Does Pholcus support distributed scraping?

Pholcus is a high-concurrency, distributed, web crawler framework written in the Go language. It does support distributed scraping, which allows it to scale across multiple machines to handle large-scale web scraping tasks.

In a distributed setup, Pholcus can distribute tasks to different nodes in a cluster, which can then scrape data independently and concurrently. This can greatly improve the efficiency of data collection by leveraging the processing power of multiple machines.

To implement distributed scraping with Pholcus, you would typically do the following:

1. Setup a Master Node: This node is responsible for managing the distribution of tasks to the worker nodes. It will assign tasks, collect data, and maintain the overall coordination among the cluster nodes.

2. Setup Worker Nodes: These nodes will receive tasks from the master node, perform the scraping jobs, and send back the results to the master node.

3. Distributed Task Queue: You need a task queue or message broker to distribute and manage the tasks among the nodes. Commonly used message brokers include RabbitMQ, Kafka, or Redis.

4. Data Storage: The scraped data needs to be stored somewhere centralized or distributed, depending on the architecture and the requirements. This could be a database like MySQL, PostgreSQL, or MongoDB, or a distributed file system like HDFS.

Here's an example of how you might set up a simple distributed scraping system with Pholcus:

Master Node Setup

On the master node, you would initialize Pholcus with a configuration that enables it to act as the coordinator for the worker nodes.

package main

import (
    "github.com/henrylee2cn/pholcus/exec"
    _ "github.com/henrylee2cn/pholcus_lib" // Any required spiders
)

func main() {
    // Set up the master node
    exec.DefaultRun("web", ":8080", "master", "127.0.0.1:9090", "yourQueueName")
}

Worker Node Setup

Each worker node would be initialized with a configuration that allows it to connect to the master node and listen for tasks.

package main

import (
    "github.com/henrylee2cn/pholcus/exec"
    _ "github.com/henrylee2cn/pholcus_lib" // Any required spiders
)

func main() {
    // Set up a worker node
    exec.DefaultRun("phantom", "", "worker", "127.0.0.1:9090", "yourQueueName")
}

Note: The above examples assume that you have a message queue system set up and running on the network address 127.0.0.1:9090 with the queue named yourQueueName. You would replace 127.0.0.1:9090 with the actual network address of your message queue system.

Also, keep in mind that implementing a distributed scraping system involves handling many challenges such as:

• Ensuring that tasks are not duplicated across workers.
• Handling failures and retries of tasks.
• Balancing the load across the worker nodes.
• Managing network communication and data serialization between nodes.
• Securing the communication between nodes to prevent unauthorized access or data leaks.

Setting up a distributed scraping system with Pholcus or any other framework is a complex task that requires careful planning and consideration of the above challenges. It's important to thoroughly test the system to ensure its reliability and efficiency before using it for large-scale scraping tasks.