Caching dramatically accelerates processing, from CPU-level operations to database interfaces. Cache invalidation—determining when to remove cached data—is a complex challenge. This post addresses a simpler, yet insidious, caching issue.
This problem, lurking for 18 months, surfaced only when users deviated from the recommended usage pattern. The issue stemmed from a custom machine learning (ML) framework (built on scikit-learn) within my organization. This framework accesses multiple data sources frequently, necessitating a caching layer for performance and cost optimization (reducing BigQuery egress costs).
Initially, lru_cache was used, but a persistent cache was needed for static data frequently accessed during development. DiskCache, a Python library using SQLite, was chosen for its simplicity and compatibility with our 32-process environment and Pandas DataFrames (up to 500MB). An lru_cache layer was added on top for in-memory access.
The problem emerged as more users experimented with the framework. Randomly incorrect results were reported, difficult to reproduce consistently. The root cause: in-place modification of cached Pandas DataFrames.
Our coding standard dictated creating new DataFrames after any processing. However, some users, out of habit, used inplace=True, modifying the cached object directly. This not only altered their immediate results but also corrupted the cached data, affecting subsequent requests.
To illustrate, consider this simplified example using dictionaries:
from functools import lru_cache
import time
import typing as t
from copy import deepcopy
@lru_cache
def expensive_func(keys: str, vals: t.Any) -> dict:
time.sleep(3)
return dict(zip(keys, vals))
def main():
e1 = expensive_func(('a', 'b', 'c'), (1, 2, 3))
print(e1)
e2 = expensive_func(('a', 'b', 'c'), (1, 2, 3))
print(e2)
e2['d'] = "amazing"
print(e2)
e3 = expensive_func(('a', 'b', 'c'), (1, 2, 3))
print(e3)
if __name__ == "__main__":
main()
lru_cache provides a reference, not a copy. Modifying e2 alters the cached data.
Solution:
The solution involves returning a deep copy of the cached object:
from functools import lru_cache, wraps
from copy import deepcopy
def custom_cache(func):
cached_func = lru_cache(func)
@wraps(func)
def _wrapper(*args, **kwargs):
return deepcopy(cached_func(*args, **kwargs))
return _wrapper
This adds a small overhead (data duplication), but prevents data corruption.
Key Takeaways:
- A deeper understanding of
lru_cache's reference behavior. - Adhering to coding standards minimizes bugs.
- Account for user deviations from best practices in the implementation. Robustness often trumps elegance.
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