csv-salvage

Read the full documentation at csv-salvage.readthedocs.io.

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csv-salvage is a high-performance, fault-tolerant CSV salvage and reconstruction engine written in Rust with native Python bindings (via PyO3 & Maturin).

It rescues corrupted, malformed, and irregular CSV data in scenarios where Polars, DuckDB, and Pandas fail and throw ParserError.

💥 The Pain Point It Solves

Real-world datasets from legacy systems, mainframe exports, and unvalidated third-party vendors often generate severely corrupted CSV files:

  • Unescaped newlines inside text cells: Line breaks breaking rows in half without proper quote enclosures.
  • Unbalanced or orphan quotes: Stray quotes inside text fields (e.g. 101,"15" Monitor",49.99) throwing parsers completely out of sync.
  • Variable delimiters: Inconsistent separators across files or mixed comma/semicolon/tab dialects.
  • Inconsistent column counts (ragged rows): Missing trailing columns, phantom extra delimiters, or unexpected column drift.

The Current State of the Art (And Why It Fails)

Today, data engineers and analysts resort to writing slow, brittle Python regular expression scripts to clean and patch corrupted files line-by-line before feeding them into Pandas, DuckDB, or Polars. These scripts take hours on multi-gigabyte datasets, consume massive RAM, and break on unseen edge cases.

⚡ Core Architecture in Rust

  • DFA & State-Machine Parser: Custom finite state automaton (DFA via manual state machine or regex-automata) engineered specifically for error-recovery, token resynchronization, and zero-panic resilience.
  • Schema Inference Heuristics: Adaptive detection of expected column counts, delimiters, and dialect conventions even in chaotic files.
  • Parallel Normalization with Rayon: Multi-threaded chunking and parallel repair using rayon to saturate multi-core CPUs during large batch salvage.

🌟 Value Proposition

  1. "Lifesaver" Mode (Zero Panic Guarantee):
    • The parser never crashes.
    • Automatically repairs orphan quotes and equalizes ragged rows.
    • If a row is mathematically unrecoverable, it cleanly routes the corrupted record into a dedicated discard / quarantine stream (with line numbers and diagnostic reasons) rather than halting execution.
  2. High-Throughput Parallel Normalization:
    • Utilizes rayon to repair and normalize large files in parallel.
    • Exports directly to cleaned, RFC-4180-compliant CSV files ready for instant loading into DuckDB, Polars, or Pandas.
    • Streams repaired dictionaries or tuples directly into Python at native C/Rust speed.

📦 Installation

# Using pip
pip install csv-salvage

# Using uv
uv add csv-salvage

(Pre-built wheels are compiled for Linux, musllinux, macOS, and Windows across major architectures).

🚀 Quickstart & Proposed API

1. Rescuing Corrupted Files for DuckDB / Polars Ingestion

import csv_salvage as cs

# Repair a corrupt CSV file and route unrecoverable rows to quarantine
summary = cs.salvage_file(
    input_path="legacy_export_corrupted.csv",
    output_path="clean_data.csv",
    quarantine_path="discarded_rows.log",
    repair_quotes=True,       # Auto-fix orphan/unbalanced quotes
    fill_ragged_rows=True,    # Pad short rows with nulls
    threads=8                 # Parallel normalization via Rayon
)

print(f"Total Rows:     {summary.total_rows}")
print(f"Salvaged Rows:  {summary.salvaged_rows}")
print(f"Quarantined:    {summary.quarantined_rows}")

# Now load clean_data.csv into Polars or DuckDB with zero errors!
import duckdb
df = duckdb.read_csv("clean_data.csv")

2. Native Python Streaming

import csv_salvage as cs

# Stream cleaned rows directly as Python tuples/dicts at native speed
with cs.open("broken_records.csv", mode="lifesaver") as stream:
    for record in stream:
        # record is a clean list of string tokens
        process_row(record)
        
    for discarded in stream.quarantine:
        print(f"Line {discarded.line_number}: {discarded.raw_line} -> {discarded.reason}")

3. Rust Crate Usage

use csv_salvage::{SalvageEngine, SalvageOptions};

let options = SalvageOptions::builder()
    .repair_quotes(true)
    .fill_ragged_rows(true)
    .build();

let engine = SalvageEngine::new(options);
let summary = engine.salvage_file_parallel("corrupted.csv", "clean.csv", Some("quarantine.csv"))?;

println!("Successfully salvaged {} rows!", summary.salvaged_rows);

🗺️ Roadmap

  • Initial scaffolding (Rust 2024 edition, PyO3, Maturin).
  • Multi-platform CI pipeline with formatting, clippy, unit testing, and Codecov.
  • Automated tag-triggered release workflow (publish.yml) with build provenance attestations, PyPI publishing, and GitHub Releases.
  • Core DFA / state-machine salvage tokenizer.
  • Orphan quote repair and newline resynchronization heuristics.
  • Multi-threaded parallel file processing with rayon.
  • Discard / quarantine stream with diagnostic error logging.
  • Direct export to Apache Arrow format for zero-copy Polars / DuckDB handoff.
  • Benchmarks against Python regex scripts, standard csv, and clevercsv.

🛠️ Development & Contributing

See CONTRIBUTING.md for local setup, development commands, and coding guidelines.

# Synchronize environment
uv sync

# Build & install locally in editable development mode
uv run maturin develop --uv

# Run Rust unit tests
cargo test

# Run Python integration and release tests
uv run python -m unittest discover -s tests -v

# Check formatting and lints
cargo fmt --check
cargo clippy --all-targets --all-features -- -D warnings

# Build documentation strictly
uv run --group docs mkdocs build --strict

📄 License

Licensed under the Apache License, Version 2.0.

Project links

Data verified by PyPI on Oct 1, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
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  • Released:
Newer release available (0.2.0)

1 maintainer

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Avatar for ezer-mackenzie from gravatar.com ezer-mackenzie

Credits

Author: Eli-ezer Reuven Ramirez Ruiz

GitHub Statistics

Data verified by PyPI on Oct 1, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

Apache-2.0
View SPDX License List

Requires

Python <3.15, >=3.9

Tags

csv salvage repair parser dataframe

Classifiers

Report project as malware

Metadata

Project links

Data verified by PyPI on Oct 1, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.2.0)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for ezer-mackenzie from gravatar.com ezer-mackenzie

Credits

Author: Eli-ezer Reuven Ramirez Ruiz

GitHub Statistics

Data verified by PyPI on Oct 1, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

Apache-2.0
View SPDX License List

Requires

Python <3.15, >=3.9

Tags

csv salvage repair parser dataframe

Classifiers

Report project as malware

Release files for csv-salvage 0.1.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for csv-salvage 0.1.0
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csv_salvage-0.1.0.tar.gz 107.9 kB Details

Built distributions (wheels)

Table of built distributions (wheels) for csv-salvage 0.1.0
File
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