From Zero to Simple Hasher: Implementations in Python, JS, and Go

Simple Hasher: Secure, Minimal, and Easy to UseHash functions are a foundational building block in software engineering and computer security. They convert arbitrary-length input into fixed-size outputs (hashes) deterministically and efficiently. A well-designed simple hasher balances three often-competing goals: security (resistance to collisions and preimage attacks), minimalism (small, easy-to-understand code and API), and usability (easy to integrate and fast enough for typical tasks). This article explores those goals, explains trade-offs, walks through practical implementations, and gives guidance for choosing or building a “Simple Hasher” for common use cases.

What a hasher should do

A hash function, from a practical point of view, should:

• Be deterministic — the same input always produces the same output.
• Produce fixed-size output — convenient for storage and indexing.
• Be fast — useful for large datasets and low-latency systems.
• Minimize collisions — different inputs should rarely produce the same hash.
• Be secure when required — resist attacks against finding collisions or preimages.

Not every use needs cryptographic security. Distinguish between:

• Non-cryptographic hashing: priorities are speed and low collision rate for typical data (e.g., hash tables, checksums, content deduplication in benign contexts).
• Cryptographic hashing: priorities are security properties (collision resistance, preimage resistance, second-preimage resistance) for authentication, signatures, password storage, or tamper detection.

Design principles for a “Simple Hasher”

1. Purpose-first

   • Define whether you need cryptographic properties. A minimal hasher for hash tables should not try to be a drop-in replacement for SHA-256 in security protocols.

2. Minimal API

   • Keep interfaces small: e.g., hash(data) → bytes or hex string; optionally a streaming API for large data (init(), update(), digest()).

3. Small, auditable code

   • Favor clarity over micro-optimizations when simplicity and auditability are important.

4. Reasonable defaults

   • Choose sensible output sizes and encodings: 64-bit or 128-bit output for non-cryptographic needs; 256-bit for cryptographic needs.

5. Portability and deterministic behavior

   • Ensure identical outputs across platforms and languages (document endianness and encoding assumptions).

6. Performance measured, not assumed

   • Benchmark on representative data and include simple tests to validate correctness.

Non-cryptographic options (fast and minimal)

For many apps (hash tables, cache keys, file deduplication where adversaries aren’t present), choose a well-known, fast, non-cryptographic hasher:

• MurmurHash3 — popular, fast, good distribution, available in many languages.
• XXHash — extremely fast, excellent distribution, has 32-, 64-, and 128-bit variants.
• CityHash/FarmHash/MetroHash — various Google-provided fast hash functions optimized for strings and large buffers.
• FNV-1a — simple and tiny, but weaker distribution for some inputs; okay for small hobby projects.

If you want minimal code and reasonable performance, XXHash (64-bit variant) is an excellent choice: high speed, low collision rate for typical inputs, small implementation footprint in C, bindings in many languages.

Cryptographic options (secure)

When security matters (digital signatures, integrity checks against adversaries, password hashing), use well-studied cryptographic hashes:

• SHA-2 family (SHA-256, SHA-512) — widely used, high confidence, hardware acceleration on many CPUs.
• SHA-3 family (Keccak) — different internal design; useful if you want algorithmic diversity.
• BLAKE3 — modern, very fast, secure, and parallelizable; good blend of speed and security and smaller API surface than older designs. BLAKE2 is also an excellent choice (BLAKE2b/2s) for keyed hashing.

For password hashing, use specialized functions (Argon2, bcrypt, scrypt), not generic cryptographic hashes.

Example implementations

Below are concise conceptual examples illustrating a minimal, practical hasher in Python and Go. These show the small API surface (hash() and streaming), while pointing to secure and fast libraries for production.

Python — cryptographic (SHA-256):

import hashlib def simple_hash(data: bytes) -> str:     return hashlib.sha256(data).hexdigest() # streaming h = hashlib.sha256() h.update(b"part1") h.update(b"part2") digest = h.hexdigest() 

Python — fast non-cryptographic (xxhash):

import xxhash def xx64(data: bytes) -> str:     return xxhash.xxh64(data).hexdigest() 

Go — minimal wrapper for BLAKE3 (using a third-party lib):

package hasher import (     "encoding/hex"     "github.com/zeebo/blake3" ) func SimpleHash(data []byte) string {     var out [32]byte     blake3.Sum256(&out, data)     return hex.EncodeToString(out[:]) } 

Notes:

• Choose a library with active maintenance and review for cryptographic use.
• For non-cryptographic tasks, prefer well-tested packages (e.g., github.com/cespare/xxhash for Go).

Streaming and chunked hashing

Large files or streams require an update/digest API. Design a minimal streaming interface:

• init() — create state
• update(chunk) — absorb data
• digest() — finalize and return hash

This pattern maps to most standard libraries (hashlib in Python, crypto/hash in Go), so implementers can keep the API familiar.

Keyed hashing and message authentication

When you need both hashing and authentication (to prevent tampering by an adversary), use a keyed hash (MAC):

• HMAC with SHA-256 — standard, simple to use.
• BLAKE2 or BLAKE3 keyed modes — faster and often simpler to integrate.