High-quality datasets are the core foundation of large model training. The ability to obtain data stably and comprehensively directly impacts model training outcomes. Faced with various challenges in the data scraping process, how can we build an efficient and sustainable acquisition solution?
This article starts from real-world difficulties and provides a complete optimization roadmap.
During the data preparation phase of large model training, teams typically encounter the following practical issues:
Limited Coverage Scope
When a single node or a small number of IP addresses access public data sources, they can easily trigger basic access restrictions on platforms, leading to interrupted data scraping or incomplete results.
Low Scraping Efficiency
Many public sites impose default frequency limits on single IP addresses. If scraping is conducted strictly at the platform-allowed rate, obtaining million-scale training data will take an extended period, thereby slowing down overall project progress.
Unstable Reliability
Factors such as network fluctuations, connection timeouts, and temporary maintenance of target sites can all affect the continuity of data scraping. Implementing resumable scraping and data completion mechanisms, in turn, adds additional development workload.
It is recommended to adopt a "task queue + distributed scraping nodes" design. When a scraping node encounters access restrictions, the system can automatically reassign tasks to other available nodes, preventing overall scraping stagnation due to a single point of failure.
At the same time, incorporating request retry mechanisms and exponential backoff strategies can improve the success rate of individual requests without increasing pressure on target sites.
By rotating through multiple IP addresses to initiate requests, the total request volume can be distributed across different network egress points, thereby increasing overall scraping throughput without violating platform rules.
LokiProxy provides IP resources covering more than 190 regions worldwide, supporting unlimited concurrent requests to help teams obtain more valid data per unit of time. Additionally, these IP resources come from legitimate ISP allocations, offering high purity and stable bandwidth. Managed by a professional operations team, the connection success rate reaches 99.9%. With flexible rotating and sticky session modes, various scraping scenarios can be effectively addressed.
To truly implement an efficient data scraping system, the following three core principles should be observed:
First, compliance first. Strictly adhere to the access rules and data scraping norms of each platform, avoiding excessive request behavior to ensure that scraping activities remain legal and compliant.
Second, adapt as needed. Flexibly adjust proxy session modes and concurrency strategies based on different application scenarios such as corpus collection, data updates, and model fine-tuning, achieving optimal resource allocation.
Third, monitor continuously. Real-time monitoring of scraping link stability and data integrity, along with timely resolution of network and node anomalies, ensures the long-term healthy operation of the scraping system.
A stable scraping architecture and appropriate use of rotating proxies form the foundation of data acquisition, while supporting monitoring and management mechanisms ensure long-term operational sustainability. Only by combining both can we build a truly sustainable and highly available data acquisition solution for large model training.
