Subsystem Patterns

When making subsystems the best thing to do is make sure that the subsystem follows a pattern. Patterns are the model for how a subsystem is used. When a pattern is used then all of the plugins in the subsystem do similar tasks and follow similar models. Some patterns are good at specific tasks, such as an rpc/rest backend. Some patterns are good at disparate tasks, like setting up and running a server.

This document covers a number of common patterns, but you can create new patterns! Just make sure that your pattern is well documented and contracted.

Spine Pattern

The spine pattern is very common because it defines the startup spine of an application. This is a pattern where your application loads up config data, starts worker processes and loads the bulk of the subsystems to be used.

The spine is typically the first subsystem loaded from the startup script. The spine should be very small and only have a few plugins. Try to keep the spine very limited, and just enough to start the application. The spine typically runs the following things:

  • Set up the core data structures used by the application
  • Load up conf and read in the application configuration
  • Load up additional subsystems
  • Start up an asyncio loop
  • Start the main coroutines or functions
  • Start the patterns in the subs’ files

Beacon Pattern

The name of the beacon pattern comes from Salt’s Beacon system. Salt’s Beacon system starts one coroutine per plugin module. In this example we will make a simple cryptocurrency tracker. This would be a simple

async def start(hub):
    Start the beacon listening process
    gens = []
    for mod in hub.beacons:
        if not hasattr(mod, 'listen'):
        func = getattr(mod, 'listen')
    async for ret in hub.pop.loop.as_yielded(gens):
        await hub.beacons.QUE.put(ret)

This example shows iterating over the modules found in the beacons sub. The plugins are defined as needing to implement an async generator function. We call the async generator function which returns an async generator that gets appended to a list. That list is then passed to the as_yielded function that yields as the next async generator yields. The yielded data is then added to a QUE that can be ingested elsewhere.

Following this pattern a plugin that emits a beacon would subsequently look like this:

import asyncio
import aiohttp

async def listen(hub):
    while True:
        async with aiohttp.ClientSession() as session:
            async with session.get('') as resp:
        await asyncio.sleep(5)

Now we have a bitcoin ticker. More modules could act as means to gather data about other cryptocurrencies.

Collection Pattern

The collection pattern is where an expansive number of modules can be added that define the on-demand collection of data. A good example here is to collect system information. This makes it simple to extend what data is being gathered and to support more platforms.

A simple example of a collection pattern file could look like this:

import asyncio

async def run(hub):
    Run the data collection
    hub.system.DATA = {}
    coros = []
    for mod in hub.system:
        if not hasattr(mod, 'gather'):
        func = getattr(mod, 'gather')
        ret = func()
        if asyncio.iscoroutine(ret):
    await asyncio.gather(coros)

This example allows for plugin modules to create both functions and async functions as well as execute the async functions in parallel. A simple module for this example of the collection patter could look like this plugin called

import sys

def gather(hub):
if sys.platform.startswith('win'):
    hub.system.DATA['kernel'] = 'windows'
elif sys.platform.startswith('linux'):
    hub.system.DATA['kernel'] = 'linux'
elif sys.platform.startswith('darwin'):
    hub.system.DATA['kernel'] = 'darwin'

The collection pattern we used here allowed the modules to populate a dict on the hub, but we could have just as easily returned the data we wanted to put on the hub and had the function in the aggregate the data.

Flow Pattern

The flow pattern is used for flow based interfaces. This follows an async pattern where data is queued and passed into and/or out of the subsystem. This is an excellent pattern for applications that do data processing. Data can be loaded into the pattern, processed and sent forward to the next interface for processing. This pattern is used to link together multiple flow subsystems or to take data from a beacon pattern and process it.

In the file start a coroutine that waits on an async queue that is feed by another subsystem.

import asyncio

async def start(hub, mod):
    while True:
        data = await hub.beacons.QUE.get()
        ret = await getattr(f'flows.{mod}.process'){data}
        await hub.flows.QUE.put(ret)

Using a flow pattern makes pipe-lining concurrent data fast and efficient. For a more elegant example take a look at the internals of the umbra project.

Router Pattern

The router pattern is used to take input data and route it to the correct function and route it back. This is typically used with network interfaces. A typical will look something like this:

import aiohttp

def start(hub):
    app = asyncio.web.Application()
    app.add_routes([asyncio.web.get('/', hub._.router)])

async def router(hub, request):
    data = request.json()
    if 'ref' in data:
        return web.json_response(getattr(hub.server, data['ref'])(**data.get('kwargs')))

Now the plugin subsystem can be populated with modules that expose request functions

Library Pattern

The Library Pattern is one of the most intuitive. This is where a collection of plugins expose somewhat arbitrary functions. Many other patterns are backed by the library pattern. The library pattern itself does not require an file, typically a library pattern is used to back up a Router pattern, where arbitrary functions are being exposed to an interface. The nice thing about the library pattern is that it allows functions to be exposed outside of the context of the interface they are associated with. This means that functions that exist behind a router could still be easily and natively exposed to any other aspect of the application.