Slow Proxy: 7 Reasons for Speed Decline and Methods to Speed Up

The speed of a proxy connection directly affects the efficiency of parsing, automation, and any tasks related to mass requests. When a proxy operates slowly, it leads to increased script execution time, timeouts, and data loss. In this article, we will analyze the technical reasons for low speed and show specific optimization methods with code examples and testing results.

Geographical Distance of the Server

The physical distance between your server, the proxy, and the target resource is a primary factor in latency. Each additional node in the chain adds milliseconds, which accumulate during mass requests.

A typical request scheme through a proxy looks like this: your server → proxy server → target website → proxy server → your server. If your parser is in Germany, the proxy is in the USA, and the target website is in Japan, the data travels tens of thousands of kilometers.

Solution: Choose a proxy geographically close to the target resource. If you are scraping Russian websites, use proxies with Russian IPs. For working with global services (Google, Amazon), proxies in the USA or Western Europe, where the main data centers are located, are optimal.

For residential proxies, pay attention to the ability to choose a specific city or region, not just the country. The ping difference between proxies from Moscow and Vladivostok when accessing a Moscow server can reach 150-200 ms.

Impact of Protocol on Data Transfer Speed

The choice of proxy protocol significantly affects speed. The main options are: HTTP/HTTPS, SOCKS4, SOCKS5. Each has its own data handling features and overheads.

HTTP proxies are optimal for web scraping as they operate at the application level and can cache data. SOCKS5 is more versatile but adds an additional processing layer. For simple HTML parsing, the speed difference between HTTP and SOCKS5 can be 10-15%.

Python Configuration Example (requests):

import requests

# HTTP proxy - faster for web requests
proxies_http = {
    'http': 'http://user:pass@proxy.example.com:8080',
    'https': 'http://user:pass@proxy.example.com:8080'
}

# SOCKS5 - more versatile, but slower
proxies_socks = {
    'http': 'socks5://user:pass@proxy.example.com:1080',
    'https': 'socks5://user:pass@proxy.example.com:1080'
}

# For web scraping, use HTTP
response = requests.get('https://example.com', proxies=proxies_http, timeout=10)

If your provider offers both options, test them on real tasks. For data center proxies, the HTTP protocol usually shows speeds 12-18% higher than SOCKS5 under identical loads.

Proxy Server Overload and IP Pools

When a single proxy server handles too many simultaneous connections, speed drops due to bandwidth and computational resource limitations. This is especially critical for shared proxies, where one IP is used by dozens of clients.

A typical overload scenario: at the beginning of the script's execution, the speed is normal (50-100 requests per minute), then it suddenly drops to 10-15 requests. This happens when the server reaches the limit of open connections or bandwidth.

Solutions:

• Use a proxy pool instead of a single IP. Rotating between 10-20 proxies distributes the load and reduces the likelihood of blocking.
• Limit the number of simultaneous connections through one proxy (recommended no more than 5-10 parallel threads).
• For high-load tasks, choose private proxies, where resources are not shared with other users.
• Monitor speed in real-time and automatically exclude slow proxies from rotation.

Example of Implementing a Pool with Speed Monitoring:

import time
import requests
from collections import deque

class ProxyPool:
    def __init__(self, proxies, max_response_time=5.0):
        self.proxies = deque(proxies)
        self.max_response_time = max_response_time
        self.stats = {p: {'total': 0, 'slow': 0} for p in proxies}
    
    def get_proxy(self):
        """Get the next proxy from the pool"""
        proxy = self.proxies[0]
        self.proxies.rotate(-1)  # Move to the end
        return proxy
    
    def test_and_remove_slow(self, url='http://httpbin.org/ip'):
        """Test and remove slow proxies"""
        for proxy in list(self.proxies):
            try:
                start = time.time()
                requests.get(url, proxies={'http': proxy}, timeout=10)
                response_time = time.time() - start
                
                self.stats[proxy]['total'] += 1
                if response_time > self.max_response_time:
                    self.stats[proxy]['slow'] += 1
                
                # Remove if more than 50% of requests are slow
                slow_ratio = self.stats[proxy]['slow'] / self.stats[proxy]['total']
                if slow_ratio > 0.5 and self.stats[proxy]['total'] > 10:
                    self.proxies.remove(proxy)
                    print(f"Removed slow proxy: {proxy}")
            except:
                self.proxies.remove(proxy)

# Usage
proxies = [
    'http://proxy1.example.com:8080',
    'http://proxy2.example.com:8080',
    'http://proxy3.example.com:8080'
]

pool = ProxyPool(proxies, max_response_time=3.0)
pool.test_and_remove_slow()

# Working with the pool
for i in range(100):
    proxy = pool.get_proxy()
    # Execute request through proxy

Connection Settings and Timeouts

Incorrectly configured connection parameters are a common reason for the apparent slowness of proxies. Too long timeouts make the script wait for unavailable proxies, while too short ones lead to the disconnection of normal connections.

Key parameters affecting speed:

• Connection timeout — the waiting time for establishing a connection. Optimal: 5-10 seconds for residential proxies, 3-5 for data center proxies.
• Read timeout — the waiting time for a response after establishing a connection. Depends on the task: 10-15 seconds for parsing, 30+ for downloading large files.
• Keep-Alive — reusing TCP connections. Saves up to 200-300 ms on each subsequent request to the same domain.
• Connection pooling — a pool of open connections. Critical for high performance during mass requests.

Optimized Configuration for requests:

import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry

# Create a session with optimized settings
session = requests.Session()

# Retry settings
retry_strategy = Retry(
    total=3,  # Maximum 3 attempts
    backoff_factor=0.5,  # Delay between attempts: 0.5, 1, 2 seconds
    status_forcelist=[429, 500, 502, 503, 504],
    allowed_methods=["GET", "POST"]
)

# Adapter with connection pooling
adapter = HTTPAdapter(
    max_retries=retry_strategy,
    pool_connections=10,  # Pool for 10 hosts
    pool_maxsize=20  # Maximum 20 connections
)

session.mount("http://", adapter)
session.mount("https://", adapter)

# Proxy settings
session.proxies = {
    'http': 'http://user:pass@proxy.example.com:8080',
    'https': 'http://user:pass@proxy.example.com:8080'
}

# Request with optimal timeouts
# (connection_timeout, read_timeout)
response = session.get(
    'https://example.com',
    timeout=(5, 15),  # 5 seconds for connection, 15 for reading
    headers={'Connection': 'keep-alive'}  # Reusing connection
)

Using sessions with Keep-Alive when parsing 1000 pages of one site speeds up the process by 30-40% compared to creating a new connection for each request. The time savings on establishing TCP connections and SSL handshakes are critical during mass operations.

Encryption and SSL/TLS Overheads

HTTPS connections require additional computational resources for encrypting/decrypting data and performing SSL/TLS handshakes. When working through a proxy, this occurs twice: between you and the proxy, and between the proxy and the target server.

Typical SSL/TLS overheads:

• Initial handshake: 150-300 ms (depends on the algorithm and distance)
• Encrypting/decrypting data: +10-20% to transmission time
• Additional CPU load on the proxy server during high traffic

Optimization Methods:

When working with mobile proxies, where the communication channel may be slower, SSL overheads become even more noticeable. In tests, the difference between HTTP and HTTPS requests through 4G proxies averaged 280 ms.

DNS Resolution and Caching

Each request to a new domain requires DNS resolution — converting a domain name into an IP address. Without caching, this adds 20-100 ms to each request, and with a slow DNS server, the delay can reach 500+ ms.

When you work through a proxy, DNS requests can be executed in three places:

• On your side (the client resolves the domain and passes the IP to the proxy)
• On the proxy server (SOCKS5, HTTP CONNECT — the proxy receives the domain and resolves it itself)
• On the target server (rarely, in specific configurations)

For SOCKS5 proxies, DNS resolution usually occurs on the proxy server side, which can be slower if the proxy provider has poor DNS servers. HTTP proxies more often resolve on the client side.

Methods to Speed Up DNS:

import socket
from functools import lru_cache

# Caching DNS resolution on the client side
@lru_cache(maxsize=256)
def cached_resolve(hostname):
    """Cache DNS query results"""
    try:
        return socket.gethostbyname(hostname)
    except socket.gaierror:
        return None

# Usage
hostname = 'example.com'
ip = cached_resolve(hostname)
if ip:
    # Use IP directly in requests
    url = f'http://{ip}/path'
    headers = {'Host': hostname}  # Specify the original host in the header

An alternative approach is to use fast public DNS servers at the system level:

• Google DNS: 8.8.8.8, 8.8.4.4
• Cloudflare DNS: 1.1.1.1, 1.0.0.1
• Quad9: 9.9.9.9

In Linux, the configuration is done through /etc/resolv.conf:

nameserver 1.1.1.1
nameserver 8.8.8.8
options timeout:2 attempts:2

For Python scripts with a large number of domains, it is recommended to pre-warm the DNS cache:

import concurrent.futures
import socket

def warmup_dns_cache(domains):
    """Pre-resolve a list of domains"""
    def resolve(domain):
        try:
            socket.gethostbyname(domain)
        except:
            pass
    
    with concurrent.futures.ThreadPoolExecutor(max_workers=20) as executor:
        executor.map(resolve, domains)

# List of domains for parsing
domains = ['site1.com', 'site2.com', 'site3.com']
warmup_dns_cache(domains)

# Now DNS is already in cache, requests will be faster

Quality of Provider Infrastructure

The speed of a proxy directly depends on the quality of the provider's equipment and communication channels. Cheap proxies often operate on overloaded servers with slow network interfaces and outdated hardware.

Critical Infrastructure Parameters:

Providers with their own infrastructure (not resellers) usually ensure consistently high speeds. They control the entire stack: from hardware to network equipment settings.

Signs of Quality Infrastructure:

• Stable speed throughout the day (deviations no more than 15-20% from the average)
• Low jitter (delay variation) — less than 10 ms
• Minimal packet loss (<0.1%)
• Quick response from technical support to issues (important for business tasks)
• Transparent information about server locations and channel characteristics

For critical tasks, it is recommended to test proxies in conditions as close to real-world scenarios as possible. Purchase test access for 1-3 days and run real scripts while monitoring all metrics.

Proxy Speed Testing Methodology

Proper testing helps identify bottlenecks and objectively compare different providers. A simple speed test is insufficient — it is necessary to measure parameters important for your tasks.

Key Metrics to Measure:

• Latency — the time it takes for a packet to travel back and forth. Critical for tasks with a large number of small requests.
• Throughput — the amount of data per unit of time. Important for uploading files, images.
• Connection time — the time it takes to establish a connection. Shows efficiency for one-off requests.
• Success rate — the percentage of successful requests. Below 95% is a poor indicator.
• Jitter — variation in latency. High jitter (>50 ms) indicates instability in the channel.

Comprehensive Testing Script:

import time
import requests
import statistics
from concurrent.futures import ThreadPoolExecutor, as_completed

def test_proxy_performance(proxy, test_url='https://httpbin.org/get', requests_count=50):
    """
    Comprehensive proxy testing
    
    Args:
        proxy: Proxy URL
        test_url: URL for testing
        requests_count: Number of test requests
    
    Returns:
        dict with metrics
    """
    results = {
        'latencies': [],
        'connection_times': [],
        'total_times': [],
        'successes': 0,
        'failures': 0,
        'errors': []
    }
    
    session = requests.Session()
    session.proxies = {'http': proxy, 'https': proxy}
    
    def single_request():
        try:
            start = time.time()
            response = session.get(
                test_url,
                timeout=(5, 15),
                headers={'Connection': 'keep-alive'}
            )
            total_time = time.time() - start
            
            if response.status_code == 200:
                results['successes'] += 1
                results['total_times'].append(total_time)
                # Approximate latency estimation
                results['latencies'].append(total_time / 2)
            else:
                results['failures'] += 1
        except Exception as e:
            results['failures'] += 1
            results['errors'].append(str(e))
    
    # Parallel execution of requests
    with ThreadPoolExecutor(max_workers=10) as executor:
        futures = [executor.submit(single_request) for _ in range(requests_count)]
        for future in as_completed(futures):
            future.result()
    
    # Calculate statistics
    if results['total_times']:
        metrics = {
            'proxy': proxy,
            'total_requests': requests_count,
            'success_rate': (results['successes'] / requests_count) * 100,
            'avg_response_time': statistics.mean(results['total_times']),
            'median_response_time': statistics.median(results['total_times']),
            'min_response_time': min(results['total_times']),
            'max_response_time': max(results['total_times']),
            'stdev_response_time': statistics.stdev(results['total_times']) if len(results['total_times']) > 1 else 0,
            'jitter': statistics.stdev(results['latencies']) if len(results['latencies']) > 1 else 0,
            'failures': results['failures']
        }
        return metrics
    else:
        return {'proxy': proxy, 'error': 'All requests failed'}

# Testing
proxy = 'http://user:pass@proxy.example.com:8080'
metrics = test_proxy_performance(proxy, requests_count=100)

print(f"Proxy: {metrics['proxy']}")
print(f"Success rate: {metrics['success_rate']:.1f}%")
print(f"Average response time: {metrics['avg_response_time']*1000:.0f} ms")
print(f"Median: {metrics['median_response_time']*1000:.0f} ms")
print(f"Jitter: {metrics['jitter']*1000:.0f} ms")
print(f"Standard deviation: {metrics['stdev_response_time']*1000:.0f} ms")

For more accurate results, test at different times of the day (morning, afternoon, evening) and on different target websites. Speed can vary significantly depending on geography and network load.

Comprehensive Optimization: Checklist

Applying all the described methods in combination yields a cumulative effect. Here is a step-by-step plan for optimizing speed through proxies:

Example of Optimized Configuration for Production:

import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
from collections import deque
import time

class OptimizedProxyPool:
    def __init__(self, proxies_list):
        self.proxies = deque(proxies_list)
        self.session = self._create_optimized_session()
        self.stats = {p: {'requests': 0, 'avg_time': 0} for p in proxies_list}
    
    def _create_optimized_session(self):
        """Create an optimized session"""
        session = requests.Session()
        
        # Retry strategy
        retry = Retry(
            total=3,
            backoff_factor=0.3,
            status_forcelist=[429, 500, 502, 503, 504],
            allowed_methods=["GET", "POST", "PUT"]
        )
        
        # Adapter with connection pooling
        adapter = HTTPAdapter(
            max_retries=retry,
            pool_connections=20,
            pool_maxsize=50,
            pool_block=False
        )
        
        session.mount("http://", adapter)
        session.mount("https://", adapter)
        
        # Keep-Alive headers
        session.headers.update({
            'Connection': 'keep-alive',
            'Keep-Alive': 'timeout=60, max=100'
        })
        
        return session
    
    def get_best_proxy(self):
        """Get the proxy with the best performance"""
        # Sort by average speed
        sorted_proxies = sorted(
            self.stats.items(),
            key=lambda x: x[1]['avg_time'] if x[1]['requests'] > 0 else float('inf')
        )
        return sorted_proxies[0][0] if sorted_proxies else self.proxies[0]
    
    def request(self, url, method='GET', **kwargs):
        """Execute a request through the optimal proxy"""
        proxy = self.get_best_proxy()
        self.session.proxies = {'http': proxy, 'https': proxy}
        
        start = time.time()
        try:
            response = self.session.request(
                method,
                url,
                timeout=(5, 15),  # connection, read
                **kwargs
            )
            
            # Update statistics
            elapsed = time.time() - start
            stats = self.stats[proxy]
            stats['avg_time'] = (
                (stats['avg_time'] * stats['requests'] + elapsed) / 
                (stats['requests'] + 1)
            )
            stats['requests'] += 1
            
            return response
        except Exception as e:
            # On error, move the proxy to the end of the queue
            self.proxies.remove(proxy)
            self.proxies.append(proxy)
            raise e

# Usage
proxies = [
    'http://user:pass@proxy1.example.com:8080',
    'http://user:pass@proxy2.example.com:8080',
    'http://user:pass@proxy3.example.com:8080'
]

pool = OptimizedProxyPool(proxies)

# Executing requests
for url in ['https://example.com', 'https://example.org']:
    try:
        response = pool.request(url)
        print(f"Success: {url}, status: {response.status_code}")
    except Exception as e:
        print(f"Error: {url}, {e}")

Applying this checklist allows for a 2-3 times increase in speed when working through proxies compared to basic settings. In real parsing projects, this reduces task execution time from hours to minutes.

Conclusion

Slow proxy performance is a solvable problem if you understand the technical reasons and apply the right optimization methods. The main factors affecting speed are geographical proximity, protocol choice, the quality of provider infrastructure, and proper client code configuration.

A comprehensive approach to optimization includes...