Data Set Segmentation Methods: Analysis of Proxy Data Set Segmentation Techniques

What does proxy dataset segmentation really do?

The old iron engaged in data collection know that the biggest headache in the collection process is the IP is blocked. For example, if you want to crawl the price data of an e-commerce platform, and use the same IP to request continuously, you will be recognized as a robot in minutes. At this time it is necessary toSplit the dataset into parts, each copy of the data is run with a different proxy IP.

Take a real case: a clothing price comparison platform needs to collect 1 million pieces of commodity data every day. They use ipipgo's dynamic residential IP pool to split the commodity links into 50 groups according to stores, and each group allocates 20 rotating IPs, which avoids triggering the anti-climbing mechanism, and the collection success rate is directly increased from 40% to 92%.

Hands down, three splits.

first movepolling and cutting method: It's like student placement in a class, where the data is divided equally among each proxy IP. suppose there are 100,000 pieces of data, processed by 100 IP polls, with 1,000 pieces processed by each IP.

import random
from ipipgo_api import get_proxies Here we use the SDK for ipipgo_.

data_list = [...]   Raw data set
proxies = get_proxies(type='dynamic', count=100) get dynamic IP pools

for index, item in enumerate(data_list):
    proxy = proxies[index % len(proxies)]
    process_data(item, proxy)

second movecharacteristic grouping (math.): Group the data according to its characteristics. For example, when collecting real estate information, the dataset is divided by city, with Beijing's data using Beijing local IP and Shanghai's data using Shanghai IP.

The third move.Dynamic weighting: Set weight values for different IPs. ipipgo's exclusive static IPs are responsive and can allocate more data volume; dynamic IP resources handle low-frequency requests.

A Guide to Avoiding the Pit (Lessons Learned Through Tears)

Three common mistakes newbies make:

Special reminder: the test phase is recommended to use ipipgo'sStatic Home PackageThe stability is better. Formal runtime switch dynamic package, 35 yuan / IP cost-effective is very capable of beating.

Practical QA triple question

Q: How often do I need to split the dataset for collection?
A: More than 500 requests per hour should be split, it is recommended to refer to the usage warning function of ipipgo backend.

Q: How do I use dynamic and static IPs together?
A: Login authentication uses static IPs to maintain the session and dynamic IP rotation for data capture. Their Enterprise package supports mixed calls.

Q: What should I do if I encounter a sudden IP failure?
A: Add an exception retry mechanism in the code, ipipgo's API returns a new IP as long as 0.8 seconds or so, which is 2 times faster than common services in the market.

the right tool saves effort and leads better results

Used seven or eight proxy services, ipipgo'sTK LineIt is indeed stable. Especially when doing cross-border e-commerce data collection, their cross-border line latency can be controlled within 200ms. The recent new SERP API interface directly eliminates the need to deal with the trouble of CAPTCHA on your own.

Package Selection Tip:
- Start-up team selectionDynamic Residential Standard($7.67/GB)
- On enterprise-level acquisitionEnterprise Dynamic Package
- Static packages for services that require fixed IP bindings

Finally, a nagging word: do not believe that those 9.9 monthly cheap IP, collection to half of the blocked is the real pit. Used ipipgo's customized program to know that the charges are flexible is not playing around, just last week to help us adjust the amount of billing mode by success, the cost of standing down 20%.