A Cloud Native Unstructured Data Storage
CubeFS is a new generation cloud-native storage that supports access protocols such as S3, HDFS, and POSIX. It is widely applicable in various scenarios such as big data, AI/LLMs, container platforms, separation of storage and computing for databases and middleware, data sharing and protection,etc.
The Key Features of CubeFS
Multi-Protocol
Compatible with various access protocols such as S3, POSIX, HDFS, etc., and the access between protocols can be interoperable
Multi-Engine
Support replicas and erasure coding engines, users can choose flexibly according to business scenarios
Highly Scalable
Easy to build a PB or EB-scale distributed storage service, and each module can be expanded horizontally
Multi-Tenancy
Supports multi-tenant management and provides fine-grained tenant isolation policies
High Performance
Supports multi-level caching, multiple high-performance replication protocols, and optimizes specific performance for small files
Cloud-Native
Easy to use CubeFS in Kubernetes via CSI Driver
CubeFS Deployment
CubeFS has developed a CSI plugin based on the Container Storage Interface (CSI) interface specification to support cloud storage in Kubernetes clusters.



CubeFS Application Scenarios
CubeFS is a cloud-native storage infrastructure that is widely used in a variety of scenarios, including big data storage, machine learning platforms, large-scale container platforms, as well as database and middleware storage and computing separation. It is also used for data sharing and protection.
Building a Highly Reliable, Highly Available, Low-Cost, and EB-Scale Independent Key-Value Storage System with CubeFS.
As data continues to grow, businesses face greater cost challenges. In order to alleviate the storage cost pressure caused by multi-copy mode, CubeFS has introduced a erasure code subsystem (also known as BlobStore), which is a highly reliable, highly available, low-cost, and EB-scale independent key-value storage system.
CubeFS's application in storage-computing separation scenario in ClickHouse.
ClickHouse can achieve the following advantages through the data tiering set by CubeFS. Fisrt is the Ultra-large capacity, because CubeFS provides storage space with almost no capacity limit, which removes the capacity limit of ClickHouse that is restricted by local resources. Second,Higher cost-effectiveness.Compared with local SSD storage arrays, CubeFS provides a bandwidth that far exceeds SSD arrays at the same price through cluster multi-machine IO concurrency.For more information,click to get it
Big Data Cold and Hot Separation Technology Practice in OPPO
OPPO has been storing its large-scale data in HDFS clusters with a total storage capacity of EB-level. However, due to the rapidly growing business, the company faced several operational issues, including lack of machine resources, high operational costs, and high redundancy in storing cold and hot data. In order to solve these problems, OPPO implemented CubeFS
The Application of CubeFS in Machine Learning
The article discusses OPPO's machine learning platform which supports over 100 AI businesses with tens of thousands of daily training tasks. The platform uses storage to manage corpora, datasets, and business models. Due to the growth of the platform, the original storage faced challenges, so they implemented continuous architecture iterations.
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