naive estimate misses 50-150%. This calculator does not.
📐 Table Configuration
Rows × bytes/row × (1 + index overhead). That is the formula. The engine computes raw data, secondary index footprint, and total in both GB and GiB — so your RDS console and your OS agree. The hidden indexing costs that the naive row-count math misses.
Database Row Storage: Why PKs Are the Tip of the Iceberg
Most engineers estimate database storage by multiplying row count by average row width — and then are surprised when the actual on-disk footprint is 1.5× to 3× larger. The primary key clustered index is only the beginning. Secondary indexes — every foreign key, every composite index on frequently queried columns, every unique constraint — duplicate the indexed columns (plus the primary key) into separate B-tree structures. A table with five secondary indexes can easily consume more storage in indexes than in the base data. This calculator applies the fundamental capacity planning formula: Total Storage ≈ Rows × Average Row Bytes × (1 + Index Overhead Ratio) to produce a realistic projection.
What Secondary Indexes Actually Cost
A secondary index in a B-tree-based engine (InnoDB, PostgreSQL, SQL Server) stores the indexed column values plus a pointer back to the primary key row. For a table with a 4-byte integer PK and a secondary index on a VARCHAR(255) column averaging 40 bytes, each index entry consumes approximately 44 bytes before page overhead. At 50 million rows, that single index consumes an additional 2.2 GB of raw storage — and B-tree pages are typically only 65-75% full due to fill factor and page split dynamics. The industry rule of thumb of 50% index overhead is conservative; production schemas with heavy indexing for query performance routinely exceed 100% overhead relative to the base data.
MVCC, WAL, and Page Fill Factor
Beyond indexes, several architectural mechanisms inflate the physical footprint. MVCC (Multi-Version Concurrency Control) engines like PostgreSQL and InnoDB retain old row versions until vacuum or purge reclaims them, causing transient bloat of 10-40% during heavy write workloads. The Write-Ahead Log (WAL) is a separate sequential log that can grow to gigabytes during checkpoint intervals. Page fill factor — typically 70-90% for B-trees — means 10-30% of every 8 KB or 16 KB page is intentionally left empty to accommodate future inserts without immediate page splits. These factors compound: a 25 GB raw data estimate at 50% index overhead yields 37.5 GB; add 20% MVCC bloat and 15% page fill factor waste, and the real footprint approaches 52 GB — more than double the naive row-count × row-width estimate.
Practical Capacity Planning
When provisioning storage for a new database deployment, start with the raw row count and average row width, then apply the index overhead multiplier from this calculator. Add a 20-30% buffer for MVCC bloat and WAL space. For cloud managed databases (RDS, Cloud SQL, Azure DB), this total is your minimum allocated storage — and remember that most cloud providers bill on provisioned storage, not utilized storage. Over-provisioning by 30% is far cheaper than a production outage caused by a full disk. Use the GiB conversion metric to reconcile your binary storage allocation with the decimal figures reported by most cloud console dashboards.