Author: Ganesh Raj
Date: 2025
Domain: Data Comparison, Database Consistency Validation, Data Engineering
Hash–Sort–Chunk Sampling (HSC) is a deterministic, statistically principled method designed to efficiently compare large datasets across heterogeneous databases.
The technique uses:
- Hashing — Converts record keys into uniformly distributed hash values (e.g., SHA-256).
- Sorting — Sorts records by their hashes to destroy clustering and ensure uniformity.
- Chunking & Sampling — Divides the dataset into equal-sized chunks and samples deterministically for efficient difference detection.
Unlike random sampling or Merkle Trees, HSC:
- Offers consistent reproducibility (same results across runs).
- Ensures uniform coverage across the entire dataset.
- Detects differences even in distributed or dense mismatch scenarios.
- Is database-agnostic, suitable for one-time validation between heterogeneous systems.
Each record’s primary/composite key is hashed:hash = SHA256(key_fields)
All records are sorted by their hash values to remove spatial correlation:dataset.sort(by='hash')
The sorted dataset is split into C equal chunks.
A deterministic sample is taken per chunk:
sample_size = 1
sample = [chunk[i % len(chunk)] for i, chunk in enumerate(chunks)]
The probability that a differing row falls into a given chunk follows a Binomial distribution: [ P(k) = \binom{D}{k} \left(\frac{1}{C}\right)^k \left(1 - \frac{1}{C}\right)^{D-k} ]
If you use this method or code, please cite:
Ganesh Raj. Deterministic Hash–Sort–Chunk Sampling for Efficient Database Comparison. ResearchGate, 2025. DOI: 10.13140/RG.2.2.27435.50721