Concurrency vs Parallelism: Key Differences Explained

Automation tools, web servers, and databases must perform efficiently well even under high loads. Modern software systems are expected to function optimally so two techniques: concurrency and parallelism, are used for efficient resource distribution.

This article will focus on the difference between parallelism and concurrency, their practical use case, and which method works better for web scraping.

What is Concurrency?

It is a technique where several processes share computing resources, and is an organizational method of task execution where all processes do not run at the same time.

The essence of concurrency is to boost system responsiveness by switching tasks frequently. For instance, when a server is attending to multifarious requests, he is not executing the requests simultaneously; in fact, he is switching and modifying them swiftly to create a perception of parallel execution, and what is also called a faulty parallelism.

In the following cases, concurrency is incredibly useful:

• Managing numerous network connections: a server, for instance, has the capability to serve thousands of users concurrently.
• Manipulation of background tasks: data-loading or message processing without causing a halt in the central program logic.
• Process management in operating systems: a single computer can execute multiple programs simultaneously, but each needs to get processor time sequentially.

Having thought through: what is concurrency, it appears that this method is executed with the use of multithreading. We will elaborate on the process of how it occurs.

Threads in Concurrency

A thread is a separate sequence of command execution within a process. In concurrent systems, a single process may have several threads, which take turns utilizing the processor’s time, thus competing for its resources.

Let’s picture the following scenario:

1. The program sends a request over a network and sits idle until it receives a response.
2. The processor used does not stay idle. Switches to another thread, for instance, to process some data.
3. When the response for the network request is received, the thread picks up where it left off.

So, it allows the system to redistribute computing resources efficiently rather than sit idle when waiting for a reply.

Example

Concurrency is widely adopted in the design of server applications, automated data processing systems, and for systems which support multitasking.

Imagine that a web server accepts a thousand requests every second.

• Without concurrency: the server can only work on one request at a time, thus the other requests will be put on hold until the server is free.
• With concurrency: the server is able to rapidly switch from one request to another, thus at any given moment, a number of clients can be served simultaneously.

All of these features enable the handling of a very high number of connections per second even with a small number of processor cores available, which is better than what can be achieved with parallel processing. In this situation, there is no need to create individual threads or processes for every single request, which helps in saving memory and reducing the load on the CPU.

Cloud platforms, data collection systems, and other databases make use of concurrency. The business databases for concurrent execution of multiple SQL queries, data collection systems for the concurrent sending of HTTP requests, and cloud platforms for the distribution of computing power.

Before we get to understanding the difference between parallelism and concurrency, let us check next how performance can be impacted by concurrency.

Optimizing with Concurrency

Optimization and proper management through the use of concurrency has the ability to achieve tremendous results in system efficacy and efficiency.

How it improves performance:

• Downtime is minimized, as the processor does not have to wait for a specific operation to finish to switch to another one.
• System load is reduced, as all pre-allocated resources do not need to be separated for each specific task.
• Provision of scalability is guaranteed as hundreds and thousands of simultaneous connections can be managed without losing efficiency.

There are several primary techniques that enable concurrency functionality:

• Use of asynchronous programming means to carry out specific actions without requiring the utilization of the main thread.
• Effective management of threads where the number of context switches is reduced.
• Load balancing — splitting operation between threads relatively evenly.

As an example, in web scraping frameworks, asynchronous HTTP requests enable fetching of data from a number of pages at the same time without allocating a thread to each task. This leads to much less work for the processor and increases the speed of operations.

What is Parallelism?

Parallelism short definition is a modern approach in computing in which a given task is carried out in parts and each part is accomplished using a distinct processor, core or machine. In parallelism, unlike concurrency, there is no turn taking of resources; everything is executed at once. The strategy makes it possible to actually parallel concurrent execution, leading to a reduction in time spent greatly.

Most effective use of parallelism is in computations where the work is particularly geared towards a specific goal and can be partitioned into segments.

To achieve parallelism in the execution of tasks, a number of techniques are employed:

• Multi-core processors, where each task is allocated a separate core;
• Distributed computing, where data is worked on at the same time on several servers;
• Parallel threads, where codes are simultaneously executed on several threads and those threads are shared among the available cores or processors.

If in parallel systems there is operation sharing, there is optimal use of computing resources unlike concurrent systems which strive for optimal use of the processor available.

Example

In the case where large volumes of data require processing in minimum possible time, the need to operate these parallel threads becomes of great importance.

Let’s assume you are to extract information from 10,000 web pages. Now let us analyze the difference between parallelism and concurrency using this example.

• Concurrent approach. The script retrieves one page at a time, downloads the data then proceeds to the next page. This approach is time consuming because it necessitates waiting for a response from every site.
• Parallelism example. The system assigns a subdivided 10,000 queries to 10 servers, each of them downloads 1000 pages at the same time. This approach increases the speed by ten times as the pages are being fetched simultaneously.

In the generation of reports, parallel processing is widely utilized. Take for instance composing a financial report, where data retrieval and processing from client databases, transactions, and budgets is done parallelly across several threads. This approach is efficient since the user can access the final document in a short period.

Tasks that can be segmented into independent units that can be processed separately benefit a lot from parallelism.

Speeding Up with Parallelism

So, why use Parallelism? Evidently, the primary merit of it stems from its ability to drastically cut down the time of task completion through subdivision.

How it speeds up workflow:

• Division of a task into parts: It can be done independently or sequentially but classifying it in blocks simplifies the task.
• Load balancing: A single core or server does not perform every task. Each segment is processed individually.
• Reintegration of Results: Once the processes are done, all the gathered data is combined to form results.

For instance, if it takes 1 second to process a single web page:

• Single thread mode, 10 thousand pages would take 10,000 seconds to complete.
• In multi threaded mode with 10 threads enabled, it would complete in 1,000 seconds.
• On a hundred machines on a distributed system, it would only take 100 seconds.

The tasks most suitable for parallelism are:

• For computation intensive tasks, take image or video processing for instance.
• For high load systems such as multithreaded transaction processing.
• Cloud services such as querying databases with distributed servers.

As with most things, it has to be said that partitioning is not always a viable option. When one parts’ execution is dependent on another part, parallel execution can make implementation difficult, if not impossible, and create additional complications.

Concurrency vs Parallelism: Comparison

The methods of concurrency and parallelism are two diverse types of multitasking, each of which has a particular impact on resource allocation and efficiency.

Let's try to analyze the difference between parallelism and concurrency in the table below.

Many systems appear to have adopted a hybrid model, which accommodates both concurrency and parallelism, even though the gap between the two is quite vast. In the following section, we will look at some examples and the benefits of a hybrid model.

Best Approach for Web Scraping

Automated data scraping from web pages is termed as web scraping. The understanding of difference between parallelism and concurrency, depending on the available features and characteristics, helps achieve the optimal function of web scraping.

Choose the concurrent model when:

• You want to send numerous HTTP requests at once, but the pages load at different speeds.
• External factors like network latency or APIs are restricting speed.
• There is a need for a reduction in resource usage without overworking the CPU.

For instance, consider multi-threaded scraping with asynchronous requests (such as the Python aiohttp library). This enables you to issue subsequent requests instantly rather than waiting for responses from the desired server.

Why do we refer to parallelism when discussing the same set of activities:

• There’s a need to quickly analyze massive data sets — for instance, extracting data from millions of web pages.
• The workload can be split across multiple servers or processor cores.
• Each sub-division of the task is self-contained and can be done at the same time as other subtasks.

Example: multi-threading scrapers where each scraper is assigned its own server and is responsible for processing a specific list of web pages.

Both techniques are common in web scraping. For instance, in the case of super busy web services, thousands of network connections are managed simultaneously using concurrency with fast context switching between requests. Parallelism is used to carry out computations that require a lot of resources, such as data processing, or report generation.

A hybrid approach includes both: parallel processing or division of a task into several threads that can be executed simultaneously, and asynchronous requests or non-blocking execution of code instructions within a thread.

How it works:

1. Parallelism. Let’s say a program is capturing data from several webpages. Instead of executing the process sequentially, it opens four threads (equal to the number of processor cores), and each thread fetches a page while disregarding what the others are doing.
2. Through asynchrony concurrency. Each thread actively sends further requests instead of simply waiting for the server response. This helps in making the best possible use of the available resources and avoids idle times.
3. Load balancing. Other threads will keep working if one of the tasks, such as a complex calculation or a slow server, consumes more time.

The hybrid form of individual concurrency and parallelism with a single or a few dominant threads is the most common in the highly loaded systems, like server requests processing, page rendering and big data estimating. Now, you understand the difference between parallelism and concurrency in the context of web scraping and can make proper decisions.

Summary

The most suitable choice between concurrency and parallelism rests with the issue surrounded by the targeted task and provided resources. In case of many simultaneous queries where every fragment of the work should be completed at once, concurrency stands to take charge in this scenario. It contributes to ensuring that the system continues to serve which minimizes processor idle time.

On the other hand, parallelism becomes more attractive when dealing with resource thirsty cell computations where work is executed on more than one core or processor simultaneously. It offers great savings on the time to perform complicated operations but leads to a need for a multithreading environment or multiple processors.

While selecting a method, it is pertinent to keep in consideration the expected outcome and the bounds of the system as well as understanding the difference between parallelism and concurrency. In cases where resources are constrained, concurrency may be more beneficial due to better load balancing. If the focus is on speed of computation, then speedup is maximized with parallelism, where work is divided.

Usually, the optimal solution is reached with some mix of these two strategies.