Koala Diff
Blazingly Fast Data Comparison for the Modern Stack.
🚀 Quickstart | 🚩 Issues | 📊 Benchmarks
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.
📈 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
XXHash64handles massive joins locally without cluster overhead.
🧐 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.
> Benchmarks run on MacBook Pro M3 Max.
🎯 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-diffin your CI pipeline to block PRs that introduce unexpected data quality regressions.
📦 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:
- Reader: Lazy Polars scan of both datasets.
- Hasher: XXHash64 computation of row values (SIMD optimized).
- Differ: fast set operations to classify rows as
Added,Removed, orModified. - Reporter: Jinja2 rendering of results.
🤝 Contributing
We welcome contributions! Whether it's a new file format reader, a performance optimization, or a documentation fix.
- Check the Issues.
- Read our Contribution Guide.
📄 License
MIT © 2026 godalida - KoalaDataLab
1 maintainer
Metadata
1 maintainer
Release files for koala-diff 0.3.2
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