moka-py

moka-py is a Python binding to the Moka cache written in Rust. It brings Moka’s high-performance, feature‑rich caching to Python.

Features

Synchronous cache: Thread-safe in-memory caching for Python.
TTL: Evicts entries after a configurable time to live (TTL).
TTI: Evicts entries after a configurable time to idle (TTI).
Per-entry TTL / TTI: Override the cache-wide TTL or TTI on individual entries.
Size-based eviction: Removes items when capacity is exceeded using TinyLFU or LRU.
Weight-based eviction: Optionally bound the cache by total entry weight (e.g. bytes) via a weigher.
Concurrency: Optimized for high-throughput, concurrent access.
Fully typed: mypy and pyright friendly.

Installation

Using uv:

uv add moka-py

Poetry Using poetry:

poetry add moka-py

Python Using pip:

pip install moka-py

Installation
Features
Usage
How it works
Eviction policies
Performance
License

Usage

Using moka_py.Moka

from time import sleep
from moka_py import Moka


# Create a cache with a capacity of 100 entries, with a TTL of 10.0 seconds
# and a TTI of 0.1 seconds. Entries are always removed after 10 seconds
# and are removed after 0.1 seconds if there are no `get`s happened for this time.
#
# Both TTL and TTI settings are optional. In the absence of an entry,
# the corresponding policy will not expire it.

# The default eviction policy is "tiny_lfu" which is optimal for most workloads,
# but you can choose "lru" as well.
cache: Moka[str, list[int]] = Moka(capacity=100, ttl=10.0, tti=0.1, policy="lru")

# Insert a value.
cache.set("key", [3, 2, 1])

# Retrieve the value.
assert cache.get("key") == [3, 2, 1]

# Wait for 0.1+ seconds, and the entry will be automatically evicted.
sleep(0.12)
assert cache.get("key") is None

Per-entry TTL / TTI

By default, TTL and TTI are set once for the entire cache. You can also set them per entry by passing ttl and/or tti to set() or get_with():

from time import sleep
from moka_py import Moka


cache = Moka(100)

cache.set("short-lived", "value", ttl=0.5)
cache.set("session", {"user": "alice"}, ttl=3600.0)
cache.set("idle-sensitive", "value", tti=1.0)
cache.set("both", "value", ttl=60.0, tti=5.0)

# Entries without per-entry ttl/tti never expire (unless the cache has global settings).
cache.set("permanent", "value")

sleep(0.6)
assert cache.get("short-lived") is None  # expired after 0.5s
assert cache.get("session") is not None  # still alive
assert cache.get("permanent") is not None

get_with() accepts the same parameters:

from moka_py import Moka


cache = Moka(100)

value = cache.get_with("key", lambda: "computed", ttl=30.0)

Concurrent `get_with` with different TTL / TTI

get_with() guarantees that only one thread executes the initializer for a given key (stampede protection). When multiple threads call get_with() for the same key concurrently with different ttl/tti values, the thread that wins the race runs its initializer — and its ttl/tti values are stored with the entry. All other threads receive the same cached value and their ttl/tti parameters are silently ignored.

import threading
from moka_py import Moka


cache = Moka(100)

# Thread A: get_with("k", compute, ttl=1.0)
# Thread B: get_with("k", compute, ttl=60.0)
#
# If thread A wins, the entry expires in 1 second.
# If thread B wins, the entry expires in 60 seconds.
# The loser's ttl is discarded — it is NOT merged or compared.

Interaction with cache-wide TTL / TTI

When the cache is constructed with global ttl or tti and an entry specifies its own, the entry expires at whichever deadline comes first.

WARNING

Per-entry TTL / TTI can only make an entry expire sooner than the cache-wide policy, not later. This is a technical limitation of the underlying Moka library: global and per-entry expiration are evaluated independently, and the earliest deadline wins.

If you need entries with different lifetimes that can exceed a common default, do not set global ttl/tti on the cache. Use per-entry values exclusively instead.

from moka_py import Moka

# Do this:
cache = Moka(1000)
cache.set("short", "v", ttl=60.0)
cache.set("long", "v", ttl=300.0)  # works as expected

# NOT this — "long" will still expire in 60 s:
cache = Moka(1000, ttl=60.0)
cache.set("long", "v", ttl=300.0)  # capped at 60 s by the global policy

from time import sleep
from moka_py import Moka


# Global TTL of 10 seconds.
cache = Moka(100, ttl=10.0)

# This entry will expire in 0.5 s (per-entry TTL wins, it is shorter).
cache.set("fast", "value", ttl=0.5)

# This entry keeps the global 10 s TTL (per-entry TTL=20 s is longer, so global wins).
cache.set("slow", "value", ttl=20.0)

sleep(0.6)
assert cache.get("fast") is None
assert cache.get("slow") is not None

As a decorator

moka-py can be used as a drop-in replacement for @lru_cache() with TTL + TTI support:

from time import sleep
from moka_py import cached


calls = []


@cached(maxsize=1024, ttl=5.0, tti=0.05)
def f(x, y):
    calls.append((x, y))
    return x + y


assert f(1, 2) == 3  # calls computations
assert f(1, 2) == 3  # gets from the cache
assert len(calls) == 1
sleep(0.06)
assert f(1, 2) == 3  # calls computations again (since TTI has passed)
assert len(calls) == 2

Async support

Unlike @lru_cache(), @moka_py.cached() supports async functions:

import asyncio
from time import perf_counter
from moka_py import cached


calls = []


@cached(maxsize=1024, ttl=5.0, tti=0.1)
async def f(x, y):
    calls.append((x, y))
    await asyncio.sleep(0.05)
    return x + y


start = perf_counter()
assert asyncio.run(f(5, 6)) == 11
assert asyncio.run(f(5, 6)) == 11  # from cache
elapsed = perf_counter() - start
assert elapsed < 0.2
assert len(calls) == 1

Coalesce concurrent calls (wait_concurrent)

moka-py can synchronize threads on keys

import moka_py
from typing import Any
from time import sleep
import threading
from decimal import Decimal


calls = []


@moka_py.cached(ttl=5, wait_concurrent=True)
def get_user(id_: int) -> dict[str, Any]:
    calls.append(id_)
    sleep(0.02)  # simulate an HTTP request (short for tests)
    return {
        "id": id_,
        "first_name": "Jack",
        "last_name": "Pot",
    }


def process_request(path: str, user_id: int) -> None:
    user = get_user(user_id)
    ...


def charge_money(from_user_id: int, amount: Decimal) -> None:
    user = get_user(from_user_id)
    ...


if __name__ == "__main__":
    request_processing = threading.Thread(target=process_request, args=("/user/info/123", 123))
    money_charging = threading.Thread(target=charge_money, args=(123, Decimal("3.14")))
    request_processing.start()
    money_charging.start()
    request_processing.join()
    money_charging.join()

    # Only one call occurred. Without `wait_concurrent`, each thread would issue its own HTTP request
    # before the cache entry is set.
    assert len(calls) == 1

Async wait_concurrent

When using wait_concurrent=True with async functions, moka-py creates a shared asyncio.Task per cache key. All concurrent callers await the same task and receive the same result or exception. This eliminates duplicate in-flight work for identical arguments.

Eviction listener

moka-py supports an eviction listener, called whenever a key is removed. The listener must be a three-argument function (key, value, cause) and uses positional arguments only.

Possible reasons:

"expired": The entry's expiration timestamp has passed.
"explicit": The entry was manually removed by the user (.remove() is called).
"replaced": The entry itself was not actually removed, but its value was replaced by the user (.set() is called for an existing entry).
"size": The entry was evicted due to size constraints.

from typing import Literal
from moka_py import Moka
from time import sleep


def key_evicted(k: str, v: list[int], cause: Literal["explicit", "size", "expired", "replaced"]):
    events.append((k, v, cause))


events: list[tuple[str, list[int], str]] = []


moka: Moka[str, list[int]] = Moka(2, eviction_listener=key_evicted, ttl=0.5)
moka.set("hello", [1, 2, 3])
moka.set("hello", [3, 2, 1])  # replaced
moka.set("foo", [4])
moka.remove("foo")  # explicit
for i in range(10):
    moka.set(f"overflow-{i}", [i])  # over capacity, so most of these go away with "size"

# Maintenance is lazy, so ask for it explicitly instead of guessing when it happens
moka.run_pending_tasks()
sleep(1.0)
moka.run_pending_tasks()  # whatever survived is past its TTL by now: "expired"

causes = {c for _, _, c in events}
assert causes == {"size", "expired", "replaced", "explicit"}, events

IMPORTANT NOTES

The listener is not called just-in-time. moka has no background threads or tasks; it runs only during cache operations.

The listener must not raise exceptions. If it does, the exception may surface from any moka-py method on any thread.

Keep the listener fast. Heavy work (especially I/O) will slow .get(), .set(), etc. Offload via ThreadPoolExecutor.submit() or asyncio.create_task()

Per-entry TTL / TTI and the eviction listener. Per-entry expiry fires the listener with "expired" just like global TTL/TTI does. The notification is delivered lazily during subsequent cache operations (e.g. get, set) after the per-entry deadline passes — it is not instant.

Removing entries

Remove an entry with Moka.remove(key). It returns the previous value if present; otherwise None.

from moka_py import Moka


c = Moka(128)
c.set("hello", "world")
assert c.remove("hello") == "world"
assert c.get("hello") is None

If None is a valid cached value, distinguish it from absence using Moka.remove(key, default=...):

from moka_py import Moka


c = Moka(128)
c.set("hello", None)
assert c.remove("hello", default="WAS_NOT_SET") is None  # None was set explicitly

# Now the entry "hello" does not exist, so `default` is returned
assert c.remove("hello", default="WAS_NOT_SET") == "WAS_NOT_SET"

Size-aware cache (weigher)

capacity bounds the total weight of all entries. Every entry weighs 1 by default — that's why capacity normally reads as "maximum number of entries". Pass a weigher — a callable (key, value) -> int — to weigh entries yourself, in any unit you like; bytes are typical:

from moka_py import Moka


# A 100-character budget for string values
c = Moka(100, weigher=lambda k, v: len(v))
c.set("a", "x" * 40)
c.set("b", "y" * 40)

# Maintenance (evictions, expirations) runs lazily. Force it when you need
# accurate numbers — e.g. in tests or metrics:
c.run_pending_tasks()
assert c.weighted_size() == 80
assert c.count() == 2

The weigher is called once per insert, on the calling thread, and its result is fixed for that entry (replacing a value re-weighs it). The contract:

The result must be a non-negative int; a negative result raises ValueError, a non-int raises TypeError.
0 is allowed and means the entry spends no capacity budget.
Values above 2**32 - 1 are clamped to that maximum.
If the weigher raises, the insert fails with that exception and the cache is left unchanged (for get_with, nothing is cached and all concurrent waiters see the error).

@cached accepts a weigher as well — maxsize bounds the total weight the same way. The key passed to the weigher is an opaque hashable object built from the call arguments, so weigh the value:

from moka_py import cached


@cached(maxsize=1_000_000, weigher=lambda k, v: len(v))
def render(name: str) -> str:
    return f"<h1>Hello, {name}!</h1>"


assert render("world") == "<h1>Hello, world!</h1>"

With wait_concurrent=True on an async function, a weigher changes what the cache stores: finished results instead of in-flight asyncio.Tasks (a Task has no result to weigh yet). Concurrent calls still share one computation; a failed computation is not cached. One asymmetry to be aware of: if the weigher raises, the error goes to the call that started the computation, while concurrent waiters still receive the computed value.

weighted_size() and run_pending_tasks() work without a weigher too: since every entry weighs 1, weighted_size() equals count().

How it works

Moka stores Python object references (by Py_INCREF) and does not serialize or deserialize values. You can use any Python object as a value and any hashable object as a key (__hash__ is used). Mutable objects remain mutable:

from moka_py import Moka


c = Moka(128)
my_list = [1, 2, 3]
c.set("hello", my_list)
still_the_same = c.get("hello")
still_the_same.append(4)
assert my_list == [1, 2, 3, 4]

Eviction policies

moka-py uses TinyLFU by default, with an LRU option. Learn more in the Moka wiki.

Performance

Measured using MacBook Pro 14-inch, Nov 2024 with Apple M4 Pro processor and 24GiB RAM

-------------------------------------------------------------------------------------------- benchmark: 9 tests -------------------------------------------------------------------------------------------
Name (time in ns)                       Min                 Max                Mean            StdDev              Median               IQR            Outliers  OPS (Mops/s)            Rounds  Iterations
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_bench_remove                   68.1140 (1.0)       68.2812 (1.0)       68.1806 (1.0)      0.0671 (1.0)       68.1621 (1.0)      0.1000 (1.0)           1;0       14.6669 (1.0)           5    10000000
test_bench_get[lru-False]           77.5126 (1.14)      78.2797 (1.15)      77.7823 (1.14)     0.2947 (4.39)      77.6792 (1.14)     0.2913 (2.91)          1;0       12.8564 (0.88)          5    10000000
test_bench_get[tiny_lfu-False]      78.0985 (1.15)      78.8168 (1.15)      78.4920 (1.15)     0.2678 (3.99)      78.4868 (1.15)     0.3429 (3.43)          2;0       12.7401 (0.87)          5    10000000
test_bench_get[lru-True]            89.1512 (1.31)      89.6459 (1.31)      89.4480 (1.31)     0.1910 (2.85)      89.5190 (1.31)     0.2458 (2.46)          2;0       11.1797 (0.76)          5    10000000
test_bench_get[tiny_lfu-True]       91.4891 (1.34)      91.9214 (1.35)      91.6827 (1.34)     0.1867 (2.78)      91.7339 (1.35)     0.3141 (3.14)          2;0       10.9072 (0.74)          5    10000000
test_bench_get_with                137.0672 (2.01)     137.8738 (2.02)     137.4143 (2.02)     0.3182 (4.74)     137.2839 (2.01)     0.4530 (4.53)          2;0        7.2773 (0.50)          5    10000000
test_bench_set_str_key             354.1709 (5.20)     355.5768 (5.21)     354.9073 (5.21)     0.5631 (8.39)     355.0415 (5.21)     0.8900 (8.90)          2;0        2.8176 (0.19)          5     1408297
test_bench_set[tiny_lfu]           355.6927 (5.22)     356.9633 (5.23)     356.3647 (5.23)     0.5645 (8.41)     356.4059 (5.23)     1.0390 (10.40)         2;0        2.8061 (0.19)          5     1405450
test_bench_set[lru]                388.7005 (5.71)     389.5825 (5.71)     389.1170 (5.71)     0.3837 (5.72)     389.0796 (5.71)     0.6915 (6.92)          2;0        2.5699 (0.18)          5     1295615
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

License

moka-py is distributed under the MIT license.

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moka-py 0.5.0

moka-py

Features

Installation

Table of Contents

Usage

Using moka_py.Moka

Per-entry TTL / TTI

Concurrent get_with with different TTL / TTI

Interaction with cache-wide TTL / TTI

As a decorator

Async support

Coalesce concurrent calls (wait_concurrent)

Async wait_concurrent

Eviction listener

Removing entries

Size-aware cache (weigher)

How it works

Eviction policies

Performance

License

Project links

Key dates

1 maintainer

GitHub Statistics

License expression

Requires

Classifiers

Download files

Source Distribution

Built Distributions

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Concurrent `get_with` with different TTL / TTI

Algorithm	Hash digest
SHA256	`c584e58ff54441e55601d0711c9e3a6805de2855623ac00f92baa1b154e8ae47`
MD5	`7b37cfaf4168dcd080393bb9120cfdca`
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Algorithm	Hash digest
SHA256	`36833be73617a57fa20bf43d71757e2ed54263a7695ba6c86e2a10c1eb37191a`
MD5	`a9a050021d52d750b8fcc6d8c1f72407`
BLAKE2b-256	`b3dd27b81b57e43cc8093680ba8afcb816997af2e1e859465983efaf9cdf6cf7`

Algorithm	Hash digest
SHA256	`7352a2fefbe03198155db516227cf03bc261a84422b34f855b185042ac9128b0`
MD5	`c5c348d9696990336c4b5d41b6c8ce1c`
BLAKE2b-256	`2d0376b8f8f31e3658a640158d20e0d5d8c584e1f8e87b67127262e9c0e3fc00`

Algorithm	Hash digest
SHA256	`c5681a2bddfc586d2d312bd03bbcf6f9d597882c26ac25f17bec59e6c1151b79`
MD5	`4b598e072ab24cec4d29c900ace148a2`
BLAKE2b-256	`cd32bf7e84563a47c3b28d9129bb383fa313a51892923058958b2281c702295b`

Algorithm	Hash digest
SHA256	`b85341de0d26a7dca56aab0fee01fdd55ebe3f5c8e8fd259ce7c54948fd5c94e`
MD5	`11ceb7ae6d52e0be257c1634426b86b9`
BLAKE2b-256	`514165c7edf72a63ccc926109ba6ce21c105d4a9e19f0df2c9a0fc3905b295fa`

Algorithm	Hash digest
SHA256	`d2cd9a54df3628edd02a851cdb3a1c83aa8125c79ae7bd73a0575c065fbe1849`
MD5	`3431b132a7ab90f6bff4efed91911b09`
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Algorithm	Hash digest
SHA256	`208372d9e0cb81ed3e1c7bb74c6d77ac41b02951dcea0414dac4ce6e478b0f46`
MD5	`069eb63947a2f8dc6a2480313ef74b36`
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Algorithm	Hash digest
SHA256	`ed9aac35c1daef39b4855481b80fb4f8ffd5ffd9ac153e9209b8e31d158082c6`
MD5	`7adfa7c1ee3775c063f94145a5af4894`
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Algorithm	Hash digest
SHA256	`b74e56af4ce7c98b892a7902a787783d3a931f777c41f54f4cb61ef29ff7fd35`
MD5	`846c1378a264bbc505b59f8d1368634a`
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Algorithm	Hash digest
SHA256	`2144bdb1633b0e8c9d1b8b66906c84938964020d1d466a9e0f74ec29ad40e038`
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