python - 运行前绘制芹菜画布

Question

有关于如何在 Celery 中运行画布作业后生成图表的文档。但是，我想在运行作业之前生成一个图表。

假设我创建了一个简单的链：

c = chain(add.s(1, 2), mul(4))

如何生成链图？

谢谢，

三木

Answer 1

我也有同样的愿望。在运行作业之前生成图表。所以我做了一些工作:)

看来芹菜不允许。这样做的原因（至少我在尝试这样做时所理解的）是在图中每个节点都必须有一个唯一的名称。一旦画布被执行，这个唯一的名字就是 celery task_id 但在执行之前没有任何东西允许这样的区别。

所以解决方案是自己生成这个图，当然每个节点都唯一标识（为此，计数器可以完成工作）。

这是这个函数的工作：

# -*- coding: utf-8 -*-
from celery.canvas import chain, group, Signature


def analyze_canvas(canvas):
    return _analyze_canvas(canvas)['dependencies']


def _analyze_canvas(canvas, previous=[], i=0):
    dependencies = []

    if isinstance(canvas, chain):
        for t in canvas.tasks:
            if not (isinstance(t, group) or isinstance(t, chain)):
                n = str(t) + " - (" + str(i) + ")"
                i += 1
                dependencies.append((n, previous))
                previous = [n]
            else:
                analysis = _analyze_canvas(t, previous, i)
                dependencies.extend(analysis['dependencies'])
                previous = analysis['previous']
    elif isinstance(canvas, group):
        new_previous = []
        for t in canvas.tasks:
            if not (isinstance(t, group) or isinstance(t, chain)):
                n = str(t) + " - (" + str(i) + ")"
                i += 1
                dependencies.append((n, previous))
                new_previous.append(n)
            else:
                analysis = _analyze_canvas(t, previous, i)
                dependencies.extend(analysis['dependencies'])
                new_previous = analysis['previous']
        previous = new_previous
    elif isinstance(canvas, Signature):
        n = str(t) + " - (" + str(i) + ")"
        i += 1
        dependencies.append((n, previous))
        previous = [n]
    return {"dependencies": dependencies,
            "previous": previous}

它会生成画布的依赖关系图。这个想法只是迭代画布的其他任务并识别组/链/签名以生成正确的依赖关系。

从这一点开始，您可以使用更多 celery utils 来生成点文件。这是一个小的用法示例：

from celery_util import analyze_canvas
from celery.datastructures import DependencyGraph
from celery import Celery, group

app = Celery()

@app.task
def t1():
    pass

@app.task
def t2():
    pass

canvas = t1.si() | t2.si() | group(t1.si(), t1.si(), t2.si()) | t2.si()

d = analyze_canvas(canvas)
dg = DependencyGraph(it=d)
pipo = open("pipo.dot", "w+")
dg.to_dot(pipo)

在这个例子中，我只是声明了虚拟任务并将它们链接/分组在一个漂亮的画布中。我使用 celery utilDependencyGraph来获得对象表示和将图形转储为 dot 的能力，这是我使用to_dot方法所做的。

漂亮的结果是：

Answer 2

我已经更新了https://stackoverflow.com/a/29105701/928489的代码以使用 celery4。它适用于链、组和和弦。

from app.instant_design import get_instant_design_tasks
from celery.canvas import _chain, group, chord


def analyze_canvas(canvas):
    return _analyze_canvas(canvas)[0]


def _analyze_canvas(canvas, previous=[], i=0):
    dependencies = []

    if isinstance(canvas, _chain):
        for i, t in enumerate(canvas.tasks, i):
            dep, previous = _analyze_canvas(t, previous, i)
            dependencies.extend(dep)
    elif isinstance(canvas, group) or isinstance(canvas, chord):
        new_previous = []
        for i, t in enumerate(canvas.tasks, i):
            dep, p = _analyze_canvas(t, previous, i)
            dependencies.extend(dep)
            new_previous.extend(p)

        if isinstance(canvas, chord):
            dep, p = _analyze_canvas(canvas.body, new_previous, i)
            return dependencies + dep, p
    else:
        t = canvas.name + " - (" + str(i) + ")"
        dependencies = [(t, previous)]
        previous = [t]

    return dependencies, previous