Python crawler distributed architecture - Redis/RabbitMQ workflow introduction

In large-scale data acquisition and processing tasks, using a distributed architecture can improve efficiency and scalability. This article will introduce the workflow of Redis and RabbitMQ, the commonly used message queue tools in the Python crawler distributed architecture, to help you understand the principles and applications of distributed crawlers.

1. Why do you need a distributed architecture?
   In data collection tasks, stand-alone crawlers may face performance bottlenecks and resource constraints. The distributed architecture can decompose the task into multiple subtasks and execute them in parallel on multiple machines to improve the collection speed and efficiency. In addition, the distributed architecture also has fault tolerance and scalability, which can meet the needs of high concurrency and large-scale data collection.
2. Redis workflow introduction
   Redis is a high-performance memory data storage and message queue tool, which is often used for task scheduling and data delivery of distributed crawlers. Its workflow is as follows:

• Step 1: Add the crawler task to the Redis queue.
• Step 2: Multiple crawler nodes obtain tasks from the Redis queue.
• Step 3: Each crawler node performs a task and stores the collected data in a database or other storage media.
• Step 4: After the crawler node completes the task, update the status and results of the task to Redis.
• Step 5: The scheduling node monitors the task status in Redis and adds new tasks as needed.
  Through the message queue mechanism of Redis, the distribution of tasks and the collection of results are realized, so that multiple crawler nodes can work together to improve the overall collection efficiency.

3. RabbitMQ workflow introduction
   RabbitMQ is a reliable message queue tool, often used for task scheduling and message delivery of distributed crawlers. Its workflow is as follows:

• Step 1: Add the crawler task to the task queue of RabbitMQ.
• Step 2: Multiple crawler nodes subscribe to the task queue and wait to receive tasks.
• Step 3: When a new task is published to the queue, RabbitMQ sends the task to an available crawler node.
• Step 4: The crawler node executes the task and stores the collected data in the database or other storage media.
• Step 5: After the crawler node completes the task, it sends the status and results of the task to RabbitMQ.
• Step 6: The scheduling node monitors the task status and results in RabbitMQ, and adds new tasks as needed.
  Through the message queue mechanism of RabbitMQ, the distribution of tasks and the collection of results are realized, so that multiple crawler nodes can work together to improve the overall collection efficiency.

4. How to choose Redis or RabbitMQ?
   Choosing to use Redis or RabbitMQ depends on specific needs and scenarios. Redis has the characteristics of high performance and ease of use, and is suitable for scenarios that require high real-time message delivery. RabbitMQ is more suitable for scenarios that require high reliability and stability of message delivery.
   I hope the above content will help you understand and apply the Python crawler distributed architecture! If you have any questions or other comments, welcome to discuss in the comment area.