Memoize an expensive pure lookup with cache

@functools.cache memoizes a function result by its arguments. In this recipe, price_in_cents() receives a SKU string and returns the matching integer price from an immutable catalog. The intentionally long generated prefix makes finding the named entries a linear lookup; the fixture remains local and deterministic. Calling the function for "paper" twice produces 1250 both times, while the body runs once for that argument. A different argument, "ruler", creates the second function evaluation.

The evaluations counter is outside the cached result and makes the cache behavior visible. It is not part of the lookup's answer: given an unchanged CATALOG and the same SKU, price_in_cents() is pure. cache is a thin unbounded cache, so it fits a bounded key space or a process with explicit invalidation. price_in_cents.cache_clear() is available when a replacement catalog must take effect.

Arguments must be hashable; a list or dictionary cannot be a cache key. Do not decorate lookups whose answer can change with time, external state, or a modified catalog unless their cache lifetime is controlled. Concurrent first calls can also compute the same missing key more than once, even though the cache structure remains coherent.

This Batu Lab Notes article was drafted with AI assistance.

from functools import cache


CATALOG = tuple((f"sku-{number:05d}", number) for number in range(20_000)) + (
    ("paper", 1250),
    ("pencil", 90),
    ("ruler", 275),
)
evaluations = 0


@cache
def price_in_cents(sku):
    global evaluations
    evaluations += 1
    for known_sku, cents in CATALOG:
        if known_sku == sku:
            return cents
    raise KeyError(sku)


first = price_in_cents("paper")
second = price_in_cents("paper")
third = price_in_cents("ruler")
assert (first, second, third) == (1250, 1250, 275)
assert evaluations == 2
print(f"prices: {first} {second} {third}")
print(f"function evaluations: {evaluations}")

Expected stdout:

prices: 1250 1250 275
function evaluations: 2

Sources

Comments

Post a Comment

Popular posts from this blog

Compare Two Text Files in Python Without Modifying Them

Batu Lab Notes · Batu When comparing two versions of a configuration or notes file, “are they the same?” is only the first question. The bytes may be identical, the visible text may be equal despite different line endings, or the content may contain a real change. For small UTF-8 text files, Python’s standard library can show these distinctions without writing to either input. Quick answer For small, stable UTF-8 files, read bounded bytes, compare them first, then decode and produce a bounded unified diff. Decide explicitly whether newline differences matter. Keep both inputs read-only and review the diff for private data before sharing it. Set a limit before reading Loading an input of unknown size directly into memory is a poor default. This example checks both sizes with Path.stat() , then performs a bounded read and applies a 256 KiB limit per file. It rejects directories and other non-regular paths. If reading raises OSError , the result includes the exception ty...
Read more

Seven CSV Quality Checks to Run Before Importing Data

Batu Lab Notes · Batu A CSV file is not ready for import merely because it opens. The wrong delimiter can collapse an entire row into one column. An unexpected encoding can damage non-ASCII characters. A repeated identifier can overwrite an existing record in the destination. A short, read-only check makes these problems visible before data reaches a spreadsheet or application. Quick answer Before importing a CSV, set its encoding and delimiter, validate the exact header and every row width, distinguish empty IDs, duplicate IDs, and duplicate rows, and keep the check read-only. 1. Specify the encoding Python may open a text file with the platform’s default encoding, which can differ across machines. If the expected encoding is UTF-8, encoding="utf-8-sig" is a practical choice: it reads ordinary UTF-8 and skips an optional UTF-8 byte order mark at the start. If decoding fails, verify the source application’s export settings instead of silently trying unrelat...
Read more

Explain why SQLite transactions cannot make an HTTP call atomic

Direct answer A SQLite transaction can atomically commit database statements handled by that connection. It cannot include an HTTP service in the same commit protocol. The example commits an outbox row first, then simulates a network failure. The durable row remains pending , which is exactly the information a later delivery worker needs instead of pretending both actions succeeded or failed together. The edge case is failure after the database commit but before remote acknowledgement. Retrying then requires an idempotency design owned by the remote endpoint or a stable request key; it is not supplied by SQLite’s COMMIT . Conversely, sending the request before storing a durable intent can leave no local evidence when the process stops. This demonstration intentionally does not make an HTTP request, implement an outbox dispatcher, or prove delivery exactly once. It shows the boundary that must be designed around. Use transactions to protect the local state transition, and test remote...
Read more