Introduction

The fastest practical way to send a very large number of HTTP requests in Python is usually asynchronous I/O with connection reuse and bounded concurrency. The important word is "practical." Launching 100,000 requests at once is rarely the real optimum, because sockets, DNS, file descriptors, remote rate limits, and server protections become the bottleneck long before Python syntax does.

Prefer Async I/O Over Blocking Loops

A plain requests loop is easy to write, but it sends one request at a time unless you add threads or processes. For a network-bound workload at this scale, asynchronous I/O is a better fit.

With aiohttp, a basic pattern looks like this:

1import asyncio
2import aiohttp
3
4SEM = asyncio.Semaphore(500)
5
6async def fetch(session, url):
7    async with SEM:
8        async with session.get(url) as response:
9            await response.read()
10            return response.status
11
12async def main(urls):
13    connector = aiohttp.TCPConnector(limit=500)
14
15    async with aiohttp.ClientSession(connector=connector) as session:
16        tasks = [fetch(session, url) for url in urls]
17        results = await asyncio.gather(*tasks)
18        print(results[:5])
19
20urls = ["https://example.com"] * 1000
21asyncio.run(main(urls))

This keeps many requests in flight without blocking a thread per request.

Reuse Connections and Bound Concurrency

The biggest performance wins usually come from:

• reusing one session
• reusing TCP connections
• limiting concurrent requests to a realistic number

Creating a new client or a new TCP connection for every request adds a lot of overhead. The shared ClientSession and connector are not optional optimizations here. They are part of the baseline for high-volume HTTP work.

The semaphore and connector limit matter too. "Fastest" does not mean "infinite concurrency." It means pushing the pipeline as hard as the network, the remote server, and your machine can actually sustain.

Threads Are Simpler but Usually Not the Best at This Scale

A thread pool can still help if you want an easier mental model:

1import concurrent.futures
2import requests
3
4def fetch(url):
5    response = requests.get(url, timeout=10)
6    return response.status_code
7
8with concurrent.futures.ThreadPoolExecutor(max_workers=100) as executor:
9    statuses = list(executor.map(fetch, ["https://example.com"] * 100))
10    print(statuses[:5])

This is often fine for moderate concurrency, but for 100,000 requests, async clients usually scale better because they avoid the overhead of large thread pools for a mostly I/O-bound problem.

Throughput Depends on the Whole System

The real bottleneck may be:

• remote rate limiting
• server-side throttling
• DNS lookup cost
• local open-file limits
• bandwidth and latency

That is why benchmarking against the actual target service matters more than arguing over libraries in the abstract. A client that is "fastest" against localhost may fail badly against a real API that enforces connection limits or retry headers.

At high scale, you may also need:

• backoff and retries
• request batching where the API supports it
• multiple source machines

Sometimes the fastest way to complete 100,000 logical operations is not 100,000 separate HTTP requests at all.

Common Pitfalls

• Launching far more concurrent requests than the operating system or remote service can handle.
• Creating a new HTTP client or session per request instead of reusing one session.
• Measuring only client-side dispatch speed and ignoring server throttling or failures.
• Assuming threads and async are interchangeable at very high request counts.
• Forgetting timeouts, retries, and backpressure, which makes the "fast" solution collapse under real network conditions.

Summary

• For very high request volume in Python, async I/O with aiohttp or a similar client is usually the fastest practical approach.
• Reuse one session and limit concurrency instead of firing everything at once.
• Connection pooling matters as much as syntax choice.
• The true limit is often the network, the OS, or the remote server, not Python itself.
• Benchmark against the real target and tune concurrency deliberately rather than chasing raw request counts blindly.