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Benchmarks
qsv's benchmark suite runs 305 benchmarks on a 1,000,000-row × 41-column, 520 MB
sample of NYC's 311 data, timed with hyperfine
(2 warmups + 3 timed runs each). See
scripts/results/README.md
for the methodology and the raw CSVs.
Looking for the full per-command timing tables? See the classic tabular benchmarks at qsv.dathere.com. This page is the interactive, visual companion to it.
➡️ Open the interactive benchmark Data Schematic
The Data Schematic is rendered by qsv's own viz command and is fully interactive (hover for
values, zoom, download PNGs). It covers the with/without-index advantage, the sqlp
schema-cache knob, throughput across every release, deep-dives into four flagship
commands (stats, frequency, validate and moarstats) showing they grew features
without losing speed, and this release's biggest speedups.
Note: cross-version numbers are not strictly apples-to-apples — commands gain features over time. Treat the historical trend as a broad trajectory, not an exact speedup.
Last generated for qsv 28.0.0 (aarch64-apple-darwin). Regenerate with
python3 scripts/gen_benchmark_viz.py and copy this page's link — the Data Schematic itself
redeploys to GitHub Pages automatically on push to master.
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Home · Why qsv? · Tier legend
- Whirlwind Tour · Notebooks · Lessons & Exercises
- Benchmarks · Interactive Benchmarks
- Visualizations Gallery
- Neuro-symbolic, Localizable, Data Schematics
- Feature Flags · Goals/Non-goals · Testing
- Talks: NYC SOD 2022 · csv,conf,v8 · PyCon US 2025 · csv,conf,v9 · NYC SOD 2026
- "Have we achieved ACI?" series: 1 · 2 · 3 · 4
- Sponsor
- All Commands (index)
- Selection & Inspection
- Transform & Reshape
- Aggregation & Statistics
- Joins & Set Ops
- SQL & Polars
- Validation & Schema
- Metadata Profiling (profile)
- Conversion & I/O
- Geospatial
- Visualization (viz)
- HTTP & Web
- Get & Disk Cache
- Scripting (Luau / Python)
- Indexing, Compression & Diff
- AI & Documentation
- Recipes index
- Inspect an Unknown CSV
- Clean & Normalize
- Geographic Enrichment
- Date Enrichment
- CKAN Integration
- DCAT-US v3 for data.gov
- JSON Schema Validation
- Round-Trip SAS / Stata / SPSS
- Build a Data Pipeline
- Stats → Insights
- Data-Quality Signals with moarstats
- Group By
- Fetch & Cache
- Larger-than-RAM CSV
- Diff & Audit
- Multi-table Joins
- Synthesize Fake Data
- Performance Tuning
- Benchmarks
- Tuned csv Fork Benchmarks
- Security & Dependency Audits
- Environment Variables
- Stats Cache & Caching
- Lookup Tables
- MiniJinja Templating
