--- title: Reducing Pydantic's memory footprint using bitsets description: >- How Pydantic replaced Python sets with Rust-powered bitsets to cut model instance memory by 55% and speed up validation date: '2026-03-24' authors: - Victorien Plot categories: - Pydantic Validation - Performance canonical: 'https://pydantic.dev/articles/pydantic-bitset-performance' --- > ## Documentation Index > Fetch the complete documentation index at: https://pydantic.dev/llms.txt > Use this file to discover all available pages before exploring further. ## Querying This Documentation **warning**: agent query parameters (`goal` and `organization`) not included. 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Example (replace the values with your own): `https://pydantic.dev/articles/pydantic-bitset-performance.md?goal=&organization=` --- > Markdown version of [Reducing Pydantic's memory footprint using bitsets](https://pydantic.dev/articles/pydantic-bitset-performance) — the canonical HTML page. > > By [Victorien Plot](https://pydantic.dev/authors/victorien-plot.md) · 2026-03-24 · Pydantic Validation, Performance > > Related: [Official skills for Pydantic Validation, Pydantic AI, and Logfire](https://pydantic.dev/articles/pydantic-ai-logfire-claude-code-skills.md) · [Emscripten wheels for Pydantic](https://pydantic.dev/articles/emscripten-wheels-pydantic.md) > > All articles: [/articles.md](https://pydantic.dev/articles.md) · Site index: [/llms.txt](https://pydantic.dev/llms.txt) --- # Reducing Pydantic's memory footprint using bitsets On July 2025, a user [reported](https://github.com/pydantic/pydantic-core/issues/1758) an issue about Pydantic potentially using more memory than needed on model instances. The user referred to [PEP 412 – Key-Sharing Dictionary](https://peps.python.org/pep-0412/), but I then found out that PEP 412 wasn't really the bottleneck. In this blog post, we are going to see how the original issue was investigated and how we can leverage bitsets to greatly reduce the memory usage of Pydantic model instances. ## Table of Contents * [Introduction](https://pydantic.dev/articles/pydantic-bitset-performance#introduction) * [dicts and PEP 412](https://pydantic.dev/articles/pydantic-bitset-performance#dicts-and-pep-412) * [Tracking model fields set](https://pydantic.dev/articles/pydantic-bitset-performance#tracking-model-fields-set) * [Bitsets to the rescue](https://pydantic.dev/articles/pydantic-bitset-performance#bitsets-to-the-rescue) * [Implementation](https://pydantic.dev/articles/pydantic-bitset-performance#implementation) * [Results](https://pydantic.dev/articles/pydantic-bitset-performance#results) * [Open source challenges](https://pydantic.dev/articles/pydantic-bitset-performance#open-source-challenges) ## Introduction ### dicts and PEP 412 > **Note** > > The explanation provided below was simplified for brevity. If you want to read the full story, I highly recommend reading > [Internals of sets and dicts](https://www.fluentpython.com/extra/internals-of-sets-and-dicts/), an extra from the > [Fluent Python](https://www.oreilly.com/library/view/fluent-python-2nd/9781492056348/) book by Luciano Ramalho. Internally, Python dictionaries are stored as a hash table. When constructing a dictionary instance, each key->value pair is inserted in this table following a specific algorithm (long story short: the hash of the key is computed, and the value gets inserted depending on whether there is a match in the table with that hash). Such a hash table has 3 columns: | Hash | Pointer to key | Pointer to value | |----------------------|----------------|------------------| | 6558844463820098693 | -> 'a' | -> 1 | | 8771437864645622406 | -> 'b' | -> 2 | | -6104801815529482393 | -> 'c' | -> 3 | (this table would be for the following dict: `{'a': 1, 'b': 2, 'c': 3}`). In the majority of cases, instances of Python classes (including Pydantic models) store their attributes in a dictionary, under the `__dict__` attribute: ```python class MyClass: a: int b: str def __init__(self, a: int, b: str) -> None: self.a = a self.b = b m = MyClass(1, 'test') m.__dict__ #> {'a': 1, 'b': 'test'} ``` The issue is, for each instance of `MyClass`, you end up storing the same hashes and pointers to keys, and only the pointers to values are different. This is precisely what PEP 412 aims at solving. The hash table is split into two: a "keys table", only stored once and tied to the `MyClass` type, and a "values table" for each instance, containing only the pointer to the attribute values. This optimization can be seen in action. Profiling the following scripts: **❌ PEP 412** ```python dict_list = [] for _ in range(1_000_000): dict_list.append({ 'a': 1, 'b': '2', 'c': 1, 'd': 1, 'e': 2, 'f': 1, 'g': 1, }) ``` Memray results (`memray run -o out.bin script.py && memray flamegraph out.bin -o out.html`): ![Memray flamegraph showing ~800MB memory usage without PEP 412 key-sharing optimization](https://pydantic.dev/assets/blog/pydantic-bitset-performance/no-pep412.png) **✅ PEP 412** ```python class A: ... a_list = [] for _ in range(1_000_000): a = A() a.a = 1 a.b = '2' a.c = 1 a.d = 1 a.e = 2 a.f = 1 a.g = 1 a_list.append(a) ``` Memray results (`memray run -o out.bin script.py && memray flamegraph out.bin -o out.html`): ![Memray flamegraph showing reduced memory usage with PEP 412 key-sharing dictionary](https://pydantic.dev/assets/blog/pydantic-bitset-performance/with-pep412.png) We notice that a good chunk of memory was saved! Now, doing the same with Pydantic models: ```python from pydantic import BaseModel class Model(BaseModel): a: int b: str c: int d: int e: int f: int g: int model_list: list[Model] = [] for _ in range(1_000_000): model = Model( a=1, b='2', c=1, d=1, e=2, f=1, g=1, ) model_list.append(model) ``` and... ![Memray flamegraph showing Pydantic BaseModel using 4x more memory than expected](https://pydantic.dev/assets/blog/pydantic-bitset-performance/with-basemodel.png) huh, we are using 4x more memory than the example *without* the PEP 412 optimization?! ### Tracking model fields set Looking at the [Memray](https://bloomberg.github.io/memray/) flamegraph report \[1\], we can notice [one particular call](https://github.com/pydantic/pydantic/blob/f2bc2561277c2936779f0d913dda030fbb589e59/pydantic-core/src/validators/model_fields.rs#L528) in the model validation logic, accounting for almost 800 MB of the total memory usage: ```rs impl ModelFieldsValidator { fn validate_by_get_item<'py>( &self, py: Python<'py>, input: &(impl Input<'py> + ?Sized), dict: impl ValidatedDict<'py>, state: &mut ValidationState<'_, 'py>, ) -> ValResult> { // Validation of fields... ... let fields_set = PySet::new(py, &fields_set_vec)?; // <- this line ... } } ``` Apart from validating each field, Pydantic also keeps track of the field explicitly set during instantiation (such fields are accessible using the [`model_fields_set`](https://pydantic.dev/docs/validation/latest/api/pydantic/base_model/#pydantic.BaseModel.model_fields_set) property): ```python from pydantic import BaseModel class Model(BaseModel): a: int b: int = 1 m = Model(a=1) m.model_fields_set #> {'a'} ``` It is currently stored as a (mutable) Python set, we can see by checking its size that it matches the extra 800 MB of memory reported by Memray: ```python # Using the `Model` class with a, b, ..., g fields above: model = Model(a=1, b='2', g=1) (sys.getsizeof(model.model_fields_set) * 1_000_000) / 1_000_000 # (1)! #> 728.0, roughly matches the 800 MB ``` 1. Multiplying by 1M to account for the total number of models, and dividing again to get the result in MBs. ## Bitsets to the rescue Say you are developing an application with a bunch of feature flags. For every user of your application, specific feature flags can be turned off and on. A naive approach would be to store the feature flags configuration as a list of boolean values: ```python feature_flag_values = [ True, # new_dark_mode False, # sso_login False, # admin_panel True, # ai_features ] ``` But this turns out to be particularly inefficient, especially for booleans. Each element of the list is a reference to the boolean value `True`/`False` (in Python, references are 8 bytes in size, 64 times more than the theoretical size to store an on/off value). Let's say we have 8 different feature flags in our application. We can represent these using a single `u8` number, by using the binary number representation: | Feature flag | `new_dark_mode` | `sso_login` | `admin_panel` | ... | `ai_features` | |---------------|-----------------|-------------|---------------|-----|---------------| | Binary number | 1 | 0 | 0 | ... | 1 | By assigning an index to the binary position for each feature flag, we can use bitwise operations to read and toggle specific feature flags \[2\]. Now I hope you can see where this is going. The `model_fields_set` attribute is used to track whether a field was explicitly set during validation. This is an on/off value! And luckily for us, Pydantic model fields [are ordered](https://pydantic.dev/docs/validation/latest/concepts/models/#field-ordering), meaning we can easily convert from a field name -> index into the binary number representation. ## Implementation There are a couple requirements for Pydantic models to be able to work with bitsets: * The number of model fields (and as such number of bits) is arbitrary. A `u8` could be enough if a model has 5 fields, but in theory it could also have thousands of fields. Depending on the number of fields, we can use the appropriate number type (`u8` for up to 8 fields, `u16` for up to 16 fields, etc). If the model happens to have more than 128 fields, a vector of numbers can be used. The [`fixedbitset`](https://docs.rs/fixedbitset/latest/fixedbitset/) crate handles this nicely for us. * [Extra fields](https://pydantic.dev/docs/validation/latest/concepts/models/#extra-data) are also tracked in `model_fields_set`: ```python from pydantic import BaseModel class Model(BaseModel, extra='allow'): a: int model = Model(a=1, extra=2) model.model_fields_set #> {'a', 'extra'} ``` If any extra fields are set during validation, we can collect them in a separate set. Putting it all together, here is a sketch API of our new set implementation \[3\]: ```python from collections.abc import MutableSet class ModelFieldsSet(MutableSet[str]): bitset: FixedBitSet # (1)! extra_keys: set[str] | None # A reference to the `model_fields` property of the model: model_fields: dict[str, FieldInfo] def _index_of(self, key: str) -> int | None: for i, k in enumerate(self.model_fields): if k == key: return i def __len__(self) -> int: bitset_len = self.bitset.count_ones() if self.extra_keys is not None: return bitset_len + len(self.extra_keys) else: return bitset_len def __contains__(self, key: object) -> bool: if not isinstance(key, str): return False idx = self._index_of(key) if idx is not None and self.bitset.contains(idx): return True if self.extra_keys is not None and key in self.extra_keys: return True return False # Other methods: def add(self, key: str) -> None: ... def discard(self, key: str) -> None: ... ``` 1. This would be the [`FixedBitSet`](https://docs.rs/fixedbitset/latest/fixedbitset/struct.FixedBitSet.html) struct in Rust. The (very much in progress) implementation can be found in [this PR](https://github.com/pydantic/pydantic/pull/12924). ## Results In terms of memory, we end up saving almost all the 800 MB used by the original set: ![Memray flamegraph showing 55% memory reduction with bitset implementation in Pydantic](https://pydantic.dev/assets/blog/pydantic-bitset-performance/with-bitset.png) A 55% reduction! Note that most of the time, the relative improvement will be lower than that, as we used really small field values in our model (e.g. we are not using more complex types such as containers or `datetime`s). In terms of validation speed, it greatly improved on some of our benchmarks, especially the ones defining models with lots of fields. Interestingly, when only one or two fields are defined on the model, validation is slightly slower, presumably because creating a Python set was faster than the new bitset implementation. This can be mitigated by only using the new implementation if the number of fields is big enough. ## Open source challenges Whenever we need to make changes to the Pydantic library, we need to be extremely careful about potential breaking changes. The library has [widespread usage](https://pydantic.dev/articles/pydantic-validation-10-billion-downloads), and as such we should always expect it to be used in unusual and undocumented ways. As of today, `model_fields_set` [is exposed](https://github.com/pydantic/pydantic/blob/4f818e02fa95b854bc2ca295e04c10422d16cd44/pydantic/main.py#L306-L314) as a property, and typed as `set[str]`. As such, we should assume that users *expect* the value to be a built-in [set](https://docs.python.org/3/tutorial/datastructures.html#sets). This means `isinstance(m.model_fields_set, set)` should work (and in theory `type(m.model_fields_set) is set` too..), it should be [(un)picklable](https://docs.python.org/3/library/pickle.html). We should also probably expect users to override it completely with a different `set` instance (and still be supported during serialization if [`exclude_unset`](https://pydantic.dev/docs/validation/latest/concepts/serialization/#excluding-and-including-fields-based-on-their-value) is enabled). Yes, `model_fields_set` is a property and as such can't be overridden, but the actual value is stored under the `__pydantic_fields_set__` attribute, which [is relied on](https://github.com/search?q=lang%3Apython+%22__pydantic_fields_set__%22&type=code) already. These are all challenges that will complicate the implementation, and may require waiting for [V3](https://github.com/pydantic/pydantic/issues/10033). As general advice to open source maintenance, be as conservative as possible when exposing your public API. If something isn't documented but possible to do, users *will* rely on it. --- **Use Pydantic Validation? Try [Pydantic AI](https://pydantic.dev/pydantic-ai) and [Pydantic Logfire](https://pydantic.dev/logfire).** Same type-safety you trust, now for AI agents, evals, and observability. --- ### References 1. The `memray run` command has a `--native` argument, useful for finding which regions of the code are responsible for holding the memory. As the Pydantic validation is performed in Rust, this can help us find where in the Rust validation memory is being used. 2. See [this video](https://www.youtube.com/watch?v=z7wVUfnm7M0) from the Core Dumped Youtube channel for an in depth explanation. It provides a really similar example to our feature flags. 3. The actual implementation lives in `pydantic-core` and is written in Rust, but a Python example is provided for clarity.