Celery

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how to add a dependency job inside another job in celery

In Celery, creating a task that depends on the completion of another task (a dependency job) involves using the results of the first task as input to the second. This pattern is commonly known as chaining tasks. Celery supports several ways to handle task dependencies: using chain, group, or manually handling task results. Here, we'll focus on chaining tasks, as it directly relates to creating dependencies between tasks.

Step 1: Define Celery Tasks

First, define the tasks in a Celery application. Let's assume you have two tasks where the second task depends on the output of the first.

from celery import Celery

app = Celery('tasks', broker='pyamqp://guest@localhost//')

@app.task
def first_task(x):
    return x + 1

@app.task
def second_task(y):
    return y * 2

Step 2: Chain Tasks Together

You can chain these tasks so that the output of first_task becomes the input to second_task.

Using chain()

Celery's chain function links tasks together so that they execute one after another, passing the result from one as the argument to the next.

from celery import chain

# Create a chain where the output of first_task will be passed to second_task
result = chain(first_task.s(10), second_task.s())()
print(result.get())  # This will print the result of the second_task

Here, first_task.s(10) initializes the first_task with an argument of 10. second_task.s() is set up to take the result of first_task as its argument. .s() is a shorthand for .signature(), which is used to create immutable signatures for tasks.

Manually Using Results

Alternatively, you can manually manage task execution by waiting for the first task to complete and then sending its result to the second task.

result_first = first_task.delay(10)
result_second = second_task.delay(result_first.get())
print(result_second.get())

Note that this approach can block your client if used in a synchronous context, as get() waits for the task to complete.

Step 3: Using Task Workflows for More Complex Dependencies

For more complex dependencies, consider using Celery's group, chord, or chain combinations to manage workflows.

• group(): Executes tasks in parallel.

• chord(): A special kind of task that executes a callback once all of the tasks in a group have finished executing.

Best Practices

• Avoid using .get() in production: This method is blocking and can reduce the performance benefits of asynchronous execution. Consider using Celery’s built-in primitives like chain, group, and chord for non-blocking task workflows.

• Error Handling: Always plan for failures in tasks. Consider what should happen if a task in the chain fails. Celery supports linking error callbacks using .on_error().

By leveraging these methods and practices, you can efficiently manage task dependencies in Celery, enabling complex asynchronous workflows tailored to your application's needs.

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