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How to use _fast_users_on_workers_copy method in locust

Best Python code snippet using locust
dispatch.py
Source:dispatch.py
...58        self._initial_users_on_workers = {59            worker_node.id: {user_class.__name__: 0 for user_class in self._user_classes}60            for worker_node in worker_nodes61        }62        self._users_on_workers = self._fast_users_on_workers_copy(self._initial_users_on_workers)63        self._current_user_count = sum(map(sum, map(dict.values, self._users_on_workers.values())))64        self._dispatcher_generator = None65        self._user_generator = self._user_gen()66        self._worker_node_generator = itertools.cycle(self._worker_nodes)67        # To keep track of how long it takes for each dispatch iteration to compute68        self._dispatch_iteration_durations = []69        self._active_users = []70        # TODO: Test that attribute is set when dispatching and unset when done dispatching71        self._dispatch_in_progress = False72        self._rebalance = False73    @property74    def dispatch_in_progress(self):75        return self._dispatch_in_progress76    @property77    def dispatch_iteration_durations(self) -> List[float]:78        return self._dispatch_iteration_durations79    def __next__(self) -> Dict[str, Dict[str, int]]:80        users_on_workers = next(self._dispatcher_generator)81        # TODO: Is this necessary to copy the users_on_workers if we know82        #       it won't be mutated by external code?83        return self._fast_users_on_workers_copy(users_on_workers)84    def _dispatcher(self) -> Generator[Dict[str, Dict[str, int]], None, None]:85        self._dispatch_in_progress = True86        if self._rebalance:87            self._rebalance = False88            yield self._users_on_workers89            if self._current_user_count == self._target_user_count:90                return91        if self._current_user_count == self._target_user_count:92            yield self._initial_users_on_workers93            self._dispatch_in_progress = False94            return95        while self._current_user_count < self._target_user_count:96            with self._wait_between_dispatch_iteration_context():97                yield self._add_users_on_workers()98                if self._rebalance:99                    self._rebalance = False100                    yield self._users_on_workers101        while self._current_user_count > self._target_user_count:102            with self._wait_between_dispatch_iteration_context():103                yield self._remove_users_from_workers()104                if self._rebalance:105                    self._rebalance = False106                    yield self._users_on_workers107        self._dispatch_in_progress = False108    def new_dispatch(self, target_user_count: int, spawn_rate: float) -> None:109        """110        Initialize a new dispatch cycle.111        :param target_user_count: The desired user count at the end of the dispatch cycle112        :param spawn_rate: The spawn rate113        """114        self._target_user_count = target_user_count115        self._spawn_rate = spawn_rate116        self._user_count_per_dispatch_iteration = max(1, math.floor(self._spawn_rate))117        self._wait_between_dispatch = self._user_count_per_dispatch_iteration / self._spawn_rate118        self._initial_users_on_workers = self._users_on_workers119        self._users_on_workers = self._fast_users_on_workers_copy(self._initial_users_on_workers)120        self._current_user_count = sum(map(sum, map(dict.values, self._users_on_workers.values())))121        self._dispatcher_generator = self._dispatcher()122        self._dispatch_iteration_durations.clear()123    def add_worker(self, worker_node: "WorkerNode") -> None:124        """125        This method is to be called when a new worker connects to the master. When126        a new worker is added, the users dispatcher will flag that a rebalance is required127        and ensure that the next dispatch iteration will be made to redistribute the users128        on the new pool of workers.129        :param worker_node: The worker node to add.130        """131        self._worker_nodes.append(worker_node)132        self._worker_nodes = sorted(self._worker_nodes, key=lambda w: w.id)133        self._prepare_rebalance()134    def remove_worker(self, worker_node: "WorkerNode") -> None:135        """136        This method is similar to the above `add_worker`. When a worker disconnects137        (because of e.g. network failure, worker failure, etc.), this method will ensure that the next138        dispatch iteration redistributes the users on the remaining workers.139        :param worker_node: The worker node to remove.140        """141        self._worker_nodes = [w for w in self._worker_nodes if w.id != worker_node.id]142        if len(self._worker_nodes) == 0:143            # TODO: Test this144            return145        self._prepare_rebalance()146    def _prepare_rebalance(self) -> None:147        """148        When a rebalance is required because of added and/or removed workers, we compute the desired state as if149        we started from 0 user. So, if we were currently running 500 users, then the `_distribute_users` will150        perform a fake ramp-up without any waiting and return the final distribution.151        """152        users_on_workers, user_gen, worker_gen, active_users = self._distribute_users(self._current_user_count)153        self._users_on_workers = users_on_workers154        self._active_users = active_users155        # It's important to reset the generators by using the ones from `_distribute_users`156        # so that the next iterations are smooth and continuous.157        self._user_generator = user_gen158        self._worker_node_generator = worker_gen159        self._rebalance = True160    @contextlib.contextmanager161    def _wait_between_dispatch_iteration_context(self) -> None:162        t0_rel = time.perf_counter()163        # We don't use `try: ... finally: ...` because we don't want to sleep164        # if there's an exception within the context.165        yield166        delta = time.perf_counter() - t0_rel167        self._dispatch_iteration_durations.append(delta)168        # print("Dispatch cycle took {:.3f}ms".format(delta * 1000))169        if self._current_user_count == self._target_user_count:170            # No sleep when this is the last dispatch iteration171            return172        sleep_duration = max(0.0, self._wait_between_dispatch - delta)173        gevent.sleep(sleep_duration)174    def _add_users_on_workers(self) -> Dict[str, Dict[str, int]]:175        """Add users on the workers until the target number of users is reached for the current dispatch iteration176        :return: The users that we want to run on the workers177        """178        current_user_count_target = min(179            self._current_user_count + self._user_count_per_dispatch_iteration, self._target_user_count180        )181        for user in self._user_generator:182            worker_node = next(self._worker_node_generator)183            self._users_on_workers[worker_node.id][user] += 1184            self._current_user_count += 1185            self._active_users.append((worker_node, user))186            if self._current_user_count >= current_user_count_target:187                return self._users_on_workers188    def _remove_users_from_workers(self) -> Dict[str, Dict[str, int]]:189        """Remove users from the workers until the target number of users is reached for the current dispatch iteration190        :return: The users that we want to run on the workers191        """192        current_user_count_target = max(193            self._current_user_count - self._user_count_per_dispatch_iteration, self._target_user_count194        )195        while True:196            try:197                worker_node, user = self._active_users.pop()198            except IndexError:199                return self._users_on_workers200            self._users_on_workers[worker_node.id][user] -= 1201            self._current_user_count -= 1202            if self._current_user_count == 0 or self._current_user_count <= current_user_count_target:203                return self._users_on_workers204    def _distribute_users(205        self, target_user_count: int206    ) -> Tuple[dict, Generator[str, None, None], typing.Iterator["WorkerNode"], List[Tuple["WorkerNode", str]]]:207        """208        This function might take some time to complete if the `target_user_count` is a big number. A big number209        is typically > 50 000. However, this function is only called if a worker is added or removed while a test210        is running. Such a situation should be quite rare.211        """212        user_gen = self._user_gen()213        worker_gen = itertools.cycle(self._worker_nodes)214        users_on_workers = {215            worker_node.id: {user_class.__name__: 0 for user_class in self._user_classes}216            for worker_node in self._worker_nodes217        }218        active_users = []219        user_count = 0220        while user_count < target_user_count:221            user = next(user_gen)222            worker_node = next(worker_gen)223            users_on_workers[worker_node.id][user] += 1224            user_count += 1225            active_users.append((worker_node, user))226        return users_on_workers, user_gen, worker_gen, active_users227    def _user_gen(self) -> Generator[str, None, None]:228        """229        This method generates users according to their weights using230        a smooth weighted round-robin algorithm implemented by https://github.com/linnik/roundrobin.231        For example, given users A, B with weights 5 and 1 respectively, this algorithm232        will yield AAABAAAAABAA. The smooth aspect of this algorithm is what makes it possible233        to keep the distribution during ramp-up and ramp-down. If we were to use a normal234        weighted round-robin algorithm, we'd get AAAAABAAAAAB which would make the distribution235        less accurate during ramp-up/down.236        """237        # Normalize the weights so that the smallest weight will be equal to "target_min_weight".238        # The value "2" was experimentally determined because it gave a better distribution especially239        # when dealing with weights which are close to each others, e.g. 1.5, 2, 2.4, etc.240        target_min_weight = 2241        min_weight = min(u.weight for u in self._user_classes)242        normalized_weights = [243            (user_class.__name__, round(target_min_weight * user_class.weight / min_weight))244            for user_class in self._user_classes245        ]246        gen = smooth(normalized_weights)247        # Instead of calling `gen()` for each user, we cycle through a generator of fixed-length248        # `generation_length_to_get_proper_distribution`. Doing so greatly improves performance because249        # we only ever need to call `gen()` a relatively small number of times. The length of this generator250        # is chosen as the sum of the normalized weights. So, for users A, B, C of weights 2, 5, 6, the length is251        # 2 + 5 + 6 = 13 which would yield the distribution `CBACBCBCBCABC` that gets repeated over and over252        # until the target user count is reached.253        generation_length_to_get_proper_distribution = sum(254            normalized_weight[1] for normalized_weight in normalized_weights255        )256        yield from itertools.cycle(gen() for _ in range(generation_length_to_get_proper_distribution))257    @staticmethod258    def _fast_users_on_workers_copy(users_on_workers: Dict[str, Dict[str, int]]) -> Dict[str, Dict[str, int]]:259        """deepcopy is too slow, so we use this custom copy function.260        The implementation was profiled and compared to other implementations such as dict-comprehensions261        and the one below is the most efficient.262        """...
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