imohash is a fast, constant-time hashing library. It uses file size and sampling to calculate hashes quickly, regardless of file size. It was originally released as a Go library.

imosum is a sample application to hash files from the command line, similar to md5sum.

pip install imohash

As a library:

from imohash import hashfile

hashfile('foo.txt')
'O\x9b\xbd\xd3[\x86\x9dE\x0e3LI\x83\r~\xa3'

hashfile('foo.txt', hexdigest=True)
'a608658926d8aa86b3db8208ad279bfe'

# just hash the whole file if smaller then 200000 bytes. Default is 128K
hashfile('foo.txt', sample_threshhold=200000)
'x86\x9dE\x0e3LI\x83\r~\xa3O\x9b\xbd\xd3[E'

# use samples of 1000 bytes. Default is 16K
hashfile('foo.txt', sample_size=1000)
'E\x0e3LI\x83\r~\xa3O\x9b\xbd\xd3[E\x23\x25'

Or from the command line:

imosum *.jpg

Because imohash only reads a small portion of a file's data, it is very fast and well suited to file synchronization and deduplication, especially over a fairly slow network. A need to manage media (photos and video) over Wi-Fi between a NAS and multiple family computers is how the library was born.

If you just need to check whether two files are the same, and understand the limitations that sampling imposes (see below), imohash may be a good fit.

Because imohash only reads a small portion of a file's data, it is not suitable for:

• file verification or integrity monitoring
• cases where fixed-size files are manipulated
• anything cryptographic

(Note: a more precise description is provided in the algorithm description.)

imohash works by hashing small chunks of data from the beginning, middle and end of a file. It also incorporates the file size into the final 128-bit hash. This approach is based on a few assumptions which will vary by application. First, file size alone tends (1) to be a pretty good differentiator, especially as file size increases. And when people do things to files (such as editing photos), size tends to change. So size is used directly in the hash, and any files that have different sizes will have different hashes.

Size is an effective differentiator but isn't sufficient. It can show that two files aren't the same, but to increase confidence that like-size files are the same, a few segments are hashed using murmur3, a fast and effective hashing algorithm. By default, 16K chunks from the beginning, middle and end of the file are used. The ends of files often contain metadata which is more prone to changing without affecting file size. The middle is for good measure. The sample size can be changed for your application.

1 Try du -a . | sort -nr | less on a sample of your files to check this assertion.

Small files are more likely to collide on size than large ones. They're also probably more likely to change in subtle ways that sampling will miss (e.g. editing a large text file). For this reason, imohash will simply hash the entire file if it is less than 128K. This parameter is also configurable.

The standard hash performance metrics make no sense for imohash since it's only reading a limited set of the data. That said, the real-world performance is very good. If you are working with large files and/or a slow network, expect huge speedups. (spoiler: reading 48K is quicker than reading 500MB.)

Inspired by ILS marker beacons.

• The "sparseFingerprints" used in TMSU gave me some confidence in this approach to hashing.
• Sébastien Paolacci's murmur3 library does all of the heavy lifting in the Go version.
• As does Hajime Senuma's mmh3 library for the Python version.