4 min read

Concurrent Connections and Multiple Threads with Proxies Explained

Multiple threads and concurrent connections

Concurrent connections are requests that are open at the same time. In proxy work they decide how much you can get done per minute: one connection at a time is slow, while dozens in parallel can finish a large job quickly. Threads are one common way to run those connections side by side. This guide explains both concepts in plain language, shows a working example, and covers the limits that matter in practice.

Quick Proxy doesn’t limit threads on any plan. Get a trial for only $1.99.

Threads, connections and concurrency explained

A connection is an open link between your program and a server, through which a request is sent and a response received.
Concurrent connections are several of those links open at the same time.
A thread is a separate line of work inside a program. Several threads let a program wait for several responses at once instead of one after another.
Asynchronous code (such as Python’s asyncio or JavaScript promises) achieves the same effect with a single thread, by switching between tasks while each one waits.

Most of a web request’s time is spent waiting for the network. That waiting is why concurrency helps so much: while one request waits for a response, others can be sent.

Why concurrency matters with proxies

Through a residential proxy, a single request might take a second or two (see what affects proxy speed). One at a time, 1,000 pages would take over half an hour. With 20 concurrent connections, the same job can take a couple of minutes.

A rotating gateway adds a second benefit: concurrent requests can leave from different residential IPs, so no single address carries the whole load. You don’t need to give each thread its own proxy list, because the gateway rotates IPs for you.

A simple example in Python

This script fetches a list of pages with 10 concurrent workers through a rotating gateway, with a timeout and one retry:

import os
from concurrent.futures import ThreadPoolExecutor, as_completed
import requests

user, pw = os.environ["QP_USER"], os.environ["QP_PASS"]
proxy = f"http://{user}-cc-us:{pw}@gate.quickproxy.io:8000"
proxies = {"http": proxy, "https": proxy}

def fetch(url):
    for attempt in range(2):  # one retry, on a fresh IP
        try:
            r = requests.get(url, proxies=proxies, timeout=20)
            if r.status_code == 200:
                return url, len(r.content)
        except requests.RequestException:
            pass
    return url, None

urls = [f"https://example.com/page/{i}" for i in range(1, 101)]

with ThreadPoolExecutor(max_workers=10) as pool:
    futures = [pool.submit(fetch, u) for u in urls]
    for f in as_completed(futures):
        url, size = f.result()
        print(url, "failed" if size is None else f"{size} bytes")

max_workers sets the number of concurrent connections. Start low, measure, then raise it gradually. For credential setup, see the authentication guide.

How many concurrent connections should you use?

There’s no single right number. It’s a balance between three limits:

The website: every site has a level at which it starts slowing down, returning errors or blocking. Stay well below it. Read published rate limits, and back off when you see HTTP 429 or 503 responses.
Your machine and code: CPU, memory, open-file limits and your HTTP library all set ceilings. Browser automation uses far more resources per connection than plain HTTP requests.
Your proxy plan: some providers cap concurrent connections. Quick Proxy doesn’t: threads are unlimited, and only bandwidth is metered.

A practical approach: start with 5–10 concurrent connections per target website, then increase step by step while watching response time and error rate. If errors rise, step back.

Common problems and how to avoid them

Problem	Sign	Fix
Too much load on one site	429 or 503 errors, CAPTCHAs	Lower concurrency per site, add delays and back off exponentially
Unbounded threads	Memory climbs, the program slows or crashes	Use a fixed-size pool, as in the example
Hanging requests	Workers stuck, throughput drops	Always set timeouts
Shared data clashes	Corrupted files or wrong counts	Collect results through the pool or a queue, or use a lock
Broken sessions	Logouts or failed multi-step flows	Keep each flow on one sticky session and in one worker
Retry storms	Bandwidth used quickly with few successes	Limit retries and pause the job when errors spike

For the meaning of specific errors, see the most common proxy errors.

Threads or async?

For a few dozen concurrent requests, threads are simple and work well. For hundreds or thousands, asynchronous libraries such as aiohttp or httpx use fewer resources. Both work the same way with proxies. Pick the one your team finds easier to maintain.

Use concurrency responsibly

High concurrency makes it easy to send a lot of traffic to one website very quickly. Keep the load considerate, respect each site’s rules, and use official APIs where they exist. Read more in legal web scraping.

Conclusion

Concurrent connections are the main lever for faster proxy workflows. Use a fixed-size pool, set timeouts, increase concurrency gradually while watching errors, and let a rotating gateway spread the load across residential IPs.

Run your jobs in parallel without thread limits: start a $1.99 trial.

Questions and Answers

Q1: What are concurrent connections in a proxy?

A1: They are requests open through the proxy at the same time. More concurrent connections mean more pages processed per minute, up to the limits of the website and your setup.

Q2: Does Quick Proxy limit concurrent connections?

A2: No. Threads are unlimited on every plan, and only bandwidth is metered.

Q3: Do I need a separate proxy for each thread?

A3: Not with a rotating gateway. All threads use the same address, and requests leave from different residential IPs automatically.

Q4: Why do I get more errors when I add threads?

A4: You may be exceeding the website’s tolerance or your machine’s capacity. Reduce concurrency, add delays and timeouts, and increase again gradually.

Put the article into practice.

Spin up real residential IPs in 150+ countries. Pay once or subscribe — from $11.

Create account See pricing