aqueue 0.3.0

An async queue with live progress display

aqueue

An async queue with live progress display. Good for running and visualizing tree-like I/O-bound processing jobs, such as website scrapes.

Example

import random
from typing import ClassVar

import trio

from aqueue import EnqueueFn, Item, SetDescFn, run_queue


class RootItem(Item):
    async def process(self, enqueue: EnqueueFn, set_desc: SetDescFn) -> None:
        # display what we're doing in the worker status panel
        set_desc("Making child items")

        for _ in range(3):
            # simulate doing work and creating more items
            await trio.sleep(random.random())
            enqueue(ChildItem())

    async def after_children_processed(self) -> None:
        print("All done!")


class ChildItem(Item):
    # track these items on the Overall Progress panel
    track_overall: ClassVar[bool] = True

    async def process(self, enqueue: EnqueueFn, set_desc: SetDescFn) -> None:
        set_desc("Doing work...")

        # Simulate doing work
        await trio.sleep(random.random())


def main() -> None:
    run_queue(
        initial_items=[RootItem()],
        num_workers=2,
    )


if __name__ == "__main__":
    main()

Usage Notes

There's two things you need to do to use aqueue:

1. Write your Item classes
2. Start your queue with one of those items

Items

Items are your units of work. They can represent whatever you'd like, such as parts of a website that you're trying to scrape: an item for the index page, for subpages, for images, etc.

Each item should be an instance of a class. It's not required, but subclassing from aqueue.Item may let your editor give you better assistance.

For example:

from typing import ClassVar
import aqueue

class MyItem(aqueue.Item):
    async def process(self, enqueue: aqueue.EnqueueFn, set_desc: aqueue.SetDescFn) -> None:
        # display what we're doing in the worker status panel
        set_desc('Processing MyItem')

        # make an HTTP request, parse it, etc
        ...

        # when you discover more items you want to process, enqueue them:
        enqueue(AnotherItem())

class AnotherItem(aqueue.Item):

    track_overall: ClassVar[bool] = True

    async def process(self, enqueue: aqueue.EnqueueFn, set_desc: aqueue.SetDescFn) -> None:
        set_desc('Processing AnotherItem')

As a rule of thumb, you should make a new item class whenever you notice a one-to-many relationship. For example, this one page has many images I want to download.

Note: process is async, but because this library uses Trio under the hood, you may only use Trio-compatible primitives inside process. For example, use trio.sleep, not asyncio.sleep. TODO: consider AnyIO to avoid this problem?

Disclaimer: aqueue, or any asynchronous framework, is only going to be helpful if you're performing work is I/O-bound.

process method, required

An item class must define an async process method. As arguments, it should accept two positional arguments:

1. a aqueue.EnqueueFn that can be called to enqueue more work. That type is simply an alias for Callable[[Item], None].
2. a aqueue.SetDescFn that can be called to update this worker's status with a string description.

after_children_processed method, optional

You can implement an after_children_processed method. After this item's process and any (recursive) child's process are called, this method will be called.

track_overall property, optional

If set to True, when this item is enqueued, the Overall Progress total count increments. After its process method completed, the Overall Progress completed count increments

Starting your Queue

Then, start your queue with an initial item(s) to kick things off.

aqueue.run_queue(
    initial_items=[MyItem()],
    num_workers=2,
    queue_type_name="stack",
    graceful_ctrl_c=True,
)

Queue type

By default, the queue is actually ...a queue -- that is to say that items are processed first-in-first-out. Here are all the types you can specify with the queue_type_name argument.

• queue - first-in-first-out processing, or breadth-first.
• stack - last-in-first-out processing, or depth-first. This one is recommended for website scraping because it yields items fast (versus queue that tries to look at all the intermediate pages first).
• priority - priority queue processing. In this case, your item objects should be orderable (with __lt__, etc). Lesser objects will be processed first, because this code uses a minheap.

Number of workers

You can specify the number of workers you'd like to be processing your items with the num_workers argument.

Ctrl-C

If you decide you want to stop your queue processing, press Ctrl-C.

If you've set the graceful_ctrl_c to False, this will stop the program immediately. If True, the default, aqueue will wait for the items currently being worked on to complete (without taking any additional items), and then stop. Put another way, the choice is between responsiveness and resource consistency.

Sharing state

Often, its beneficial to share state between the items. Using the website scrape example again, you may want to keep track of the URLs you've visited so you don't scrape them twice.

If this is needed, simply keep a global set/dict/list and store a key for the item. For example, a URL string may be a good key.

If you don't want to or can't use a global variable, consider a ContextVar.

Persisting state

During development, its probably likely that your program will crash after doing some work. For example, maybe your HTTP request timed out or you had a bug in your HTML parsing.

It's a shame to lose that work that's been done. So, if you're looking for a really handy way to persist state across runs, check out the built-in shelve module. It's like a dict that automatically saves to a file each time you set a key in it.

Other cool things

The API is fully docstringed and type-hinted 🥳

Installation

pip install "git+https://github.com/t-mart/aqueue"