csv-stream-diff 0.2.3

Stream and compare very large CSV files with multiprocessing.

csv-stream-diff

csv-stream-diff compares very large CSV files with streaming I/O, hashed bucket partitioning, and multiprocessing. It is designed for datasets that are too large to load fully into memory.

https://pypistats.org/packages/csv-stream-diff

Features

• Compare CSVs by configurable key columns, even when left and right headers differ
• Stream files in chunks with configurable chunk_size
• Partition by stable hashed key to keep worker memory bounded
• Use all CPUs by default, or set a worker count explicitly
• Write machine-usable output artifacts for left-only, right-only, row-level differences, duplicate keys, and run summary
• Support exact random sampling for validation runs with sampling.size > 0
• Warn on duplicate keys and continue using the first occurrence per key
• Include a fixture generator and both pytest and behave tests

Installation

pip install csv-stream-diff

For local development:

poetry install

CLI

csv-stream-diff --config config.yaml

Optional overrides:

csv-stream-diff \
  --config config.yaml \
  --left-file ./left.csv \
  --right-file ./right.csv \
  --chunk-size 100000 \
  --sample-size 100000 \
  --sample-seed 20260321 \
  --workers 8 \
  --output-dir ./output \
  --output-prefix run_

The YAML config is the default source of truth. CLI flags override it for a single run.

Configuration

See config.example.yaml for a full example.

Main sections:

• files.left, files.right: input CSV paths
• csv.left, csv.right: dialect and encoding settings
• keys.left, keys.right: key columns used to match rows
• compare.left, compare.right: value columns to compare
• comparison: normalization options
• sampling: size: 0 means full comparison; any positive value means exact random sample by left-side unique key with a fixed seed
• performance: chunking, worker count, bucket count, temp directory, progress reporting
• output: output directory, filename prefix, whether to include serialized full rows once per differing key, and whether to write a text summary

Output Files

The tool writes these artifacts to output.directory:

• <prefix>only_in_left.csv
• <prefix>only_in_right.csv
• <prefix>differences.csv
• <prefix>duplicate_keys.csv
• <prefix>summary.json
• <prefix>summary.txt when output.summary_format is text or both

differences.csv contains one row per differing key with:

• difference_count
• differences_text
• differences_json

differences_json contains the field-level left/right mismatches for that key. This keeps the diff output far smaller than writing one CSV row per changed field.

Sampling

• sampling.size: 0 runs the full comparison.
• sampling.size > 0 selects an exact random sample of left-side unique keys using reservoir sampling.
• Sampling is reproducible when sampling.seed stays the same.
• Duplicate keys do not expand the sampling population because only the first occurrence per key is considered.

Value Normalization

By default the comparison is more tolerant of equivalent values that often appear differently in CSV exports:

• NULL and empty string can be treated as equal
• 0 and 0.000000000 can be treated as equal for numeric-looking values
• NULL and 0 can be treated as equal for numeric-looking values

These behaviors are controlled in the comparison section:

• treat_null_as_equal
• normalize_numeric_values
• treat_null_as_zero_for_numeric
• numeric_decimal_places
• numeric_tolerance
• normalize_boolean_values

Examples:

• NULL, "", and " " can be treated as equal
• 14.3553 and 14.355344355 can compare equal with numeric_decimal_places: 4
• 1.14725 and 1.14724961 can compare equal with numeric_tolerance: 0.0001
• 1 and True can compare equal when normalize_boolean_values is enabled

Duplicate Keys

Duplicate keys do not stop the run. They are written to duplicate_keys.csv, counted in the summary, and the main comparison uses the first occurrence of each key on each side.

Generator

The generator creates two baseline-identical CSVs, applies controlled mutations, writes a matching config, and saves an expected manifest:

python generator/generate_fixtures.py --output-dir ./generated --rows 10000 --seed 42

Generated artifacts:

• left.csv
• right.csv
• config.generated.yaml
• expected.json

Tests

Run unit tests:

poetry run pytest

Run BDD acceptance tests:

poetry run behave tests/features

Run a package build:

poetry build

PyPI Packaging

Build source and wheel distributions:

poetry build

Upload after verifying artifacts:

poetry publish