Database Storage Calculator
Size production disk volumes and IOPS for PostgreSQL, MySQL InnoDB, and MongoDB with exact row header padding, B-Tree index bloat, and Write-Ahead Log turnover models.
Schema & Workload Parameters
Select a database scenario preset or customize row sizes, indexes, and write velocity.
Database Footprint & IOPS
PostgreSQL (5.0M Rows)| Total Database Table Records | 5,000,000 Records (300 bytes/row) |
| Table Data Disk Footprint | 1.84 GB |
| Total Secondary Indexes Footprint | 1.51 GB (4 secondary indexes) |
| Index-to-Data Storage Ratio | 82.1% of table size |
| Write-Ahead Log (WAL / Binlog) Requirement | 5.05 GB (24h retention at 150 TPS) |
| MVCC Dead Tuple & Vacuum Headroom | 0.46 GB (25% MVCC reserve) |
| Estimated Sustained Disk IOPS | 765 IOPS (390 write, 375 read) |
| Recommended Cloud Storage Tier | AWS gp3 (3,000 baseline IOPS) |
Engineering Database Disk Sizing: The Hidden Cost of Indexes, Headers, and Logs
A frequent error among software developers and database administrators is estimating storage needs by multiplying row counts by raw column data lengths. In production database management systems (RDBMS) like PostgreSQL and MySQL, or document databases like MongoDB, the physical disk volume requirement is routinely two to four times larger than the raw serialized data payload.
1. Engine-Specific Row Header Padding and Alignment
Every stored tuple requires structural metadata for transaction tracking and column nullability. In PostgreSQL, each table row includes a 23-byte `HeapTupleHeader`, a 4-byte line pointer (`ItemId`) in the page directory, and a null bitmap, resulting in a mandatory 31-byte overhead per row. Furthermore, PostgreSQL aligns data along CPU word boundaries (typically 4-byte or 8-byte increments), introducing trailing padding bytes. In MySQL InnoDB, each clustered index record prepends a 5-byte header, a 6-byte transaction ID (`DB_TRX_ID`), and a 7-byte rollback pointer (`DB_ROLL_PTR`).
2. B-Tree Secondary Index Overhead
Secondary B-Tree indexes provide logarithmic search speed ($O(\log N)$) at the cost of substantial disk footprint. Each secondary index entry stores the indexed column values, a pointer to the heap tuple (or primary key in InnoDB), and internal B-Tree page navigation pointers. Because indexes must accommodate arbitrary insertions without frequent rebalancing, pages operate with a fill factor between 85% and 93%. In transactional applications with four to six indexed columns, the cumulative index footprint regularly matches or exceeds the primary table data footprint.
3. Write-Ahead Logging (WAL) and Binary Log Sizing
Relational databases guarantee ACID atomicity and durability by writing changes to an append-only Write-Ahead Log (WAL in PostgreSQL, redo log and binary log in MySQL) before flushing dirty buffers to data pages. In systems processing 200 or more write transactions per second (TPS), log generation rates reach several gigabytes per hour. For point-in-time recovery (PITR) and read-replica replication, systems must retain 24 to 72 hours of transaction logs on local disk, requiring dedicated high-speed log volumes.
4. MVCC Table Bloat and Space Reclamation
Multi-Version Concurrency Control (MVCC) enables non-blocking concurrent reads and writes by treating updates as a delete followed by an insert. The superseded row remains on disk as a "dead tuple" to maintain transaction snapshot isolation. These dead tuples consume disk space until cleaned by background autovacuum or purge processes. Production database sizing must include a 20% to 35% bloat safety factor to avoid storage exhaustion during heavy batch update operations.
Frequently Asked Questions
Databases enforce structural metadata on every record. PostgreSQL adds a 23-byte HeapTupleHeader, 4-byte ItemId, and null bitmap; MySQL InnoDB adds an 18-byte internal header including transaction IDs and roll pointers. Additionally, pages operate at 85% to 93% fill factors, leaving empty space for in-place updates.
Secondary B-Tree indexes typically consume 40% to 120% of the underlying table data size. Each index stores indexed column values, leaf page pointers, and internal node keys. Tables with 5 or more indexes often have index footprints larger than the table itself.
WAL or binary logs record all transactional mutations before writing to data pages. Disk requirements equal Write Transactions per Second (TPS) * Row Bytes * Log Overhead Multiplier (1.1x to 1.3x) * 3,600 seconds * Retention Hours.
Multi-Version Concurrency Control (MVCC) never overwrites data in place during an UPDATE or DELETE. Instead, it marks old rows as dead tuples and writes new versions. Until autovacuum or purge threads reclaim these pages, dead tuples create 15% to 35% table bloat.
A single write transaction generates multiple I/O operations: appending to the sequential WAL/redo log, modifying the clustered table leaf page, and updating each secondary B-Tree index structure. Sustained write IOPS scale as TPS * (1 + 0.4 * Secondary Indexes).