Proxy IP Data Aggregation Solution: A Technical Guide to Multi-Source Data Proxy Aggregation

What exactly does proxy IP aggregation do?

Engaged in data collection friends understand, rely solely on one or two proxy IP simply can not support the volume of business. For example, climb the price of e-commerce, the other site found the same IP crazy brush data, minutes to you closed. At this time it is necessary to integrate the IP in the pool of multiple agents to use, like frying vegetables to put seasoning, different sources of IP mixed use is not easy to be recognized.

Take a real case: there is a price comparison tool team, the original use of a single agent service provider, triggering at least 20 times a day CAPTCHA. Later, they used ipipgo's dynamic residential + static residential hybrid program to poll the IP pools of three different regions, and the trigger rate dropped directly to 3 times a week.

How do you choose the right type of agent?

The types of proxies on the market can be dizzying to look at, so it's good to keep this comparison table in mind:

Here's the kicker.Dynamic Residential AgentsThis thing is like an actor who changes faces. ipipgo's dynamic IP pool automatically changes the exit IP every 15 minutes, which is especially good for scenarios where you need to switch identities frequently. Their enterprise version also comes with intelligent routing that automatically avoids blocked IP segments.

Three Tips for Aggregation Solutions

Tip #1: Polling Mashups

import random
from ipipgo_client import IPPool This is the sample code base.

proxy_pools = [
    IPPool.get_dynamic(type='standard'),
    
    IPPool.get_custom(protocol='socks5')
]

def get_proxy().
    return random.choice(proxy_pools).next_ip()

The second move: intelligent meltdown. A proxy pool will automatically pause for 10 minutes if it fails 3 times in a row to avoid deadlocking failed nodes. ipipgo's API comes with health monitoring that can return node status in real time.

The third trick: protocol conversion. HTTP proxy and Socks5 proxy unified into the same format, so that the business code does not have to change around. For example, this conversion:

def format_proxy(ip_obj).
    if ip_obj.protocol == 'socks5': return f'socks5://{ip_obj.user}:{ip_obj.host}:{ip_obj.pwd}@{ip_obj.host}:{ip_obj.pwd}
        return f'socks5://{ip_obj.user}:{ip_obj.pwd}@{ip_obj.host}:{ip_obj.port}'
    else: return f'{ip_obj.user}
        return f'http://{ip_obj.host}:{ip_obj.port}'

QA time

Q: What should I do if my proxy IP fails frequently?
A: three ways: 1. use ipipgo's static residential IP (survival time 30 days +) 2. set the automatic replacement threshold 3. enable the failure retry mechanism

Q: How do I control costs with multiple agent pools?
A: RecommendedDynamic residential (standard)The package hits the bottom at $7.67/GB enough for daily collection. Critical services are then stacked with static IPs, which saves money and is stable.

Q: What if I need to use different country IPs at the same time?
A: ipipgo's API supports geographical filtering, just add a country_code parameter in the code. For example, to rotate the use of U.S., Japan and Korea IP:

pool = IPPool.multi_country(['us','jp','kr'])

Why ipipgo?

theirTK LineIt is true that, specifically for e-commerce data collection optimization. The last time a customer collection of a Southeast Asian platform, with ordinary proxy success rate of only 23%, cut to TK line directly soared to 89%. say a real, 35 yuan / month static IP cheaper than peers at least 15%, and support for hourly billing.

One last reminder, don't just stare at the price. ipipgo's1v1 Customized SolutionsIndeed can save lives, the last time I encountered a need to run at the same time 200 + independent IP pool of the project, their technical brother two days to get the automatic scheduling system, than their own toss to save a lot of heart.