A server architecture must adapt as data grows. Storing all data in memory works for small datasets. For large datasets, a database is needed to manage memory and ensure fast access.
By Pawan Nayar · from the LLOS AI Learning record
A server architecture must adapt as data grows.
Storing all data in memory works for small datasets. For large datasets, a database is needed to manage memory and ensure fast access.
A server architecture needs to change when data grows too large for memory. Moving data to a database like SQLite helps manage large files. This change keeps the system fast and stable. It also allows for more advanced search features. The goal is to keep the system low-cost and quick to start.
A developer creates an AI application. At first, the application stores 100,000 shapes in the server's memory. This works well. When the number of shapes grows to 2 million, the server runs out of memory. The developer must change the architecture to store shapes in a database, loading only small parts into memory when needed.
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A developer needs to store many questions for a quiz application. Storing questions in JSON files can be slow.
An AI model should use a database like SQLite for all data storage. This means all information, such as images or text, is saved in the database.
The Expressive Explorer is a large AI model that uses many visual styles. It has a flexible design with three ways to operate.
A system needs a plan for growth. This plan covers data storage, user access, and how the system changes over time.
A server architecture must adapt as data grows. Storing all data in memory works for small datasets. For large datasets, a database is needed to manage memory and ensure fast access.