What is the fastest (to access) struct-like object in Python?

This question is fairly old (internet-time), so I thought I'd try duplicating your test today, both with regular CPython (2.7.6), and with pypy (2.2.1) and see how the various methods compared. (I also added in an indexed lookup for the named tuple.)

This is a bit of a micro-benchmark, so YMMV, but pypy seemed to speed up named tuple access by a factor of 30 vs CPython (whereas dictionary access was only sped up by a factor of 3).

from collections import namedtuple

STest = namedtuple("TEST", "a b c")
a = STest(a=1,b=2,c=3)

class Test(object):
    __slots__ = ["a","b","c"]

    a=1
    b=2
    c=3

b = Test()

c = {'a':1, 'b':2, 'c':3}

d = (1,2,3)
e = [1,2,3]
f = (1,2,3)
g = [1,2,3]
key = 2

if __name__ == '__main__':
    from timeit import timeit

    print("Named tuple with a, b, c:")
    print(timeit("z = a.c", "from __main__ import a"))

    print("Named tuple, using index:")
    print(timeit("z = a[2]", "from __main__ import a"))

    print("Class using __slots__, with a, b, c:")
    print(timeit("z = b.c", "from __main__ import b"))

    print("Dictionary with keys a, b, c:")
    print(timeit("z = c['c']", "from __main__ import c"))

    print("Tuple with three values, using a constant key:")    
    print(timeit("z = d[2]", "from __main__ import d"))

    print("List with three values, using a constant key:")
    print(timeit("z = e[2]", "from __main__ import e"))

    print("Tuple with three values, using a local key:")
    print(timeit("z = d[key]", "from __main__ import d, key"))

    print("List with three values, using a local key:")
    print(timeit("z = e[key]", "from __main__ import e, key"))

Python Results:

Named tuple with a, b, c:
0.124072679784
Named tuple, using index:
0.0447055962367
Class using __slots__, with a, b, c:
0.0409136944224
Dictionary with keys a, b, c:
0.0412045334915
Tuple with three values, using a constant key:
0.0449477955531
List with three values, using a constant key:
0.0331083467148
Tuple with three values, using a local key:
0.0453569025139
List with three values, using a local key:
0.033030056702

PyPy Results:

Named tuple with a, b, c:
0.00444889068604
Named tuple, using index:
0.00265598297119
Class using __slots__, with a, b, c:
0.00208616256714
Dictionary with keys a, b, c:
0.013897895813
Tuple with three values, using a constant key:
0.00275301933289
List with three values, using a constant key:
0.002760887146
Tuple with three values, using a local key:
0.002769947052
List with three values, using a local key:
0.00278806686401

A couple points and ideas:

1. You're timing accessing the same index many times in a row. Your actual program probably uses random or linear access, which will have different behavior. In particular, there will be more CPU cache misses. You might get slightly different results using your actual program.

2. OrderedDictionary is written as a wrapper around dict, ergo it will be slower than dict. That's a non-solution.

3. Have you tried both new-style and old-style classes? (new-style classes inherit from object; old-style classes do not)

4. Have you tried using psyco or Unladen Swallow? (2020 Update - these two projects are dead)

5. Does your inner loop to modify the data or just access it? It might be possible to transform the data into the most efficient possible form before entering the loop, but use the most convenient form elsewhere in the program.

One thing to bear in mind is that namedtuples are optimised for access as tuples. If you change your accessor to be a[2] instead of a.c, you'll see similar performance to the tuples. The reason is that the name accessors are effectively translating into calls to self[idx], so pay both the indexing and the name lookup price.

If your usage pattern is such that access by name is common, but access as tuple isn't, you could write a quick equivalent to namedtuple that does things the opposite way: defers index lookups to access by-name. However, you'll pay the price on the index lookups then. Eg here's a quick implementation:

def makestruct(name, fields):
    fields = fields.split()
    import textwrap
    template = textwrap.dedent("""\
    class {name}(object):
        __slots__ = {fields!r}
        def __init__(self, {args}):
            {self_fields} = {args}
        def __getitem__(self, idx): 
            return getattr(self, fields[idx])
    """).format(
        name=name,
        fields=fields,
        args=','.join(fields), 
        self_fields=','.join('self.' + f for f in fields))
    d = {'fields': fields}
    exec template in d
    return d[name]

But the timings are very bad when __getitem__ must be called:

namedtuple.a  :  0.473686933517 
namedtuple[0] :  0.180409193039
struct.a      :  0.180846214294
struct[0]     :  1.32191514969

ie, the same performance as a __slots__ class for attribute access (unsurprisingly - that's what it is), but huge penalties due to the double lookup in index-based accesses. (Noteworthy is that __slots__ doesn't actually help much speed-wise. It saves memory, but the access time is about the same without them.)

One third option would be to duplicate the data, eg. subclass from list and store the values both in the attributes and listdata. However you don't actually get list-equivalent performance. There's a big speed hit just in having subclassed (bringing in checks for pure-python overloads). Thus struct[0] still takes around 0.5s (compared with 0.18 for raw list) in this case, and you do double the memory usage, so this may not be worth it.

This problem may be obsolete soon. CPython dev has evidently made significant improvements to the performance of accessing named tuple values by attribute name. The changes are scheduled for release in Python 3.8, near the end of Oct. 2019.

See: https://bugs.python.org/issue32492 and https://github.com/python/cpython/pull/10495.