Concurrency vs. Parallelism: What You Need To Know For Effective Web Scraping
Web scraping has become one of the most powerful tools for businesses of all sizes. Everyone from Chief Technology Officers in large software companies to small business owners to entrepreneurs can benefit from the quantifiable data provided by an effective web scraping strategy. After all, accessing large quantities of relevant data in short periods can provide essential information on a particular market or offer potential customers a major asset that can help grow your business and attract new clients.
However, anyone whose business requires effective web scraping also knows the downsides of inefficient web scraping. In today’s busy world, the quantities of data necessary for a business to grow and excel are increasing exponentially. This means that an effective web scraping program must process millions of scrapes daily on just one website.
Each scrape must acquire vast quantities of data and balance this data against the data coming in from every other website. Not only that, but the web scraper must then organize all incoming data, sort it into a coherent schema, and convey it in an easy-to-digest interface.
The sheer size of data scraped from the web, alongside the complexity of managing and organizing all of that data, means that an inefficient web scraping program can often lead to disastrous results. Inefficient web scraping will take up far too much time and resources and may require extensive manual data adjustment. This waste of time and resources can cause your company to rapidly fall behind competitors who can perform web scraping operations much more effectively.
Fortunately, you can often improve your web scraping results through a more thorough understanding of the qualities of efficient web scraping. A major component of the best web scraping applications is a successful balance of concurrency and parallelism, both within a single processor and between multiple processors.
This is especially true when working with Python, which remains one of the more popular and effective programming languages for web scraping. Read on to better understand concurrency vs. parallelism in Python and how partnering with Rayobyte can provide the most efficient and effective web scraping solutions for your business needs.
Concurrent vs. Parallel Processing in Python
At a glance, the terms “concurrency” and “parallelism” may seem like they refer to the same thing. After all, two events that are “concurrent” happen at the same time. Therefore, events that occur concurrently also occur “parallel” to one another. In the world of programming, the difference is also somewhat murky. Both concurrent and parallel programming involves processing multiple tasks simultaneously to complete complicated, multitask operations (such as web scraping) more efficiently.
However, there are important differences between concurrency and parallelism, especially when it comes to programming effective web scrapes. This is especially true when working with a programming language such as Python. After all, Python-based web scraping (and related programs) do not just require both concurrent and parallel programming. These applications must effectively coordinate both concurrency and parallelism to gather vast amounts of data, coherently organize them, and present them in a workable interface for your business or client needs. Understanding concurrent vs. parallel programming in Python, in terms of their similarities and differences, is important both for programming effective web scraping applications in Python and finding the best web scraping proxy partners.
What Is Concurrency in Programming?
The limits of a CPU
In the simplest terms, concurrent processing is the act of an application managing two or more tasks at the same time. But to get a more comprehensive understanding of how concurrency works, it’s important to understand the functions and limitations of a single processing core. A Central Processing Unit, or CPU, is a complex electronic circuit conduit that executes tasks within a computer program. Every computer will have at least one CPU for executing necessary tasks. Web scraping, therefore, requires at least one CPU.
However, a single CPU can only perform one specific task at a single point in time. Despite this, most applications require individual CPUs to perform multiple tasks over a given increment. These tasks can either be unrelated or, as is the case in web scraping, related to a single desired outcome.
Synchronous vs. asynchronous task management
There are two ways a single CPU can perform multiple tasks. The simplest way is to perform the tasks “synchronously.” In other words, it will perform each task to completion in sequential order, without going back and forth between different tasks. The CPU would perform that task entirely without pausing and resuming it later. Once complete, and only then, it will move on to the second task in the sequence.
This synchronous process may seem straightforward. However, the major disadvantage of performing tasks in this manner is, of course, inefficiency. If a CPU could not manage multiple tasks at once and instead needed to perform them synchronously in a single sequential order, completing all of the tasks would take much, much longer. If this were applied to a highly complex series of tasks, such as in web scraping, the time lost waiting for the CPU to perform each task would far exceed your business’s real-world deadline to acquire the necessary data from the web. This will, of course, equal resources and money lost and ultimately result in your initial investment in a web scraping proxy losing all of its worth.
Fortunately for you, individual CPUs do not need to process multiple tasks in a single, synchronous order. Modern CPUs can manage multiple tasks at once, switching back and forth between each in quick succession. While the CPU cannot perform multiple tasks simultaneously, it can switch back and forth between multiple tasks so quickly and efficiently that, to human perception, it is performing the tasks at more or less the same time. This is known as “asynchronous” processing.
Concurrent applications: an analogy
Concurrency is the effective and efficient asynchronous management of multiple tasks by a single CPU. In the simplest terms, concurrency in computer processing is the management of two or more tasks over a discrete length of time by a single CPU or processing core. While the CPU cannot perform these tasks at the same time, it can switch back and forth so quickly that the tasks appear to resolve in concurrence.
Let’s look at an analogy from your own body to understand this better. Think of your own eyes as representing a single CPU. In your day-to-day life, your eyes must perform several different tasks so that you can function at a base level of capacity. For example, your eyes must perform the task of seeing. They must take in visible light from the surrounding world and transfer it to visual nerve signals to be sent to your brain. However, your eyes must also blink. When performing this task, your eyes must temporarily close their lids to flush out any irritants from the surface of the eyeballs.
If you look at the requirements of these two tasks, you will note that your eyes can’t perform both simultaneously. For your eyes to see, their lids must be open. But blinking requires them to close the lids to remove irritants from the eye. If your eyelids are closed, you are not taking in visible light. If your eyelids are open, you are taking in visible light but not flushing out irritants. Therefore, your eyes, as a single “processing core,” cannot perform both the tasks of seeing and blinking at the same time.
Imagine, for a moment, if your eyes needed to perform these two tasks in an inefficient synchronous manner. You would go long periods without blinking, during which times you would be in somewhat significant amounts of discomfort as your eyes become painful and inflamed at the influx of irritants. Then, when your eyes switch to the task of blinking, you would need to close your eyes for an extended period, at which point you would be effectively blind. To put it mildly, this would not be a particularly efficient way to go about your daily life.
However, in your actual daily life, this is not generally a problem. That is because your eyes can jump back and forth so quickly and efficiently between seeing and blinking that neither disrupts your overall visual functioning. Your eyes can perform the task of blinking so quickly that you rarely even notice it. Though you are not seeing in the fraction of a second in which your eyelids blink, this happens so fast that it does not affect your overall visual processing stream.
Your eyes have to perform two tasks that they cannot perform simultaneously, but they can easily switch back and forth between the two for the best dual outcome (i.e., continuing to see while also cleansing your eyes of irritants). Thus, your eyes can manage the tasks of seeing and blinking concurrently.
This is precisely how a single CPU uses concurrency to manage multiple tasks at once. Like your eyes, a CPU can switch back and forth between different tasks in a way that makes it seem like both tasks are happening at the same time. The gaps between each when the other is performed are rendered irrelevant to your practical purposes.
Concurrent processing of threads
When a CPU works through multiple tasks concurrently, it organizes these tasks into “threads.” In the context of Python programming, a thread is essentially the smallest subset of individual tasks that an operating system can manage at once. Within a thread, these individual tasks must have no dependencies on each other, meaning that the completion of one is not essential for the CPU to begin another. Organizing tasks into threads allows a CPU to switch back and forth between each in a much more efficient manner.
This efficiency is essential for web scraping, as concurrent processing allows each thread of tasks to be performed much more quickly. Without cumbersome lengths of time and excessive resource consumption, concurrent processing enables complex tasks like web scraping to occur quickly enough to meet all practical needs for you and your business and provide much-needed efficiency and cost-effectiveness.
For this reason, an important thing to look for when developing your own web scraping program, or searching for a prebuilt web scraper, is how the programming application will create and manage threads. For a programming language to be effective at concurrency, it must have excellent modules for creating and managing different threads of individual tasks. If a programming language has the necessary threading modules and can process these threads in an efficient and user-friendly manner, you will have a better chance of receiving a positive return on your initial investment.
What makes up a concurrent processing system?
As you’ve already seen, one of the main benefits of concurrency in programming is its speed and efficiency in complex operations. For this to be achieved, however, the concurrent processing system must have a few properties.
Specific rule sets
For a processing system to run through multiple tasks concurrently in a single CPU, it needs to have a few key sets of rules that allow it to navigate through what tasks it is to perform and how to best switch back and forth between them. These rules include locks, memory sharing, modifications, and so on.
For example, take a look at the issue of “race condition” in Python programming. This occurs when two different tasks attempt to access or modify a single resource at the same time. When a single task thread attempts a modification on a shared data set at the exact same time that another task thread is attempting its own modification on the data set, this will result in one of the tasks becoming idle and unable to continue through its necessary progressions.
If two task threads that need to modify a single data are being performed concurrently by a single CPU, the CPU will need a specific set of rules that regulates that access. This regulation will allow the CPU to switch each thread’s data access of modification back and forth and ensure that both threads do not attempt to modify the data at the exact same time and induce a race condition.
The CPU will often do this through something like a locking tool. In Python, a “lock” is a type of code that switches back and forth between a “lock” and “unlock” mechanism. By continuously locking and unlocking each thread, a single CPU can maintain concurrent data access and modification for both task threads without allowing them to overlap and induce a race condition state. These locking programs, modification rules, and other tools to regulate data and memory sharing allow the CPU to switch back and forth between multiple tasks and threads with the utmost efficiency.
Concurrent processing systems must often utilize multiple resources to perform multiple tasks effectively. They must also move back and forth between these resources to achieve efficient concurrency. Examples of the resources that the application may utilize are things like memory disks, printers, graphics cards, etc.
A concurrent program must be able to arrive at the “correct” outcome for each task it works through. Therefore, the program must have the desired outcome already preprogrammed into the core application. For example, a web scraping application can only function concurrently in a single CPU if it is programmed to correctly identify the specific web data it should scrape, which websites it should scrape from, and how it should organize the data.
Data, websites, and organizational schemas outside these desired parameters would be “incorrect” outcomes. The application must be programmed to avoid all incorrect outcomes such as these and arrive at only the correct outcomes (in this case, the relevant web data from specific sites).
A concurrent system must have the capacity to remain within predetermined “safe” parameters. When performing concurrent tasks, the system must be trusted to never go outside these parameters. Or in other words, never do anything “bad.”
For example, going back to the previous instance of data sharing between two task threads, the CPU must be able to maintain both tasks within parameters that exclude either task from going into an idle state due to a race condition. Recall that a race condition occurs when a task thread attempts to access or modify a shared resource at the same time as another thread.
As you’ve seen before, the CPU must have a clearly-defined set of rules that allow it to switch resource modification and data access between two or more threads without any overlapping access. However, the CPU must also have clearly-defined safety parameters that would exclude a state of race condition. This state of “safety” would allow it to avoid any “unsafe” outcome in which one or more tasks become inefficient, idle, or otherwise unable to be completed.
A concurrent program must effectively be “alive.” In other words, it must be able to consistently move toward a predetermined outcome, which regulates constant switching back and forth between different tasks.
When a concurrent system can run multiple tasks in multiple threads over a discrete period, these individual tasks are known as “actors.” The program must understand each actor in terms of a unique endpoint, allowing it to move through each task toward each endpoint in the most efficient manner possible.
Data sharing in concurrent systems
One of the biggest challenges that concurrent systems often face is sharing data between multiple tasks. This becomes relevant if there is a shared set of data that each distinct task thread must access for them to arrive at their desired outcomes. As CPUs can only perform one task at a given instant, all concurrent tasks can access the shared data set only one at a time.
If two tasks attempt to access that data set simultaneously, it can result in at least one of them getting locked out entirely. This would cause that particular task to freeze and fail to move further toward its desired outcome. If this were to happen, the speed and efficiency of the concurrent programming system would be severely compromised, which would have devastating results for a complex application such as web scraping.
For this reason, an effective concurrent programming system must have adequate locks that maintain a coherent and consistent level of access for each task. The best concurrent programming systems must utilize the lock capabilities in their respective programming languages to ensure the most efficient means of moving data access back and forth between each task without one getting locked out.
What Is Parallelism in Programming?
Now that you’ve looked at the basics of concurrency in programming languages like Python, you can better understand the related but distinct process of “parallelism.” Like concurrency, parallelism generally involves the performance of multiple tasks over a set period to arrive at the desired outcome. In a complex application like web scraping, you can also define parallelism as an essential mechanism for achieving the necessary speed and efficiency that makes web scraping worth your business’s time and money.
The key difference between concurrency and parallelism lies in the number of CPUs or processing cores involved in the application. Recall that concurrency involves multiple tasks assigned to one single CPU and that individual CPUs can perform only one task at a given time, resulting in the need for concurrent management of multiple tasks over a discrete time frame.
However, most computers and systems (especially those involved in applications like web scraping) have multiple CPUs or processing cores. Though individual CPUs can perform only one task at a given time, a system that possesses multiple CPUs can perform multiple tasks at once. When each task is arranged into coherent threads, this multiple-CPU system can manage several threads simultaneously, depending on the number of CPUs available for individual tasks. This type of system is known as “parallelism.”
Organizing and coordinating multiple processors
Parallelism, like concurrency, is essential for a complex application to function with the necessary degree of speed and efficiency. A parallel programming system within an application like web scraping must be able to manage multiple tasks across multiple CPUs without sacrificing speed and resources. However, complex applications like this still need their subset of tasks to be performed comprehensively, or the system loses that much-needed efficiency.
When it comes to parallel computing, simply letting each CPU handle separate tasks is insufficient. Rather, you will need to make use of specialized programming to manage each CPU across a set of multiple tasks effectively. Python, for example, is an excellent programming language for organizing the processing of multiple CPUs across distinct but related tasks. A good parallel program will keep the system’s processors in harmony, allowing all necessary tasks and task threads to move toward their desired outcomes in the least amount of time.
Eyes vs. ears
To better understand how parallel computing works, refer to the human body. Recall how your eyes can be understood as a single processing core, and their management of two mutually exclusive tasks is like concurrency. In that analogy, your eyes could only perform one task at once and needed to efficiently move back and forth between these tasks to minimize disruption to their practical functioning.
Consider your eyes and ears for an example of parallelism in your body. Both of these systems are analogous to two different CPUs in one machine. Here, you have two distinct tasks that need to be performed. You need to take in visible light for your brain to process as “sight,” and you also need to take in vibrations in the air for your brain to process as “sound.” You have two “processors” to accomplish this: your eyes and ears.
Since these two processors are distinct from one another, both of them can work on both tasks at the same time. Your eyes can take in visual data for you to see, and your ears can take in auditory data for you to hear. Unlike your eyes concurrently managing the tasks of “seeing” and “blinking,” both “seeing” and “hearing” can occur at the same time. This is because you are working with two separate processors.
However, to live your life efficiently, these two processing units cannot merely work through their respective tasks independently of each other. Instead, your brain must coordinate seeing and hearing to arrive at one desired outcome: a singular picture of the world comprised of multiple strands of sense data. If your eyes could see at the exact time your ears were hearing, but neither feed of sense data was organized coherently, you would be unable to function in your day-to-day life.
Consider this example. Suppose you are sitting across from another person at a table, and this person asks you a question. In this example, your eyes and ears perform two distinct tasks parallel to each other. Your ears receive the auditory vibrations from that person’s mouth and send them to your brain to be processed as what that person is saying. At the same time, your eyes are receiving the visual data that will show your brain that the other person’s mouth is moving in your direction.
However, if these two tasks were not coordinated together quickly and efficiently, you would not be able to function appropriately in this social situation. You would hear the person’s voice asking you a question and see the mouth movement on their face. But without coordination between two parallel tasks, you would not be able to arrive at the composite understanding that this particular person is speaking to you.
You would either ignore that person or respond to someone else entirely. In this analogy, your brain needs to organize and delineate the performance of each task by each processing unit parallel to the other.
What is parallelism in Python?
To this extent, parallelism in computing requires the same management of separate tasks performed at the same time. To effectively organize the parallel tasks across multiple CPUs, an application must employ special programming to coordinate each CPU in the system. Programming languages like Python are excellent tools for achieving this needed degree of parallelism, given then user-friendly nature and extensive libraries of modules available to programmers.
Parallelism is useful when working through tasks that can be broken down into subtasks. In many cases, the most efficient path toward achieving the desired outcome of a single task requires multiple subtasks performed simultaneously in an organized manner. However, this can only work with multiple CPUs since a single CPU cannot work on more than one subtask at once.
However, if you have multiple CPUs at your disposal, an effective parallel programming system can break necessary tasks into different subtasks and coordinate each subtask across multiple CPUs at the same time. This will reduce the time the system needs to complete the tasks, often significantly, and allows you to achieve the desired results more quickly and cost-effectively. In short, parallel computing is essential for multitasking when working with a particular application.
The Difference Between Concurrency and Parallelism
The previous examples should give you a clearer indication of the similarities and differences between concurrency and parallelism in programming languages such as Python. In short, concurrency involves the management of multiple tasks by a single CPU across a given period. Parallelism involves organized multitasking by more than one CPU in a given system. Concurrency manages the task-performance limitations of one CPU by allowing it to jump back and forth between multiple tasks in the most efficient manner. Parallelism organizes different CPUs to work together in performing multiple tasks or subtasks in a complex application.
Concurrency and parallelism in one application
Of course, concurrency and parallelism are not mutually exclusive when it comes to complex applications like web scraping. Web scraping requires systems that utilize both concurrency and parallelism at the same time. To better understand this, take a look at the different combinations of concurrency and parallelism that are possible in complex applications.
Exclusively concurrent applications can run only one task at a time. Therefore, these applications must find the most efficient way to switch back and forth between different tasks and subtasks. Concurrent-exclusive applications usually have just one CPU to work with.
For an application to be parallel only, it can work through one broader task over a set period. However, it can break these tasks down into different subtasks that it can then perform simultaneously.
Applications that are both concurrent and parallel
These applications can manage multiple tasks simultaneously across multiple CPUs or processing cores. At the same time, they can also allow each CPU to switch back and forth between necessary tasks or subtasks through the most efficient means, making up for the fact that the single CPU is limited in what it can do in a given instance. These applications are by far the most effective and time-saving and are usually required for complex tasks like web scraping.
Applications that are neither concurrent nor parallel
An application capable of neither concurrency nor parallelism will only be able to work on one task at a time and cannot manage multiple subtasks for a single CPU. Unsurprisingly, these applications are the least efficient and most time-consuming. For this reason, you will rarely, if ever, find them when working with complex operations such as web scraping.
Concurrency vs. Parallelism in Python
For most users, the Python programming language is an ideal means of achieving an effective balance of concurrency and parallelism. If you run a business that requires accurate web scraping results in a timely and cost-effective process, it’s a good idea to understand how a programming language like Python makes both concurrency and parallelism much more achievable in complex operations. Python’s user-friendly design, ease of learning, and extensive library of useful modules make it the ideal programming language to blend concurrency and parallelism into one complex application.
Threading in Python
Python offers the tools that either you or a web scraper will need to effectively manage both concurrency and parallelism for extensive web scraping operations. Recall that efficient concurrent programs often require complex threading of different tasks within a large application. When individual tasks are organized into discrete threads, a single CPU can more easily move back and forth between different tasks in a process that appears (to human eyes at least) that the tasks are working toward their desired outcomes at the same time.
The Python programming language is particularly useful for threading in concurrent processing because of its powerful and useful threading module. Thanks to this module, a single CPU can be programmed to manage several different tasks at once. Python’s threading module will, therefore, likely reduce the amount of time a CPU takes to perform its designated array of tasks. For a complex application like web scraping, Python’s threading module is an excellent means of achieving the necessary data results in the quickest amount of time, with the least amount of resources expended.
Multitasking in Python
On the other hand, Python also provides programmers and proxies with the necessary means of performing essential multitasking in parallel processing. Recall that effective parallelism requires complex and organized coordination between multiple processors performing separate but related tasks simultaneously. The program needs to understand the number of processors available to it, how many tasks it can delineate to each processor, and how to organize each task’s performance efficiently.
Python’s cpu_count() module allows programs to quickly determine the exact number of processors available to a given application. With this module, the application can then organize each CPU according to the necessary number of tasks that need to be performed. With this information in place, the application will then have a composite understanding of the number of specific tasks that it can perform parallel to each other at the same time. The quick and user-friendly delineation of this information makes parallel multitasking much quicker and easier for you on the user end.
Concurrent and parallel applications in Python
Thanks to Python’s effective modules, a Python-based application can apply both concurrency and parallelism to a given task. Not only that, but the application can also easily use both concurrency and parallelism at the same time. This means it can manage multiple task threads with individual CPUs and multitask several tasks across multiple CPUs simultaneously. In other words, Python allows for the most efficient use of concurrent and parallel programming and the most efficient combination of both in a single application.
Concurrency vs. Parallelism in Web Scraping: Why Rayobyte Is the Best Proxy Partner
A thorough understanding of parallelism vs. concurrency, and their importance in complex applications like web scraping, will help you find the best partners for the proxies you may need for your particular business or clientele. If you run a business that requires a high degree of web scraping, there’s a good chance that you’ve already experienced frustration with web scraping programs and proxies in the past.
If former proxy partners have let you down, you’re probably familiar with the consequences of inefficient web scraping. Things like long turnaround times, bans, and disorganization can prevent you from getting an adequate return on your initial investment in the proxy and can quickly result in your business falling behind.
But there’s good news. Rayobyte’s web scraping solutions utilize the best modules available to manage concurrent task threads and multitask with any number of different processing cores, and their proxies keep your scraping going with minimal downtime.
So, if you think that a top-notch proxy is what your business needs to get to the next level in web scraping, get in touch with Rayobyte to begin your risk-free trial today.
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