JuiceFS

JuiceFS is a Distributed File System (DFS) (storage infrastructure) that separates compute from storage by using object storage as the data layer and a database as the metadata engine to provide POSIX-compatible file access across cloud and on-premises (on-prem) environments.

• Distributed POSIX file system built on object storage (storage infrastructure)
• Metadata stored in external databases such as Redis, MySQL, and others (database integration)
• Supports standard file system access via FUSE and Kubernetes Critical Supplier Identification (CSI) driver (container storage)
• Compatible with big data and AI/ML workloads including Hadoop and similar ecosystems (data analytics storage)
• Available as an open-source community edition and a managed cloud service (deployment options)

More About JuiceFS

JuiceFS is a DFS (storage infrastructure) designed to provide a cloud-native, POSIX-compliant namespace over object storage. It addresses the problem of storing large volumes of unstructured data while retaining traditional file system semantics that applications expect. By decoupling metadata from data and placing data in commodity object storage, JuiceFS targets scenarios where scalability, compatibility with existing tools, and integration with cloud storage services are required.

The architecture of JuiceFS uses object storage (object storage infrastructure) as the persistent data layer and an external database (metadata database layer) to store file system metadata. Supported metadata backends include common relational and key-value databases, such as Redis and MySQL, among others, which can be selected based on performance and operational requirements. Clients mount JuiceFS volumes through FUSE (file system in userspace), enabling applications to interact with JuiceFS as a standard POSIX file system. This design allows multiple compute nodes to share the same filesystem namespace while reading and writing data stored in cloud or on-prem object stores.

JuiceFS provides integrations that align with enterprise environments, including a Kubernetes CSI driver (container storage) for provisioning persistent volumes in containerized workloads. It is compatible with big data frameworks and AI/ML platforms (data analytics and Machine Learning (ML) storage), and is used to store training data, analytical datasets, logs, and other large files. The system supports features such as data caching on local disks or SSDs (performance optimization) to reduce latency when accessing frequently used objects, and it uses the object storage layer for durable, scalable capacity.

The project is available as an open-source community edition (open-source software) that organizations can deploy and operate themselves, and also as a managed service operated by Juicedata (managed cloud storage service). This allows teams to choose between self-managed deployments in their own infrastructure or consumption as a cloud service. JuiceFS interoperates with mainstream public cloud object storage services and compatible S3 APIs (cloud storage integration), which positions it as a file system layer that can unify storage across different cloud providers or hybrid setups.

Within enterprise IT taxonomies, JuiceFS fits primarily into distributed file systems and cloud-native storage platforms (storage infrastructure). It is relevant for scenarios that require shared file access across many compute nodes, such as container platforms, big data processing clusters, and ML training environments. By leveraging object storage for capacity and external databases for metadata, JuiceFS offers a file system abstraction that aligns with modern cloud architectures while remaining accessible through familiar POSIX file operations.