Index Size Calculator

Estimate database index storage size for PostgreSQL, MySQL, SQL Server, Oracle, MariaDB, and SQLite. Compare index types, fill factor, and compression before deployment.

Inputs

Estimates only — actual index size varies by engine version and configuration.

Database Engine

Index Type

Standard balanced tree index — the baseline for this estimate.

Number of Rows

Indexed Column Size (Bytes)

Primary Key Size (Bytes)

Column type:

Fill Factor90%

Estimated Overhead20%

Page Size

Compression

Examples:

Compare with a second index type

Estimated Index Size

254.31 MB

≈ 32,553 pages · 33.3% overhead

Formula Breakdown

Entry Size = Column + PK + Pointer(8) + Metadata(8) = 40 Bytes

Raw Index = Entry × Rows = 190.73 MB

Compressed = Raw × Type × Compression = 190.73 MB

Fill-Factor Adjusted = Compressed ÷ Fill Factor = 211.93 MB

Final = Adjusted × (1 + Overhead%) = 254.31 MB

Item pointer (6 bytes) plus tuple header overhead, rounded to typical alignment.

Storage Breakdown

Compressed Data: 190.73 MB

Fill Factor Impact: 21.19 MB

Overhead: 42.39 MB

Raw Index Size

190.73 MB

Compression Saved

0 Bytes

Estimated Pages

32,553

Fill Factor Impact

21.19 MB

Overhead Impact

42.39 MB

Storage Overhead

33.3%

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What Is an Index Size Calculator?

An index size calculator is a free browser-based tool that estimates the approximate storage size of a database index before you create it — helping you plan disk usage across PostgreSQL, MySQL, MariaDB, SQL Server, Oracle, and SQLite.

Enter your row count, indexed column size, and primary key size, and the calculator instantly estimates the raw index size, compression savings, fill-factor impact, and final estimated storage footprint — plus the estimated page count for your chosen page size.

This tool is intended as an estimation calculator, not an exact database profiler. Actual index size depends on engine internals, storage engine, version, alignment, and compression settings, so treat these numbers as planning estimates rather than precise measurements. It's built for database administrators, backend developers, data engineers, DevOps engineers, cloud architects, and students learning database optimization — and it runs entirely in your browser.

How the Index Size Calculator Works

The calculator builds up an estimate in layers: it starts from a per-row entry size, multiplies by your row count, then applies index-type, compression, fill-factor, and overhead adjustments.

Core Formulas

Entry Size = Column Size + Primary Key Size + Pointer + Metadata

Raw Index Size = Entry Size × Number of Rows

Compressed Size = Raw Size × Index Type × Compression Ratio

Fill-Factor Adjusted = Compressed Size ÷ (Fill Factor ÷ 100)

Final Estimated Size = Adjusted Size × (1 + Overhead %)

→Database Pointer & Metadata: Each engine has a typical per-entry pointer and row-header overhead used automatically based on your selected database engine.
→Index Type Multiplier: Different index types (B-Tree, GIN, BRIN, Bitmap, and others) have very different typical storage footprints relative to a standard B-Tree, applied as an estimated multiplier.
→Fill Factor: A lower fill factor leaves more free space per page for future updates, increasing the effective storage footprint.
→Estimated Overhead: An adjustable buffer for internal structures like free space maps, visibility maps, and index maintenance metadata not captured elsewhere.

How to Use the Index Size Calculator

Step-by-Step Guide

1Choose a Database Engine: Select PostgreSQL, MySQL, MariaDB, SQL Server, Oracle, SQLite, or Custom.
2Choose an Index Type: Select B-Tree, Hash, Composite, GIN, BRIN, or another type — each has a different typical storage profile.
3Enter Rows & Column Sizes: Type your row count, indexed column size, and primary key size in bytes.
4Adjust Fill Factor & Overhead: Use the sliders to model page density and additional engine overhead.
5Set Page Size & Compression: Choose your database's page size and an optional compression level.
6Read the Live Result: Estimated index size, storage breakdown, and page count update instantly as you type.
7Export or Share: Copy the report, download a CSV or JSON file, print it, or copy a shareable URL.

Key Features

✓Support for 7 database engines and 11 index types with engine-specific defaults
✓Composite index estimator for multi-column indexes
✓Compare two index types side-by-side with a bar chart
✓Storage breakdown pie chart (compressed data, fill-factor impact, overhead)
✓Fill factor and overhead sliders with live recalculation
✓Column type quick-fill suggestions (UUID, BIGINT, VARCHAR, and more)
✓Automatic Bytes → KB → MB → GB → TB formatting
✓Estimated page count based on your selected page size
✓Large index warning when write performance may be impacted
✓Quick example presets matching common real-world scenarios
✓Shareable calculation URL using query parameters
✓Export report as CSV or JSON, plus a printable layout
✓Calculation history — save and reload up to 20 past results
✓All processing runs locally — no schema details are ever uploaded

Real-World Use Cases

Pre-Deployment Storage Planning

A DBA estimates the storage footprint of a new B-Tree index on a 5-million-row PostgreSQL table before creating it, confirming disk capacity is sufficient ahead of a production migration.

Choosing Between Index Types

A backend engineer compares a standard B-Tree index against a BRIN index for a large, naturally-ordered timestamp column, discovering the BRIN index is dramatically smaller for the same query benefit.

VARCHAR Column Overhead Estimation

A data engineer estimates the index size for a VARCHAR(40) column across 25 million MySQL rows before deciding whether to index the full column or a shorter prefix.

Composite Index Cost Analysis

A software engineer estimates the storage cost of adding a 3-column composite index on a 150-million-row SQL Server table before approving the schema change.

Compression Strategy Evaluation

A cloud architect compares estimated index sizes at different compression levels to decide whether enabling index compression is worth the added CPU overhead.

Database Optimization Coursework

A student learning database internals uses the calculator to understand how fill factor, pointer size, and index type each contribute to overall index storage.

Tips & Common Mistakes

Pro Tips

💡Use the Compare feature before choosing between two index types on the same table — the size difference can be substantial.
💡BRIN indexes are dramatically smaller than B-Tree for naturally-ordered data like timestamps, but only useful for range queries on that ordering.
💡A lower fill factor increases index size but reduces page splits on tables with frequent updates — it's a storage-for-performance trade-off.
💡Use the column type quick-fill buttons to avoid guessing byte sizes for common types like UUID or BIGINT.
💡Treat every result as an estimate — always validate against your actual database's index statistics before final capacity planning.

Common Mistakes to Avoid

✕Don't treat this tool as an exact profiler — real index size depends on engine version, alignment, and internal implementation details this calculator can't fully model.
✕Don't forget that composite indexes add bytes for every additional column, not just the primary indexed column.
✕Don't assume all index types shrink with compression the same way — some engines don't support compression for certain index types at all.
✕Don't ignore the large index warning — very large indexes can noticeably slow down write-heavy workloads.
✕Don't confuse fill factor with compression — fill factor controls page density for future updates, while compression reduces the raw stored bytes.

Index Type Size Reference

Index Type	Relative Size	Typical Use
B-Tree	1.0×	General-purpose ordered index — the most common type
Hash	0.85×	Equality lookups only, no range queries
BRIN	0.05×	Extremely compact — naturally-ordered columns like timestamps
Bitmap	0.3×	Low-cardinality columns in analytical workloads
GIN	1.8×	Multi-value columns like arrays, JSON, and full-text search
Full Text	2.0×	Term dictionaries and positional search data
Clustered	1.3×	Data rows stored together with the index

Frequently Asked Questions

What is an index size calculator?

An index size calculator is a free browser-based tool that estimates the approximate storage size of a database index before you create it, based on row count, column sizes, index type, and database engine.

How is index size calculated?

Entry Size = Column Size + Primary Key Size + Pointer + Metadata, then Raw Index Size = Entry Size × Number of Rows. For example, a PostgreSQL B-Tree index on 5 million rows with a 16-byte column and 8-byte primary key has an entry size of 40 bytes, producing a raw index size of about 191 MB before compression, fill factor, and overhead adjustments.

Is this an exact measurement of my database's index size?

No — this is an estimation tool, not a database profiler. Actual index size depends on your specific database engine version, page alignment, storage engine internals, and configuration, which this calculator approximates but cannot replicate exactly.

Why is a BRIN index so much smaller than a B-Tree index?

BRIN (Block Range INdex) stores summary information per block range rather than an entry for every row, making it dramatically more compact for naturally-ordered data like timestamps — at the cost of being less precise for arbitrary lookups.

What does fill factor do to index size?

A lower fill factor leaves more free space on each page to accommodate future updates without page splits, which increases the effective storage footprint — the calculator divides the compressed size by the fill factor to model this.

What's the difference between compression and fill factor?

Compression reduces the actual number of bytes stored for your data. Fill factor controls how densely those bytes are packed onto disk pages, leaving room for future growth — they affect size in opposite directions.

How does the Composite Index option work?

When you select Composite as the index type, you can specify additional columns beyond the primary indexed column. Each additional column is estimated using the same indexed column size you've entered, and added to the per-row entry size.

Is my schema information private when using this calculator?

Yes. All calculations run entirely in your browser using JavaScript. No table names, column names, row counts, or schema details are ever transmitted to any server.