Concurrency and parallelism are commonly encountered concepts, especially with respect to IT. In data parsing, they help, among other things, to maximize performance and system effectiveness. Though these terms are often employed in synonymous contexts, they simultaneously refer to different methods of efficiently processing large volumes of data. Therefore, both terms are equally relevant for the purpose of constructing scalable systems for data collection and analysis. In this article, we consider concurrency vs parallelism, their differences, and examples of their use in data parsing.
In parsing, concurrency refers to the performance of multiple tasks in a time overlapping fashion within a single processor’s time slice. This means that although only one task can be executed at a time, to the outside systems, it appears that many tasks are being executed simultaneously. In other words, each task execution competes for resources from the processor but is not able to use them all at the same time.
In particular, the strategy works best in I/O bound concurrency that includes any kind of operation in which the program either sends or receives data from another device. One of these tasks is web scraping. Concurrency enables a scraper to issue multiple requests at the same time rather than waiting for one to be fulfilled before the next one is issued. In this case, productivity increases as the time needed for requests to be completed decreases.
Important to note, sometimes concurrency might be defined as what is called – faulty parallelism, but in the following blocks we will break through it.
Threads serve as the primary building blocks of concurrently running data-collection systems. In order to grasp the full extent of how threads operate, it is necessary to look closely at the definition of a process. A working process contains a number of activities, each of which is accomplished by a single thread. So, a thread may be considered as the smallest indivisible unit of a computer’s work.
From a concurrency perspective, many threads can run at roughly the same time. This enables the system to make the most of the available resources of the processors.
Modern programming languages and operating systems allow managing threads: creating, suspending, and synchronizing them. Threads are especially preferred in tasks involving data parsing because they make it possible to deal with several data streams simultaneously, which reduces waiting times and increases efficiency.
In practical terms, concurrency is a component of nearly all contemporary software systems. This feature makes it possible to run a great number of processes with small resources. A textbook example of concurrency is serving several requests at the same time on the web server.
To illustrate, let’s assume an online marketplace where a user can place orders, look for products, and check the status of their orders all at once. The server literally cannot handle all requests at the same time because of the limited number of processors. However, it is able to perform concurrency which allows it to share time with tasks by switching between user requests. For example, one user can place an order, and a different user can request product information. The server can execute these two processes in a cyclic fashion rather than waiting for the first to finish before starting the second. Because of that, systems’ responsiveness is greatly improved, and the system appears to be executing tasks in parallel.
A different example in respect to web scraping is if a user wants to collect data from 100 web pages. Without concurrency, downloading follows a wait-request-process cycle which will take an unnecessarily long time. With concurrency, however, the user could send, say, 10 requests at once, and then while the first pages are loading, already process the data that has already been received. Compared to waiting for each individual page to load, this saves a lot more time.
When it comes to web parsing, employing concurrency can boost efficiency. For example, a web scraper can deploy concurrency in order to fetch data from several web pages at the same time, thereby shortening the total time needed to collect information. Below are a few points on how concurrency helps in reducing time for processes:
Furthermore, concurrency is applied for information processing in a manner that does not block the main execution thread, so that the computer can be used without any decline in performance.
Parallelism short definition refers to accomplishing a specific set of computational processes on different computational resources simultaneously. In parallelism, resources are used at the same time as opposed to in concurrency where resources are just alternated and a simulation of simultaneous execution takes place. To put it differently, parallelism is the simultaneous processing of data on several CPU cores or even on several servers.
Let’s go further and consider: why use parallelism. The benefits include the following:
Modern multi-core processors enable a split of tasks for independent and simultaneous parallel concurrent execution.
Parallel execution of tasks reduces computational time and involves breaking a computation into chunks. The system processes can be executed in a nonsynchronous fashion as the workload is split across different processors and cores.
In modern parallelism an example may be observed in image processing. Suppose a specific filter has to be applied to a high-resolution image. If we were to process every pixel one by one, it would take an unreasonable amount of time to finish. However, with the use of parallelism, the image can be divided into multiple parts and each processor does its part at the same time. This enables maximal incrementation in the speed of the application and enhances its performance. It is also well known that this approach is used in AI information processing as well as in video games.
While developing software systems, one should learn the specific timing of using concurrency as opposed to parallelism and understand the relationship between these techniques and performance. Here are the two most important points to understand:
Below is a table that visually illustrates the points of difference between parallelism and concurrency:
| Criterion | Parallelism | Concurrency |
|---|---|---|
| Task execution | Simultaneous | Alternating |
| Resource management | Multiple processors/cores | One processor |
| Performance | Speeds up execution | Enhances responsiveness |
| Task type | Computation-intensive | I/O operations |
To sum up, each approach has its advantages, requiring selection based on the specific system needs. Under limited computing power, concurrency assists in making efficient use of resources, while parallelism helps in speeding up the operations by segmenting the load across various processors.
Even though concurrency vs parallelism can be studied as separate phenomena, their fusion is often extraordinarily productive. In systems with complex applications needing high responsiveness, their combination is very important as it greatly improves overall efficiency. A combined approach enables optimal computing resource use and accelerates data processing.
An example of such an approach would be the processing of a large data set. In this case parallelism deals with the splitting of tasks to multiple processors, while concurrency controls the processes on each processor.
Benefits of combining these methods include but are not limited to:
The combination of these techniques allows the design of very powerful and scalable systems in the field of large information processing and heavy-duty computing tasks.
When extracting information from websites, it is the user’s activities that determine whether they prefer concurrency or parallelism, or, indeed, none of the two. In reality, a concurrent approach is only practical when there is a prolonged idle time in the issuing of requests, or when the web scraping is not very CPU intensive. The opposite is true for parallelism, which is useful for situations where there is a high degree of post-visit processing of the page content, or there is significant overload of the processor due to the parsing.
A combination of strategies can be pursued, which optimally would be implementing concurrency in sending asynchronous requests, and parallelism in processing them. The latter has one main benefit: you are able to visit pages exhaustively and process the information with an equally high intensity.
This write-up has analyzed in detail concurrency vs parallelism, describing how each one can operate in different circumstances and situations. Concurrency refers to a set of techniques for managing performing activities by switching between them to maximize the efficiency of the use of processor time available. Parallelism means doing more than one thing at the same time, such as using multiple processors or multiple cores of a single processor. The key distinguishing feature of these strategies stems from the fact that in concurrency, some resources are idle while being spent, whilst parallelism optimizes available resources by redistributing them.
As we see it, the best choice of approach relies on the peculiarities of the problem: concurrency is preferable for asynchronous tasks, while parallelism is more suitable for complex calculations. In some situations, combining the two yields the best outcome.
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