The Benefits of AIOHTTP Python Web Scraping

In the world of business, the difference between effective and ineffective web scraping is often a matter of fractions of a second. Of course, if you run a business that requires web scraping to provide prompt and satisfactory services to your clients, you probably know this already. Web scraping has allowed you to collect extensive amounts of data from the web and provide it to your clients in a comprehensive format. However, in the business world, time is money, and even slight delays in the web scraping process can turn into costly errors.


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A Python AsyncIO Tutorial for Efficient Web Scraping

A Python AsyncIO Tutorial for Efficient Web Scraping

This is especially true if you rely on proxies to provide you with your web scraping services. After all, your benefits to your client are limited by the speed and efficiency of the web scraping proxies that your company uses. If you are using a web scraping proxy that accumulates costly delays in updating relevant data, that loss of time gets passed off to your customers. And the longer this continues, the more likely it is that your clients will seek out other services for their web scraping needs.

An AIOHTTP Tutorial for Web Scraping Proxy Partners

An AIOHTTP Tutorial for Web Scraping Proxy Partners

Many businesses that rely on web scraping are unhappy with their current web scraping proxies for just this reason. You already know the benefits of getting fast and comprehensive web-scrapped data to your clients in your own business. However, you also know that individual delays with your data can accumulate over time. Maybe you’ve had to turn to complicated, multi-layered solutions to try and fix these web-scraping delays. Of course, a single web-scraping solution that could handle all of your business’s web-scraping needs would be ideal. But, given the stakes of fast and accurate data, you need to know that you can rely on any proxy partner before placing your trust in it.

Therefore, it’s a good idea to understand how the best web scraping proxy partners can mitigate the problems that cause costly delays and inefficiency in the web scraping process. If a web scraping proxy can use novel techniques in Python programming to achieve significantly greater speed when scraping the web, this partner would provide you with the best web data for your and your clients.

That’s where AIOHTTP Python code and the AIOHTTP library come in. The AIOHTTP Python program is an HTTP client/server for the AsyncIO library, which allows for asynchronous programming in the Python language. In web scraping, most lost time stems from the time a function spends awaiting a response from the server. AIOHTTP Python and AsyncIO are excellent resources for mitigating this common cause of delays in web scraping. Therefore, a web scraping proxy partner that utilizes AIOHTTP Python coding for asynchronous web scraping could solve any issues you may have with previous proxies and provide you with quick, efficient, and accurate web data. For an in-depth AIOHTTP tutorial, read on to learn about the benefits of AIOHTTP Python functions and the AIOHTTP library for web scraping, and how Rayobyte uses these functions to provide the best web scraping proxies on the market today.

Python Asynchronous and Concurrent Codes

Python Asynchronous and Concurrent Codes

To understand how Python asynchronous coding works, you should first understand how “concurrent” processing works in Python. Concurrency is common in most applications, to the point where it is often misunderstood. In general terms, concurrent programming occurs when a single processor, or CPU, completes multiple tasks simultaneously. However, the reality is a bit more complicated than this.

The limits of individual processors

A single processor is only capable of completing a single task at one given increment of time. Despite this, processors are usually asked to complete multiple tasks at once. The way that a CPU gets around this is through “concurrency.” In general, a CPU will jump back and forth between different tasks in such a way that it appears as if the two tasks are being completed simultaneously.

When a CPU is effectively using concurrency to complete multiple tasks, it may appear to the human eye as if these tasks are happening at the same time. But at the level of milliseconds at which computers operate, the tasks are broken down into the smallest components and completed one at a time. The CPU can jump back and forth between tasks and subtasks so efficiently that, to us, there’s no discernible difference between this quick jumping back and forth and actual simultaneous task completion.

Threading in Python

When concurrently working through multiple tasks, a CPU will organize them into what’s known as “threads.” A “thread” is essentially the smallest subset of individual tasks that a CPU can complete in a single increment of time. By organizing these tasks into threads, the CPU can achieve the most efficient organization of how the tasks will be completed. Thanks to this concurrent processing of threads, the CPU does not need to wait for one task to complete before beginning another. Rather, it can send a request for a particular task and, while awaiting a response, begin work on a separate task instead of wasting what would otherwise be functionless computing time.

Concurrency in web scraping

Concurrency is essential for fast and efficient processing when it comes to things like web scraping. This is because web scraping involves a wide variety of tasks that a CPU must complete over a single timespan. While most computers used in web scraping will have multiple CPUs that can process multiple tasks parallelly, each CPU in the system will still need to process several different tasks concurrently. Further, since the biggest loss of CPU time in web scraping comes from the time spent awaiting responses for a server, concurrency allows these CPUs to move on to other tasks while they await this response.

While the amount of time made up in a single switch from one task to another may be a small fraction, if only a whole second, this time can accumulate rapidly over the entire web scraping process. Thanks to concurrency, the result will be much faster data returns from the proxy. This speed and efficiency will then roll over as benefits for your customers.

Synchronous vs Asynchronous Python AsyncIO Requests

Synchronous vs Asynchronous Python AsyncIO Requests

The capacity for CPUs to process tasks concurrently has allowed for the development of Python asynchronous code. Asynchronous code is code that can facilitate concurrent processing within a single CPU. The distinction between synchronous and asynchronous code — and of synchronous and asynchronous processing — is essential to understanding how AsyncIO and AIOHTTP Python can provide significant improvements to web scraping operations.

In a general sense, the word “synchronous” refers to two or more events that occur at the same time. “Asynchronous,” on the other hand, refers to events that do not occur at the same time. These definitions are somewhat broad, and can generally refer to events that are unrelated to each other but which occur coincidentally within a single timespan, or outside of a single timespan.

Synchronicity as the interdependence of tasks

However, in the context of asynchronous computer processing, “synchronous” and “asynchronous” have more specific definitions. For these processes, “synchronous” events can be understood not only as events that occur at the same time, but whose function is dependent upon the functioning of the other within that timespan. That is, one function within a synchronous process cannot be completed until certain tasks within the other function are completed.

An example of synchronous conversations

If this seems a bit confusing, take a look at this example from day-to-day life. Suppose that you are sitting at a café having a conversation with someone over coffee. This person, let’s assume, is an old friend whom you haven’t seen in a while, and you would like to use this coffee date as a way of catching up and finding out what he/she has been up to over the past year or so. In computing terms, you might say that you need to complete the overall task of “catching up with a friend over coffee.”

For this, both you and your friend need to perform certain subtasks within this overall process. Specifically, you need to ask your friend a question about her life over the past year. Your friend, in turn, needs to respond and then perform the next task of asking you a question. You then need to perform the task of responding to that question, and so on.

In computing terms, these tasks are “synchronous,” not only because both of your tasks occur at the same time, but because they are dependent on each other to function. When your friend performs the task of asking you a question, you need to perform the task of “answering” before you can perform the task of asking a question of your own. You will then need to wait for your friend to perform the task of answering that question before you can ask another question. In short, neither you nor your friend can complete your subtasks without waiting for the completion of a reciprocal task from the other. This is why this process is “synchronous.”

Imagine if you attempted an in-person conversation asynchronously. Your friend would ask you a question about what you did over the summer. Instead of responding as soon as she finishes the question, you ignore her for a time and do some other task. Maybe you take out your phone and check your email, maybe you get some more coffee, maybe you start reading a book, etc. You leave your friend hanging like that for several minutes while you do other things instead of completing your task of responding to her.

You, of course, cannot do this. (Well, technically you can, but not without violating the normal rules of in-person conversation and coming across as rude or weird). This is why in-person conversations like this are synchronous; you cannot perform each subtask in the process without waiting for your conversation partner to complete their tasks in response.

Asynchronous communication

Now, let’s look at another form of communication. Instead of meeting your friend face-to-face over coffee, maybe you two could not coordinate your schedules and decided to communicate via email instead. You send your friend an email asking her about her life over the past year, what she’s been doing, how her family is, how her job’s going, and so on. In this case, you perform the individual subtasks of “asking your friend about her life” all at once.

But, more importantly, social rules do not prohibit you from beginning other tasks while you await your friend’s response. You would probably go about your day, complete errands, get something to eat, do some work, or possibly just browse the internet or watch TV. You would occasionally check your email to see if your friend has gotten back to you, but before she does you are not prohibited by any social rules from beginning other tasks unrelated to catching up with your friend.

For this reason, communication via email is an “asynchronous” process. Not only are the times you and your friend send your respective emails separate from one another, but neither of you is prevented from beginning other tasks while you await the completion of the response task to each email. The tasks you complete in these time frames do not require the completion of individual subtasks for their completion.

In computer processing, synchronous and asynchronous processes work the same way. When a process is synchronous, no function in that process can be completed without certain subtasks also being completed. When a process is asynchronous, tasks can complete independently of each other, and a CPU can move from one task to another while awaiting the completion of the first task.

Blocking vs non-blocking code

Asynchronous coding is also called “ non-blocking code” for this reason. You can understand the principles of a synchronous process in terms of any task being “blocked” by a task that has not yet been completed. To go back to the analogy of a conversation with your friend, if you are talking to your friend face-to-face over coffee, and she asks you a question, you cannot (without appearing rude or careless) complete any other tasks until you complete the task of responding to her. In this process, your friend’s task of asking you a question has “blocked” you from performing any other task until you respond. However, if your friend has sent you an email, you are not blocked from performing any other tasks while she awaits your response.

In synchronous code, new tasks are blocked from beginning execution by other tasks that have not yet been completed. In asynchronous code, however, new tasks are not blocked by other tasks that are not yet completed. Asynchronous code can run effectively while not blocking other code from performing its tasks.

Python Asynchronous vs Synchronous Web Scraping

Python Asynchronous vs Synchronous Web Scraping

Asynchronous coding provides an innovative path toward much greater speed and efficiency in web scraping. Recall that the greatest source of costly delays in the web scraping process is when programs have to wait for replies from other servers. If individual tasks are blocked from completion by another task awaiting a server reply, these delays can turn into significant failures of time efficiency throughout the entire process.

Web scraping, as you are already aware, is a complex process that requires multiple threads of tasks occurring concurrently. This is especially true in the business world, where web scraping usually means getting data from hundreds, or possibly thousands, of websites all at once. The more websites you need to scrape for data, the more possibilities that a single task would be delayed by a non-responsive server at that website’s end.

Web scraping delays caused by synchronous code

In the nightmarish world in which you could only run a web scraping application synchronously, you would need to wait for the server on each website to respond individually before the program can scrape any other site. Even if the delay from the server is only for a second or two, this would result in far too much time passing before you get adequate data from every website. Even if your scraping proxy is working with multiple processors, synchronous coding will still result in costly delays on your end. These delays would, of course, be passed off to the clients who are relying on you for a quick turnaround time in getting their web data. Too many such delays and your clients will soon be looking for new web scraping services.

Therefore, asynchronous coding is essential for efficient time management for a web scraping program or API. To get results as quickly as possible, each code needs to operate independently of other codes to complete its tasks. While one function in the program may expend time waiting for a response from a server, the other functions need not wait for that one to complete its task before beginning and completing theirs. For web scraping programs to minimize the amount of time it takes to get meaningful data back, the code in question will need a mechanism for utilizing the time it takes for one task to hear back from a server, instead of letting that entire processor go dormant during that time. The tools afforded by asynchronous coding in Python are essential for efficient web scraping as a whole.

Asynchronous tools in synchronous processes

Let’s go back to the analogy of a conversation with your friend over drinks. The general rules of social interaction block you from engaging in other tasks while your friend awaits your response to a question that she asked. But though the rules of social interaction are synchronous in this sense (meaning that tasks are blocked from completion by other tasks still awaiting completion), it is still extremely useful if you had asynchronous tools at hand in these kinds of situations.

Imagine that, during your conversation over coffee, you ask your friend if she has seen any good movies lately. However, before she can respond, your friend’s phone begins to ring. Your friend sees that this is an important call, and apologetically asks if she can interrupt your conversation by taking the call right then. Being a good friend, you of course tell her it’s not a problem. She then proceeds to spend several minutes completing a separate task of talking on the phone.

While she is on the phone, you are still awaiting a response to your question about the movies she’s seen recently. However, while she is engaged, you need not be prevented from completing other tasks by this lag in response. Indeed, it would look somewhat strange and awkward if you spent the entire timeframe of your friend’s phone conversation doing absolutely nothing, instead merely sitting there with your hands at your side, staring blankly ahead.

You could check your email on your phone, get another coffee, use the restroom, or complete any number of other tasks unrelated to your conversation with your friend. Though the rules of social interaction are based on the synchronous blocking of tasks, you have asynchronous tools at your disposal to use if something delays the required response to a particular task within a conversation.

Asynchronous tools in Python

Python-based web scraping operates much in the same way. Similar to the rules of social interaction, the Python programming language is designed around synchronous functions. In basic Python coding, each line of code is blocked by other codes until those codes are completed. However, if Python only operated within a synchronous framework, then efficient web scraping with Python would be impossible. But thanks to the AsyncIO and AIOHTTP library, web scrapers have excellent tools for performing asynchronous functions within Python. This allows Python web scrapers to collect, organize, and return data much more quickly than they would if they could only operate in a synchronous format.

Asynchronous Web-Scraping with the AsyncIO and AIOHTTP Library

Asynchronous Web-Scraping with the AsyncIO and AIOHTTP Library

The AsyncIO library

The AsyncIO library, now available for Python, provides web scrapers with new, useful tools for reducing web scraping time and increasing efficiency. AsyncIO is a Python library that provides asynchronous codes for Python applications. By using the AsyncIO AIOHTTP library, Python programmers can code asynchronous functions into the normally synchronous Python coding language.

The AsyncIO library contains numerous modules that are highly useful for performing asynchronous web scraping. However, two main keywords warrant particular mention in this capacity: “async” and “await.” These two keywords form the basis for the main benefit of the AsyncIO library, which is forming “coroutines.”

To understand what a coroutine is, look back at the basic differences between a synchronous and an asynchronous process. In a synchronous process, a single function is not independent of other functions within the program. Each function, or task, within the program is blocked from completion by other, unresolved functions. If one function is not completed, other functions within the program end up being “oh hold” for an indefinite period while the unresolved function awaits response or completion.

For a code to operate asynchronously, each function in a program must have the capacity to suspend its execution before it reaches “return.” By suspending its execution, the function can hand off control to other functions. These functions, therefore, will no longer be blocked by the initial function being on hold as it awaits execution. In this sense, these functions will move from “synchronous” to “asynchronous,” as they are no longer dependent on the completion of another function within the program.


This is the basic definition of a “coroutine” in the AsyncIO library. By using the AsyncIO keywords of “async” and “await,” Python code can arrange respective functions into coroutines that could operate without being blocked by other functions. Instead of the processor remaining idle during the execution process of one function, these keywords allow the processor to switch back and forth between tasks with maximum efficiency, moving to complete different tasks while other tasks await execution.

Event loops

Inherent in these asynchronous processes from the AsyncIO library is something called an “event loop.” In the simplest terms, an event loop is a process within an asynchronous Python code that monitors each task and takes note of what is idle and what is moving toward completion. In doing this, the event loop allows the code to switch back and forth between idle and active tasks in the most efficient manner.

How do asynchronous tools work?

To better understand this, let’s go back to the analogy of a conversation with your friend. Recall that your friend had to take an important phone call before she could respond to your question. Now, your task of “listening to your friend’s response” is idle as you wait for her to finish her call. As you have asynchronous tools at your disposal, you can use this idle time to complete another task.

However, as you still want to complete your conversation with your friend, you periodically check back with her to see if she has finished her phone conversation. Once you see that she has completed her conversation, you move from the task of “checking your email on your phone” to your earlier task of “listening to your friend’s response to your question about movies.”

This basic ability to keep tabs on an idle task while working asynchronously on another is equivalent to an event loop in AsyncIO’s asynchronous programming. Using this type of event loop, not only can the Python asynchronous coding switch back and forth between different tasks in a coroutine, but it can work through each event as quickly and efficiently as possible.

This has significant implications for web scraping in particular. Not only does effective web scraping require multiple tasks running concurrently, but with frequent communication with different websites, there is a much higher chance of one particular task being held up by a slow or unresponsive server. Thanks to the coroutines and event loops from the AsyncIO library, an asynchronous web scraping program can use what would otherwise be an idle time when one task is awaiting a server for the completion of additional tasks. This efficiency allows web scraping proxies to provide comprehensive data much more quickly than they would without these tools.

AIOHTTP Tutorial

AIOHTTP Tutorial

When working with AsyncIO for web scraping programs, it is particularly useful to utilize AIOHTTP. AIOHTTP is an HTTP client and server for AsyncIO that allows you to program asynchronous HTTP AsyncIO requests to different servers. Since web scraping involves significant HTTP Python requests AsyncIO functions for a large number of websites, the ability to program these AsyncIO requests asynchronously through AIOHTTP Python is the most significant tool for cutting down the time it takes a web scraping program to return data.

AIOHTTP Python HTTP AsyncIO requests

Thanks to AIOHTTP Python, a web scraping proxy can arrange all HTTP requests to different websites in an asynchronous format. This development in Python code resolves the biggest source of hassle and inefficiency when it comes to web scraping. Under an AIOHTTP Python client/server, a web scraping program can maintain a comprehensive event loop for all HTTP AsyncIO requests that it sends to the group of websites it is scraping.

AIOHTTP event loops

By using this event loop, the program can maintain tabs on which functions are receiving quick feedback from their respective sites and which ones are still awaiting a response. Rather than blocking subsequent HTTP requests, AIOHTTP Python allows the program to quickly switch to a different request as one request awaits a response from a server. Because of this capacity for asynchronous HTTP requestions, AIOHTTP Python can reduce web scraping return times from twenty seconds or more to less than a second. And even seemingly small amounts of time saved can turn into greater financial returns on your business end.

Importing “async” and “await” functions

Take a look at this example of a Python code that uses the “async” and “await” functions from the AsyncIO library:

#!/usr/bin/env python3


import asyncio

async def count():


await asyncio.sleep(1)


async def main():

await asyncio.gather(count(), count(), count())

if __name__ == “__main__”:

import time

s = time.perf_counter()

elapsed = time.perf_counter() – s

print(f”{__file__} executed in {elapsed:0.2f} seconds.”)

On the surface, this may seem like a normal line of Python code. But with the asynchronous tools provided by AsyncIO, the “await asyncio.sleep(1)” function takes place of what would normally be a basic “sleep” function in synchronous Python code. During a sleep function, a task will “sleep” while it awaits completion, wasting valuable processor time that could be spent on other tasks within the program.

But thanks to the new asynchronous tools provided by AsyncIO, the “await asyncio.sleep(1)” instead creates an event loop that allows the task to sleep and passes the processor function to another task within the program. In doing so, the “async” and “await” functions allow the tasks in a given program to organize into coroutines that can pass processing time back and forth between each other via an event loop. With these functions in place, the total time it takes the tasks to complete is shaved down to several seconds compared to the synchronous application.

Better APIs with AIOHTTP Python Programming

Better APIs with AIOHTTP Python Programming

Another major benefit of the AIOHTTP Python library in AsyncIO is its extensive offerings of state-of-the-art APIs that can be used for more efficient web scraping functions. AsyncIO offers both high- and low-level APIs for asynchronous operations within Python. With access to these APIs, an AIOHTTP Python web scraping program can further minimize the most common causes of costly delays and inefficiency in data retrieval.

High-level AIOHTTP Python APIs

  • AIOHTTP Python coroutine runners, which allow you to run Python code concurrently and control their execution
  • Subprocess controls
  • Queues that help you distribute tasks more efficiently
  • Synchronization primitives that help you synchronize concurrent code
  • Streams featuring await/async primitives that work with network connections to perform network IO and IPC

Low-level AIOHTTP Python APIs

  • Event loops that provide asynchronous APIs for networking, subprocess running, and OS signal handling
  • Future objects that bridge low-level callback code with high-level await/async code
  • Transports and protocols that utilize callback-based programming to implement high-performance network or IPC protocols
  • Event loop policies that create new loops and set current event loops
  • AsyncIO module platform support
  • AsyncIO extending tasks that write custom event loop classes

More Efficient Python Web-Scraping with AIOHTTP and AsyncIO

More Efficient Python Web-Scraping with AIOHTTP and AsyncIO

As you’ve already seen, the reason web scraping becomes such a headache for businesses is because of the sheer number of complex moving parts that a web scraping program needs to juggle for getting results back on time and in an easily digestible format. You’ve now explored how the AIOHTTP Python client/server can minimize the lags in server response times when paired with the AsyncIO library.

These lags are the most common source of costly delays in web scraping operations. But for an asynchronous web scraping program to complete its tasks in the most time-and cost-efficient manner possible, it must balance additional tasks while it manages server requests. Asynchronous python code through AsyncIO allows a web scraping program to manage these tasks while providing comprehensive data quickly and efficiently.

With the asynchronous codes and APIs available from AsyncIO, and the asynchronous HTTP clients and servers available from AIOHTTP Python HTTP, web scraping operations can not only coordinate data scrapes from hundreds of websites at once, but also organize the data into a convenient format such as a CSV_reader file, as per different industry and client needs.

Using AsyncIO commands for a CSV_reader

Suppose, for example, you wish to gather product prices from several commercial websites and then organize them in a CSV_reader file. You would not only need to coordinate the multiple URLs for the websites you want to scrape, but you also need the program to identify the relevant price data from each website, collect it, and then arrange it in a coherent format in a CSV_reader file.

Using the asynchronous AIOHTTP Python code available from AsyncIO, a web scraping proxy can import the following commands:

  • import asyncio
  • import csv
  • import json
  • import time
  • import aiohttp

With these commands in place, the web scraping program can now arrange all of its numerous tasks into an efficient event loop, in which the numerous coroutines can execute their respective tasks without being blocked from tasks still awaiting completion. Without this blocking, the process will save valuable processor time by not waiting for a single task to complete. This time saved will, of course, translate to money saved on your end.

Suppose that your web scraping proxy needs to gather price data from two different commercial websites. One Python request gets a response from the server quickly, but the other python requests wait for page to load before it can gather data. If the thread gathering data from the responsive website needs to wait for the other thread to get a response from its website’s server, the entire process will be put on hold. But with asynchronous coding through AsyncIO, the first thread can use the downtime to collect its data and import it into the CSV_reader file. Thanks to AsyncIO and AIOHTTP Python coding functions, the process of scraping hundreds of websites at once can be completed in a fraction of a second.


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The Best Asynchronous Python Web Scraping Proxies with Rayobyte

The Best Asynchronous Python Web Scraping Proxies with Rayobyte

As a business professional who depends on effective web scraping to do your job, you likely often find yourself stuck between a rock and a hard place. On one hand, you depend on your web scraping proxy partners for your business needs, but on the other hand, any delays or inefficiency on your proxy’s end will inevitably get passed off to you, and ultimately, to your clients. Finding the best web scraping proxy is essential, but without a clear understanding of how effective web scraping works, it can be hard to know which proxy to choose.

AsyncIO and AIOHTTP Tutorial

By using innovative asynchronous Python coding available from AsyncIO and AIOHTTP, Rayobyte provides business partners with only the best web scraping solutions. Rayobyte’s web scraping proxies can cut down precious seconds, or even minutes, from web scraping functions thanks to these and other innovations with AIOHTTP Python code. If you would like to learn more about how Rayobyte uses asynchronous code to improve web scraping proxies, you can visit our blog for in-depth AIOHTTP tutorials or Python AsyncIO tutorials. Otherwise, you can browse our proxy options for the web proxy solutions for you.

Rayobyte offers both data center proxies and residential proxies, which offer ideal solutions for the web scraping needs of our business clients. If you run a business that requires web scraping data to serve your clients but is unhappy with your current proxy partner, get in touch with Rayobyte today to see how our AsyncIO and AIOHTTP Python offerings can provide the best web scraping proxy applications for you.

The information contained within this article, including information posted by official staff, guest-submitted material, message board postings, or other third-party material is presented solely for the purposes of education and furtherance of the knowledge of the reader. All trademarks used in this publication are hereby acknowledged as the property of their respective owners.

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