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How to use _get_user_current_count method in locust

Best Python code snippet using locust

test_dispatch.py

Source:test_dispatch.py

...2883                        list(users_dispatcher)2884                        for user_class in user_classes:2885                            if user_class.fixed_count:2886                                self.assertEqual(2887                                    users_dispatcher._get_user_current_count(user_class.__name__),2888                                    user_class.fixed_count,2889                                )2890                        # Ramp-down to go to `down_to_count`2891                        # and ensure the fixed users was decreased too2892                        users_dispatcher.new_dispatch(target_user_count=down_to_count, spawn_rate=1)2893                        users_dispatcher._wait_between_dispatch = 02894                        list(users_dispatcher)2895                        for user_class in user_classes:2896                            if user_class.fixed_count:2897                                self.assertNotEqual(2898                                    users_dispatcher._get_user_current_count(user_class.__name__),2899                                    user_class.fixed_count,2900                                )2901                        # Ramp-up go back to `target_user_count` and ensure2902                        # the fixed users return to their counts2903                        users_dispatcher.new_dispatch(target_user_count=target_user_count, spawn_rate=1)2904                        users_dispatcher._wait_between_dispatch = 02905                        list(users_dispatcher)2906                        for user_class in user_classes:2907                            if user_class.fixed_count:2908                                self.assertEqual(2909                                    users_dispatcher._get_user_current_count(user_class.__name__),2910                                    user_class.fixed_count,2911                                )2912def _aggregate_dispatched_users(d: Dict[str, Dict[str, int]]) -> Dict[str, int]:2913    user_classes = list(next(iter(d.values())).keys())2914    return {u: sum(d[u] for d in d.values()) for u in user_classes}2915def _user_count(d: Dict[str, Dict[str, int]]) -> int:2916    return sum(map(sum, map(dict.values, d.values())))  # type: ignore2917def _user_count_on_worker(d: Dict[str, Dict[str, int]], worker_node_id: str) -> int:...

dispatch.py

Source:dispatch.py

...228            self._current_user_count -= 1229            self._try_dispatch_fixed = True230            if self._current_user_count == 0 or self._current_user_count <= current_user_count_target:231                return self._users_on_workers232    def _get_user_current_count(self, user: str) -> int:233        count = 0234        for users_on_node in self._users_on_workers.values():235            count += users_on_node.get(user, 0)236        return count237    def _distribute_users(238        self, target_user_count: int239    ) -> Tuple[240        Dict[str, Dict[str, int]], Generator[Optional[str], None, None], itertools.cycle, List[Tuple["WorkerNode", str]]241    ]:242        """243        This function might take some time to complete if the `target_user_count` is a big number. A big number244        is typically > 50 000. However, this function is only called if a worker is added or removed while a test245        is running. Such a situation should be quite rare.246        """247        user_gen = self._user_gen()248        worker_gen = itertools.cycle(self._worker_nodes)249        users_on_workers = {250            worker_node.id: {user_class.__name__: 0 for user_class in self._user_classes}251            for worker_node in self._worker_nodes252        }253        active_users = []254        user_count = 0255        while user_count < target_user_count:256            user = next(user_gen)257            if not user:258                break259            worker_node = next(worker_gen)260            users_on_workers[worker_node.id][user] += 1261            user_count += 1262            active_users.append((worker_node, user))263        return users_on_workers, user_gen, worker_gen, active_users264    def _user_gen(self) -> Generator[Optional[str], None, None]:265        """266        This method generates users according to their weights using267        a smooth weighted round-robin algorithm implemented by https://github.com/linnik/roundrobin.268        For example, given users A, B with weights 5 and 1 respectively, this algorithm269        will yield AAABAAAAABAA. The smooth aspect of this algorithm is what makes it possible270        to keep the distribution during ramp-up and ramp-down. If we were to use a normal271        weighted round-robin algorithm, we'd get AAAAABAAAAAB which would make the distribution272        less accurate during ramp-up/down.273        """274        def infinite_cycle_gen(users: List[Tuple[Type[User], int]]) -> itertools.cycle:275            if not users:276                return itertools.cycle([None])277            # Normalize the weights so that the smallest weight will be equal to "target_min_weight".278            # The value "2" was experimentally determined because it gave a better distribution especially279            # when dealing with weights which are close to each others, e.g. 1.5, 2, 2.4, etc.280            target_min_weight = 2281            # 'Value' here means weight or fixed count282            normalized_values = [283                (284                    user.__name__,285                    round(target_min_weight * value / min(u[1] for u in users)),286                )287                for user, value in users288            ]289            generation_length_to_get_proper_distribution = sum(290                normalized_val[1] for normalized_val in normalized_values291            )292            gen = smooth(normalized_values)293            # Instead of calling `gen()` for each user, we cycle through a generator of fixed-length294            # `generation_length_to_get_proper_distribution`. Doing so greatly improves performance because295            # we only ever need to call `gen()` a relatively small number of times. The length of this generator296            # is chosen as the sum of the normalized weights. So, for users A, B, C of weights 2, 5, 6, the length is297            # 2 + 5 + 6 = 13 which would yield the distribution `CBACBCBCBCABC` that gets repeated over and over298            # until the target user count is reached.299            return itertools.cycle(gen() for _ in range(generation_length_to_get_proper_distribution))300        fixed_users = {u.__name__: u for u in self._user_classes if u.fixed_count}301        cycle_fixed_gen = infinite_cycle_gen([(u, u.fixed_count) for u in fixed_users.values()])302        cycle_weighted_gen = infinite_cycle_gen([(u, u.weight) for u in self._user_classes if not u.fixed_count])303        # Spawn users304        while True:305            if self._try_dispatch_fixed:306                self._try_dispatch_fixed = False307                current_fixed_users_count = {u: self._get_user_current_count(u) for u in fixed_users}308                spawned_classes: Set[str] = set()309                while len(spawned_classes) != len(fixed_users):310                    user_name: Optional[str] = next(cycle_fixed_gen)311                    if not user_name:312                        break313                    if current_fixed_users_count[user_name] < fixed_users[user_name].fixed_count:314                        current_fixed_users_count[user_name] += 1315                        if current_fixed_users_count[user_name] == fixed_users[user_name].fixed_count:316                            spawned_classes.add(user_name)317                        yield user_name318                        # 'self._try_dispatch_fixed' was changed outhere,  we have to recalculate current count319                        if self._try_dispatch_fixed:320                            current_fixed_users_count = {u: self._get_user_current_count(u) for u in fixed_users}321                            spawned_classes.clear()322                            self._try_dispatch_fixed = False323            yield next(cycle_weighted_gen)324    @staticmethod325    def _fast_users_on_workers_copy(users_on_workers: Dict[str, Dict[str, int]]) -> Dict[str, Dict[str, int]]:326        """deepcopy is too slow, so we use this custom copy function.327        The implementation was profiled and compared to other implementations such as dict-comprehensions328        and the one below is the most efficient.329        """330        # type is ignored due to: https://github.com/python/mypy/issues/1507...

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