Concurrency vs. Parallelism: How To Use Them To Accelerate Web Scraping

Scraping Robot
December 7, 2022
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Concurrency and parallelism are both used with multi-threaded programs — programs that can handle multiple requests at the same time. However, concurrency and parallelism are otherwise distinct.

Table of Contents

 

Read on to learn more about concurrent vs. parallel execution in Python and how to increase web scraping speed through concurrency and parallelism. We’ll also cover how Scraping Robot can help you scrape more effectively and efficiently.

What Is Concurrency in Programming?

learn about concurrency

Concurrency is when multiple tasks start, run, and complete within the same time frame, in no specific order. Another term for concurrent is asynchronous.

For computers with only one CPU, applications may not be able to make progress on more than one task at exactly the same time. However, the applications can switch between tasks so fast that they look like they’re multitasking.

You can achieve concurrency in Python via threading, a powerful way to create and manage threads. Threads are small sets of tasks that can be used and managed by the operating system without any dependencies on each other.

Types of concurrency

There are three levels of concurrency:

Low-level concurrency

This concurrent processing level features explicit use of atomic operations — sequences of instructions that guarantee updates of shared single variables and accesses to memory regions. Developers should not use this type of concurrency for creating applications since it is incredibly error-prone and hard to debug. Python and other programming languages do not support low-level concurrency.

Mid-level concurrency

Mid-level concurrency does not feature the use of explicit atomic operations. Instead, it uses explicit locks, which means only one person can access, read, or write the file at a given time. Python and other coding languages support mid-level concurrency. Many application programmers use mid-level concurrency.

High-level concurrency 

High-level concurrency does not use explicit atomic operations or explicit locks. Programmers can use the Python concurrent.futures module to support this type of concurrency.

Pros and cons of concurrency

As with all things, concurrency offers advantages and disadvantages.

Pros:

  • The ability to run multiple applications concurrently: Concurrency enables a CPU to run multiple applications at the same time.
  • Higher average response time: Without concurrency, a CPU must run each application to completion before running the next one.
  • Improved resource utilization: Concurrency lets resources that are unused by an application be used for other applications.

Cons: 

  • Requires additional overhead and complexities: Concurrency may require coordinating multiple applications via additional mechanisms. Accordingly, you may have to invest in additional performance complexities and overheads.
  • May lead to subpar performance: Running too many tasks concurrently may lead to mediocre performance.

What Is Parallelism in Python?

learn about parallelism in python

Now that you know what concurrency is let’s define parallelism.

Parallelism involves splitting tasks into subtasks that can be processed at the same time. Unlike concurrent execution, parallel execution uses multiple CPUs to execute more than one process in parallel or synchronously. You can perform parallel execution in Python by using multitasking.

However, parallel execution is not the same as parallel concurrent execution. The former features different CPUs performing processes simultaneously, but the latter executes threads on the same CPU concurrently.

Pros and cons of parallelism

Like concurrency, parallelism has pros and cons:

Pros:

  • Efficient code execution: Parallel computing is much faster than traditional serial computing, which executes instructions one after the other with no overlap.
  • Cost savings: Running your code more efficiently means cost savings. You can go through big data sets faster than ever.
  • The ability to solve more complex problems: Increased efficiency lets you bring and use more resources to the table. This, in turn, allows you to solve complex problems faster and better.

Cons:

  • Difficult to learn: Executing parallel architecture can be difficult, especially for beginners.
  • Extra costs: Parallelization may require extra costs due to data transfers, communication, synchronization, increased power consumption, and thread destruction and creation.
  • May result in distributed denial of service (DDoS): You may encounter an involuntary DDoS if you send too many requests to a small site.

What Is The Difference Between Concurrency and Parallelism?

difference between concurrency and parallelism

To recap, here are the main differences between parallelism vs. concurrency:

Parameter Concurrency Parallelism
Definition Concurrency is when two or more tasks happen in the same time frame in no specific order. Parallelism is when two or more tasks or subtasks literally run at the same time on hardware with multiple CPUs.
How to use it in Python Concurrency is achieved in Python through threading. Parallelism is achieved in Python through multitasking.
Is it about interruptions or isolation? Concurrency is about interruptions — resources that alert or interrupt the CPU to the fact that something has occurred so it can attend to it. Parallelism is about isolation — the property that a task can access shared data without other tasks’ interference.
Number of CPU cores required Concurrency only requires one CPU core. Parallelism requires more than one CPU core.
Use cases
  • Cyber-physical and embedded systems like microwaves
  • Microcontrollers
  • Smartphones
  • ILLIAC IV
  • Desktops and laptops
  • The most powerful computer on earth, the American Summit
  • Bitcoin
  • Internet of Things (IoT) devices

Now that you understand the differences between concurrency and parallelism, note that an application can be:

Concurrent but not parallel

A program can be concurrent but not parallel when it processes more than one task simultaneously without dividing the tasks into subtasks.

Parallel but not concurrent

A program can be parallel but not concurrent when it only works on a single task at a time and divides the tasks into subtasks that are processed in parallel.

Both parallel and concurrent

An application is parallel and concurrent when it works on multiple tasks simultaneously and divides the task into subtasks for parallel execution.

Neither parallel nor concurrent

A program is neither parallel nor concurrent when it only works on one task at a time and does not divide the task into subtasks.

How Concurrent Processing and Parallelism Can Increase Web Scraping Speed

importance of concurrency and parallelism

Software developers aren’t the only people who use concurrency and parallelism — small business owners and marketing professionals can also use the processes to increase web scraping speed.

Web scraping is when users extract data from websites into readable spreadsheets. Although it’s possible to perform web scraping manually, most business owners and marketers use web scrapers to accelerate the process. Specialized tools for quickly and accurately extracting data from the internet, web scrapers use intelligent automation to fetch thousands or even billions of data points. Specifically, scrapers parse webpages’ HTML elements to give you the desired data.

Unfortunately, web scrapers don’t always work as fast as they should, especially when you have millions or billions of target web pages. Here’s how you can use Python concurrency and parallelism to boost web scraping speed:

How to accelerate the web scraping process with Python concurrency

Follow these steps to use Python concurrency for your web scraper:

  1. Install Python 3 if you don’t have it already. Then, use pip install to install all required libraries.
  2. Run asyncio, a Python library for executing asynchronous frameworks.
  3. Import requests from BeautifulSoup and define your extraction details, including the name, identification, price, and URL of the pages you want to scrape.
  4. Define asyncio functions and how many concurrent instances you want to run.
  5. If your scraping speed isn’t fast enough, consider limiting the number of concurrent requests against a single domain. You can use Semaphore to set maximum concurrency requests per instance.

How to accelerate the web scraping process with Python parallelism

Follow these steps to use Python parallelism for your web scraper:

  1. Install Python 3 and use pip install to install all required libraries.
  2. Import Pool from multiprocessing. Pool creates multiple Python processes in the background and distributes your computations across multiple CPU cores. Meanwhile, multiprocessing supports spawning processes through an application programming interface (API) similar to the threading module. The multiprocessing package offers both remote and local concurrency.
  3. Import requests from BeautifulSoup and define your extraction details, including the name, identification, price, and URL of the pages you want to scrape.
  4. Initialize Pool.
  5. Call the scrape function and map function scrape with all_urls.

Accelerate Your Web Scraping By Using Scraping Robot

increase web scraping speed and accuracy using Scraping Robot

Concurrency vs. parallelism in Python scraping can be time-consuming and exhausting, especially if you have limited scraping and programming expertise.

That’s where Scraping Robot comes in. A user-friendly scraping solution for beginners and pros, Scraping Robot empowers you to scrape a wide range of sites and data, including real-time data, dynamic sites, and big data. We also help you handle all the headaches that come with scraping, such as proxy management and rotation, browser scalability, server management, staying on top of new anti-scraping updates from target websites, and CAPTCHA solving.

Scraping Robot is also incredibly affordable. A free account will give you 5,000 scrapes per month and access to all of our features, including seven-day storage, 24/6/365 customer support, and frequent module and improvement updates. If you want more than 5,000 scrapes per month, you can sign up for the following tiers:

  • Business tier: This tier offers up to 500,000 scrapes per month at only $0.0018 per scrape.
  • Enterprise tier: This tier offers over 500,000 scrapes per month, with each scrape being as low as $0.00045 per scrape. You also get access to custom API requests.

Interested in exceeding the speed of concurrency vs. parallelism scraping in Python? Create a Scraping Robot account today.

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