Process and thread model

The design described here is based on a two process model. These two processes are:

1. The IPython engine/kernel. This process contains the user's namespace and is responsible for executing user code. If user code uses :mod:`enthought.traits` or uses a GUI toolkit to perform plotting, the GUI event loop will run in this process.
2. The GUI application. The user facing GUI application will run in a second process that communicates directly with the IPython engine using a suitable RPC mechanism. The GUI application will not execute any user code. The canonical example of a GUI application that talks to the IPython engine, would be a GUI based IPython terminal. However, the GUI application could provide a more sophisticated interface such as a notebook.

We now describe the treading model of the IPython engine. Two threads will be used to implement the IPython engine: a main thread that executes user code and a networking thread that communicates with the outside world. This specific design is required by a number of different factors.

First, The IPython engine must run the GUI event loop if the user wants to perform interactive plotting. Because of the design of most GUIs, this means that the user code (which will make GUI calls) must live in the main thread.

Second, networking code in the engine (Twisted or otherwise) must be able to communicate with the outside world while user code runs. An example would be if user code does the following:

import time
for i in range(10):
    print i
    time.sleep(2)

We would like to result of each print i to be seen by the GUI application before the entire code block completes. We call this asynchronous printing. For this to be possible, the networking code has to be able to be able to communicate the value of stdout to the GUI application while user code is run. Another example is using :mod:`IPython.kernel.client` in user code to perform a parallel computation by talking to an IPython controller and a set of engines (these engines are separate from the one we are discussing here). This module requires the Twisted event loop to be run in a different thread than user code.

For the GUI application, threads are optional. However, the GUI application does need to be able to perform network communications asynchronously (without blocking the GUI itself). With this in mind, there are two options:

• Use Twisted (or another non-blocking socket library) in the same thread as the GUI event loop.
• Don't use Twisted, but instead run networking code in the GUI application using blocking sockets in threads. This would require the usage of polling and queues to manage the networking in the GUI application.

Thus, for the GUI application, there is a choice between non-blocking sockets (Twisted) or threads.

Interprocess communication

The GUI application will use interprocess communication (IPC) to communicate with the networking thread of the engine. Because this communication will typically happen over localhost, a simple, one way, non-secure protocol like XML-RPC or JSON-RPC can be used. These options will also make it easy to implement the required networking in the GUI application using the standard library. In applications where secure communications are required, Twisted and Foolscap will probably be the best way to go for now.

Using this communication channel, the GUI application will be able to perform the following actions with the engine:

• Pass code (as a string) to be executed by the engine in the user's namespace as a string.
• Get the current value of stdout and stderr.
• Pass a string to the engine to be completed when the GUI application receives a tab completion event.
• Get a list of all variable names in the user's namespace.
• Other similar actions.

Engine details

As discussed above, the engine will consist of two threads: a main thread and a networking thread. These two threads will communicate using a pair of queues: one for data and requests passing to the main thread (the main thread's "input queue") and another for data and requests passing out of the main thread (the main thread's "output queue"). Both threads will have an event loop that will enqueue elements on one queue and dequeue elements on the other queue.

The event loop of the main thread will be of a different nature depending on if the user wants to perform interactive plotting. If they do want to perform interactive plotting, the main threads event loop will simply be the GUI event loop. In that case, GUI timers will be used to monitor the main threads input queue. When elements appear on that queue, the main thread will respond appropriately. For example, if the queue contains an element that consists of user code to execute, the main thread will call the appropriate method of its IPython instance. If the user does not want to perform interactive plotting, the main thread will have a simpler event loop that will simply block on the input queue. When something appears on that queue, the main thread will awake and handle the request.

The event loop of the networking thread will typically be the Twisted event loop. While it is possible to implement the engine's networking without using Twisted, at this point, Twisted provides the best solution. Note that the GUI application does not need to use Twisted in this case. The Twisted event loop will contain an XML-RPC or JSON-RPC server that takes requests over the network and handles those requests by enqueing elements on the main thread's input queue or dequeing elements on the main thread's output queue.

Because of the asynchronous nature of the network communication, a single input and output queue will be used to handle the interaction with the main thread. It is also possible to use multiple queues to isolate the different types of requests, but our feeling is that this is more complicated than it needs to be.

One of the main issues is how stdout/stderr will be handled. Our idea is to replace sys.stdout/sys.stderr by custom classes that will immediately write data to the main thread's output queue when user code writes to these streams (by doing print). Once on the main thread's output queue, the networking thread will make the data available to the GUI application over the network.

One unavoidable limitation in this design is that if user code does a print and then enters non-GIL-releasing extension code, the networking thread will go silent until the GIL is again released. During this time, the networking thread will not be able to process the GUI application's requests of the engine. Thus, the values of stdout/stderr will be unavailable during this time. This goes beyond stdout/stderr, however. Anytime the main thread is holding the GIL, the networking thread will go silent and be unable to handle requests.

Refactoring of IPython.core

We need to go through IPython.core and describe what specifically needs to be done.