How to use _prepare_rebalance method in locust

Best Python code snippet using locust

dispatch.py

Source:dispatch.py Github

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...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.contextmanager...

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