Most efficient property to hash for numpy array

Question

I need to be able to store a numpy array in a dict for caching purposes. Hash speed is important.

The array represents indicies, so while the actual identity of the object is not important, the value is. Mutabliity is not a concern, as I'm only interested in the current value.

What should I hash in order to store it in a dict?

My current approach is to use str(arr.data), which is faster than md5 in my testing.

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I've incorporated some examples from the answers to get an idea of relative times:

In [121]: %timeit hash(str(y)) 10000 loops, best of 3: 68.7 us per loop  In [122]: %timeit hash(y.tostring()) 1000000 loops, best of 3: 383 ns per loop  In [123]: %timeit hash(str(y.data)) 1000000 loops, best of 3: 543 ns per loop  In [124]: %timeit y.flags.writeable = False ; hash(y.data) 1000000 loops, best of 3: 1.15 us per loop  In [125]: %timeit hash((b*y).sum()) 100000 loops, best of 3: 8.12 us per loop 

It would appear that for this particular use case (small arrays of indicies), arr.tostring offers the best performance.

While hashing the read-only buffer is fast on its own, the overhead of setting the writeable flag actually makes it slower.

Answer 1

You can simply hash the underlying buffer, if you make it read-only:

>>> a = random.randint(10, 100, 100000) >>> a.flags.writeable = False >>> %timeit hash(a.data) 100 loops, best of 3: 2.01 ms per loop >>> %timeit hash(a.tostring()) 100 loops, best of 3: 2.28 ms per loop 

For very large arrays, hash(str(a)) is a lot faster, but then it only takes a small part of the array into account.

>>> %timeit hash(str(a)) 10000 loops, best of 3: 55.5 us per loop >>> str(a) '[63 30 33 ..., 96 25 60]' 

Answer 2

You can try xxhash via its Python binding. For large arrays this is much faster than hash(x.tostring()).

Example IPython session:

>>> import xxhash >>> import numpy >>> x = numpy.random.rand(1024 * 1024 * 16) >>> h = xxhash.xxh64() >>> %timeit hash(x.tostring()) 1 loops, best of 3: 208 ms per loop >>> %timeit h.update(x); h.intdigest(); h.reset() 100 loops, best of 3: 10.2 ms per loop 

And by the way, on various blogs and answers posted to Stack Overflow, you'll see people using sha1 or md5 as hash functions. For performance reasons this is usually not acceptable, as those "secure" hash functions are rather slow. They're useful only if hash collision is one of the top concerns.

Nevertheless, hash collisions happen all the time. And if all you need is implementing __hash__ for data-array objects so that they can be used as keys in Python dictionaries or sets, I think it's better to concentrate on the speed of __hash__ itself and let Python handle the hash collision[1].

[1] You may need to override __eq__ too, to help Python manage hash collision. You would want __eq__ to return a boolean, rather than an array of booleans as is done by numpy.