EdgeNext Documentation

Overview

Object Storage is a storage architecture that manages and stores unstructured data as discrete units known as "objects." Unlike traditional file systems or block storage, object storage encapsulates each piece of data as an independent object. Each object consists of three core elements: the data itself, metadata (descriptive attributes associated with the data), and a unique identifier (used for retrieval and access). With its inherent advantages of low cost, high scalability, and ease of integration, object storage has become the preferred solution for storing massive volumes of unstructured data in the cloud computing era.

Key Features

• Flat Namespace: Object storage abandons the hierarchical directory structure of traditional file systems. All objects reside in a single flat namespace and are addressed using unique keys, which greatly simplifies the complexity of managing massive-scale data.

• High Scalability: Object storage natively supports horizontal scaling, enabling it to effortlessly accommodate data growth from terabytes to exabytes without requiring significant architectural overhauls.

• HTTP/RESTful Access: Object storage utilizes standard HTTP/HTTPS interfaces for read and write operations, facilitating seamless integration with applications, microservices, and cloud-native architectures.

• High Durability and Availability: Technologies such as three-copy replication and erasure coding ensure data durability. Object storage typically achieves 99.99% data durability.

Application Scenarios

Object storage is widely used in the following scenarios:

• Static Resource Hosting: Storage and distribution of web assets such as images, videos, audio files, CSS, and JavaScript.

• Data Backup and Archiving: Long-term, cost-effective storage for cold data with high reliability.

• Big Data and Data Lakes: Serving as the foundational layer for data lakes, enabling storage and analysis of massive volumes of raw data.

• Log and Audit Data: Centralized storage for system logs and operational records.

• AI/ML Training Datasets: Management and sharing of large-scale datasets for model training.

• Content Distribution: Integration with CDNs to achieve low-latency content delivery on a global scale.