rust-py-cache

An ultra-fast local cache for Python, powered by Rust.

A local, in-memory, thread-safe cache with TTL, lazy expiration, and metrics. The core is written in Rust (PyO3 + maturin) on top of a concurrent DashMap; the Python API is minimal. Think of it as a "mini Redis" living inside your Python process.

🌐 Website: rust-py-cache.vercel.app

Installation

pip install rust-py-cache

To work on it locally (requires Rust + maturin):

python -m venv .venv && source .venv/bin/activate
pip install maturin pytest
maturin develop          # compiles the Rust core and installs into the venv
pytest                   # runs the tests

Usage

from rust_py_cache import Cache

cache = Cache()

cache.set("user:1", {"name": "Roberto"}, ttl=60)   # ttl in seconds
user = cache.get("user:1")                          # {"name": "Roberto"}
cache.get("missing", default=0)                     # 0

cache.exists("user:1")        # True (honors TTL)
cache.delete("user:1")        # True if removed, False if absent
cache.len()                   # approximate size
cache.keys()                  # list of keys
cache.cleanup_expired()       # remove expired entries; returns the count
cache.clear()                 # remove everything (keeps counters)
cache.stats()                 # {'hits','misses','sets','deletes','expired','evicted','size'}

Bounded cache with LRU eviction

# Cap the number of keys. When full and a new key arrives, evict the
# least-recently-used entry instead of rejecting the write.
cache = Cache(max_size=1000, eviction_policy="lru")
cache.eviction_policy            # "lru"

# Default policy is "reject": set() returns False when full (and the key is new).
cache = Cache(max_size=1000)     # eviction_policy="reject"
cache.set("a", 1)                # True / False

Background expiration

# A background thread reclaims expired entries every N seconds, so you don't
# have to call cleanup_expired() yourself. It stops when the cache is collected.
cache = Cache(cleanup_interval=30)   # seconds (int/float)

Memoization decorator

@cache.cached(ttl=60)
def add(a, b):
    return a + b

add(2, 3)   # runs and caches
add(2, 3)   # served from cache

# custom key (fixed string or callable):
@cache.cached(ttl=300, key=lambda user_id: f"user:{user_id}")
def load_user(user_id):
    ...

See full examples under examples/ (FastAPI and Django).

API

Constructor: Cache(max_size=None, eviction_policy="reject", cleanup_interval=None). eviction_policy must be "reject" or "lru" (any other value raises ValueError). cleanup_interval (seconds, > 0) enables the background sweeper.

Method	Description
`set(key, value, ttl=None)`	Store a value. `ttl` in seconds (`int`/`float`); `None` = no expiration; `ttl <= 0` → `ValueError`. Overwrites. Returns `True`, or `False` when full under `eviction_policy="reject"` and the key is new.
`get(key, default=None)`	The value, or `default` if missing/expired (expired entries are removed).
`delete(key)`	`True` if removed, `False` if it didn't exist.
`exists(key)`	`True`/`False`, honoring TTL.
`keys()`	List of keys (may include expired-but-not-yet-collected ones).
`len()` / `len(cache)`	Approximate size.
`clear()`	Remove everything (does not reset counters).
`cleanup_expired()`	Remove expired entries; returns how many.
`eviction_policy` (property)	The active policy: `"reject"` or `"lru"`.
`stats()`	`dict` with `hits, misses, sets, deletes, expired, evicted, size`.
`@cache.cached(ttl=None, key=None)`	Memoization decorator.

How it works

Serialization: in the MVP, values are serialized with pickle (on the Python side, via PyO3) and stored as opaque bytes (Vec<u8>) in the Rust core.
Concurrency: DashMap (a HashMap with per-shard locks) plus AtomicU64 counters, with no global lock on the hot path. Thread-safe, no busy loop.
TTL: expiration is lazy by default — an expired key is removed when accessed (get/exists) or via cleanup_expired(). Pass cleanup_interval to also run a background sweeper thread that reclaims expired keys on its own.
Eviction: with max_size + eviction_policy="lru", a full cache evicts the least-recently-used entry (recency updated on every get hit) to admit a new key.

Limitations

The cache is process-local: multiple workers = multiple independent caches.
It does not replace Redis for distributed caching.
Data is lost when the process restarts.
pickle must not be used to deserialize untrusted data.
Lazy TTL by default: without cleanup_interval, expired items may linger until accessed or until cleanup_expired() runs.

Development

cargo test          # Rust core tests
maturin develop     # rebuild and install
pytest              # Python tests

If maturin develop complains about both VIRTUAL_ENV and CONDA_PREFIX being set, run conda deactivate first, or use env -u CONDA_PREFIX maturin develop.

Roadmap

Stages and next steps (LRU/LFU eviction, background expiration, configurable serializer, namespaces, etc.) are in ROADMAP.md.

License

MIT

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Jun 22, 2026 4 files

Jun 22, 2026 4 files

rust-py-cache 0.2.2

rust-py-cache

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Bounded cache with LRU eviction

Background expiration

Memoization decorator

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1 maintainer

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