Concurrency vs Parallelism

Speed and efficiency are essential in data programming, especially when handling large-scale data extraction. Two concepts that often come into play when optimizing these processes are concurrency and parallelism. While they might sound similar, they serve distinct purposes and offer different advantages.

In this article, we'll explore the details of parallelism vs concurrency, highlight their key differences, and demonstrate how they can be leveraged in Python and JavaScript to enhance performance across various data programming tasks. Let's get started!

What is Concurrency?

Concurrency refers to the ability of a system to handle multiple tasks simultaneously by managing their execution in overlapping time periods. It's about dealing with a lot of tasks at once, allowing each task to make progress without blocking.

In the context of web scraping, concurrency enables your scraper to initiate multiple HTTP requests simultaneously, handling them as responses come in. This means your scraper doesn't have to wait for one request to finish before starting another, leading to a much faster data extraction process.

Key Takeaway: Concurrency is about efficiently managing multiple tasks by avoiding blocking, It allows tasks to overlap, especially when waiting for I/O, making better use of system resources.

For some taks concurrency can be limiting as we're only eliminating wait not actually multiplying the processes and this is where parellelism comes into play.

What is Parallelism?

Parallelism, on the other hand, involves performing multiple tasks simultaneously, typically by utilizing multiple CPU cores or processors. It's about executing multiple operations at the exact same time, which can significantly speed up computation-heavy tasks.

In web scraping, parallelism can be utilized for tasks like parsing large volumes of data or processing complex datasets. By distributing these tasks across multiple cores, you can achieve faster data processing and reduce the overall scraping time.

Key Takeaway: Parallelism is about executing multiple tasks at the same exact time, leveraging multiple CPU cores to enhance performance.

With a clear understanding of both concurrency and parallelism, it's crucial to highlight the distinctions between these two concepts to apply them effectively in your projects.

Key Differences

Understanding the distinction between concurrency vs parallelism is crucial for optimizing your web scraping strategies:

Execution Model:

• Concurrency manages multiple tasks by interleaving their execution.
• Parallelism executes multiple tasks simultaneously on different processors or cores.

Use Cases:

• Concurrency is ideal for IO-bound tasks like making HTTP requests.
• Parallelism excels in CPU-bound tasks such as data parsing and processing.

Resource Utilization:

• Concurrency optimizes resource usage by overlapping tasks.
• Parallelism maximizes computational power by distributing tasks.

Understanding these differences allows you to choose the right approach based on the nature of your web scraping tasks.

CPU-Bound Tasks vs IO-Bound Tasks

Understanding the distinction between CPU-bound and IO-bound tasks is essential when deciding whether to use concurrency or parallelism in your scraping or programming workflows:

CPU-Bound Tasks

These tasks rely heavily on processing power and computation, consuming significant CPU resources. Examples include mathematical calculations, data parsing, or image processing. Parallelism is highly effective in these cases because it distributes the workload across multiple CPU cores, speeding up task execution.

IO-Bound Tasks

These tasks are primarily waiting for input/output operations, such as making HTTP requests, reading from databases, or interacting with files. Concurrency is better suited for IO-bound tasks because it allows the system to handle multiple operations simultaneously by overlapping the waiting times, improving overall efficiency without the need for extra computational power.

By identifying whether your task is CPU-bound or IO-bound, you can apply the correct optimization approach to maximize performance.

Practical Implementation

Now that we've covered the key differences between concurrency and parallelism, it's time to put these concepts into practice. We'll explore concurrency vs parallelism with examples on how you can implement both concurrency and parallelism in Python and JavaScript to improve your web scraping tasks.

Concurrency in Python

If you want to use python with concurrency in mind, it is primarily achieved using the asyncio library or threading. Below is an example demonstrating how asyncio can handle multiple HTTP requests concurrently, showcasing significant speed improvements for IO-bound tasks.

import asyncio
import httpx
import time


# Asynchronous function to fetch the content of a URL
async def fetch(url):
    async with httpx.AsyncClient(timeout=10.0) as client:
        response = await client.get(url)
        return response.text


# Concurrently fetch multiple URLs using asyncio.gather
async def concurrent_fetch(urls):
    tasks = [fetch(url) for url in urls]
    return await asyncio.gather(*tasks)


# Synchronous version to demonstrate performance difference
def sync_fetch(urls):
    results = []
    for url in urls:
        response = httpx.get(url)
        results.append(response.text)
    return results


def run_concurrent():
    urls = ["http://httpbin.org/delay/2"] * 100  # Use the same delay for simplicity
    start_time = time.time()

    # Running fetch requests concurrently
    asyncio.run(concurrent_fetch(urls))

    duration = time.time() - start_time
    print(f"Concurrent fetch completed in {duration:.2f} seconds")


def run_sync():
    urls = ["http://httpbin.org/delay/2"] * 100  # Use the same delay for simplicity
    start_time = time.time()

    # Running fetch requests synchronously
    sync_fetch(urls)

    duration = time.time() - start_time
    print(f"Synchronous fetch completed in {duration:.2f} seconds")


if __name__ == "__main__":
    print("Running concurrent version:")
    # Concurrent fetch completed in 2.05 seconds

    run_concurrent()

    print("Running synchronous version:")
    # Synchronous fetch completed in 200.15 seconds
    run_sync()

Using asyncio for concurrent tasks, especially IO-bound tasks like HTTP requests, results in a massive speedup compared to processing the same requests synchronously.

Parallelism in Python

In Python, parallelism can be achieved using the concurrent.futures module, specifically the ProcessPoolExecutor, which allows you to run multiple processes in parallel, each utilizing separate CPU cores. Below is an example that demonstrates how to perform a CPU-intensive task, like squaring numbers, in parallel.

from concurrent.futures import ProcessPoolExecutor
import time


def fibonacci(n):
    if n <= 1:
        return n
    else:
        return fibonacci(n - 1) + fibonacci(n - 2)


def run_parallel(numbers):
    start_time = time.time()
    with ProcessPoolExecutor() as executor:
        results = executor.map(fibonacci, numbers)
    duration = time.time() - start_time
    t = f"Parallel processing completed in {duration:.2f} seconds"
    return list(results), t


def run_non_parallel(numbers):
    start_time = time.time()
    results = [fibonacci(n) for n in numbers]
    duration = time.time() - start_time
    t = f"Non-parallel processing completed in {duration:.2f} seconds"
    return results, t


if __name__ == "__main__":
    numbers = [30, 31, 32, 33, 34]  # Change these values as needed

    parallel_results, parallel_time = run_parallel(numbers)
    non_parallel_results, non_parallel_time = run_non_parallel(numbers)

    # Compare results
    print(f"Parallel results: {parallel_time}")
    # Example Output: Parallel processing completed in 1.50 seconds
    print(f"Non-parallel results: {non_parallel_time}")
    # Example Output: Non-parallel processing completed in 7.50 seconds

In this example, parallel processing speeds up the computation of Fibonacci numbers by distributing the workload across multiple processors, resulting in a substantial performance improvement compared to the sequential approach.

Concurrency in JavaScript

If you want to use javascript with concurrency in mind, it is managed using async/await and Promises. Below is an example that performs multiple HTTP requests concurrently, similar to the Python example.

const axios = require("axios");

async function fetch(url) {
  const response = await axios.get(url);
  return response.data;
}

async function concurrentFetch(urls) {
  const startTime = Date.now();
  const promises = urls.map((url) => fetch(url));
  await Promise.all(promises);
  const duration = (Date.now() - startTime) / 1000;
  console.log(`Concurrent fetch completed in ${duration.toFixed(2)} seconds`);
}

// Generate 100 URLs with varying delays (cycling between 1 to 5 seconds)
const urls = Array.from(
  { length: 100 },
  (_, i) => `http://httpbin.org/delay/${(i % 5) + 1}`
);

concurrentFetch(urls);
// Output: Concurrent fetch completed in X.XX seconds (depending on delay and execution)

Similar to the Python example, the concurrent HTTP requests complete in approximately the time of the longest single delay (5 seconds), showcasing effective concurrency in JavaScript.

Parallelism in JavaScript

In JavaScript, you can achieve parallelism using the Worker Threads module. This allows you to run CPU-intensive tasks in separate threads, taking advantage of multi-core processors. Below is an example that shows how to calculate squares of numbers in parallel.

const {
  Worker,
  isMainThread,
  parentPort,
  workerData,
} = require("worker_threads");

function fibonacci(n) {
  if (n <= 1) {
    return n;
  } else {
    return fibonacci(n - 1) + fibonacci(n - 2);
  }
}

function runNonParallel(numbers) {
  const startTime = Date.now();
  const results = numbers.map(fibonacci);
  const duration = (Date.now() - startTime) / 1000;
  const t = `Non-parallel processing completed in ${duration.toFixed(
    2
  )} seconds`;
  return t;
}

function runWorker(workerData) {
  return new Promise((resolve, reject) => {
    const worker = new Worker(__filename, { workerData });
    worker.on("message", resolve);
    worker.on("error", reject);
    worker.on("exit", (code) => {
      if (code !== 0)
        reject(new Error(`Worker stopped with exit code ${code}`));
    });
  });
}

if (isMainThread) {
  const numbers = [30, 31, 32, 33, 34]; // Change these values as needed

  const runParallel = async (numbers) => {
    const startTime = Date.now();
    const promises = numbers.map((number) => runWorker(number));
    await Promise.all(promises);
    const duration = (Date.now() - startTime) / 1000;
    const t = `Parallel processing completed in ${duration.toFixed(2)} seconds`;
    return t;
  };

  (async () => {
    const parallelResults = await runParallel(numbers);
    const nonParallelResults = runNonParallel(numbers);

    // Compare results
    console.log(parallelResults);
    console.log(nonParallelResults);
  })();
} else {
  const result = fibonacci(workerData);
  parentPort.postMessage(result);
}

The script utilizes worker_threads to run the fibonacci function in parallel for different numbers. This allows for CPU-bound tasks like calculating Fibonacci sequences to run concurrently across multiple worker threads.

Execution Times

Concurrent Fetching (I/O-bound):

• Python's concurrent fetching of five URLs with delays from 1 to 5 seconds completed in approximately 5 seconds. JavaScript's similar implementation finished in about 5.29 seconds, illustrating the efficiency of handling multiple I/O-bound tasks simultaneously.

Parallel Processing (CPU-bound):

• In Python, parallel processing for Fibonacci calculations took around 1.50 seconds, while non-parallel execution lasted about 7.50 seconds. In JavaScript, the parallel approach using Worker Threads completed in about 0.12 seconds, compared to 3.12 seconds for the non-parallel version. This demonstrates how parallelism effectively utilizes multiple CPU cores for significant time savings.

Concurrency excels in reducing wait times for I/O-bound tasks, while parallelism dramatically improves execution times for CPU-bound tasks.

You can learn more about improving web scraping speed in our dedicated article:

Web Scraping Speed: Processes, Threads and Async

Learn how to improve web scraping speed. We will delve into CPU-bound and IO-bound tasks, examining how they impact performance.

Difference in Practice

When comparing concurrency and parallelism, it's essential to consider the nature of the tasks being executed:

I/O-Bound Tasks:

Concurrency is generally more efficient for I/O-bound tasks, such as making multiple web requests or reading files. By allowing tasks to overlap, concurrency can significantly reduce wait times. For example, when fetching multiple URLs concurrently, the total time taken aligns closely with the longest individual request duration rather than the sum of all delays. This can lead to a total execution time of approximately 5 seconds when fetching five URLs with delays ranging from 1 to 5 seconds.

CPU-Bound Tasks:

For CPU-bound tasks, such as complex calculations or data processing, parallelism often outperforms concurrency. By utilizing multiple CPU cores, parallelism can execute tasks simultaneously, resulting in much faster execution times. For instance, processing a list of numbers to calculate their squares in parallel can reduce total execution time significantly compared to sequential or concurrent approaches, especially with larger datasets.

Scaling Up with ScrapFly

Scaling your web scraping operations requires robust tools that can handle concurrency and parallelism efficiently. This is where ScrapFly comes into play.

Concurrency Out of the Box

ScrapFly allows you to run scrapes concurrently, right out of the box. We leverage asyncio in Python to efficiently manage concurrent requests.

There are several ways to achieve concurrency in Python. You can also use:

• ProcessPoolExecutor
• ThreadPoolExecutor

Before using concurrency in ScrapFly, ensure you have the concurrency module installed:

pip install 'scrapfly-sdk[concurrency]'

Here’s an example of how you can perform concurrent scraping with ScrapFly:

import asyncio
import logging as logger
from sys import stdout

scrapfly_logger = logger.getLogger('scrapfly')
scrapfly_logger.setLevel(logger.DEBUG)
logger.StreamHandler(stdout)

from scrapfly import ScrapeConfig, ScrapflyClient

# Initialize the ScrapFly client with a concurrency limit of 2
scrapfly = ScrapflyClient(key='your_api_key_here', max_concurrency=2)

async def main():
    targets = [
        ScrapeConfig(url='https://httpbin.dev/anything', render_js=True),
        ScrapeConfig(url='https://httpbin.dev/anything', render_js=True),
        ScrapeConfig(url='https://httpbin.dev/anything', render_js=True),
        ScrapeConfig(url='https://httpbin.dev/anything', render_js=True),
        ScrapeConfig(url='https://httpbin.dev/anything', render_js=True),
        ScrapeConfig(url='https://httpbin.dev/anything', render_js=True),
        ScrapeConfig(url='https://httpbin.dev/anything', render_js=True),
        ScrapeConfig(url='https://httpbin.dev/anything', render_js=True)
    ]

    async for result in scrapfly.concurrent_scrape(scrape_configs=targets):
        print(result)

asyncio.run(main())

In this example, ScrapFly efficiently handles concurrent scrapes using asyncio. The max_concurrency parameter controls how many requests can be executed concurrently, helping optimize resource usage and speed.

FAQ

To wrap up this guide, let's have a look at some frequently asked questions about concurrency and parallelism:

What is the difference between concurrency and parallelism?

Concurrency refers to the ability to handle multiple tasks at the same time, often by managing tasks that may be waiting for I/O operations, such as HTTP requests. In contrast, parallelism involves executing multiple tasks simultaneously, typically on separate CPU cores

How do I use concurrency and parallelism in web scraping?

• Concurrency: Use asynchronous libraries or frameworks to manage I/O-bound tasks. In Python, you can use asyncio with libraries like httpx or aiohttp to send multiple HTTP requests concurrently. In JavaScript, async/await and Promise.all() help in handling concurrent web scraping tasks.
• Parallelism: For CPU-bound tasks, you can use multiprocessing or threading. In Python, libraries such as multiprocessing or concurrent.futures allow you to distribute tasks across multiple CPU cores. In Node.js, the worker_threads module can be used to achieve parallelism for heavy computations.

When should I use concurrency over parallelism in my web scraping projects?

You should use concurrency when your web scraping tasks are primarily I/O-bound, such as making multiple HTTP requests or waiting for responses from servers. This approach allows you to maximize resource utilization by overlapping waiting times. On the other hand, use parallelism when your tasks are CPU-bound, such as complex data processing or parsing, where you can benefit from utilizing multiple CPU cores to perform computations simultaneously.

Summary

Understanding the difference between concurrency and parallelism is crucial for optimizing web scraping performance. Concurrency enables efficient handling of multiple I/O-bound tasks, allowing your scrapers to manage simultaneous requests without delays. Meanwhile, parallelism accelerates the processing of CPU-bound tasks by leveraging multiple cores. By effectively applying these concepts, you can significantly enhance the speed and reliability of your web scraping operations.