River: A Python-based Framework for Distributed Programming
River is a parallel and distributed programming environment written in Python. The River core interface is based on a few fundamental concepts that enables the execution of code on multiple virtual machines and provides a flexible mechanism for communication. These concepts are supported by the River run-time system, which manages automatic discovery, connection management, and naming. River can be used directly by an application programmer to implement distributed programs or it can be used as a framework for implementing programming models. We have found the the simplicity and elegance of the River core combined with Python's dynamic typing and concise notation make it easy to rapidly develop a variety of distributed applications and
A River program consists of one or more virtual resources (VRs) that execute on River virtual machines (VMs). VMs can exist locally or remotely and VRs are named using UUIDs. A VM is simply a Python interpreter running the River run-time system. An initiating VR can discover existing VMs and deploy itself or other VRs onto selected VMs. Once running, VRs communicate with each other using a mechanism called super flexible messaging (SFM) for sending and receiving dynamically-typed packets.
A virtual resource (VR) is a process-like abstraction for Python programs. A VR is specified by subclassing VirtualResource and each VR has its own thread of execution. Therefore, a VR encapsulates state using the standard Python class type. In addition, each VR is named using a UUID. A message queue is associated with each VR and is used to support our super flexible messaging mechanism. The VirtualResource base class provides methods for VM discovery, VR creation, and message passing.
Super Flexible Messaging
In River we introduce a novel message passing mechanism called super flexible messaging (SFM). Our mechanism leverages Python's dynamic typing and named arguments to send structured data as (attribute,value) pairs between VRs. Our mechanism allows for an arbitrary number of attributes and the values can be any Python data type that can be serialized using the pickle module.