Estimate database storage requirements from row count, row size, index overhead, and growth projections. Plan capacity for any RDBMS.
Estimating database size before deployment prevents the all-too-common scenario of running out of disk space in production. Database storage is more than just rows multiplied by row size—indexes, overhead structures, WAL/transaction logs, temporary space, and MVCC bloat all consume significant space. A database that looks like 10 GB of raw data can easily occupy 25–40 GB on disk.
This calculator models total database size by combining data volume (rows × row size), index overhead, page/block overhead, and a configurable general overhead factor. It also projects growth over time, giving you a multi-month capacity forecast. Use it for initial sizing, migration planning, or capacity reviews of existing databases.
Integrating this calculation into monitoring and reporting workflows ensures that engineering decisions are grounded in real data rather than assumptions about system behavior. Precise measurement of this value supports informed infrastructure decisions and helps engineering teams optimize system architecture for both performance and cost efficiency.
Under-sizing database storage causes outages. Over-sizing wastes expensive SSD capacity. This calculator accounts for the overhead that raw data size alone misses, giving you an accurate estimate for procurement and capacity planning. Data-driven tracking enables evidence-based infrastructure decisions, reducing the risk of over-provisioning costs or under-provisioning that leads to performance bottlenecks.
data_size = rows × avg_row_bytes; index_size = data_size × (index_pct / 100); total = (data_size + index_size) × (1 + overhead_pct / 100); future = total × (1 + monthly_growth_pct / 100) ^ months
Result: 3.50 GB total
10 million rows × 200 bytes = 2,000 MB (1.95 GB) data. Indexes at 40% add 800 MB. Subtotal: 2,800 MB. General overhead at 25% adds 700 MB. Total: 3,500 MB (3.42 GB). With 5% monthly growth, this reaches 5.6 GB in 12 months.
PostgreSQL: Add 23 bytes per row for tuple header, plus 8 bytes page header per 8 KB page. MySQL InnoDB: Add 13–20 bytes per row for record header and row versioning. SQL Server: Add 7–14 bytes per row depending on nullable columns. Oracle: Add 3 bytes per row plus 24 bytes per block.
Never use more than 80% of available database storage. Above this threshold, auto-vacuum in PostgreSQL, index maintenance, and sort operations may fail due to insufficient temporary space. Plan your capacity alerts at 60%, 70%, and 80%.
Cloud managed databases (RDS, Cloud SQL, Azure SQL) have maximum storage limits per instance type. Check that your projected growth stays within the instance's storage ceiling. Scaling storage is possible but may require downtime on some platforms.
Sum the byte sizes of all columns: INT=4, BIGINT=8, VARCHAR(n)=average actual length +1–4 bytes overhead, TEXT=average length, TIMESTAMP=8, BOOLEAN=1, UUID=16. Add tuple header overhead (23 bytes for PostgreSQL, 8–16 bytes for MySQL).
A single B-tree index on an integer column uses about 30% of the table's size. A unique index is similar. Composite indexes and covering indexes use more. Total index overhead of 30–60% of data size is common for well-indexed OLTP tables.
General overhead covers WAL/redo logs (1–5 GB), temporary tablespace for sorts and joins, MVCC dead tuples (10–30% for PostgreSQL), page fill factor losses (typically 10–15%), and system catalogs. A 20–30% overhead factor is a good starting point.
Table partitioning adds overhead for partition metadata and may reduce index efficiency slightly. However, it improves query performance on large tables and makes maintenance operations faster. The space overhead is typically under 1%.
Size for peak. Databases need temporary space for sorts, hash joins, and maintenance operations. Provision at least 20% free space above your projected data size. Performance degrades significantly when disks approach 85–90% utilization.
Check your row insert rate (rows/day) and average row size. Multiply to get daily data growth. Factor in index growth proportionally. Track actual growth monthly and adjust projections. Most databases grow faster than initially estimated.