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compara

License: MIT CI Crates.io PyPI npm

Extremely fast table comparison: two tables on a composite key, with counts, per-column statistics and a self-contained HTML report. Key columns, compared columns and normalisation rules are parameters, so one tool serves every recurring comparison: a nightly export against yesterday's, a CSV against the Parquet a warehouse emits, a migration's source against its target.

Faster software for a better planet — less energy, less compute, more time for life.

  • Reads CSV, newline-delimited JSON and Parquet, and the two sides need not be in the same format.
  • Ten million rows × 20 columns in about a second on four cores (numbers below).
  • One static binary; the report is a single HTML file with nothing to host.
  • Exit codes made for pipelines: 0 identical, 1 differences, 2 error, 3 duplicate keys (with --fail-on-dups).

Install

Prebuilt binaries for Linux (x86_64, aarch64), macOS (arm64, x86_64) and Windows (x64) are on the releases page, and the same binaries through package managers:

Cargo cargo install --locked compara
npm npm install -g compara (or npx compara …)
pip / uv pip install compara · uv tool install compara
Homebrew brew install andrey-usa/tap/compara
Scoop scoop bucket add andrey-usa https://github.com/andrey-usa/scoop-bucket then scoop install compara

Use

compara compare a.csv b.csv -k account_id,txn_id -i updated_at
compara compare a.csv b.csv -k account_id,txn_id -i updated_at -o report.html
compara compare a.csv b.csv -k account_id,txn_id --trim --tolerance 0.005
compara compare export.csv warehouse.parquet -k id
compara compare a.csv b.csv --profile orders --json summary.json --export-dir out/
compara columns data.parquet          # column names (reads only the footer)
compara head data.ndjson -n 5         # first rows, aligned

compare prints the counts and writes nothing else unless asked. -o PATH writes the HTML report: counts, per-column statistics, and the changed, added, removed and duplicate rows, each section capped at --max-rows (counts are always exact). examples/report.html is one, made from the two files next to it:

compara compare examples/orders_2026-08.csv examples/orders_2026-09.csv -k order_id,line_no -i updated_at -o examples/report.html
Option Effect
-k, --key composite key, required (or from a profile)
-c, --compare columns to diff; default every column present in both files except the key
-i, --ignore columns to skip (timestamps, run ids)
--trim, --ignore-case, --empty-is-null normalisation before comparing (applies to key and values)
--tolerance absolute numeric tolerance where both sides parse as numbers
--delimiter, --encoding override auto-detection (CSV only)
--threads how wide the engines run; the default is every core
--memory memory to plan for (e.g. 8G); decides when large text files are joined in one pass instead of two, and how many columns of two Parquet files are read at once (a Parquet pair that needs more than a stated budget is refused)
--max-rows rows embedded per report section (default 50 000)
-o, --out write the HTML report here; there is no report without it
--export-dir full, uncapped changed/added/removed CSVs
--json write a JSON summary (counts and column stats; schema in docs/contract.schema.json)
--summary print the counts and refuse any flag that writes a file (the default without -o, --json or --export-dir)
--engine auto (default), turbo, sortmerge or native; sortmerge and native read CSV only
--no-compress plain JSON payload in the report, for pre-2023 browsers (with -o)
--fail-on-dups exit 3 when either file has duplicate keys

Duplicate keys are counted and listed per file; the first occurrence of each key takes part in the join.

Profiles. A compara.toml in the working directory (or ~/.config/compara/compara.toml, or --config PATH) names recurring comparisons: key, compared and ignored columns, normalisation, engine. See compara.example.toml. The command line overrides the profile.

Speed

Ten million rows × 20 columns, keyed on (account_id, txn_id), --ignore updated_at, one GitHub Actions runner (AMD EPYC 7763, 4 vCPU / 16 GB), best of five. Each cell is wall time · CPU time · memory above the mapped input.

Format Input a side compara
CSV 3,509 MB 1.20 s · 4.0 s · 591 MB
ndjson 8,487 MB 2.79 s · 10.4 s · 592 MB
Parquet 2,074 MB 1.41 s · 4.7 s · 1,342 MB

Contender benchmarks rerun weekly (bench-contenders.yml) against the latest DuckDB and pandas.

Engines

Three backends, one result contract: every engine returns identical counts and column statistics for the same input, and the test suite asserts it cell for cell. --engine auto takes the first one that can load the input, in this order.

Engine Implementation Memory model Use it for
turbo mapped file, byte-level index; CSV, JSON and Parquet off-heap bytes, index in memory the default, and the fastest
sortmerge external sort, merge join bounded memory, spills to disk files past what memory can index
native over the csv crate in-memory, row-oriented the dependency-light baseline

All three read values as text, with no type inference, so 1.0 and 1 stay different unless a tolerance is set, and all three treat an empty field as absent whether or not it is quoted. A Parquet column is the exception, on either side: it stores typed values, so a column it holds as numbers compares by value with the other file's text (31.40 against the DOUBLE 31.4 is no change), and a date, time or timestamp column by instant (T or a space, a zero fraction or none, Z or nothing).

Input formats

turbo decides what a file is by its content rather than its name: a Parquet magic number, a leading {, or a CSV header.

Format How it is read
CSV mapped, scanned eight bytes at a time; a field is an offset and a length; a UTF-8 byte order mark is skipped
newline-delimited JSON the same, addressed by key rather than by column number; \uXXXX is decoded, so an escaped and a literal character compare equal
Parquet pages decoded into an arena; dictionary-encoded columns point at the dictionary entry, so a repeated value costs eight bytes a row

The Parquet reader is written here rather than taken from a crate, to keep that representation instead of converting to Arrow arrays. It reads PLAIN, dictionary (PLAIN_DICTIONARY, RLE_DICTIONARY), RLE booleans and the version-2 delta encodings; uncompressed, snappy, gzip, zstd and LZ4-raw pages; data pages v1 and v2; definition levels for optional columns. It refuses, by name, nested or repeated schemas, BYTE_STREAM_SPLIT, LZO and Brotli.

A typed Parquet value is rendered to text by a fixed rule: byte arrays as their bytes, booleans true/false, integers and decimals in full, floats in the shortest round-trip form, dates YYYY-MM-DD, timestamps ISO 8601. tests/fixtures/formats/typed.csv is that rule written down.

Two Parquet files take a columnar path instead, which never builds a row: it joins on the key columns, then compares one column at a time, string columns by dictionary id or bytes and booleans, integers, floats, dates, times, timestamps and decimals of up to eight bytes as one word per value. A cell is rendered by the rule above only when the report shows it. A column the two files store as different types, or with a codec or encoding outside PLAIN, dictionary, snappy and v1 pages, goes the row-by-row way instead, with the same answer.

Contributing

See CONTRIBUTING.md.

License

MIT

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