Celery and RabbitMQ are two powerful tools often used together in the realm of distributed computing and task queue management, especially within the Python ecosystem.
Distributed computing refers to the use of multiple computers or nodes working together in a network to solve a computational problem or perform a task. Instead of relying on a single powerful machine, distributed computing distributes the workload across multiple machines, which can be located in different geographical locations.
Task queue management involves managing a queue of tasks that need to be executed asynchronously by one or more workers. It provides a mechanism for distributing, prioritizing, and executing tasks in a controlled manner.
This video explain very well the below:
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A queue is a line of things waiting to be handled. Starts at the beggining of the line and is processed in sequential order.
- Like a queue of people or cars:
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A message queue also called a message broker is a queue of messages placed between 2 parts of the system, in order for them to communicate with each other:
- A message is the data transported between the sender and receiver.
- An example of a message could be a party of the system, telling another part of the system to start a task (an image scaling request or logs that should be stored).
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- Producers create messages and and deliver them to the message queue.
- Consumers connect to the queue and subscribe messages from the queue.
- Messages placed onto the queue are stored until the consumer acknowledge them (i.e, the consumer tells the message broker that the message has been received or handled).
A message queue provides an asynchronous communication protocol, meaning that one system puts a message onto a message queue and does not require an immediate response to continue processing.
(https://www.youtube.com/watch?v=hFIkEGtW6vE - high level overview)
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Celery is a distributed task queue framework for Python, which allows you to run asynchronous tasks in a distributed manner across multiple workers.
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These tasks can be anything from small data processing operations to complex, long-running computations.
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Some important concepts in celery are:
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Tasks:
- Tasks are units of work that you want to execute asynchronously.
- They can be defined as simple Python functions decorated with
@celery.taskor as classes implementing thecelery.Taskinterface.
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Workers:
- Workers are processes that execute tasks.
- Celery supports multiple types of workers, including regular workers for executing tasks, beat workers for handling periodic tasks, and more.
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Brokers:
- Celery requires a message broker to handle the communication between the client and the workers.
- This is where RabbitMQ comes into play.
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Schedulers:
- Celery supports periodic tasks, which are managed by a scheduler.
- The default scheduler in Celery is Celery Beat.
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- Celery simplifies the process of distributing tasks across multiple workers, making it easier to scale your application horizontally.
- It helps in decoupling different parts of your application, allowing for better scalability and fault tolerance.
- Additionally, Celery provides monitoring and management tools to monitor the status of tasks and workers.
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RabbitMQ is a message broker that implements the Advanced Message Queuing Protocol (AMQP).
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It acts as a middleman between producers and consumers of messages, allowing them to communicate asynchronously and reliably.
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Some important concepts in RabbitMQ are:
- Queues:
- Queues are the basic building blocks of RabbitMQ.
- They store messages that are sent by producers and consumed by consumers.
- Exchanges:
- Exchanges receive messages from producers and route them to one or more queues based on routing rules defined by the exchange type.
- Bindings:
- Bindings define the relationship between exchanges and queues, specifying which queues should receive messages from which exchanges.
- Consumers:
- Consumers are applications that receive and process messages from queues.
- Queues: