Excerpt
Discover what a murmur hash is, how it works, its advantages and use cases, limitations to consider, and how to implement it in various programming languages.
Hashing is a technique used in many areas of computing and programming. One popular hash family is the murmur hash, providing good performance and distribution for non-cryptographic uses. This post covers what murmur hashes are, how they work, their advantages, use cases, limitations, and implementation.
Introduction
A murmur hash is a fast, non-cryptographic hash function producing fairly uniform 32-bit or 128-bit hash values. Murmur hashes are optimized for speed and low collision rates which makes them well-suited for hash-based data structures, network protocols, and other applications requiring decent distribution with minimal processing overhead.
Some common uses of murmur hashes include hash tables, checksums, bloom filters, caching, data matching, and fingerprinting. Let’s look at how murmur hashes work and their key characteristics.
How does a murmur hash work?
The murmur hashing algorithm involves:
- Breaking input data into blocks
- Seeding the hash with initialization vectors
- Performing multiplication, rotation, and bitwise operations on each block
- Combining block hashes through addition and XORing
- Finalizing the total hash from all blocks
This provides good diffusion and statistical randomness in the hash values while executing fast on modern processors. Variants like MurmurHash2 and MurmurHash3 have tweaked and optimized the original algorithm.
Advantages of murmur hashes
Some benefits of using murmur hashes:
- Very fast computation suitable for real-time applications
- Efficient implementation in software and hardware
- Good distribution quality with low collisions
- Portable with consistent output across platforms
These make murmur an excellent choice for general purpose non-cryptographic hashing needs.
Common use cases
Typical uses of murmur hashes:
- Hash tables and hash maps
- Network protocol checksums
- Data matching and change detection
- Caching mechanisms and load balancing
- Bloom filters for probabilistic set membership
- Fingerprinting data records and database keys
Murmur provides the speed, distribution quality, and consistency required in such applications.
Limitations and considerations
Some limitations to note:
Murmur offers poor cryptographic strength unlike SHA-2 or BLAKE2.
Hash collisions are possible, so extra steps may be needed to handle them.
Small input changes can significantly alter the hash, which can be problematic for similarity hashing.
So murmur may not be suitable where cryptographic security or fuzzy matching is required.
Implementing murmur hash
Murmur hash is easy to implement in languages like C, C++, Go, Rust, Java, Python etc. Many standard libraries provide murmur hash utilities that can be readily used. For bespoke implementations, the specification is simple and portable.
For example, here is a Python murmur3 hash for a string:
1import mmh3
2
3data = "Sample input string"
4
5hash = mmh3.hash(data)
Conclusion
Murmur provides a fast and effective hashing solution suitable for a wide range of non-cryptographic applications where performance matters. Its speed, quality distribution, and simplicity makes it a great choice for tasks like data structures, caching, verification, and fingerprinting. Like any hash, care should be taken to handle collisions. But overall, understanding the role of murmur hashes enables leveraging their advantages while being aware of limitations.
