DiskCache sqlerror when throttled / memoized

dc_utils.py

##################################################################
### Overriding DiskCache's decorators to make them pickle-able ###
##################################################################

import diskcache as dc
from functools import update_wrapper
import time

ENOVAL = dc.core.ENOVAL

class Memoized:
    NOTICE = (
        'NOTE: This function is memoised.'
        '\n    The results for a given combination of parameters are kept'
        '\n    for {expire} seconds in the cache at {cache_dest}'
    )
    def __init__(self, func, cache, name=None, typed=False, expire=None, tag=None) -> None:
        """Memoizing cache decorator.

        Decorator to wrap callable with memoizing function using cache.
        Repeated calls with the same arguments will lookup result in cache and
        avoid function evaluation.

        This is the class version of the [diskcache's own implementation](#1).
        Class version is required for having a pickle-compatible callable

        #1: https://github.com/grantjenks/python-diskcache/blob/c4ba1f78bb8494bcf6aba9d7d1c3aa49a1093508/diskcache/core.py#L1782
        """
        self.base = dc.core.full_name(func) if name is None else name
        self.base = (self.base,) # 1-tuple due to use in args_to_key

        self.func = func
        # update name, qualname etc, but do not copy dict ! https://stackoverflow.com/q/53602825
        update_wrapper(self, func, updated=())
        notice = self.NOTICE.format(expire=expire, cache_dest=cache.directory)
        if self.__doc__:
            self.__doc__ += '\n\n' + notice
        else:
            self.__doc__ = notice

        self.cache = cache
        self.typed = typed
        self.expire = expire
        self.tag = tag

    def __cache_key__(self, *args, **kwargs):
        "Make key for cache given function arguments."
        return dc.core.args_to_key(self.base, args, kwargs, self.typed)


    def __call__(self, *args, **kwargs):
        "Wrapper for callable to cache arguments and return values."
        key = self.__cache_key__(*args, **kwargs)
        result = self.cache.get(key, default=ENOVAL, retry=True)

        if result is ENOVAL:
            result = self.func(*args, **kwargs)
            if self.expire is None or self.expire > 0:
                self.cache.set(key, result, self.expire, tag=self.tag, retry=True)

        return result

class Throttled:
    NOTICE = (
        'NOTE: This function is throttled!'
        '\n    At most {count} calls can be made every {seconds} seconds.'
        '\n    It synchronises using the cache at {cache_dest}.'
    )

    def __init__(self, func, cache, count, seconds, name=None, expire=None, tag=None,
                 time_func=time.time, sleep_func=time.sleep):
        """Decorator to throttle calls to function.

        This is exact implementation of DiskCache's own [`throttle()`](#1),
        but in a class object, which is required for having pickle-compatible decorator.
        The only modification is that we don't allow starting with two-times the rate (#2)

        #1 https://github.com/grantjenks/python-diskcache/blob/c4ba1f78bb8494bcf6aba9d7d1c3aa49a1093508/diskcache/recipes.py#L227
        #2 https://stackoverflow.com/a/6415181/786559
        """
        self.cache = cache
        self.count = count
        self.seconds = seconds
        self.expire = expire
        self.tag = tag
        self.time_func = time_func
        self.sleep_func = sleep_func

        self.func = func
        # update name, qualname etc, but do not copy dict ! https://stackoverflow.com/q/53602825
        update_wrapper(self, func, updated=())
        notice = self.NOTICE.format(count=count, seconds=seconds, cache_dest=cache.directory)
        if self.__doc__:
            self.__doc__ += '\n\n' + notice
        else:
            self.__doc__ = notice

        self.rate = self.count / float(self.seconds)
        self.key = dc.core.full_name(func) if name is None else name
        self.now = self.time_func()
        # in the original code, there is an allowance of `count` to start with
        # but this results in double the calls for the first interval
        # we set it to zero so that it is initialised with the time of the first call
        self.cache.set(self.key, (self.now, 0), expire=self.expire, tag=self.tag, retry=True)


    def __call__(self, *args, **kwargs):
        while True:
            with self.cache.transact(retry=True):
                last, tally = self.cache.get(self.key)
                now = self.time_func()
                tally += (now - last) * self.rate
                delay = 0

                if tally > self.count:
                    self.cache.set(self.key, (now, self.count - 1), self.expire)
                elif tally >= 1:
                    self.cache.set(self.key, (now, tally - 1), self.expire)
                else:
                    delay = (1 - tally) / self.rate

            if delay:
                self.sleep_func(delay)
            else:
                break

        return self.func(*args, **kwargs)

##################################################################

dummy_api.py

import diskcache as dc
import time
import random
from dc_utils import Throttled, Memoized

class API:
    def __init__(self, cache_dir, expire=7*24*60*60) -> None:
        self.expire = expire
        self.cache = dc.Cache(cache_dir)

        self.__setup_memoization_throttling()

    def __setup_memoization_throttling(self):
        """
        The following lines are equivalent to these decorators:

        @memoized(expire=0)
        @throttled(count=1, seconds=2)
        @memoized(expire=duration)
        def _check_online(self):
        """
        # inner memoization -- read/write
        check_memoized_rw = Memoized(self._check_online, self.cache, expire=self.expire)

        # throttle the API -- API spec is max 30 calls per 60s, 
        # but they average somehow weirdly and large bursts result errors with API Limit Exceeded
        # hence 1 call / 2s always respects that limit
        # since we are mocking the API, we can increase that -- the error persists
        check_30per_min = Throttled(check_memoized_rw, self.cache, count=20, seconds=2)

        # outer memoization -- read-only
        check_memoized_ro = Memoized(check_30per_min, self.cache, expire=0)

        self._check_online = check_memoized_ro

    def _check_online(self, n):
        # here we would query the API using requests
        time.sleep(random.random())
        return n

    def get_n(self, n):
        """Retrieve data associated with given SIREN number

        These calls are memoized and throttled! See `__init__`
        """
        # we need this indirection to avoid dead-locking when used in parallel
        return self._check_online(n)

test_throttled.py

from dummy_api import API
from concurrent.futures import ProcessPoolExecutor
import sys
import numpy as np
from random import choices

mapping = None
def init_worker():
    global mapping
    mapping = {'my_api': API('./api-cache')}

def check(n, api_name):
    api = mapping[api_name]
    return (api.get_n(n), api)

def run_serial(items):
    init_worker()
    check_names = ['my_api'] * len(items)
    results = list(map(check, items, check_names))
    return results

def run_parallel(num_threads, items):
    check_names = ['my_api'] * len(items)
    with ProcessPoolExecutor(num_threads, initializer=init_worker) as pool:
        results = pool.map(check, items, check_names)
    return results


if __name__ == "__main__":
    threads = int(sys.argv[1])
    nums = 1800
    tests = 60000
    ws = np.abs(np.random.normal(0.5, 0.3, size=nums)) # approx normally distributed items
    items = choices(range(nums), k=tests, weights=ws)
    if threads == 1:
        results = run_serial(items)
    else:
        results = run_parallel(threads, items)
    if len(sys.argv) > 2:
        # use the returned `api` from the main thread
        with open('/tmp/dc_results.out', 'w') as f:
            for result, api in results:
                print(result, file=f)

Thanks !
Indeed, it is very different ! I had removed too many comments when stripping down the example 😅. Although the specification is 30 calls / min, it errors out when we have bursts, so I had to reduce it to 1 call every 2s.

I've increased it now to 20 calls / 2s, and this still errors for me, while filling the cache much faster.