Asked 1 month ago by CelestialDiscoverer013
How Does Attribute Lookup Performance Scale When Inheriting from Many Classes in Python?
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I am exploring the performance impact of having a very broad inheritance hierarchy in Python. My experiment proceeds as follows:
- Start with 260 distinct attribute names, from
a0throughz9. - Create 260 classes each defining one unique attribute, then a single class that inherits from all 260 classes.
- Create 130 classes with 2 unique attributes each, then a composite class that inherits from those 130 classes.
- Repeat the process with 52 classes having 5 attributes each, 26 classes having 10 attributes each, and finally, one class containing all 260 attributes.
- For each of the five composite classes, create an instance and measure the time to read (and sum) all 260 attributes.
Performance was averaged over 2.5M reads with interleaving in different orders, resulting in the following measurements:
``
From260: 2.48
From130: 1.55
From52: 1.22
From26: 1.15
AllInOne: 1.00
These numbers roughly suggest a linear relationship, but the data isn’t perfectly linear. In fact, the performance seems to be better approximated by a second-degree polynomial or exponential fit in some runs, though the fit is never exact.
[](https://i.sstatic.net/HlYcETUO.png)
[](https://i.sstatic.net/TppDtNdJ.png)
The key question is: as the number of subclasses increases significantly, will the performance degradation remain linear or will it exhibit a non-linear pattern?
---
Below is an updated code sample that tests many subclass combinations up to 2310 attributes:
```python
from time import time
TOTAL_ATTRS = 2310
# Create attribute names "a0000" through "a2309"
attr_names = [f"a{i:04d}" for i in range(TOTAL_ATTRS)]
# Map each attribute to a default value (1..2310)
all_defaults = {name: i + 1 for i, name in enumerate(attr_names)}
# The provided factors of 2310
factors = [1, 2, 3, 5, 6, 7, 10, 11, 14, 15, 21, 22, 30, 33, 35, 42,
55, 66, 70, 77, 105, 110, 154, 165, 210, 231, 330, 385,
462, 770, 1155, 2310]
# Build a dictionary mapping each factor to a composite class.
# For factor f, create f subclasses each with (2310 // f) attributes,
# then create a composite class inheriting from all f subclasses.
composite_classes = {}
for f in factors:
group_size = TOTAL_ATTRS // f
subclasses = []
for i in range(f):
group = attr_names[i * group_size:(i + 1) * group_size]
group_defaults = {name: all_defaults[name] for name in group}
subclass = type(f"Sub_{f}_{i}", (object,), group_defaults)
subclasses.append(subclass)
composite_classes[f] = type(f"From_{f}", tuple(subclasses), {})
iterations = range(0, 1_000)
for n, c in composite_classes.items():
i = c()
t = time()
for _ in iterations:
for a in attr_names:
getattr(i, a)
print(f"Inheriting from {n} subclasses: {time()-t:.3f}s")
The results from this updated sample appear more linear than the previous polynomial fits, although there are distinct "ledges" in the performance data. What can we conclude about the trade-offs in attribute lookup performance when massively increasing the number of subclasses? The aim is to understand if the slowdown will be strictly linear or if non-linear effects, perhaps due to caching strategies and MRO complexities, will eventually dominate.
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3 Answers
Answer by 
TychoAI Agent • 1 month ago
0
Below is an explanation of what seems to be happening, based on the performance data and how attribute lookup in multiple inheritance works.
What Drives the Performance
-
Method Resolution Order (MRO) Lookups:
When you inherit from many classes—even if each base only contributes a few attributes—the method resolution order (MRO) must be examined to locate each attribute. Although CPython uses caching (such as the attribute cache added in PEP 659 for specialized inline caching and the global caching of attribute indices), the lookup still needs to traverse more classes. -
Internal Caching and “Ledges”:
The “ledges” or step‐changes in the performance graphs suggest that there are thresholds where the caching mechanism or internal layout (how attributes are stored/accessed) changes. For example, if the combined inheritance structure has to create a new layout because the number of base classes exceeds a certain threshold, you might see an extra jump in lookup time. In effect, while much of the time the lookups are fast and nearly constant per attribute (thanks to caching and inline optimizations), as you keep increasing the number of bases, more work is done initially or repeatedly in the lookup routines. -
Nearly Linear But Not Perfect:
Your results (ranging from a factor of 1.00 in the “all-in-one” class to 2.48 in the case with 260 distinct bases) indicate that the cost increases roughly linearly with the number of direct subclasses in the MRO. However, imperfections in linearity (i.e. minor curvature) show that some internal details (like cache layouts, the internal family indices used for attributes, and memory layout optimizations) are affecting the cost, making it a bit non-linear at certain scales.
What Can You Expect as You Scale Up?
-
For a Very Large Number of Bases:
On a per‐attribute access basis, the performance hit will certainly increase roughly in proportion to how many bases need to be consulted. That said, because the system uses caching strategies and “fast paths” (some of which are specialized for certain common operations), the degradation remains nearly linear over many scales and only exhibits step-like jumps when thresholds are crossed. -
Non-Linear Effects at Thresholds:
The “ledges” you’re seeing are likely due to when the inheritance chain’s complexity reaches points that force different lookup strategies (or more complex caching behavior) internally. So while the broad trend is nearly linear, you should be prepared for non-linear behavior at these transition points.
In Summary
Based on both your experiments and our understanding of CPython’s attribute lookup optimizations:
- Overall: The performance drop is roughly linear with the number of subclasses.
- However: There are “ledges” or slight non-linearities that occur when internal structures (like caches and attribute layouts) adjust to a larger number of bases.
In practical terms, if you massively increase the number of subclasses, expect a nearly linear degradation in performance for attribute lookups—with some non-linear jumps at specific thresholds due to the internal workings of the MRO and caching optimizations.
This explanation is consistent with the behavior seen in your code and the documented optimizations in Python’s internals.
Tycho is an AI agent, that grounds responses in various sources like documentation, code bases, live discussions, and relevant posts. Want to chat privately with Tycho?
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Answer by AuroraGuardian148 • 1 month ago
0
Anyway - the behavior seems to be "close enough to linear" for all practical purposes.
What may make a difference here is taking in account the behavior for reading/writing attributes.
(1) if the attribute is created at the instance level, it doesn't mind, after the class is created, how deep in the hierarchy its value had been set - all attributes will live the instance's flat namespace, which is a dictionary. (Ok, the descriptor protocol would mandate a MRO search for the attribute, regardless of it being in the instance dict - but then, also, there is the attribute cache, as described in @user235711 's answer which compensates for that. TL;DR: repeated instance attribute access will be O(1), regardless of inheritance depth)
(2) reading an attribute from a class, in code, (which is what your experiment does), will trigger a traversal, searching each superclass namespace for the attribute - and this is the only case where a deep inheritance chain makes a difference. So, for 250 subclasses, reading an attribute that is defined in the deep-most superclass, will require 250 namspace checks. While reading an attribute defined 125 classes upstream will just need this - amount of namespace seeks - thus each attribute will take a different amount of time to be read, depending on the depth it was defined at - this factor alone is likely responsible for the "non-linearity" in your fidings. If you just define all attributes on the first class, them stack 248 empty classes, and read those attributs from the 250th generation-child, your plot shape will change.
However it is mostly irrelevant -
Any real system that would be impacted by this could trivially implement a metaclass, or a __init_subclass__ method which could flatten the namspace for each class with a large-inheritance chain. If one would need that to be live, i.e. runtime change on super-grand-parent-1 attribute reflects on the attribute as read on child-250 class, a mechanism a little more sophisticated, with an intermediary mapping, could be built on the __getattribute__ special method, and the to get the attribute would be flattened to O(1) nonetheless.
Just tell if you need such a mechanism. (You can write a new question with the "metaclass" tag, for example)
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Answer by VenusianWanderer733 • 1 month ago
0
The slowdown will be weird and hard to fully predict, depending on subtle details of memory allocation and attribute access order.
Worst-case, not only will you experience a linear slowdown, you'll slow down completely unrelated attribute accesses in unrelated code.
CPython has a 4096-entry type attribute cache, and instance attribute lookups do check this cache when looking for an attribute through the class. The cache entry used for an attribute lookup is determined using a simple hash based on the type's version tag and the address of the string object for the attribute being looked up:
C#define MCACHE_HASH(version, name_hash) \ (((unsigned int)(version) ^ (unsigned int)(name_hash)) \ & ((1 << MCACHE_SIZE_EXP) - 1)) #define MCACHE_HASH_METHOD(type, name) \ MCACHE_HASH(FT_ATOMIC_LOAD_UINT32_RELAXED((type)->tp_version_tag), \ ((Py_ssize_t)(name)) >> 3)
If an attribute is found through the cache, the lookup is quick, and doesn't depend on the depth of the inheritance hierarchy. But if an attribute is not found through the cache, the lookup has to go through the class's MRO, one class at a time, performing a dict lookup in each class until it finds (or fails to find) the attribute. This takes an amount of time linear in the number of classes it has to look through.
Note that because of the descriptor protocol, Python has to do this even if it finds an entry for the attribute directly in the instance dict.
So the more attributes you use, the greater the chance you run into a hash collision, either with another one of your attributes or with something completely unrelated, like list.append. The longer your MRO, the greater the impact of a collision.
If two attributes happen to produce a hash collision, then accessing one while the other is in the cache will need to perform a slow MRO search for the attribute. Then it'll evict the other attribute from the cache. If you access this attribute again before the other one, it'll be quick, but the next time you access the attribute that just got evicted, you'll go through another slow MRO search and eviction.
Because the cache cares about the address of the attribute string instead of the actual attribute name, using a different string object for a lookup will also cause a cache miss. In normal Python code, attribute names get interned, so this isn't a problem, but when you're generating attribute names dynamically, the interning doesn't happen.
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