Dictionary of (named) tuples in Python and speed/RAM performance

Question

I'm creating a dictionary d of one million of items which are tuples, and ideally I'd like to access them with:

d[1634].id       # or  d[1634]['id']
d[1634].name     # or  d[1634]['name']
d[1634].isvalid  # or  d[1634]['isvalid']

rather than d[1634][0], d[1634][1], d[1634][2] which is less explicit.

According to my test:

import os, psutil, time, collections, typing
Tri = collections.namedtuple('Tri', 'id,name,isvalid')
Tri2 = typing.NamedTuple("Tri2", [('id', int), ('name', str), ('isvalid', bool)])
t0 = time.time()
# uncomment only one of these 4 next lines:
d = {i: (i+1, 'hello', True) for i in range(1000000)}                                 # tuple
# d = {i: {'id': i+1, 'name': 'hello', 'isvalid': True} for i in range(1000000)}      # dict
# d = {i: Tri(id=i+1, name='hello', isvalid=True) for i in range(1000000)}            # namedtuple
# d = {i: Tri2(id=i+1, name='hello', isvalid=True) for i in range(1000000)}            # NamedTuple
print('%.3f s  %.1f MB' % (time.time()-t0, psutil.Process(os.getpid()).memory_info().rss / 1024 ** 2))

"""
tuple:       0.257 s  193.3 MB
dict:        0.329 s  363.6 MB
namedtuple:  1.253 s  193.3 MB  (collections)
NamedTuple:  1.250 s  193.5 MB  (typing)
"""

• using a dict doubles the RAM usage, compared to a tuple
• using a namedtuple or NamedTuple multiplies by 5 the time spent, compared to a tuple!

Question: is there a tuple-like data structure in Python 3 which allows to access the data with x.id, x.name, etc. and also is RAM and CPU efficient?

---

Notes:

• in my real use case, the tuple is something like a C-struct of type (uint64, uint64, bool).

• I've also tried with:

  • slots (to avoid the interal object's __dict__, see Usage of __slots__?)

  • dataclass:

    @dataclasses.dataclass
    class Tri3:
        id: int
        ...
    

  • ctypes.Structure:

    class Tri7(ctypes.Structure):
        _fields_ = [("id", ctypes.c_int), ...]
    

  but it was not better (all of them ~ 1.2 sec.), nothing close to a genuine tuple in terms of performance

• Here are other options: C-like structures in Python

Answer 1

Cython's cdef-classes might be what you want: They use less memory than the pure Python classes, even if it comes at costs of more overhead when accessing members (because fields are stored as C-values and not Python-objects).

For example:

%%cython
cdef class CTuple:
    cdef public unsigned long long int id
    cdef public str name
    cdef public bint isvalid
    
    def __init__(self, id, name, isvalid):
        self.id = id
        self.name = name
        self.isvalid = isvalid

which can be used as wished:

ob=CTuple(1,"mmm",3)
ob.id, ob.name, ob.isvalid # prints (2, "mmm", 3)

Timings/memory consumption:

First, the baseline on my machine:

0.258 s  252.4 MB  # tuples
0.343 s  417.5 MB  # dict
1.181 s  264.0 MB  # namedtuple collections

with CTuple we get:

0.306 s  191.0 MB

which is almost as fast and needs considerable less memory.

If the C-type of members isn't clear at compile time, one could use simple python-objects:

%%cython
cdef class PTuple:
    cdef public object id
    cdef public object name
    cdef public object isvalid
    
    def __init__(self, id, name, isvalid):
        self.id = id
        self.name = name
        self.isvalid = isvalid

The timings are a little bit surprising:

0.648 s  249.8 MB

I didn't expect it to be so much slower than the CTuple-version, but at least it is twice as fast as named tuples.

---

One disadvantage of this approach is that it needs compilation. Cython however offers cython.inline which can be used to compile Cython-code created on-the-fly.

I've released cynamedtuple which can be installed via pip install cynamedtuple, and is based on the prototype bellow:

import cython

# for generation of cython code:
tab = "    "
def create_members_definition(name_to_ctype):
    members = []
    for my_name, my_ctype in name_to_ctype.items():
        members.append(tab+"cdef public "+my_ctype+" "+my_name)
    return members

def create_signature(names):
    return tab + "def __init__(self,"+", ".join(names)+"):"

def create_initialization(names):
    inits = [tab+tab+"self."+x+" = "+x for x in names]
    return inits

def create_cdef_class_code(classname, names):
    code_lines = ["cdef class " + classname + ":"]
    code_lines.extend(create_members_definition(names))
    code_lines.append(create_signature(names.keys()))
    code_lines.extend(create_initialization(names.keys()))
    return "\n".join(code_lines)+"\n"

# utilize cython.inline to generate and load pyx-module:
def create_cnamedtuple_class(classname, names):
    code = create_cdef_class_code(classname, names)
    code = code + "GenericClass = " + classname +"\n"
    ret = cython.inline(code)
    return ret["GenericClass"]

Which can be used as follows, to dynamically define CTuple from above:

CTuple = create_cnamedtuple_class("CTuple", 
                                 {"id":"unsigned long long int", 
                                  "name":"str",
                                  "isvalid":"bint"})

ob = CTuple(1,"mmm",3)
... 

---

Another alternative could be to use jit-compilation and Numba's jitted-classes which offer this possibility. They however seem to be much slower:

from numba import jitclass, types

spec = [
    ('id', types.uint64), 
    ('name', types.string),
    ('isvalid',  types.uint8),
]

@jitclass(spec)
class NBTuple(object):
    def __init__(self, id, name, isvalid):
        self.id = id
        self.name = name
        self.isvalid = isvalid

and the results are:

20.622 s  394.0 MB

so numba jitted classes are not (yet?) a good choice.

Answer 2

You can try to reverse it (store as struct of arrays) and access the values as x['id'][1634]. In other words, x is a dictionary with three keys and value for each key is a list. It will be space efficient.

Or you can use pandas dataframes. Dataframes are stored in a matrix form where the rows have numeric IDs and columns have labels (strings like 'name' etc.). For a dataframe df, df.iloc[i] points to the $i^th$ row and you can access the name in that row by df.iloc[i].name or df.iloc[i]['name']

Answer 3

There is one another fast and compact way with the help of recordclass library:

pip3 install recordclass

import recordclass
TriDO = recordclass.make_dataclass("TriDO", 
           [('id', int), ('name', str), ('isvalid', bool)],
           fast_new=True)

Here are the values performance counters (linux, 64-bit, python3.9, recordclass >= 0.15):

tuple:

t0 = time.time()
d = {i: (i+1, 'hello', True) for i in range(1000000)}
print('%.3f s  %.1f MB' % (time.time()-t0,
      psutil.Process(os.getpid()).memory_info().rss / 1024 ** 2))
0.235 s  259.1 MB

dict:

t0 = time.time()
d = {i: {'id':i+1, 'name':'hello', 'isvalid':True} for i in range(1000000)}
print('%.3f s  %.1f MB' % (time.time()-t0,
      psutil.Process(os.getpid()).memory_info().rss / 1024 ** 2))
0.332 s  428.6 MB

namedtuple:

t0 = time.time()
d = {i: Tri(i+1, 'hello', True) for i in range(1000000)}
print('%.3f s  %.1f MB' % (time.time()-t0,
      psutil.Process(os.getpid()).memory_info().rss / 1024 ** 2))
1.195 s  275.6 MB

'NamedTuple:'

t0 = time.time()
d = {i: Tri2(i+1, 'hello', True) for i in range(1000000)}
print('%.3f s  %.1f MB' % (time.time()-t0,
      psutil.Process(os.getpid()).memory_info().rss / 1024 ** 2))
1.059 s  275.2 MB

dataobject:

t0 = time.time()
d = {i: TriDO(i+1, 'hello', True) for i in range(1000000)}
print('%.3f s  %.1f MB' % (time.time()-t0,
      psutil.Process(os.getpid()).memory_info().rss / 1024 ** 2))
0.256 s  244.2 MB

Here more accurate timings:

%timeit d = {i:(i+1, 'hello', True) for i in range(1000000)} # tuple
162 ms ± 756 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
%timeit d = {i:{'id':i+1, 'name':'hello', 'isvalid':True} for i in range(1000000)} # dict
250 ms ± 2.77 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
%timeit d = {i:Tri(i+1,'hello',True) for i in range(1000000)} # namedtuple
318 ms ± 422 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)
%timeit d = {i:Tri2(i+1,'hello',True) for i in range(1000000)} # NamedTuple
330 ms ± 5.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
%timeit d = {i:TriDO(i+1,'hello',True) for i in range(1000000)} # dataobject
188 ms ± 823 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

Answer 4

You could keep tuples and name your indices (2nd option below)

from enum import IntEnum

# int variable
ID, NAME, IS_VALID = 0, 1, 2

# IntEnum
class Index(IntEnum):
    ID = 0
    NAME = 1
    IS_VALID = 2
   

# Create tuples
d = {i: (i+1, 'hello', True) for i in range(int(1e6))}  

t0 = time.time()

# check data access performance
# uncomment only one of these 3 next lines:
# for i in range(len(d)): _ = d[i][0], d[i][1], d[i][2]
# for i in range(len(d)): _ = d[i][ID], d[i][NAME], d[i][IS_VALID]
for i in range(len(d)): _ = d[i][Index.ID], d[i][Index.NAME], d[i][Index.IS_VALID]

print('%.3f s' % (time.time()-t0))

"""
int           0.307 s
int variable  0.312 s
IntEnum       0.749 s
"""