Concurrency vs. Parallelism: Key Differences and When To Use Each

TL;DR

Concurrency manages multiple tasks by rapidly switching between them, whereas parallelism uses multiple processors to execute tasks simultaneously. Choosing the right model depends on your resources and the program's workload (I/O-bound, CPU-bound, or both).

Why concurrency vs. parallelism matters

When your program completes processes one after the other, it spends a bulk of its time idle: waiting for network responses, disk writes, etc. For small-scale projects, this inefficiency can be negligible. However, it becomes a bottleneck as you scale.

Concurrency and parallelism are critical aspects of modern computing; therefore, understanding the difference between them isn't limited to software developers. It's equally important to data engineers and anyone building automated systems. A project that misunderstands these concepts risks directly impacting system architecture, resource management, and overall infrastructure cost.

Nowhere is this more apparent than in web scraping and data pipelines. Scrapers mostly handle thousands of network requests, which are I/O (Input/Output) bound, meaning that the program's performance relies on external resources, such as waiting for HTTP responses. Similarly, data pipelines involve transformation, processing, and storage, which are a mix of I/O-bound and CPU-bound tasks. Knowing the difference between concurrency and parallelism, and when to use either or both, is the difference between efficiently scraping 10,000 URLs and crawling to a halt.

What is concurrency?

Concurrency is the ability to handle multiple tasks during overlapping time periods – not necessarily at the same instant. 

Think of it as a librarian with a truckload of books to shelve across 3 aisles. He/she places a fixed number of books on aisle 1, then does the same to aisle 2, then aisle 3, and repeats this workflow till the end. Each aisle makes progress at overlapping time periods, even though only one aisle receives books at any given time. 

The same applies to a concurrent single-CPU core. It interleaves between tasks, working on a slice of each one, and then moving to the next when a task blocks or the operating system schedules a different task. This process, also known as rapid context switching, occurs thousands of times per second, giving the illusion that processes are running simultaneously, but in reality, only one task runs at any given time.

Threading and asynchronous programming are two common approaches to initiating concurrency. While both mechanisms aim to manage and execute tasks efficiently, they differ in implementation and use case. 

Threads are the smallest unit of a process that can run independently. They allow for multiple sequence executions within that process. A simple way to think of it is as a kitchen with multiple chefs. The kitchen is the process, and the chefs are the executable entity. All the chefs share the same resources (the same pot, counter, ingredients, etc.). Each chef takes turns cooking the same meal, and the meal finishes faster because multiple "threads are running concurrently."

Asynchronous programming is a model that uses non-blocking operations, event loops, and callback functions to pause long-running tasks (such as network requests) while continuing other work, rather than blocking and waiting. Returning to the kitchen-chef analogy, instead of hiring multiple chefs, one hardworking chef can chop vegetables, put water on the stove, and then proceed to prep the sauce all at the same time, rather than stand idle, waiting. When water boils, the chef goes back and continues. 

In a nutshell, concurrency is effectively managing independently executable entities or processes at once. However, as the number of processes increases, the time it takes to regain CPU access also increases, and this affects performance.

What is parallelism?

Parallelism is the actual simultaneous execution of tasks across multiple processing units. Unlike concurrency, where a single core alternates between tasks, parallelism involves individual cores handling different tasks independently at the same time. 

If concurrency is 1 librarian shelving books on 3 aisles, parallelism is 3 librarians, one per aisle, performing the same operation simultaneously. Parallelism equals more hardware requirements.

In large computations, parallel programming can split a single task into independent subtasks. The goal isn't merely to run multiple tasks at the same time but also to maximise throughput and computational speed.

A modern computing environment, which includes multicore processors, GPU computing, and distributed systems, enables parallelism at scale. However, more hardware doesn't always result in better performance. For example, adding more librarians only helps if you also include more carts to move the books to respective aisles or more library ladders to reach the top shelf. Each worker must be completely independent for parallelism to be effective.

Key differences between concurrency and parallelism

Both concurrency and parallelism aim to improve performance. However, they do so differently and must be used for the right types of tasks for positive results. Here's a breakdown of the key differences between the two.

Conceptual differences

Rob Pike, one of the creators of the Go programming language, gave an almost perfect conceptual distinction between the two mechanisms. In his talk, "Concurrency Is Not Parallelism," he described concurrency as dealing with a lot of things at once, and parallelism as actually executing multiple things simultaneously.

From his definition, you can see that both concepts are related but distinctive in their approaches. Concurrency is about structure – designing a program to handle multiple tasks at once. Each task makes progress in overlapping time periods, but isn't executed simultaneously. Parallelism, on the other hand, is about execution – running multiple processes, which may or may not be related, at the same time, across multiple processing units. 

Hardware requirements

While concurrency isn't limited to single-core systems, it only requires a single processing unit. On the other hand, parallelism requires hardware with multiple cores. Some cases may even involve machines with more than one processor or distributed systems, which allows you to split computational workloads across different machines. 

Task handling

Concurrency handles tasks by rapidly context-switching between them in overlapping time periods, and only one task actually runs at a time. In parallelism, processes run simultaneously and don't alternate between tasks, since each runs independently on separate processing units.

Primary use cases

Concurrency is most effective for I/O-bound and high-latency operations, such as network requests, database calls, and file operations, where programs depend on external resources. 

Since parallelism executes multiple tasks at the same time, it's ideal for CPU-bound tasks where the program's performance depends on the processor's speed rather than I/O responses. High-CPU-usage tasks, such as data processing, mathematical computation, image analysis, and so on, can improve performance with parallelism.

Debugging complexity

Both concurrent and parallel programs introduce higher levels of debugging complexity compared to sequential programs. When multiple unit processes interact, especially through shared state, as is the case in both mechanisms, execution order is no longer predictable. The program's behavior, instead, depends on timing and operating system scheduling. This can introduce race conditions and synchronization challenges as multiple threads can access shared resources.

In concurrent systems, common issues such as deadlocks, livelocks, and starvation often arise from uncoordinated communication among processes or tasks. Deadlocks occur when multiple threads wait indefinitely for each other to release processing resources. Starvation occurs when some threads never get a chance to run because other threads "monopolize" resources. In a livelock, every thread is working but not making any progress.

Since parallel systems execute tasks simultaneously on separate cores, multiple threads can access shared memory at the same time. This can lead to data races, where threads can read and modify data inconsistently because there's no synchronization that mandates order between processes.

The four combinations: Concurrent, parallel, both, or neither

Concurrency and parallelism aren't two sides of the same coin. An efficient system can be concurrent, parallel, neither, or both, depending on your project type and needs.

Let's break down these four possible states:

1. Concurrent but not parallel

In a purely concurrent system, multiple tasks make progress through interleaving on a single CPU. This rapid context switching creates the illusion of running processes simultaneously, but in reality, each task receives processing resources one at a time. 

A good example is a single-core machine running a web scraper that sends requests to multiple URLs using async I/O. While waiting for one response, it initiates another request. 

To help you better understand, below is an asyncio-based scraper (script) that handles 50 concurrent HTTP requests on a single core.

# pip install aiohttp
import asyncio
import aiohttp
import time
import threading

URL = "https://httpbin.org/delay/1"  
# this endpoint waits 1 second before responding

async def fetch(session, request_id):
    print(f"Request {request_id} started")
    # send asynchronous HTTP request.
    async with session.get(URL) as response:
        # await the response body 
        data = await response.text()

    print(f"Request {request_id} finished with status {response.status}")
    return data

async def main():
    print("Running on thread:", threading.current_thread().name)

    start_time = time.time()
    
    # create a shared HTTP session 
    async with aiohttp.ClientSession() as session:
        # create 50 requests and schedule them immediately
        tasks = [
            asyncio.create_task(fetch(session, i))
            for i in range(1, 51)
        ]
        # wait for all 50 tasks to complete concurrently
        await asyncio.gather(*tasks)

    end_time = time.time()
    print(f"\nTotal execution time: {end_time - start_time:.2f} seconds")

if __name__ == "__main__":
    asyncio.run(main())

# pip install aiohttp
import asyncio
import aiohttp
import time
import threading

URL = "https://httpbin.org/delay/1"  
# this endpoint waits 1 second before responding

async def fetch(session, request_id):
    print(f"Request {request_id} started")
    # send asynchronous HTTP request.
    async with session.get(URL) as response:
        # await the response body 
        data = await response.text()

    print(f"Request {request_id} finished with status {response.status}")
    return data

async def main():
    print("Running on thread:", threading.current_thread().name)

    start_time = time.time()
    
    # create a shared HTTP session 
    async with aiohttp.ClientSession() as session:
        # create 50 requests and schedule them immediately
        tasks = [
            asyncio.create_task(fetch(session, i))
            for i in range(1, 51)
        ]
        # wait for all 50 tasks to complete concurrently
        await asyncio.gather(*tasks)

    end_time = time.time()
    print(f"\nTotal execution time: {end_time - start_time:.2f} seconds")

if __name__ == "__main__":
    asyncio.run(main())

This program makes 50 concurrent requests to an endpoint that waits 1 second before responding. This means that sequentially processing the same 50 requests will take approximately 50 seconds. However, the code above uses aiohttp (a Python library) to make asynchronous requests, allowing other requests to run while waiting for a response. Therefore, the 50 seconds are reduced to just about 2-3 seconds.

2. Parallel but not concurrent

Unlike concurrent systems, parallel systems split tasks into sub-tasks that can run independently on separate cores. Each sub-task is distributed across multiple processing units and runs simultaneously. A data pipeline that splits a 10GB CSV file into chunks, processes each chunk on a separate core, and then combines results is a perfect example of a parallel system. 

To put things in a web scraping context, below is a script that uses multiprocessing to parse HTML from 1,000 already-downloaded files simultaneously.

import os
import time
from multiprocessing import Pool, cpu_count

# pip install beautifulsoup4
from bs4 import BeautifulSoup

# directory containing already-downloaded HTML files
HTML_DIR = "html_files"


def parse_html(file_path):
    # read the HTML file content
    with open(file_path, "r", encoding="utf-8") as f:
        content = f.read()

    # parse HTML and extract title
    soup = BeautifulSoup(content, "html.parser")

    title = soup.title.string if soup.title else "No Title"

    return (file_path, title)


def main():
    start_time = time.time()

    # get all HTML file paths
    files = [
        os.path.join(HTML_DIR, filename)
        for filename in os.listdir(HTML_DIR)
        if filename.endswith(".html")
    ]

    print(f"Found {len(files)} files")
    print(f"Using {cpu_count()} CPU cores")

    # create a pool of worker processes equal to CPU core count
    with Pool(processes=cpu_count()) as pool:

        # distribute files across processes in parallel
        results = pool.map(parse_html, files)

    end_time = time.time()

    print(f"\nParsed {len(results)} files in {end_time - start_time:.2f} seconds")

    # output
    for file_path, title in results[:5]:
        print(f"{file_path} → {title}")


if __name__ == "__main__":
    main()

import os
import time
from multiprocessing import Pool, cpu_count

# pip install beautifulsoup4
from bs4 import BeautifulSoup

# directory containing already-downloaded HTML files
HTML_DIR = "html_files"


def parse_html(file_path):
    # read the HTML file content
    with open(file_path, "r", encoding="utf-8") as f:
        content = f.read()

    # parse HTML and extract title
    soup = BeautifulSoup(content, "html.parser")

    title = soup.title.string if soup.title else "No Title"

    return (file_path, title)


def main():
    start_time = time.time()

    # get all HTML file paths
    files = [
        os.path.join(HTML_DIR, filename)
        for filename in os.listdir(HTML_DIR)
        if filename.endswith(".html")
    ]

    print(f"Found {len(files)} files")
    print(f"Using {cpu_count()} CPU cores")

    # create a pool of worker processes equal to CPU core count
    with Pool(processes=cpu_count()) as pool:

        # distribute files across processes in parallel
        results = pool.map(parse_html, files)

    end_time = time.time()

    print(f"\nParsed {len(results)} files in {end_time - start_time:.2f} seconds")

    # output
    for file_path, title in results[:5]:
        print(f"{file_path} → {title}")


if __name__ == "__main__":
    main()

This script creates a pool of worker processes based on the number of your CPU cores and distributes files across them in parallel. 

3. Both concurrent and parallel (Parallel concurrent execution)

In a parallel concurrent system, multiple tasks run simultaneously on separate cores, and each core manages sub-tasks by interleaving between them. While this model is the most complex of the four, it makes for the most efficient and scalable systems. A good example is a distributed scraping system where multiple worker nodes each run async scrapers.

Below is a script showing 4 worker processes, each running 50 concurrent async requests, yielding 200 effective concurrent connections:

import asyncio

# pip install aiohttp
import aiohttp
from multiprocessing import Process

URL = "https://httpbin.org/delay/1"
REQUESTS_PER_WORKER = 50
NUM_WORKERS = 4

async def fetch(session, worker_id, request_id):
    # perform an asynchronous GET request
    async with session.get(URL) as response:
        # wait for the response body to be read
        await response.text()

    # log the completion with the current Process ID
    print(
        f"Worker {worker_id} | "
        f"Request {request_id} completed"
    )

async def worker_async(worker_id):
    # create a shared HTTP session
    async with aiohttp.ClientSession() as session:
        # create a list of 50 task objects
        tasks = [
            asyncio.create_task(fetch(session, worker_id, i))
            for i in range(REQUESTS_PER_WORKER)
        ]

        # run all tasks concurrently and wait for them to finish
        await asyncio.gather(*tasks)

def worker_process(worker_id):
    # start the asyncio event loop for this specific process
    asyncio.run(worker_async(worker_id))

def main():
    processes = []

    # spawn 4 independent OS processes
    for i in range(NUM_WORKERS):
        p = Process(target=worker_process, args=(i,))
        p.start() # begin process execution
        processes.append(p)

    # wait for all 4 processes to exit before continuing
    for p in processes:
        p.join()

    print("\nAll workers completed.")

if __name__ == "__main__":
    main()

import asyncio

# pip install aiohttp
import aiohttp
from multiprocessing import Process

URL = "https://httpbin.org/delay/1"
REQUESTS_PER_WORKER = 50
NUM_WORKERS = 4

async def fetch(session, worker_id, request_id):
    # perform an asynchronous GET request
    async with session.get(URL) as response:
        # wait for the response body to be read
        await response.text()

    # log the completion with the current Process ID
    print(
        f"Worker {worker_id} | "
        f"Request {request_id} completed"
    )

async def worker_async(worker_id):
    # create a shared HTTP session
    async with aiohttp.ClientSession() as session:
        # create a list of 50 task objects
        tasks = [
            asyncio.create_task(fetch(session, worker_id, i))
            for i in range(REQUESTS_PER_WORKER)
        ]

        # run all tasks concurrently and wait for them to finish
        await asyncio.gather(*tasks)

def worker_process(worker_id):
    # start the asyncio event loop for this specific process
    asyncio.run(worker_async(worker_id))

def main():
    processes = []

    # spawn 4 independent OS processes
    for i in range(NUM_WORKERS):
        p = Process(target=worker_process, args=(i,))
        p.start() # begin process execution
        processes.append(p)

    # wait for all 4 processes to exit before continuing
    for p in processes:
        p.join()

    print("\nAll workers completed.")

if __name__ == "__main__":
    main()

This code spawns 4 independent worker processes, each running its own asynchronous event loop that handles 50 concurrent HTTP requests.

4. Neither concurrent nor parallel (sequential execution) 

Sequential execution is your conventional program processing model, where one task completes before the next begins. This is often the simplest to implement but can also be the slowest, depending on your use case. A basic for loop making HTTP requests one at a time is a perfect example of sequential processing.

Below is a basic scraper using requests.get() in a loop without any optimization.

# pip install requests
import requests
import time


URL = "https://httpbin.org/delay/1"
TOTAL_REQUESTS = 10  


def main():
    start_time = time.time()

    for i in range(TOTAL_REQUESTS):
        print(f"Request {i} started")

        # make a get request to the URL (this will block until the response is received)
        response = requests.get(URL)

        print(f"Request {i} finished with status {response.status_code}")

    end_time = time.time()

    print(f"\nTotal execution time: {end_time - start_time:.2f} seconds")


if __name__ == "__main__":
    main()

# pip install requests
import requests
import time


URL = "https://httpbin.org/delay/1"
TOTAL_REQUESTS = 10  


def main():
    start_time = time.time()

    for i in range(TOTAL_REQUESTS):
        print(f"Request {i} started")

        # make a get request to the URL (this will block until the response is received)
        response = requests.get(URL)

        print(f"Request {i} finished with status {response.status_code}")

    end_time = time.time()

    print(f"\nTotal execution time: {end_time - start_time:.2f} seconds")


if __name__ == "__main__":
    main()

When to use concurrency vs parallelism

The choice between concurrency and parallelism depends on your project's needs: the type of tasks your program intends to run and the best design to maximize performance. 

I/O-bound vs CPU-bound

If your program spends most of its time waiting for external resources, such as network responses, disk writes, or user input, use concurrency because it allows your program to run other coroutines while waiting. 

Similarly, if your program spends most of its time computing, processing, calculating, transforming, or performing any CPU-bound task, use parallelism. This approach distributes tasks across multiple cores to speed up performance.

Web scraping decision tree 

The fundamental question in the previous subsection translates to web scraping. If you're primarily making HTTP requests, use concurrency. Mechanisms such as asynchronous scraping and threading allow your scraper to process other requests while waiting for a network response. 

Are you parsing or processing large data sets? Parallelism (multiprocessing) is the right approach because it can split large datasets into chunks and distribute them across multiple cores, each running simultaneously. 

That said, if you're doing large-scale web scraping, which mostly involves high-volume requests and large data sets at scale, a combination of concurrency and parallelism is the best model for maximum performance. 

Is your task simple and small-scale? Then no need to complicate things. Sequential processing will yield your desired result.

Resource management considerations 

It's also important to consider resource and system constraints. Running numerous tasks in parallel is memory-intensive, while too many concurrent requests can hit server, OS, or proxy pool limits. For web scrapers, excessive concurrent requests can trigger anti-bot measures that'll most likely block your script. 

System design implications

From a design perspective, concurrency can add complexity when shared state isn't managed properly, while parallelism requires coordinated communication between processes. All in all, both require careful error handling. 

Common limitations and challenges 

When building efficient, scalable systems, the challenge isn't only in choosing the best model (concurrency vs. parallelism); it's also about dealing with runtime, network, and system limitations. 

Below are realistic challenges developers face when implementing concurrent and parallel systems. 

Technical limitations

One major technical limitation is Python's Global Interpreter Lock (GIL), which prevents parallel execution of threads by ensuring that only one thread can run at a time. This means that you'll need multiprocessing; while this, in and of itself, can improve performance, spawning multiple processes is memory-intensive. 

Keep in mind, more concurrency doesn't always mean more speed. Your program can reach a point of diminishing returns where adding more threads or workers does nothing to improve performance, but rather incurs overhead.

To bypass Python's GIL, use multiprocessing for CPU-bound work and keep threads for I/O operations. Also, add concurrency only when it positively impacts performance to avoid unnecessary overhead. 

Anti-bot and rate-limiting challenges

Generally, modern websites employ anti-bot and rate-limiting measures to regulate traffic and also protect data and resources. Sending excessive concurrent requests can quickly trigger these measures, most of which respond with often impossible CAPTCHA challenges. Even with techniques like proxy rotation, advanced defenses, such as session-based detection and fingerprinting techniques, can track and deny your requests. 

To mitigate this challenge, keep concurrency under control to avoid triggering anti-bot defenses. Adding delays between requests can also improve your chances of avoiding detection. For the best result, use high-quality rotating proxies, such as Decodo's residential proxies, which distribute requests across real IPs to simulate actual user traffic. 

Infrastructure challenges

Infrastructure can also introduce challenges, particularly at scale. For example, operating systems impose limits on open sockets and file descriptors, and high concurrency can quickly exhaust this limit. If you scale faster than your proxy pool can rotate, you exhaust your pool and risk triggering anti-bot defenses. Long-running concurrent processes can also result in memory leaks, which can impact performance. 

To mitigate infrastructure challenges, start conservative, scale gradually, and continuously monitor resources. 

Final thoughts

Concurrency and parallelism are core aspects of how operating systems work, how multi-threaded applications are built, and how effective scalable models are designed. Understanding the differences between the 2 concepts and when to use each can determine the success of your project.  Concurrency is best for I/O-bound operations, while CPU-bound tasks benefit from parallelism. In some cases (I/O-bound plus CPU-bound tasks at scale), combining both models will maximize performance.  Keep in mind that running multiple operations in parallel is memory-intensive, and excessive concurrency often triggers anti-bot systems.