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How to use infinite_cycle_gen method in locust

Best Python code snippet using locust

dispatch.py

Source:dispatch.py

...270        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)...

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