Index Size Estimator Calculator

Formula Used

The estimator builds an average entry size, then packs entries into pages with your header, reserved space, and fill factor. Leaf pages store the full entry; non-leaf pages store smaller routing entries.

How to Use This Calculator

1. Start with realistic row count and key size from your schema.
2. Select index type and set compression ratio if used.
3. Set page size and overhead to match your storage engine.
4. Pick a fill factor aligned with insert churn and growth.
5. Run the estimate, then export CSV for comparisons.

Why Index Size Matters

Oversized indexes increase memory pressure, enlarge backups, and raise read amplification. Even when a query uses an index, each extra page can add latency through cache misses and storage fetches. Estimating footprint early helps you pick sensible keys, avoid redundant secondary indexes, and right-size buffer pools. Use this calculator to translate schema choices into concrete storage numbers before you migrate, shard, or change retention targets.

Key Inputs That Drive Footprint

Row count sets the scale, but entry size sets the slope. Average key bytes should include collation, prefixes, and variable-length headers. Pointer bytes represent row identifiers or child page references. Entry overhead and null maps capture per-record metadata that often surprises teams during growth. Compression ratio mostly affects the key portion, while the duplicate factor represents duplication, prefix compression limits, and varlen expansion under real data distributions.

Page Geometry and Fill Factor

Indexes live in fixed-size pages. Page header bytes reduce usable payload, and reserved space accounts for fragmentation, MVCC bloat, and split slack. Fill factor is a policy knob: higher values pack more entries today, lower values trade space for cheaper future inserts. For write-heavy workloads, a 80–90% target often reduces page splits and keeps latency stable. For append-only tables, higher fill can be acceptable.

B-tree Levels and Fanout

A B-tree adds internal pages that route searches to leaf pages. Fanout is the number of child pointers an internal page can store, driven by usable bytes and internal entry size. Higher fanout reduces tree height and usually lowers I/O per lookup. Larger pages can improve fanout, but they may waste space if entries are small and updates are frequent. Track estimated levels; a jump from 3 to 4 levels can noticeably change read paths.

Capacity Planning Checklist

Start with your expected peak rows, not today’s count. Add headroom for growth, rebuilds, and maintenance copies. Compare several key sizes, especially if you are debating composite keys or long text prefixes. Run multiple fill factors to see the split headroom effect. Finally, validate the estimate with engine-specific reports after loading a representative sample, then lock sizing assumptions into your runbooks. And monitor bloat over time.

FAQs

Does this match my database exactly?

No. It’s a sizing model that uses averages and page packing rules. Engines differ in tuple headers, prefix compression, and free space management. Use it for planning, then validate with a loaded sample and native catalog statistics.

Which key size should I enter for composite keys?

Add the typical stored bytes for all key columns plus any per-column headers. If you index a prefix, enter the prefix length. Use a weighted average if values vary widely across tenants or time ranges.

How do I pick a compression ratio?

If your engine compresses index keys, estimate savings from a representative sample. For example, 0.80 means keys shrink about 20%. If compression is off, set 1.00. When unsure, test both 0.70–1.00 to bound outcomes.

Why does fill factor change size so much?

Fill factor reduces entries per page, increasing page count. Lower fill leaves space for inserts and reduces splits, which can protect latency. Higher fill minimizes storage today but can increase fragmentation and maintenance cost in write-heavy workloads.

What is the duplicate or varlen factor?

It’s a practical multiplier to reflect duplicates, variable-length headers, and cases where compression is less effective. Use 1.00 for clean, fixed-length keys. Use 1.05–1.30 when data has many repeats or wide distributions.

Should I include row identifiers?

Include them for most secondary indexes, where leaf entries must point to a row. Disable only if your structure is clustered or the key itself locates the row. If unsure, leave it enabled; it produces safer estimates.

Stores up to 20 runs in your current session.