Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsMemoization can speed up a Python function when it is called repeatedly with the same arguments: instead of recalculating a result, Python returns a value saved from an earlier call. Use functools.cache when the set of cached inputs is safely bounded; choose functools.lru_cache when you need to limit how many results stay in memory. Neither is a universal speed boost: the function must be safe to reuse, and the benefit depends on the workload.
How memoization works in Python
A memoized function stores results by call arguments. On a cache hit, the wrapper returns the stored result; on a miss, it calls the original function and saves the result for later. This is most useful when the same inputs recur and the calculation costs more than looking up a result.
Python provides both decorators in functools. The official Python 3.14.8 functools documentation describes the LRU cache as appropriate when you want to reuse previously computed values.
Choose between cache and lru_cache
| Decorator | Storage behavior | When it fits |
|---|---|---|
@cache |
Unbounded; equivalent to @lru_cache(maxsize=None). |
Use when the set of distinct inputs is finite or otherwise safely bounded and you want a lightweight cache without eviction. |
@lru_cache |
Defaults to at most 128 entries; accepts an explicit maximum size and evicts least-recently-used entries when full. | Use when recent inputs are likely to recur and a long-running process needs a cap on retained entries. |
The default of 128 is not a universal tuning target. Set a size based on the workload, the memory cost of cached keys and values, and how often useful inputs recur. A bound that is too small can evict results before they are reused; an unbounded cache can keep growing as new inputs arrive.
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How to add a cache safely
For example, a parser whose output depends only on its schema text could use a bounded cache:
from functools import lru_cache
@lru_cache(maxsize=256)
def parse_schema(schema_text: str) -> object:
...The example is appropriate only if repeated calls with the same schema text should produce an interchangeable result. If the output can change while the arguments stay the same, caching needs a deliberate invalidation strategy or should not be used.
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Cache keys are built from the function’s positional and keyword arguments, which must be hashable. Mutable types such as lists and dictionaries cannot be used directly as arguments to a cached call. Also, calls using the same values but a different keyword-argument order may be treated as separate entries, so normalize call patterns if duplicate entries would undermine reuse.
Cases where memoization causes problems
- Side effects: A cached call is skipped on a hit, so side effects such as writing a file or updating shared state will not happen on every call.
- Changing results: If results depend on current time, external data, or mutable state not included in the arguments, a hit may return stale data. Clear or otherwise invalidate entries when appropriate, or avoid caching.
- Fresh mutable results: A cache returns the same stored object on a hit. Do not cache a function when each call must provide a newly created list, dictionary, or other mutable object.
- Generators and async functions: Caching a generator or coroutine object does not cache a fresh sequence or completed asynchronous result for each invocation. These decorators are not suitable for those use cases.
- Memory retention: Cache entries retain their arguments and results until eviction or clearing. An unbounded cache can grow indefinitely; even a bounded cache retains up to its configured number of entries.
Methods: cached_property or lru_cache?
For a value that belongs to one object and requires no extra call arguments, functools.cached_property is often a better fit: it stores the computed value on that instance. By contrast, decorating a method with lru_cache includes self in the key. The official CPython programming FAQ notes this distinction; cached method entries can keep instances alive until those entries are evicted or the cache is cleared.
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Threads and duplicate calculations
The Python Software Foundation’s official documentation says the cache is threadsafe, meaning the wrapped function can be used across multiple threads without corrupting the cache structure. That does not guarantee only one calculation for a missing key: if two threads request the same uncached input at nearly the same time, both may run the underlying function before either result is stored.
Check whether caching helped
Use the wrapper’s cache_info() method to inspect hits, misses, maximum size, and current size. A high hit count suggests repeated calls are being reused, but it does not by itself prove that total runtime improved.
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- Time the uncached function on a representative workload, including realistic repeated and unique inputs.
- Apply
@cacheor@lru_cacheand run the same workload under comparable conditions. - Review
cache_info()and check memory use, output correctness, and whether results need invalidation. - Keep the decorator only if the measured workload benefits without unacceptable memory use or stale results.
To inspect or manage the wrapper, call function.cache_clear() to remove stored entries. The function.__wrapped__ attribute provides access to the original function when you need to call or time it without the cache.
Further reading
For broader coverage of Python decorators and caching, O’Reilly’s publisher page for Fluent Python, 2nd Edition lists material on functools.cache and lru_cache.
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