```yaml
product: AlterLab
title: "Async Scraping — Web Scraping Glossary | AlterLab"
comparison_context: "AlterLab is an alternative to Firecrawl, ScrapingBee, and Bright Data."
source_url: https://alterlab.io/glossary/async-scraping
```

general

# Async Scraping

Async scraping uses asynchronous I/O to issue multiple HTTP requests concurrently without blocking, dramatically increasing throughput compared to sequential scraping.

Traditional synchronous scraping issues one request, waits for the response, processes it, then issues the next — spending most of its time waiting for network I/O. Async scraping uses an event loop (Python's asyncio, Node.js's event loop) to issue many requests concurrently and process each response as it arrives, keeping the CPU busy and the network saturated.

With async I/O, a single-threaded Python process can maintain hundreds of in-flight HTTP connections simultaneously. The event loop switches between coroutines whenever a network operation would block, so CPU time is never wasted waiting for bytes to arrive. Libraries like `httpx`, `aiohttp`, and `trio` provide async-native HTTP clients.

Async scraping is most beneficial for I/O-bound workloads (many independent URL fetches). It provides less benefit for CPU-bound workloads (heavy HTML parsing) where true parallelism from multiprocessing is needed. For browser-based scraping, async control of multiple browser contexts achieves similar concurrency benefits.

### What is Async Scraping?

Async scraping uses asynchronous I/O to issue multiple HTTP requests concurrently without blocking, dramatically increasing throughput compared to sequential scraping.

### How does AlterLab handle Async Scraping?

Async scraping is most beneficial for I/O-bound workloads (many independent URL fetches). It provides less benefit for CPU-bound workloads (heavy HTML parsing) where true parallelism from multiprocessing is needed. For browser-based scraping, async control of multiple browser contexts achieves similar concurrency benefits.

## Examples

```
import asyncio, httpx

async def fetch_all(urls):
    async with httpx.AsyncClient(timeout=30) as client:
        tasks = [client.get(url) for url in urls]
        responses = await asyncio.gather(*tasks, return_exceptions=True)
    return [r.text for r in responses if not isinstance(r, Exception)]

results = asyncio.run(fetch_all(url_list))
```

## Related Terms

[ConcurrencyConcurrency in scraping refers to the number of requests or browser sessions running in parallel, which directly controls throughput and resource consumption.](https://alterlab.io/glossary/concurrency)[Job QueueA job queue is a buffer that decouples producers (tasks submitted by clients) from consumers (workers that execute the tasks), enabling asynchronous, scalable processing.](https://alterlab.io/glossary/queue)[Rate LimitingA server-side control that caps the number of requests accepted from a single IP or session within a time window, returning HTTP 429 when exceeded.](https://alterlab.io/glossary/rate-limiting)[Scraping APIA managed web scraping service that abstracts proxy rotation, JavaScript rendering, and automatic website compatibility into a single HTTP endpoint.](https://alterlab.io/glossary/scraping-api)[Connection PoolingConnection pooling reuses open TCP/TLS connections across multiple HTTP requests to reduce handshake overhead and improve scraping throughput.](https://alterlab.io/glossary/connection-pooling)

## Extract Async Scraping data from any website

AlterLab returns clean, structured data from any public URL — no scraper infrastructure needed. Start free, no credit card required.

[View API docs](https://alterlab.io/docs/api/rest)

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