Benchmarks

All benchmarks measured with hyperfine on a MacBook Pro M4 Pro, 200+ runs each. The benchmark suite tests four scenarios to give a fair picture of performance.

Interactive SVG charts are available in the GitHub README.

Core Queries (500 entries, flat seeding)

Raw query speed with 1 visit per path — the simplest comparison between tp and zoxide.

This scenario tests the baseline overhead of the frecency lookup engine with a uniform visit distribution.

Realistic Visit Patterns (hot/warm/cold)

300 directories with varied visit counts:

• 50 "hot" paths — 20 visits each
• 100 "warm" paths — 5 visits each
• 150 "cold" paths — 1 visit each

This exercises frecency ranking under real-world conditions, where some paths are visited far more often than others.

Stale Path Handling

200 directories, 40% deleted after seeding. tp checks Path::exists() on every candidate and self-heals stale entries — this costs extra I/O but keeps your results clean. Stale paths are pruned automatically so they don't pollute future results.

Scale (5,000 entries)

Tests whether performance holds at larger database sizes. With 5,000 tracked directories, tp exercises its SQLite indexes and fuzzy matching at a scale that approximates a power user's database after months of use.

Note on add Performance

tp's add command does more work than zoxide's equivalent — it detects project roots by walking up the directory tree for .git, Cargo.toml, etc., and logs session data. This is the cost of project-scoped search and session recall. Query performance is where it matters most, and tp matches or exceeds zoxide there.

Running Benchmarks Yourself

cargo build --release
./bench/bench.sh
python3 bench/chart.py   # generate SVG charts

The benchmark scripts are in the bench/ directory. bench.sh runs hyperfine comparisons and outputs JSON results. chart.py reads those results and generates the SVG charts.