The Tool Desk
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Start with the bottleneck: network wait or CPU work?
Profile one representative run before rewriting your scraper. Record total elapsed time, successful pages per second, timeout and retry counts, memory, CPU utilization, and time spent waiting for responses versus parsing documents. A scraper that spends most of its time awaiting DNS, TCP/TLS, server response, or download bytes benefits from concurrency that overlaps those waits. A scraper that spends most of its time extracting, normalizing, decompressing, or running machine-learning code needs CPU parallelism or cheaper parsing.
Keep request rates and concurrency within the destination’s terms and technical limits. More simultaneous requests can increase throttling, failures, memory use, and pressure on the target rather than improving useful throughput.
Choose the model
| Approach | Best fit | Main trade-off | Implementation cue |
|---|---|---|---|
| Async/asyncio | Many network waits, an async-capable client, and an application already using async code | Every operation on the event-loop path must be non-blocking; a blocking call stalls other tasks | Use an async client such as HTTPX AsyncClient and await its methods |
| Threads | Blocking synchronous HTTP libraries or an existing synchronous scraper | Thread coordination and shared-state hazards; ordinary CPython’s GIL limits parallel Python bytecode for CPU-bound work | Submit blocking functions to a thread pool |
| Processes | CPU-heavy parsing or transformation that needs parallel Python execution | Process startup and data-transfer overhead; arguments, results, and callables must be pickleable, and the main module must be importable | Isolate a serializable CPU function in a process pool |
Python’s official concurrency guidance frames the choice around CPU-bound versus I/O-bound work and cooperative event-driven versus preemptive multitasking. Asyncio is a cooperative event-loop scheduler: a task runs until it reaches an await point. A coroutine does not make a synchronous network call non-blocking. Likewise, a process pool is not automatically faster for downloading pages; it is usually most useful after responses arrive.
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Async scraping with HTTPX
Async is a strong default when you need to coordinate many requests and can keep the entire request path non-blocking. Reuse one client so connections can be pooled, bound concurrency with a semaphore, set explicit timeouts, and handle failures per URL.
import asyncio
import httpx
URLS = [
"https://example.com/one",
"https://example.com/two",
]
async def fetch(client: httpx.AsyncClient, url: str, limit: asyncio.Semaphore):
async with limit:
try:
response = await client.get(url)
response.raise_for_status()
return {"url": url, "status": response.status_code, "html": response.text}
except httpx.HTTPError as exc:
return {"url": url, "error": str(exc)}
async def main():
limit = asyncio.Semaphore(20)
timeout = httpx.Timeout(30.0, connect=10.0)
async with httpx.AsyncClient(timeout=timeout, follow_redirects=True) as client:
results = await asyncio.gather(
*(fetch(client, url, limit) for url in URLS)
)
for result in results:
print(result["url"], result.get("status", result.get("error")))
if __name__ == "__main__":
asyncio.run(main())Keep the event loop cooperative
- Do not call a synchronous HTTP library, blocking file operation, or long CPU parser directly inside an async task.
- Replace blocking sleeps with
await asyncio.sleep(). - For unavoidable blocking work, use
await asyncio.to_thread(function, ...)or an executor. - For substantial CPU work, use a process pool and return only compact, serializable results.
An async client improves scheduling, not the remote server’s response time. Connection limits, DNS behavior, TLS setup, server throttling, retries, and your semaphore still determine throughput.
Threads for synchronous scrapers
Threads are the least disruptive upgrade when your parser and HTTP code are synchronous. A worker can wait on a socket while another worker runs. They do not provide ordinary CPython parallelism for CPU-bound Python bytecode because of the GIL, so use them primarily for blocking I/O.
from concurrent.futures import ThreadPoolExecutor, as_completed
import requests
URLS = [
"https://example.com/one",
"https://example.com/two",
]
def fetch(url: str):
try:
response = requests.get(url, timeout=(10, 30))
response.raise_for_status()
return url, response.status_code, response.text, None
except requests.RequestException as exc:
return url, None, None, str(exc)
if __name__ == "__main__":
with ThreadPoolExecutor(max_workers=20) as pool:
futures = [pool.submit(fetch, url) for url in URLS]
for future in as_completed(futures):
url, status, html, error = future.result()
print(url, status or error)Choose a worker count experimentally. A larger pool can exhaust file descriptors, sockets, memory, or the target’s rate limit. Protect shared output with a queue, lock, or single writer; avoid having threads mutate one shared parser or session unless that library documents the behavior as safe.
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Processes for CPU-heavy parsing
Use processes when profiling shows that parsing or transformation, rather than downloading, dominates runtime. ProcessPoolExecutor uses multiple processes to sidestep the GIL, but workers must receive pickleable arguments and return pickleable results. Put pool creation behind if __name__ == "__main__" so the main module can be imported by worker subprocesses.
from concurrent.futures import ProcessPoolExecutor
from bs4 import BeautifulSoup
def parse_title(item):
url, html = item
soup = BeautifulSoup(html, "html.parser")
title = soup.title.get_text(strip=True) if soup.title else None
return {"url": url, "title": title}
if __name__ == "__main__":
pages = [
("https://example.com/one", "<html><title>One</title></html>"),
("https://example.com/two", "<html><title>Two</title></html>"),
]
with ProcessPoolExecutor() as pool:
for result in pool.map(parse_title, pages):
print(result)Passing complete HTML documents between processes consumes memory and serialization time. Batch work where practical, keep results small, and compare the pool’s total time—including startup and transfer—with a sequential parser.
A hybrid architecture often wins
Separate stages: an async or threaded downloader obtains responses, then a bounded process pool parses only the CPU-heavy subset. Use a queue or bounded batches so downloaded pages do not accumulate without limit. If parsing is light, process-pool overhead can erase any gain; parse in the event-loop process or worker thread instead.
How to measure a fair comparison
- Freeze the URL set, response sizes, parser, extraction rules, retry policy, timeout values, and output format.
- Run sequential, async, threaded, and (where relevant) process versions in the same environment and with the same concurrency or worker limit.
- Warm up once, then collect several runs. Record elapsed time, successful pages per second, errors, retries, peak memory, CPU utilization, and network-wait versus parse time.
- Check result equivalence. A faster run that silently drops pages or changes retry behavior is not an improvement.
- Increase concurrency gradually until throughput stops improving or errors, memory, or rate limiting rise. Keep the setting that meets your reliability and policy requirements.
No generally applicable speedup percentage or maximum worker count is established here. Results depend on target latency, response size, local CPU, network, library versions, and server behavior.
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Reliability and production controls
Timeouts and retries
Set separate connect and read timeouts. Retry only transient failures, use exponential backoff with jitter, and cap attempts. Do not retry authentication failures, invalid URLs, or permanent HTTP responses indefinitely.
Backpressure and cancellation
Bound in-flight requests with a semaphore, queue, or limited future set. Cancel outstanding tasks when a job is stopped and close clients, sessions, and pools in finally or context managers.
Sessions, cookies, and identity
Reuse connections, but keep per-account cookies and authorization isolated. Respect robots directives, terms, privacy obligations, and explicit rate limits. Concurrency does not bypass bot checks or access controls.
Troubleshooting
Async is no faster than sequential
Look for a synchronous call, blocking parser, excessive semaphore limits, connection setup on every request, or a target that serializes responses. Move blocking work off the loop, reuse one client, and profile wait time.
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The event loop freezes
A long CPU function or blocking library is running on the loop thread. Use an executor or process pool, or rewrite the operation with an async-compatible library.
Threads increase errors
Reduce worker count, add bounded retries and backoff, verify thread-safe use of sessions and output, and inspect socket/file-descriptor limits.
Processes fail to start or raise pickling errors
Move pool creation under the main guard, define worker functions at module scope, and pass plain serializable values. Do not send open sockets, locks, nested local functions, or live client objects.
More workers use more memory
Bound queued URLs and response retention, stream or discard bodies after extraction, reduce batch size, and measure peak memory rather than average memory.
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Collect every future or task result, log exceptions, distinguish timeout from HTTP status, and verify that retries and cancellation do not drop work.
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Frequently Asked Questions
Should I use async or threads with an async web framework?
Use the framework’s async flow and an async HTTP client. Offload any unavoidable blocking library to an executor rather than calling it directly on the event loop.
Can a process pool download pages faster?
It can add overhead without helping I/O. Use async or threads for blocking downloads, reserving processes for measured CPU-heavy parsing or transformation.
Does Python’s free-threaded development work change this advice?
Development documentation for Python 3.16.0a0 discusses free-threaded support, but those pre-release, version-specific details should not be generalized to ordinary stable CPython builds.
What should I log in a scraper benchmark?
Log the fixed URL set and environment plus elapsed time, successful pages per second, status and exception counts, retries, memory, CPU, and network-wait versus parsing time.
Quick Recap
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