Koala Diff

Blazingly Fast Data Comparison for the Modern Stack.

🚀 Quickstart | 🚩 Issues | 📊 Benchmarks

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Koala Diff is the "git diff" for your data lake. It compares massive datasets (CSV, Parquet, JSON) instantly to find added, removed, and modified rows.

Built in Rust 🦀 for speed, wrapped in Python 🐍 for ease-of-use. It streams data to compare datasets larger than RAM and generates beautiful HTML reports.

🚀 Why Koala Diff?

• Zero-Copy Streaming: Compare 100GB files on a laptop without crashing RAM.
• Rust-Powered Analytics: Go beyond row counts. Track Value Variance, Null Drift, and Match Integrity per column.
• Professional Dashboards: Auto-generates premium, stakeholder-ready HTML reports with status badges and join attribution.
• Deep-Dive API: Extract mismatched records as Polars DataFrames for instant remediation.

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📈 The "Magic" Benchmark

"Process 100M rows on a laptop in seconds, not minutes."

⚡ Performance at a Glance

• Time: 🟦🟦 1x (Koala) vs 🟦🟦🟦🟦🟦 3x (Polars) vs 🟦🟦...🟦 30x+ (Pandas)
• RAM: 🟩 0.4GB (Koala Diff) vs 🟩🟩🟩🟩🟩🟩🟩🟩 12GB+ (Polars)
• Edge: Native Rust XXHash64 handles massive joins locally without cluster overhead.

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🧐 Why not just use Polars/Spark?

While Polars and Spark are incredible for general data processing, Koala Diff is a specialized tool for Data Quality & Regression:

Feature	Polars / Spark	🚀 Koala Diff
Specialization	General Purpose ETL	Data Quality & Diffing
Memory	High (Join-heavy)	Ultra-Low (Streaming)
Output	Raw DataFrames	Pro Dashboards + Metrics
Logic	Manual Join/Filter code	Out-of-the-box Analytics
Stakeholders	Engineer-facing	Business-Ready Reports

Koala Diff doesn't replace your processing engine; it verifies that its output is correct.

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> Benchmarks run on MacBook Pro M3 Max.

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🎯 Common Use Cases

• ETL Regression Testing: Automatically verify that your daily pipeline didn't accidentally mutate 1 million rows after a code change.
• Data Migration Validation: Ensure 100% parity when moving data between systems (e.g., Hive to Snowflake or S3 to BigQuery).
• Environment Drift Detection: Compare Production vs. Staging datasets to find out why your model is behaving differently.
• Compliance Auditing: Generate unalterable HTML snapshots of data changes for regulatory or financial reviews.
• CI/CD for Data: Run koala-diff in your CI pipeline to block PRs that introduce unexpected data quality regressions.

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📦 Installation

pip install koala-diff

⚡ Quick Start

1. Generate a "Pro" Report

from koala_diff import DataDiff, HtmlReporter

# Initialize with primary keys
differ = DataDiff(key_columns=["user_id"])

# Run comparison
result = differ.compare("source.parquet", "target.parquet")

# Generate a professional dashboard
reporter = HtmlReporter("data_quality_report.html")
reporter.generate(result)

2. Mismatch Deep-Dive

Need to fix the data? Pull the exact differences directly into Python:

# Get a Polars DataFrame of ONLY mismatched rows
mismatch_df = differ.get_mismatch_df()

# Analyze variance or push to a remediation pipeline
print(mismatch_df.head())

2. CLI Usage (Coming Soon)

koala-diff production.csv staging.csv --key user_id --output report.html

🏗 Architecture

Koala Diff uses a streaming hash-join algorithm implemented in Rust:

1. Reader: Lazy Polars scan of both datasets.
2. Hasher: XXHash64 computation of row values (SIMD optimized).
3. Differ: fast set operations to classify rows as Added, Removed, or Modified.
4. Reporter: Jinja2 rendering of results.

🤝 Contributing

We welcome contributions! Whether it's a new file format reader, a performance optimization, or a documentation fix.

1. Check the Issues.
2. Read our Contribution Guide.

📄 License

MIT © 2026 godalida - KoalaDataLab