In today's data-driven world, web scraping has become an important means of obtaining critical information and market data. However, with the increase in the size of target websites and the continuous advancement of anti-crawler technology, traditional crawling methods can no longer meet the needs for efficient and stable data extraction. At this time, concurrent execution and parallel execution have become the core technologies to improve the efficiency of web crawling. By rationally utilizing these two technologies, combined with high-quality residential proxy services, data scrapers can significantly increase scraping speed and success rate while avoiding being banned. In this article, we will explore in detail the basic concepts of concurrency and parallel execution, their application in web scraping, and how to optimize your scraping strategy with these techniques for faster and more reliable data collection.

What is Concurrent Execution? What Is the Basic Unit?

Concurrency refers to the ability of a system to handle multiple tasks at the same time. It does not mean that these tasks must be executed completely in parallel at the same time, but it means that the system can effectively manage and schedule multiple tasks so that multiple tasks appear to be performed at the same time. The core of concurrent execution lies in task switching and scheduling. By quickly switching between tasks, the system can handle multiple tasks, improving the system's responsiveness and resource utilization.

The basic unit of concurrent execution is usually a thread or a coroutine:

• Threads are the basic execution units at the operating system level. Each thread can execute tasks in parallel, and multiple threads can share the memory and resources of a process. The creation and switching of threads involves context switching of the operating system, which may bring certain overhead.
• Coroutines are user-level execution units that are lighter than threads. Coroutines are scheduled and switched in the same thread, and achieve concurrency in a collaborative way. They are usually very efficient when processing I/O (Input/Output Operation) intensive tasks in a single-threaded environment.

In concurrent execution, multiple threads or coroutines share system resources (such as CPU, memory, etc.), and the operating system switches between these tasks through the scheduler, making all tasks appear to be running at the same time. Concurrency mainly solves the problem of efficient scheduling of tasks and rational use of resources.

What is Parallel Execution? What Is the Difference between parallel Execution and Concurrent Execution?

Parallel execution refers to the ability to process multiple tasks at the same time. Unlike concurrent execution, parallel execution emphasizes the true simultaneous execution of tasks, using multiple processors or computing cores to achieve simultaneous execution of tasks, thereby improving the system's processing power and efficiency.

The basic unit of parallel execution is usually a process or a thread:

• A process is the basic unit for allocating resources to the operating system. Each process has its own memory space and resources and can be executed simultaneously on different CPU cores.
• A thread is an execution unit in a process, and threads share the memory space of the process. Multiple threads can be executed simultaneously within a process, and tasks can be processed in parallel on multiple cores.

The difference between concurrent execution and parallel execution is:

• Concurrent execution emphasizes the efficient management and scheduling of tasks, allowing multiple tasks to share system resources even if they do not necessarily execute at the same time.
• Parallel execution emphasizes the true simultaneous execution of tasks, improving processing power and efficiency through multiple processors or cores.

In short, concurrency is the management and scheduling of tasks, while parallelism is the simultaneous execution of tasks.

What Are Some of the Reasons for Slow Web Crawling?

Web scraping refers to the process of automatically extracting data from the Internet. It usually involves visiting a website, downloading the webpage content, and then parsing and extracting the required data. The reasons for slow web scraping may involve multiple aspects. Here are some common factors that cause slow web scraping:

• Network latency: Network latency refers to the time it takes for data to travel across a network. High latency can be caused by network congestion, link problems, or a long distance from the target server. High latency can cause requests to respond more slowly, which can slow down crawling.
• Target website response speed: The target website's server response speed may be affected by its load, server configuration, or technical problems. If the target website's server responds slowly or fails, it will cause the crawl request to take longer to respond.
• Crawler efficiency: The performance and configuration of the crawler may affect the crawling speed. For example, how the tool is implemented, how optimized the code is, or how threads/coroutines are managed. If the crawler is inefficient or not optimized, the crawling speed will be limited.
• Page content complexity: The content of a web page may contain a large number of resources (such as images, scripts, style sheets) or dynamically loaded content, which will increase the complexity of crawling. High page content complexity will cause the crawler to take more time to parse and extract the required data.
• Data processing bottleneck: The captured data needs to be processed and stored. There may be bottlenecks in the data processing link, such as data parsing and storage system performance limitations. If the data processing link is inefficient, the overall crawling speed will be slowed down.
• Insufficiency of concurrency or parallelism strategies: The crawler may not effectively utilize concurrency or parallelism techniques for task processing. Single-threaded or serial crawling will slow down crawling. Use concurrency or parallel technology to process multiple requests at the same time, making crawling more efficient. If the crawling strategy is insufficient, it can result in slower task processing.

Concurrency in Web Scraping Using Python

During the web scraping process, using residential proxies can significantly improve the concurrency and efficiency of scraping. Residential proxies bypass website access restrictions and anti-crawler mechanisms by providing real residential IP addresses, allowing crawling to occur at higher concurrency.

1.Thread or coroutine concurrency:

Threads: Use multi-threading technology to perform web page crawling tasks concurrently in multiple threads. Each thread uses a different residential proxy to send requests.

Coroutines: Coroutines are lightweight concurrent units suitable for I/O intensive tasks. In Python, you can use libraries such as asyncio and aiohttp to implement coroutine concurrent crawling.

2.Request Scheduling:

Task queue: The crawling tasks are placed in the queue, and multiple threads or coroutines retrieve the tasks from the queue and execute them. Through reasonable scheduling, the concurrency of task processing can be improved.

Rate limit control: To avoid excessive load on the target website, set appropriate request rate and concurrency limit.

3.Load Balancing:

Proxy pool: Use proxy pool to manage and distribute residential proxies, ensuring that each proxy is used evenly and avoiding overuse of some proxies.

IP Rotation: Here, using 911 Proxy as an example, you can regularly rotate residential proxy IPs in more than 195 locations to prevent being blocked for using the same IP for a long time.

4.Python example: Concurrent fetching with aiohttp and asyncio

import asyncio
import aiohttp

# Residential Proxy List
proxies = [
'http://user:pass@proxy1:port',
'http://user:pass@proxy2:port',
# More Agents
]

async def fetch(session, url, proxy):
try:
async with session.get(url, proxy=proxy) as response:
return await response.text()
except Exception as e:
print(f"Request failed: {e}")
return None

async def main(urls):
tasks = []
async with aiohttp.ClientSession() as session:
for url in urls:
for proxy in proxies:
tasks.append(fetch(session, url, proxy))
results = await asyncio.gather(*tasks)
for result in results:
if result:
print(result)

# List of URLs to crawl
urls = ['http://example.com/page1', 'http://example.com/page2']

# Run the crawl
asyncio.run(main(urls))

When faced with crawling tasks that require high concurrency, combining residential proxies and concurrent crawling technology can effectively improve the efficiency and stability of web crawling.

How Parallelism Speeds Up Web Scraping

Parallelism allows multiple web page requests to be processed at the same time by distributing the crawling tasks across multiple processors or cores. This approach is different from concurrency, which is achieved by rapidly switching tasks, while parallelism is the execution of multiple tasks at the same time.

Multi-process parallelism: Create multiple processes on the same machine, and each process runs simultaneously on different cores. In Python, you can use the multiprocessing module to implement multi-process parallel crawling. Each process has its own independent memory space and resources, so it can better handle CPU-intensive tasks.

from multiprocessing import Pool
import requests

urls = ['http://example.com/page1', 'http://example.com/page2', ...] # List of URLs to be crawled

def fetch(url):
response = requests.get(url)
return response.text

if __name__ == '__main__':
with Pool(processes=4) as pool: # Create 4 processes
results = pool.map(fetch, urls)
for result in results:
print(result)

Multithreaded parallelism: Create multiple threads in the same process, and each thread runs the crawling task independently. In Python, the threading module can be used for multithreaded crawling tasks.

import threading
import requests

def fetch(url):
response = requests.get(url)
print(response.text)

urls = ['http://example.com/page1', 'http://example.com/page2', ...]

threads = []
for url in urls:
thread = threading.Thread(target=fetch, args=(url,))
threads.append(thread)
thread.start()

for thread in threads:
thread.join()

Summarize

In the process of web crawling, the reasonable application of concurrent and parallel execution technology is the key to improving efficiency. Whether it is multi-threading, coroutines or multi-process parallelism, choosing the right technical strategy and combining it with residential proxies are the core of achieving efficient web crawling. Ultimately, through these optimization methods, you can obtain the required data at a faster speed and higher success rate in a complex network environment, providing strong data support for your business.