Magic Jug Methods¶

This is an advanced use of jug and you can shoot yourself in the foot doing this. If you cannot figure out why this functionality could be useful, then you probably should not be using it.

Custom hash functions¶

Sometimes, you may want to give your objects a special hash function, either to add functionality or for efficiency. There are two ways to do it: (1) use a CustomHash object for simple cases or (2) add a __jug_hash__ method for more complex ones.

Using the CustomHash wrapper¶

For example, here is how timed_path is implemented (minus the comments in the real code):

def hash_with_mtime_size(path):
from .hash import hash_one
st = os.stat_result(os.stat(path))
mtime = st.st_mtime
size = st.st_size
return hash_one((path, mtime, size))

def timed_path(path):
return CustomHash(path, hash_with_mtime_size)


The return value from timed_path is an object which behaves exactly like path (i.e., as a file path), but when jug needs to hash it, it calls the function hash_with_mtime_size.

Implementing a __jug_hash__ method¶

When jug wants to hash an object, first it checks whether the object has a __jug_hash__ method. If so, that method should return bytes (or a bytes-like object that can be used by a hashlib object. If there is no __jug_hash__ method, jug checks whether the object is one of its known types (dict, list, tuple, numpy array, …). If so, it will use optimized code. Otherwise; it resorts to calling pickle on the object and then hashing the pickled representation.

This fallback can be very inefficient. For example, let’s say you have an object which is basically just a numpy array loaded from disk, which remembers its initial location. The standard pickling method would be very inefficient compared to the optimized numpy code.

The way to solve this is to define a __jug_hash__ method. Inside it, we can rely on the jug hashing machinery to access the optimized version!

Here is how we’d do it:

import numpy as np
class NamedNumpy(object):
def __init__(self, ifile):
self.name = ifile

def transform(self, x):
self.data *= x

def __jug_hash__(self):
from jug.hash import hash_one

return hash_one({
'type': 'NumpyPair',
'data': self.data,
'name': self.name,
})


The function hash_one takes one object and hashes it using the jug machinery. Because we are passing it a dictionary, it recursively build a hash for it. Thus, our NamedNumpy object now has a very fast hash function.

In fact, the CustomHash object we saw above, just defined its __jug_hash__ function to call whatever you pass it in.

Overriding the value function¶

Similarly to overriding the hashing, we can override the value call which jug used internally to load objects.

Again, value(x) works in the following way:

1. Does the x.__jug_value__ member exist? If so, call it.
2. Is x one of the composite types it knows about (dict, list,…). If so, use special code to recursively get all the sub objects. For a list value([x,y]) == [value(x), value(y)].
3. Is it a Task or a Tasklet? If so, load it from the store.
4. Otherwise, value(x) == x.

What if you have your own sequence object? Then you can set a __jug_value__ method, which will be called whenever value(self) is needed. This is a pretty advanced use case: if you cannot figure out why this may be useful, then you probably don’t need to use it.