cachey
Caching mindful of computation/storage costs
Decision gist · record as of 2026-08-14
No. While the package has low install friction and addresses a real problem in analytic computing, it is abandoned and unmaintained. The last release was in 2020, the last commit in 2021, and no security or compatibility updates are forthcoming. For new projects, use an actively maintained alternative; for existing code already using cachey, consider migration to reduce technical debt.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.6 or later.
- Low install friction with a single dependency (heapdict).
- However, the package is abandoned—last commit was 2021-02-10 and no releases since 2020-03-11—so maintenance and bug fixes are not forthcoming.
License · maintenance · safety
BSD (permissive) — BSD license is permissive, allowing use in most projects without significant legal constraints.
last release 2020-03-11 (2347 days) · last repo commit 2021-02-10 · 142 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 225,252 downloads/mo, #9,225 on PyPI
Alternatives
Verify before relying
from cachey import Cache
c = Cache(1e9, 1) # 1 GB cache, cost threshold 1
c.put('x', 'some value', cost=3)
result = c.get('x')- Whether the package's scoring algorithm remains suitable for modern analytic workloads.
- Real-world performance and memory overhead compared to alternatives in active maintenance.
- Compatibility with current versions of scientific libraries.
What it is and what it does
Cachey is a caching library designed for analytic computations where standard policies like LRU are ineffective because computation and storage costs vary wildly. It scores cached items based on their recomputation cost, storage size, frequency of use, and recency, keeping results that are expensive to recompute but cheap to store while evicting cheap-to-recompute but large results. The package provides a Cache class for manual cache management and a memoize decorator for automatic function result caching.
The library targets scientific and data-analysis workflows where a single computation might be costly but yield a small result, while another might be cheap but produce large data. It accomplishes this through a scoring formula that balances these trade-offs with exponential decay of old items. However, the package is no longer maintained—its last release was in 2020 and the last commit in early 2021—so it receives no updates, bug fixes, or compatibility improvements.
Use it for
- Cache expensive computations that produce small results in analytical workflows.
- Memoize costly data-processing functions in analytical pipelines where storage is limited.
- Manage memory in long-running data science workflows by automatically evicting cheap-to-recompute intermediate results.
- Optimize task graphs or similar lazy-evaluation systems where computation cost varies widely.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
No.
While the package has low install friction and addresses a real problem in analytic computing, it is abandoned and unmaintained. The last release was in 2020, the last commit in 2021, and no security or compatibility updates are forthcoming. For new projects, use an actively maintained alternative; for existing code already using cachey, consider migration to reduce technical debt.
Install
cachey on PyPI
Before you install
Low install friction with a single dependency (heapdict). However, the package is abandoned—last commit was 2021-02-10 and no releases since 2020-03-11—so maintenance and bug fixes are not forthcoming.
Requires Python 3.6 or later.
License in practice
BSD license is permissive, allowing use in most projects without significant legal constraints.
Quickstart
from cachey import Cache
c = Cache(1e9, 1) # 1 GB cache, cost threshold 1
c.put('x', 'some value', cost=3)
result = c.get('x')Verify before relying
- Whether the package's scoring algorithm remains suitable for modern analytic workloads.
- Real-world performance and memory overhead compared to alternatives in active maintenance.
- Compatibility with current versions of scientific libraries.
Package facts
| License | BSD permissive |
| Python support | Supports the current Python release >=3.6 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packageheapdict |
| Maintenance | Abandoned 2,347 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 225,252 / month, #9,225 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Topic :: Scientific/Engineering |
Evidence: cachey-0.2.1-py3-none-any.whl
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See also repoze-lru · flexcache · cacheout · django-memoize · memoization · pylru · cachebox · py-memoize · asyncache · boltons