← Glossary / Coroutine-Based Scraping

What is Coroutine-Based Scraping?

Coroutine-based scraping is an asynchronous execution model where a single thread handles thousands of concurrent network requests by yielding control during I/O wait times. Instead of blocking the CPU while waiting for a target server to return HTML, the event loop suspends the current task and resumes another. For high-throughput data pipelines, it is the fundamental architecture that makes scaling to millions of pages per hour economically viable without spinning up massive server clusters.

Async I/OEvent LoopConcurrencyPython asyncioHigh Throughput

// 02 — definitions

Yielding to
the network.

Why waiting for HTTP responses is the most expensive thing a scraper does, and how coroutines eliminate that waste.

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TL;DR

Coroutine-based scraping uses an event loop to manage thousands of concurrent requests on a single thread. When a request hits the network, the coroutine yields control. It's the architecture behind aiohttp and Scrapy, allowing a single worker to achieve 100x the throughput of synchronous, thread-based models.

01Definition & structure

A coroutine is a specialized function that can pause its execution (yield) and resume later. In scraping, coroutines are used to handle network I/O. When a scraper sends an HTTP request, it doesn't wait for the server to reply. Instead, the coroutine yields control back to an event loop, which immediately starts or resumes another request. This allows a single CPU thread to manage thousands of open connections simultaneously, drastically reducing the hardware footprint required for large-scale data extraction.

02How it works in practice

You define your fetch logic using async and await keywords. When the code hits await session.get(url), the underlying framework registers the socket with the OS (via epoll or kqueue) and suspends the function. The event loop continuously polls the OS to see which sockets have received data. As soon as a response arrives, the event loop wakes up the corresponding coroutine exactly where it left off to process the HTML.

03The semaphore pattern

Because coroutines are so lightweight, it's easy to accidentally launch 500,000 of them at once. This will instantly exhaust your system's file descriptors or crash the target server. Production async scrapers use a semaphore—a concurrency lock—to bound the maximum number of active requests. If the semaphore is set to 1,000, the 1,001st coroutine will wait in line until an active request finishes.

04How DataFlirt handles it

We run heavily optimized uvloop event loops in Python, completely isolating I/O from CPU tasks. Our fetch workers do nothing but push bytes over the wire. Once a coroutine receives a payload, it immediately offloads the parsing to a separate worker pool. This strict separation prevents CPU-heavy tasks from blocking the event loop, allowing us to maintain sub-millisecond loop lag even when processing thousands of requests per second.

05The synchronous DNS trap

The most common mistake in coroutine-based scraping is using the default OS DNS resolver. Standard DNS lookups are synchronous and blocking. If you launch 1,000 coroutines and they all try to resolve hostnames using the default resolver, the event loop will freeze. You must configure your async HTTP client (like aiohttp) to use an asynchronous DNS resolver (like aiodns or c-ares) to keep the loop moving.

// 03 — concurrency math

How many requests
can one core handle?

In a synchronous model, concurrency is bound by OS threads. In a coroutine model, it's bound by memory and open file descriptors. DataFlirt tunes event loops to maximize socket utilization.

Theoretical Concurrency = RAM_available / RAM_{per_socket}

Usually ~50KB per active TLS socket. Linux kernel defaults

Effective Throughput = C_active × (1 / Latency_avg)

1,000 coroutines at 500ms latency = 2,000 req/sec. Little's Law

DataFlirt Worker Density = 10,000 sockets / CPU_core

Our standard async worker density. DataFlirt infra SLO

// 04 — event loop trace

One thread,
10,000 sockets.

A snapshot of an asyncio event loop managing a high-concurrency crawl. Notice how tasks yield on network I/O and resume instantly when bytes arrive.

asyncioaiohttpepoll

edge.dataflirt.io — live

CAPTURED

// event loop initialized
loop.policy: uvloop
max_open_files: 65535

// task scheduling
task-1042: GET https://target.com/page/1042 AWAITING_SOCKET
task-1043: GET https://target.com/page/1043 AWAITING_SOCKET
task-0018: RESUMED // TLS handshake complete

// I/O multiplexing (epoll)
epoll.wait(): 412 events ready
task-0891: READ 14.2KB // HTML received
task-0891: yield to parser

// worker metrics
active_coroutines: 8,402
cpu_utilization: 84%
throughput: 1,840 req/s

// 05 — bottlenecks

Where async
scrapers choke.

Coroutines solve network waiting, but they introduce new failure modes. If you block the event loop with heavy CPU tasks, the entire concurrent pipeline stalls.

WORKERS PROFILED · · · 1,200+

FRAMEWORK · · · · · asyncio/uvloop

UPDATED · · · · · · 2026-05-19

Blocking the event loop

CPU bound · Running heavy HTML parsing without yielding.

File descriptor exhaustion

OS limit · Hitting ulimit -n before running out of RAM.

DNS resolution blocking

Network · Using synchronous DNS resolvers in async context.

Memory bloat

RAM bound · Queueing 1M coroutines instead of bounding semaphore.

Proxy pool exhaustion

Infra · Async speed overwhelms the available proxy rotation.

// 06 — DataFlirt's async engine

Never block the loop,

offload the heavy lifting.

In DataFlirt's architecture, the event loop does exactly one thing: manage network sockets. We strictly separate I/O from CPU. When an aiohttp coroutine finishes fetching a payload, it doesn't parse it. It hands the bytes off to a separate Rust-based parsing worker pool via a fast message queue. This ensures our fetch workers maintain microsecond-level event loop responsiveness, allowing a single 2-core container to sustain 3,000+ requests per second without dropping connections.

Async Worker Telemetry

Live metrics from a single fetch worker on a high-speed catalog crawl.

worker.id fetch-eu-west-04

loop.lag 0.4ms

active.tasks 9,240

dns.resolver c-ares (async)

cpu.usage 78%

throughput 3,142 req/s

dropped.conns 0

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// 07 — FAQ

Common
questions.

Common questions about asynchronous scraping, event loops, and scaling data pipelines.

Ask us directly →

What is the difference between multithreading and coroutines for scraping? +

Multithreading uses the operating system to switch between tasks, which requires significant memory per thread (often 1MB+) and high context-switching overhead. Coroutines operate in a single thread, managed by an event loop in user space. They use kilobytes of memory and switch instantly, allowing 10,000+ concurrent requests where threads would crash the OS.

Why does my async scraper slow down when parsing HTML? +

Because you are blocking the event loop. Coroutines only yield on I/O (like network requests or disk writes). If you run a heavy CPU task like BeautifulSoup parsing inside a coroutine, the event loop stops. No other requests can be sent or received until parsing finishes. You must offload parsing to a separate thread or process pool.

Is it legal to scrape a site asynchronously at high speeds? +

Speed does not dictate legality, but it dictates impact. Scraping at 5,000 requests per second can constitute a Denial of Service (DoS) attack, which violates the Computer Fraud and Abuse Act (CFAA) and similar international laws. You must respect robots.txt Crawl-delay directives and target infrastructure capacity, regardless of how fast your async engine can go.

How does DataFlirt handle rate limits with such high concurrency? +

We use distributed token buckets and dynamic semaphores. Our scheduler monitors HTTP 429 responses and target latency. If latency spikes, the central control plane automatically reduces the concurrency semaphore across all async workers in real-time, ensuring we stay below the target's breaking point.

Do I need async if I'm using headless browsers like Playwright? +

Yes, but differently. Playwright itself is inherently asynchronous (communicating with the browser via WebSockets). However, browsers are incredibly CPU and RAM heavy. You cannot run 10,000 concurrent browser contexts on a single machine like you can with raw HTTP requests. Async here manages orchestration, not massive socket concurrency.

What happens if a proxy connection hangs in an async loop? +

Without strict timeouts, hanging connections will permanently consume your concurrency slots (semaphores). In DataFlirt's engine, every coroutine is wrapped in a strict 15-second total timeout and a 5-second read timeout. If the proxy stalls, the coroutine is cancelled, the socket is destroyed, and the URL is requeued.

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hello@dataflirt.com · Bengaluru · IST · typical reply < 4h

What is Coroutine-Based Scraping?

Yielding tothe network.

TL;DR

How many requestscan one core handle?

One thread,10,000 sockets.

Where asyncscrapers choke.

Blocking the event loop

File descriptor exhaustion

DNS resolution blocking

Memory bloat

Proxy pool exhaustion

Never block the loop,

Async Worker Telemetry

Stay ahead of the pipeline

Data engineeringintel, weekly.

Commonquestions.

Tell us whatto extract.We do the rest.

Related glossary terms

Async Scraping

aiohttp

Event Loop Performance

Worker Concurrency