Crawling with Proxies and Multi-threading: Improving Efficiency and Stability

Data collection and competitive analysis is now becoming critical in a variety of industries. If you want to collect large amounts of data, whether it's keyword rankings or competitor analysis, large-scale crawling will inevitably involve concurrency and Proxies.However, as the frequency of crawling increases, many target sites will limit or even outright block large numbers of requests from the same IP. Among them, Proxies IP and multi-threading for concurrency are the key tools to achieve successful crawling. Today, I will introduce how to utilize Proxies IP and concurrency control to avoid being banned and improve the efficiency of data crawling.

What is concurrency?

Concurrency may sound complicated, but it actually means performing multiple tasks in the same time period. Concurrency" is a common term in the data crawling world, but many people are not particularly clear about its concept. Simply put, concurrency refers to performing multiple tasks at the same time over a period of time. For example, when crawling with Proxies, concurrency refers to how many active TCP connections there are at the same time. Reasonable concurrency can effectively speed up the efficiency of data collection while reducing the risk of being blocked by the target site.

How can Proxies IPs improve concurrency?

Proxies IPs increase concurrency by spreading multiple requests to different IP addresses. The site will start to wonder, "Why is this IP so active? Is it a bot?" Then, it will probably ban you. At this point, you can solve this problem by using Proxies IPs. Proxies are like different "identities" that change every time you send a request, so that the website doesn't realize that all the requests are coming from the same place. As a result, you can send requests concurrently from multiple IP addressesand still avoid getting banned.

Why are Proxies and Concurrency Control a must?

The purpose of a crawler is to quickly and comprehensively grab data from a target site, but going fast can be risky, especially if the concurrency count is too high, and you may run into several problems:

IP blocked: If too many requests come from the same IP at the same time, the target server will think you are a malicious crawler and block you directly.

Excessive server pressure: Too many concurrent requests can put tremendous pressure on the target server, causing it to trigger an anti-crawler mechanism that restricts you from getting data.

Proxies IP resource waste: If you use Proxies IP uncontrollably, you may waste resources, such as enabling too many proxies at the same time, resulting in a high concurrency count.

The relationship between concurrency, threading, and HTTP connections

Let's explain some common terms:

1. Concurrency: In data crawling, concurrency refers to the number of active TCP connections going on at the same time when using Proxies IPs, which reflects how many tasks are currently being processed at the same time.

2. Multithreading: Multithreading is the technique of executing multiple threads simultaneously on software or hardware. Systems that support multithreading can run multiple tasks at the same time, thus improving overall performance.

3. HTTP Connections: refers to the number of HTTP connections generated by all the resources (such as JS, CSS, images, etc.) loaded when accessing the target page.

The relationship between these three is not linear or simply equivalent. For example, if you have 100 threads, each initiating 3 HTTP requests, then you will actually have 300 concurrent HTTP connections. This also means that the number of concurrency, threadsand connectionsare not equivalent and need to be configured flexibly depending on the task.

How to control concurrency: from theory to practice

To better explain concurrency control, we can walk through a simple Python code example that shows how to use Proxies IP poolingand multithreadingfor efficient data crawling. Here, we manage the number of concurrent requests through a queueand ensure that each request uses a different Proxies IP.

code

In the code, proxy_pool is the pool of proxy IP addresses, which stores the available proxy IP addresses. Whenever you make a request, the program gets a proxy IP address from the pool and ensures that it is not used by multiple threads at the same time (using a locking mechanism). After the request is complete, the proxy IP address is released so that it can be reused for the next request.

Rotating the number of concurrency: optimizing crawling according to the actual situation

The number of concurrent requests is not fixed, it needs to be Rotating according to the actual situation. If the response speed of Proxies IP becomes slower, or the target server starts to make frequent errors, you need to reduce the number of concurrent requeststo avoid further aggravating the burden of the target server. On the contrary, if everything goes well and the target server responds quickly and stably, you can appropriately increase the number of concurrent requeststo accelerate data capture.

How to realize this Rotating adjustment? You can Rotating the number of threads or connections by monitoring the response timeand success rate of the Proxies IPto ensure that the whole system is always in the best condition. You can keep the crawler system efficient and stable by writing an adaptive adjustment algorithm to adjust the concurrency count in real time.

Resource Management: How to utilize Proxies IP wisely?

In addition to concurrency control, it's critical to manage your Proxies' IP resourceswell. Here are a few practical strategies:

1. Update Proxies IP Pool Regularly: Proxies are not permanently valid and may expire or be banned for various reasons. Therefore, it is very important to update the IP pool regularly to make sure you always have enough available IPs. You can get the latest Proxies IP list through the API interface and update it to the IP pool.

Here's a sample code for periodically updating the Proxies IP pool, I'm using the code from the PROXY.CC I'm using an AIP obtained from PROXY.CC:

import requests import schedule import time Function to update the proxy pool def update_proxy_pool(). response = requests.get('https://api.proxy.cc/web_v1/ip/get-ip-v3?app_key=XILN3cJFvNDDG9xzYWmV') Replace with actual API new_proxies = response.json() Assuming the API returns a JSON response global proxy_pool proxy_pool = new_proxies Schedule the proxy pool update every hour schedule.every(1).hours.do(update_proxy_pool) while True: schedule.run_pending() time.sleep(1)

In the above code, the latest Proxies IPs are periodically fetched from the Proxies APIand updated to the proxy_pool. the process is as follows:

• updateproxypool(): get the list of Proxies IPs via API request and store them in the global variable proxy_pool.

• schedule.every(1).hours.do(update_proxies_pool): call update_proxies_pool() every hour to make sure the Proxies IP pool is updated in time.

• while True:: Checks once a second in an infinite loop if there is a timed task that needs to be executed, ensuring that the program keeps running and updating the Proxies on time.

This code ensures that the crawler uses the latest Proxies IPs to improve crawl stability.

1. Proxies IP Quality Assessment: Not all Proxies IPs are suitable for data crawling. Some IPs have slow response time or are frequently banned. By monitoring the success rate and response time of each Proxies IP, you can filter out high-quality IPs and prioritize their use. For poorly performing IPs, you should temporarily move them out of the IP pool or lower their usage priority.

   

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1. Proxies IP reuse and release: It is very important to utilize Proxies IP resources wisely. By managing the reuseand releaseof Proxies, you can reduce the cost of frequently requesting new IPs and improve the efficiency of crawling. In practice, you can consider using connection poolingtechniques to manage connections to Proxies, so that each IP can be effectively utilized multiple times.

Real-time monitoring and strategy adjustment

In a long-running data crawling task, it is not enough to set up the concurrency count and Proxies IP pool. You need to constantly monitor the real-time situation of crawling, including the use of Proxies, the response speed of the target server and so on. Through these real-time monitoring data, you can Rotating the number of concurrency, Proxies IP allocation strategy, and even switch the crawling strategy.

Monitor Proxies IP Quality and Server Response

You can monitor the status of Proxies IPs and target servers by using several key metrics:

• Response Time: The average response time for each Proxies IP request. A long response time may indicate that the IP is not very stable or that there is a problem with the target server.

• Success Rate: Whether or not the Proxies IP is successfully acquiring data. If the success rate of the IP is too low, consider replacing or removing the IP.

• Blocking rate: If an IP is frequently blocked by the target server, it means that the IP is of poor quality and should be prioritized for elimination.

Adaptive adjustment strategies

Based on these monitored metrics, you can Rotating your crawling strategy. For example:

• If you find that certain IPs have longer response times or lower success rates, you can lower the priority of those IPs or remove them from the IP pool.

• When the response of the target server is slow, you can reduce the number of concurrency appropriately to avoid putting too much pressure on the server.

• If everything runs smoothly, you can gradually increase the number of concurrent requests to speed up the crawling process.

These Rotating adjustment strategies not only improve the crawling efficiency, but also ensure the stability of the crawler, avoiding the failure of the crawling task due to the changes of the target server or the quality of the Proxies' IPs.

The Critical Role of Proxies IP and Concurrency Control

Whether it's a small-scale data collection or a large-scale competitive analysis, Proxies IP and concurrency control are key to ensure the success of the crawling task. With Proxies, you can avoid being blocked due to frequent requests; with reasonable concurrency control, you can increase the crawling speed without putting too much pressure on the target server.

In addition, Rotating the strategy and real-time monitoring are necessary to ensure the long-term stable operation of the crawler. You can optimize the use of Proxies IP and adjust the concurrency count by monitoring the real-time situation of the crawling to ensure the efficiency and stability of the system.

Conclusion

Proxies IP and concurrency control are very important technical aspects in a crawler system. By making reasonable use of the Proxies IP pool, optimizing the number of concurrencies, Rotating the policy, and monitoring the quality of Proxies IPs, you can build an efficient, stable, and scalablecrawler system. Although this may sound a bit complicated, by accumulating experience through real-world combat, you will be able to master these techniques and greatly improve the success rate of data crawling.