Python in-memory cache with time to live

Question

I have multiple threads running the same process that need to be able to to notify each other that something should not be worked on for the next n seconds its not the end o

Answer 1

In case you don't want to use any 3rd libraries, you can add one more parameter to your expensive function: ttl_hash=None. This new parameter is so-called "time sensitive hash", its the only purpose is to affect lru_cache.

For example:

from functools import lru_cache
import time


@lru_cache()
def my_expensive_function(a, b, ttl_hash=None):
    del ttl_hash  # to emphasize we don't use it and to shut pylint up
    return a + b  # horrible CPU load...


def get_ttl_hash(seconds=3600):
    """Return the same value withing `seconds` time period"""
    return round(time.time() / seconds)


# somewhere in your code...
res = my_expensive_function(2, 2, ttl_hash=get_ttl_hash())
# cache will be updated once in an hour

Answer 2

You can also go for dictttl, which has MutableMapping, OrderedDict and defaultDict(list)

Initialize an ordinary dict with each key having a ttl of 30 seconds

data = {'a': 1, 'b': 2}
dict_ttl = DictTTL(30, data)

OrderedDict

data = {'a': 1, 'b': 2}
dict_ttl = OrderedDictTTL(30, data)

defaultDict(list)

dict_ttl = DefaultDictTTL(30)
data = {'a': [10, 20], 'b': [1, 2]}
[dict_ttl.append_values(k, v) for k, v in data.items()]

Answer 3

You can use the expiringdict module:

The core of the library is ExpiringDict class which is an ordered dictionary with auto-expiring values for caching purposes.

In the description they do not talk about multithreading, so in order not to mess up, use a Lock.

Answer 4

Something like that ?

from time import time, sleep
import itertools
from threading import Thread, RLock
import signal


class CacheEntry():
  def __init__(self, string, ttl=20):
    self.string = string
    self.expires_at = time() + ttl
    self._expired = False

  def expired(self):
    if self._expired is False:
      return (self.expires_at < time())
    else:
      return self._expired

class CacheList():
  def __init__(self):
    self.entries = []
    self.lock = RLock()

  def add_entry(self, string, ttl=20):
    with self.lock:
        self.entries.append(CacheEntry(string, ttl))

  def read_entries(self):
    with self.lock:
        self.entries = list(itertools.dropwhile(lambda x:x.expired(), self.entries))
        return self.entries

def read_entries(name, slp, cachelist):
  while True:
    print "{}: {}".format(name, ",".join(map(lambda x:x.string, cachelist.read_entries())))
    sleep(slp)

def add_entries(name, ttl, cachelist):
  s = 'A'
  while True:
    cachelist.add_entry(s, ttl)
    print("Added ({}): {}".format(name, s))
    sleep(1)
    s += 'A'



if __name__ == "__main__":
  signal.signal(signal.SIGINT, signal.SIG_DFL)

  cl = CacheList()
  print_threads = []
  print_threads.append(Thread(None, read_entries, args=('t1', 1, cl)))
  # print_threads.append(Thread(None, read_entries, args=('t2', 2, cl)))
  # print_threads.append(Thread(None, read_entries, args=('t3', 3, cl)))

  adder_thread = Thread(None, add_entries, args=('a1', 2, cl))
  adder_thread.start()

  for t in print_threads:
    t.start()

  for t in print_threads:
    t.join()

  adder_thread.join()

Answer 5

I absolutely love the idea from @iutinvg, I just wanted to take it a little further. Decouple it from having to know to pass the ttl and just make it a decorator so you don't have to think about it. If you have django, py3 and don't feel like pip installing any dependencies, try this out.

import time
from django.utils.functional import lazy
from functools import lru_cache, partial, update_wrapper


def lru_cache_time(seconds, maxsize=None):
    """
    Adds time aware caching to lru_cache
    """
    def wrapper(func):
        # Lazy function that makes sure the lru_cache() invalidate after X secs
        ttl_hash = lazy(lambda: round(time.time() / seconds), int)()
        
        @lru_cache(maxsize)
        def time_aware(__ttl, *args, **kwargs):
            """
            Main wrapper, note that the first argument ttl is not passed down. 
            This is because no function should bother to know this that 
            this is here.
            """
            def wrapping(*args, **kwargs):
                return func(*args, **kwargs)
            return wrapping(*args, **kwargs)
        return update_wrapper(partial(time_aware, ttl_hash), func)
    return wrapper


@lru_cache_time(seconds=10)
def meaning_of_life():
    """
    This message should show up if you call help().
    """
    print('this better only show up once!')
    return 42


@lru_cache_time(seconds=10)
def multiply(a, b):
    """
    This message should show up if you call help().
    """
    print('this better only show up once!')
    return a * b
    
# This is a test, prints a `.` for every second, there should be 10s 
# between each "this better only show up once!" *2 because of the two functions.
for _ in range(20):
    meaning_of_life()
    multiply(50, 99991)
    print('.')
    time.sleep(1)

Answer 6

Regarding an expiring in-memory cache, for general purpose use, a common design pattern to typically do this is not via a dictionary, but via a function or method decorator. A cache dictionary is managed behind the scenes. As such, this answer somewhat complements the answer by User which uses a dictionary rather than a decorator.

The ttl_cache decorator in cachetools==3.1.0 works a lot like functools.lru_cache, but with a time to live.

import cachetools.func

@cachetools.func.ttl_cache(maxsize=128, ttl=10 * 60)
def example_function(key):
    return get_expensively_computed_value(key)


class ExampleClass:
    EXP = 2

    @classmethod
    @cachetools.func.ttl_cache()
    def example_classmethod(cls, i):
        return i * cls.EXP

    @staticmethod
    @cachetools.func.ttl_cache()
    def example_staticmethod(i):
        return i * 3