It's a simple framework based on three composed concepts:
- Task: A task is the smaller unit of execution or simple a node in the DAG, everyone of them has a function to execute and a set of events that indicate when to run this task.
- Multitask: The multitask can be seen as a DAG with the ability to orquestate when and how run every task, based on events that can be modified during the execution.
- Multitask Queue: It's an extention to run multiple multitasks in a first-in-first-out (FIFO) order (this behaviour can be modified, for example you can use a priority queue or a class that has a put and a get method)
The positive parts of this framework are the nexts:
- The tasks can be created with a simple decorator over a function.
- The tasks can be run in parallel depending on the decorator that you use, internally it can use async code, multithreads or multiprocess.
- The parameters of the functions are stored to speed up every call.
- The resulting DAG from the tasks allows to optimize the parallelization of your tasks when you run them using the Multitask class.
- The MultitaskQueue class is really easy to use, all the functions that you decorate are automatically registered to be use by this class, so you don't have to make a lot of imports and then send every task to the class to use it.
- Enqueue new multitasks to the MultitaskQueue class is easy to do and can be done during the run process using a task so, you can add as many as you want and this allow you to recreate some workflow like the ones used in the backtesters.