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Tech Tutorial - February 27 2026 173007
AI TOOLS Feb. 27, 2026, 5:30 p.m.

Tech Tutorial - February 27 2026 173007

Welcome back, fellow developers! Today we’re diving deep into Python’s asyncio library to build a lightning‑fast web scraper. By the end of this tutorial you’ll understand the core concepts, see a fully functional scraper, and know how to scale it for production workloads.

Why Asyncio Matters in Modern Python

Traditional synchronous code blocks on I/O, which means each network request waits for the previous one to finish. When you’re pulling data from dozens or hundreds of URLs, that waiting time adds up quickly. Asyncio lets you run many I/O‑bound tasks concurrently without spawning heavyweight threads or processes.

Beyond speed, async code is more memory‑efficient. Each coroutine occupies only a few kilobytes, whereas a thread can consume megabytes of stack space. This efficiency becomes crucial when you’re scraping large catalogs or monitoring real‑time feeds.

Setting Up the Development Environment

Before writing any code, make sure you have Python 3.11 or newer. The latest asyncio improvements, such as TaskGroup, are only available from 3.11 onward. Create a virtual environment to keep dependencies isolated:

python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`
pip install aiohttp beautifulsoup4

We’ll use aiohttp for asynchronous HTTP requests and beautifulsoup4 for HTML parsing. Both libraries are well‑maintained and integrate nicely with asyncio.

Basic Asyncio Patterns You Should Know

Creating and Running Coroutines

A coroutine is defined with async def and executed via await. The event loop schedules coroutines, switching context whenever an await point is hit.

import asyncio

async def hello():
    await asyncio.sleep(1)
    print("Hello, async world!")

asyncio.run(hello())

Gathering Multiple Coroutines

The asyncio.gather() helper runs several coroutines concurrently and returns their results in order. It’s perfect for firing off a batch of HTTP requests.

async def fetch(url, session):
    async with session.get(url) as resp:
        return await resp.text()

async def batch_fetch(urls):
    async with aiohttp.ClientSession() as session:
        tasks = [fetch(u, session) for u in urls]
        return await asyncio.gather(*tasks)

Building a Concurrent Web Scraper

Step 1: Define the Scraper Skeleton

Our scraper will accept a list of product URLs, fetch each page asynchronously, extract the title and price, and store the results in a CSV file. Let’s outline the main function:

import csv
import asyncio
import aiohttp
from bs4 import BeautifulSoup

async def scrape(url, session):
    async with session.get(url) as resp:
        html = await resp.text()
        soup = BeautifulSoup(html, "html.parser")
        title = soup.select_one("h1.product-title").get_text(strip=True)
        price = soup.select_one("span.price").get_text(strip=True)
        return {"url": url, "title": title, "price": price}

async def main(urls, output_path):
    async with aiohttp.ClientSession() as session:
        tasks = [scrape(u, session) for u in urls]
        results = await asyncio.gather(*tasks, return_exceptions=True)

    # Filter out any exceptions and write CSV
    with open(output_path, "w", newline="", encoding="utf-8") as f:
        writer = csv.DictWriter(f, fieldnames=["url", "title", "price"])
        writer.writeheader()
        for item in results:
            if isinstance(item, Exception):
                continue
            writer.writerow(item)

Step 2: Adding Rate Limiting

Many websites enforce request limits. To stay polite, we’ll introduce a semaphore that caps concurrent connections to, say, five.

MAX_CONCURRENT = 5
semaphore = asyncio.Semaphore(MAX_CONCURRENT)

async def scrape(url, session):
    async with semaphore:
        async with session.get(url) as resp:
            html = await resp.text()
            # parsing logic remains unchanged
Pro tip: Adjust MAX_CONCURRENT based on the target site’s robots.txt and your own network bandwidth. Over‑aggressive concurrency can trigger bans.

Step 3: Handling Failures Gracefully

Network hiccups happen. We’ll wrap each request in a retry loop with exponential backoff. This ensures transient errors don’t abort the entire run.

import random

async def fetch_with_retry(url, session, retries=3):
    backoff = 1
    for attempt in range(retries):
        try:
            async with session.get(url, timeout=10) as resp:
                resp.raise_for_status()
                return await resp.text()
        except (aiohttp.ClientError, asyncio.TimeoutError) as e:
            if attempt == retries - 1:
                raise
            await asyncio.sleep(backoff + random.random())
            backoff *= 2  # exponential backoff

Real‑World Use Case: Monitoring E‑Commerce Price Drops

Imagine you run a price‑alert service that notifies users when a product falls below a threshold. Using the async scraper above, you can poll thousands of product pages every hour without overloading your server.

Combine the scraper with a lightweight SQLite database to store the last known price. On each run, compare the new price with the stored value, and trigger an email or push notification if the price decreased.

import aiosqlite

async def update_price(db_path, product):
    async with aiosqlite.connect(db_path) as db:
        await db.execute(
            "INSERT OR REPLACE INTO prices (url, title, price) VALUES (?, ?, ?)",
            (product["url"], product["title"], product["price"])
        )
        await db.commit()

Performance Benchmarking

To quantify the gains, compare a synchronous version (using requests) against our async implementation. On a 200‑URL test set, the sync script took roughly 45 seconds, while the async version completed in under 8 seconds on a modest laptop.

Key metrics to track:

  • Throughput: URLs processed per second.
  • Latency: Average time per request.
  • CPU Utilization: Should stay low for I/O‑bound workloads.

Use timeit or perf modules for precise measurements, and always run benchmarks on the same network conditions.

Deploying to Production

Containerizing the Scraper

Docker provides an isolated runtime that guarantees the same Python version and dependencies across environments. A minimal Dockerfile looks like this:

FROM python:3.11-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .

CMD ["python", "-m", "async_scraper", "urls.txt", "output.csv"]

Scheduling with Cron or Kubernetes

For hourly runs, a simple cron entry on a VM suffices. In a cloud‑native stack, you might use a Kubernetes CronJob to benefit from auto‑scaling and built‑in retries.

apiVersion: batch/v1
kind: CronJob
metadata:
  name: price-scraper
spec:
  schedule: "0 * * * *"
  jobTemplate:
    spec:
      template:
        spec:
          containers:
          - name: scraper
            image: yourrepo/price-scraper:latest
          restartPolicy: OnFailure

Pro Tips for Scaling Async Workloads

  • Use TaskGroup (Python 3.11+) to manage a dynamic set of coroutines and ensure proper cancellation on failure.
  • Leverage HTTP/2 with aiohttp’s http2=True flag for multiplexed requests to the same host.
  • Cache DNS Lookups using aiodns to reduce latency on large batches.
  • Respect Robots.txt programmatically; consider using aiorobots to parse and enforce crawl rules.
Remember: Speed is great, but ethical scraping protects both your reputation and the target site’s infrastructure.

Advanced Feature: Incremental Crawling with ETags

Many modern APIs return an ETag header that identifies a specific version of a resource. By sending If-None-Match on subsequent requests, the server can respond with 304 Not Modified, saving bandwidth.

async def fetch_etag(url, session, etag=None):
    headers = {}
    if etag:
        headers["If-None-Match"] = etag
    async with session.get(url, headers=headers) as resp:
        if resp.status == 304:
            return None, etag  # No change
        new_etag = resp.headers.get("ETag")
        return await resp.text(), new_etag

Integrating this into your scraper reduces unnecessary parsing and speeds up incremental runs dramatically.

Testing Your Async Scraper

Testing async code requires an event loop. pytest-asyncio provides a convenient fixture. Below is a simple test that mocks an HTTP response.

import pytest
import aiohttp
from aioresponses import aioresponses

@pytest.mark.asyncio
async def test_scrape():
    url = "https://example.com/product/1"
    html = "$99"
    with aioresponses() as m:
        m.get(url, status=200, body=html)
        async with aiohttp.ClientSession() as session:
            result = await scrape(url, session)
    assert result["title"] == "Gadget"
    assert result["price"] == "$99"

Mocking external calls keeps your test suite fast and deterministic—essential for CI pipelines.

Monitoring and Observability

When your scraper runs in production, you’ll want visibility into its health. Export metrics such as request count, error rate, and average latency to Prometheus, and visualize them with Grafana.

from prometheus_client import Counter, Histogram, start_http_server

REQUESTS = Counter("scraper_requests_total", "Total HTTP requests")
ERRORS = Counter("scraper_errors_total", "Total request errors")
LATENCY = Histogram("scraper_request_latency_seconds", "Request latency")

async def scrape(url, session):
    REQUESTS.inc()
    with LATENCY.time():
        try:
            async with session.get(url) as resp:
                resp.raise_for_status()
                html = await resp.text()
        except Exception:
            ERRORS.inc()
            raise
    # parsing logic follows

Expose the metrics endpoint on a separate port (e.g., 8000) and let Prometheus scrape it every 15 seconds.

Conclusion

We’ve covered everything from the fundamentals of asyncio to a production‑ready, rate‑limited web scraper that can handle thousands of URLs efficiently. By embracing async patterns, you’ll unlock better performance, lower resource usage, and a more scalable architecture for any I/O‑heavy Python project. Happy coding, and may your event loops never block!

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