Concurrency vs Parallelism: A Web Scraping Expert‘s Guide

As a web scraping expert with over 5 years of experience, I‘m often asked to explain the difference between concurrency and parallelism. While they sound similar, these concepts are quite distinct and leveraging them properly can significantly impact a scraper‘s performance. In this guide, I‘ll use my expertise to explain concurrency and parallelism in depth, provide code examples, and share tips on applying them to web scraping.

A Quick Intro to Concurrency and Parallelism

Before diving in, let‘s briefly define both terms:

• Concurrency: Multiple tasks making progress in overlapping time periods by rapidly switching between them.

• Parallelism: Multiple tasks executing simultaneously on separate processing units.

The key distinction is that concurrency rapidly switches between tasks on one CPU, while parallelism spreads work across multiple CPUs to achieve true simultaneity.

These techniques emerged decades ago to improve performance in computing systems. Back in the 1960s and 70s, systems could only execute one program at a time. Concurrency allowed more efficient use of resources by multitasking. In the 1990s, multicore CPUs enabled parallel execution.

Now concurrency and parallelism are indispensable performance techniques used in all types of applications. Next, we‘ll explore exactly how they work.

Understanding Concurrency

Concurrency coordinates multiple tasks on a single CPU by efficiently switching between them as they make progress. This allows the CPU to stay utilized even when tasks are waiting on I/O or other events.

Here are some key aspects of concurrency:

Multithreading – Concurrency is often implemented via multithreading. Threads represent asynchronous tasks that run independently but share memory space.

Interleaved Execution – The processor interleaves execution of threads by switching between them. This happens so fast it seems simultaneous.

Non-Blocking Operations – Threads perform non-blocking operations like I/O to avoid wasting cycles waiting. The CPU simply switches to another ready thread.

Synchronization – Mechanisms like locks prevent race conditions as threads access shared memory. This coordinates the concurrency.

Faster Performance – By keeping the CPU active, concurrency improves performance at less hardware cost vs scaling vertically.

Here is a diagram contrasting sequential vs concurrent execution:

With concurrency, threads 1 and 2 make progress in overlapping periods by rapidly switching between them. The result is faster overall execution.

Concurrency in Action: Web Crawler Example

Let‘s see concurrency in action with a simple web crawler example. Say we need to crawl 50 web pages and extract links from each page.

Here is some Python code to handle one page sequentially:

import requests
from bs4 import BeautifulSoup

def crawl_page(url):
  resp = requests.get(url)
  soup = BeautifulSoup(resp.text, ‘html.parser‘)
  links = []

  for link in soup.find_all(‘a‘):
    links.append(link.get(‘href‘))

  print(f"Found {len(links)} links on {url}")

crawl_page(‘https://example.com‘) 

This works but is slow doing pages sequentially. We can speed it up with threads:

from concurrent.futures import ThreadPoolExecutor

links_per_page = []

def crawl_page(url):
  # crawling logic...
  return links_per_page

pages = [
  ‘https://example.com‘,
  ‘https://example.org‘,
  #...
]

with ThreadPoolExecutor(50) as executor:
  results = executor.map(crawl_page, pages)

all_links = [link for result in results for link in result]
print(f"Total links: {len(all_links)}")

By using a thread pool, we can crawl 50 pages concurrently in overlapping time periods. The CPU efficiently switches between threads to keep utilized.

This simple example demonstrates the power of concurrency – improved performance through more efficient utilization of a single CPU.

Understanding Parallelism

In contrast to concurrency, parallelism spreads work across multiple processors/cores to achieve true simultaneous execution.

Here are some key aspects of parallelism:

Multiprocessing – Parallelism is often achieved by multicore processors or multiprocessing.

Simultaneous Execution – Tasks literally execute at the same instant on separate CPUs/cores.

Separate Memory – Parallel processes have separate memory space and aren‘t as coupled.

Partioning – Data and tasks must be partitioned across the parallel processes.

Improved Throughput – By utilizing more CPUs, parallelism improves throughput and scalability.

Here is a diagram showing parallel vs sequential execution:

With two CPUs, tasks 1 and 2 can run in parallel, literally executing simultaneously. This cuts the total execution time in half.

Adding more CPUs/cores allows you to scale up parallelism further for higher throughput.

Parallelism in Action: Stock Price Checker Example

Let‘s look at a stock price checker example to see parallelism in action. Say we need to check prices for a list of 50 stocks once per minute.

Here is some Python code to check sequentially:

import time
import requests

symbols = [‘AAPL‘, ‘TSLA‘, ‘NVDA‘, #... ]

while True:
  for symbol in symbols:
    url = f‘https://financialmodelingprep.com/api/v3/quote/{symbol}‘  
    res = requests.get(url)
    price = res.json()[0][‘price‘]
    print(f"{symbol} : {price}")

  time.sleep(60)

This works but is slow checking prices one by one. We can speed this up with parallelism:

from multiprocessing import Pool

# Define price check function
def check_price(symbol):
  url = f‘https://financialmodelingprep.com/api/v3/quote/{symbol}‘
  res = requests.get(url)
  return res.json()[0][‘price‘]

if __name__ == ‘__main__‘:

  with Pool(10) as p:  
    while True:
      prices = p.map(check_price, symbols)

      for i in range(len(symbols)):
        print(f"{symbols[i]} : {prices[i]}")

      time.sleep(60)

By creating a pool of 10 processes, we can check 10 stocks simultaneously due to parallel execution on the multiple CPUs. This makes the price checker much more efficient.

This example highlights how parallelism can significantly improve performance through simultaneous execution across multiple processors.

Key Differences Between Concurrency and Parallelism

Now that we‘ve seen concurrency and parallelism in action, let‘s summarize some of the key differences:

Concurrency	Parallelism
Uses a single CPU by rapidly switching between threads	Spreads work across multiple CPUs for true simultaneity
Achieved via multithreading	Achieved via multiprocessing
Focuses on efficient coordination of threads	Focuses on partitioning data/tasks effectively
Uses locks, events for thread coordination	Uses separate processes with own memory space
Optimizes use of limited CPU resources	Scales up throughput with more CPUs
Susceptible to race conditions	Mostly isolated processes, less coupling

In summary:

• Concurrency rapidly switches between multiple tasks on a single CPU

• Parallelism spreads work across multiple CPUs to achieve true simultaneous execution

Both approaches have their merits. Concurrency offers efficient utilization of limited resources. Parallelism enables scalability.

In terms of challenges:

• Concurrency faces issues like deadlocks, race conditions due to tight coupling.

• Parallelism faces challenges around partitioning schemes, splitting data effectively.

Next we‘ll dig deeper into applying both approaches.

Concurrency in Python

Python provides great support for concurrency via threading. Here are some tips:

• Use the Thread class directly to create/manage threads

• threading.Lock() enables synchronizing access to shared resources

• ThreadPoolExecutor from concurrent.futures manages a thread pool

• Use queues (e.g. Queue) for message passing between threads

• Avoid race conditions using locks, semaphores, etc.

Here is an example using a ThreadPoolExecutor:

from concurrent.futures import ThreadPoolExecutor

def task(n):
  print(f"Processing {n}")

with ThreadPoolExecutor(max_workers=4) as executor:
  executor.map(task, range(20)) 

This will process the tasks concurrently with a 4 thread pool, efficiently utilizing the CPU.

According to benchmarks, multithreading in Python can achieve 2-3x speedup on average depending on workload. So concurrency is very worthwhile.

Some key Python threading limitations:

• The GIL prevents true parallelism – only one thread executes Python bytecode at a time
• Threading in Python is best for I/O bound vs CPU bound tasks
• Avoid oversubscribing threads – target 1.5x num of cores

Despite limits, concurrency in Python is very useful. But for CPU intensive work on multicore machines, parallelism is needed.

Parallelism in Python

In Python, parallelism can be achieved via multiprocessing. Some tips:

• Use Process class to create processes you can manage

• Leverage Pool for simple process pools like thread pools

• Use shared memory (Value, Array) or queues for communication

• Divide workload effectively to minimize overhead

Here is an example using a multiprocessing pool:

from multiprocessing import Pool
import time

def job(n):
  print(f"Processing {n}")
  time.sleep(1)

if __name__ == ‘__main__‘:

  p = Pool(processes=4)
  p.map(job, range(20))

This allows processing tasks in parallel using 4 processes.

According to benchmarks, on an 8 core CPU Python multiprocessing achieves 6-7x speedup on average depending on workload.

Some Python multiprocessing considerations:

• Adding too many processes can increase overhead
• Use all available cores, but avoid oversubscribing
• Beware of GIL limiting parallelism in pure Python code
• Use Process pools for data parallel workloads

Overall multiprocessing provides excellent parallelism capabilities in Python.

Concurrency, Parallelism & Web Scraping

As a web scraping expert, I utilize both concurrency and parallelism to optimize scraper performance. Here are some tips:

Concurrency Tips

• Use threads to scrape multiple pages concurrently
• Process responses asynchronously as they complete
• Use queues to coordinate scraping workflow
• Limit threads based on site load and latency

Parallelism Tips

• Run parsers/data processing in parallel processes
• Distribute scrapers across proxies for large crawls
• Leverage process pools for data parallel work
• Monitor CPU usage to find optimization sweet spot

Here is a diagram showing how I leverage both:

Scrapers run concurrently while parsers process data in parallel. This maximizes efficiency.

I also suggest starting with concurrency as it‘s simpler to coordinate. Introduce parallelism judiciously based on workload needs and CPU resources.

Challenges and Limitations

While concurrency and parallelism offer performance benefits, some challenges can arise:

Concurrency Challenges

• Race conditions from shared memory access
• Deadlocks if coordination blocked
• Overheads from excessive context switching
• Difficulty reproducing bugs

Parallelism Challenges

• Partitioning skews from uneven workloads
• Bottlenecks around shared resources
• Overheads from inter-process communication
• Non-determinism from process timing

Therefore it‘s critical to test for these issues and optimizations. Some other limitations include:

• Concurrency limited by dependence on single CPU speed
• Parallelism limited by number of cores and memory bandwidth
• Diminishing returns adding too many threads/processes
• GIL limiting Python threads and multiprocessing

Understanding these challenges helps avoid pitfalls when implementing concurrency/parallelism.

Key Takeaways

Let‘s recap the key takeaways from this guide:

• Concurrency coordinates multiple tasks on one CPU by efficiently switching between them.

• Parallelism spreads work across multiple CPUs simultaneously for higher throughput.

• Concurrency improves speed on single CPU systems. Parallelism improves scalability.

• In Python, use threading for concurrency and multiprocessing for parallelism.

• Thread pools provide a simple way to process concurrently. Process pools enable parallel processing.

• Concurrency and parallelism can significantly improve performance but introduce overheads if overused.

• Monitor resource usage and bottlenecks to optimize concurrency/parallel levels.

• Concurrency simplifies coordination but faces race conditions. Parallelism requires partitioning schemes.

• For web scraping, combine concurrency for scraping with parallelism for data processing.

I hope these explanations and tips provide you a helpful introduction to concurrency and parallelism. Please let me know if you have any other questions!