Python JIT instantiation This code is about instantiating object attributes Just in Time.

I'm working on a REST API, where I currently create Python objects from the JSON data returned by the server, though not all attributes of the created objects will be called upon in user code. Hence only instantiating the attributes the user access via dot notation seems like a valid way to improve performance.

lazy_evaluate is a function, with a signature similar to functools.partial. 
lazy_evaluate returns a function that when called multiple times only computes the result once.

I then take advantage of the builtin property function to create a getter that calls the function returned by lazy_evaluate.

There is clearly a trade off here. This only helps performance if a subset of the attributes are required in user code. Otherwise the extra code will slow it down.

The core of the idea is to use Python scoping to mutate a list in the scope outside the function evaluate. I dislike this the most.


Do you think it's a good idea?
Do you have a better solution?
Do you think the complexity is worth it?


I will try and create more context here as to why I want this. I have implemented this idea in my package async_v20 found here. I have written docs here.

I left this out as I felt it was a distraction from the concept.

JSON from the server does get parsed into a python dict. Though aiohttp does this for me.

When the server sends JSON:


All attributes are converted to snake_case via a dictionary lookup
Time data is turned into a pandas.TimeStamp (requires extra parsing)


In some cases there may be 5000 objects to instantiate or objects may be nested around 3 deep. Objects get instantiated in this module.

This example runs a benchmark:

from time import time

class Time(object):
    def __enter__(self):
        self.start = time()
        return self
    def __exit__(self, exc_type, exc_val, exc_tb):
        self.end = time()
        self.interval = self.end - self.start

def lazy_evaluate(func, *args, **kwargs):
    acc = []
    def evaluate():
        if acc == []:
            print('EVALUATING')
            acc.append(func(*args, **kwargs))
        return acc[0]
    return evaluate



class JitAttributes(object):

    foo = property(lambda self: getattr(self, '_foo')())
    bar = property(lambda self: getattr(self, '_bar')())
    baz = property(lambda self: getattr(self, '_baz')())

    def __init__(self, foo, bar, baz):
        self._foo = foo
        self._bar = bar
        self._baz = baz

class Attributes(object):

    def __init__(self, foo, bar, baz):
        self.foo = foo
        self.bar = bar
        self.baz = baz


a = 500 # Careful making this too big!
b = a * 2
c = b * 2

print('RUNNING JitAttributes')
repeats = range(100000)

with Time() as t:
    for _ in repeats:
        JitAttributes(
            lazy_evaluate(list, range(a)),
            lazy_evaluate(list, range(b)),
            lazy_evaluate(list, range(c)),
        )

print('TOOK ', t.interval, ' seconds')


print('RUNNING Attributes')

with Time() as t:
    for _ in repeats:
        Attributes(
            list(range(a)),
            list(range(b)),
            list(range(c)),
        )

print('TOOK ', t.interval, ' seconds')


Outputs:



RUNNING JitAttributes
TOOK  0.2956113815307617  seconds
RUNNING Attributes
TOOK  4.880501985549927  seconds


Console demonstration:

&gt;&gt;&gt; jit_instance = JitAttributes(
...                 lazy_evaluate(list, range(a)),
...                 lazy_evaluate(list, range(b)),
...                 lazy_evaluate(list, range(c)),
...             )
&gt;&gt;&gt; jit_instance.foo
# EVALUATING            &lt;- Only evaluates once :)
# [0, 1, 2, 3, ... ]
&gt;&gt;&gt; jit_instance.foo
# [0, 1, 2, 3, ... ]
&gt;&gt;&gt; non_jit_instance = Attributes(
...                 list(range(a)),
...                 list(range(b)),
...                 list(range(c)),
...             )

&gt;&gt;&gt; non_jit_instance.foo
# [0, 1, 2, 3, ...]
&gt;&gt;&gt; non_jit_instance.foo
# [0, 1, 2, 3, ...]


Update

New review following on from this one
I have a problem with your code, in that the code seems unrelated to the problem statement you give at the top.

You say you are "working on a REST API, where I currently create python objects from the JSON data returned by the server. Though not all attributes of the created objects will be called upon in user code."

None of your code involves REST, or JSON. So while it's an interesting demonstration of certain parts of Python, it doesn't much address your issue.

IMO, the feasibility of what you are discussing is going to be determined by the nature and character of the JSON data, and the REST API. Parsing JSON is generally a one-shot deal. If you get JSON data like this:

'{ "name":"John", "age":31, "city":"New York" }'


how would it benefit you to "defer" the evaluation or construction of the age and city attributes? It would make far more sense to just parse and assign them all.

On the other hand, if your API returns a list of 1,000's of, say, homes with lat/long coordinates, and part of the object initialization process involves computing the distance from a given origin address, then perhaps delaying the computation or delaying object instantiation makes sense.

Please share with us the nature of the problem you're really trying to solve, and we'll be able to comment effectively on whether your proposed solution is a good idea.

EDIT:

I had a look at your linked code. The key was, I think, in the helpers.py:get_attribute() function.

You appear to be re-implementing the behavior of the __getattr__ or __getattribute__ dundermethods. I suggest that you look at implementing a simpler approach, where you store (say) '_attrname' as the lazy-evaluating function, and use __getattr__ to expand that value into 'attrname' when you fetch it the first time. 

Since Python will check for obj.attrname and only call obj.__getattr__() if not found, this would provide the advantage of doing the subsequent lookups in C code rather than Python.