Draft:Juice File System

Juice File System (JuiceFS)

JuiceFS is an open-source, distributed POSIX file system designed for cloud-native environments. Released under the Apache License 2.0, it enables users to utilize cloud storage as if it were a local file system, offering high performance, scalability, and full POSIX compatibility. JuiceFS is widely adopted for applications in big data, machine learning, artificial intelligence (AI), and cloud-native deployments due to its robust feature set and flexibility.

Features

JuiceFS provides a range of powerful features that distinguish it as a versatile storage solution:

• POSIX Compatibility: Fully POSIX-compliant, JuiceFS integrates seamlessly with existing applications without requiring code changes, behaving like a traditional local file system.
• Hadoop Compatibility: Supports Hadoop 2.x and 3.x, along with various components of the Hadoop ecosystem, making it a strong choice for big data workloads.
• S3 Compatibility: Includes an S3-compatible gateway, enabling interaction with the file system using S3 tools and APIs.
• Cloud Native: Offers a Kubernetes CSI driver, simplifying its use in Kubernetes environments for containerized applications.
• Shareable: Can be mounted and accessed concurrently by multiple clients, supporting thousands of simultaneous users.
• Strong Consistency: Ensures that file system changes are immediately visible across all mounted instances.
• High Performance: Delivers low-latency access (as low as a few milliseconds) and scalable throughput, leveraging the underlying object storage.
• Data Encryption: Provides encryption for data both in transit and at rest, enhancing security.
• Global File Locks: Supports BSD locks (flock) and POSIX record locks (fcntl) for distributed file locking.
• Data Compression: Utilizes LZ4 or Zstandard algorithms to compress data, optimizing storage efficiency.

Architecture

JuiceFS employs a unique architecture that separates data and metadata storage to maximize performance and scalability:

• Data Storage: Files are divided into chunks (default size: 64 MiB), which are further split into slices and blocks (default size: 4 MiB). These blocks are stored in an object storage system, such as Amazon S3, Google Cloud Storage, or any S3-compatible service.
• Metadata Storage: Metadata—including file names, sizes, permissions, and directory structures—is stored in a high-performance database engine. Supported options include Redis, MySQL, PostgreSQL, SQLite, and TiKV, allowing flexibility based on deployment needs.

This separation enables rapid metadata operations while leveraging the virtually unlimited scalability of object storage for file data.

Use Cases

JuiceFS is well-suited for a variety of modern storage needs, including:

• Big Data: Acts as a scalable and cost-effective storage layer for big data frameworks like Hadoop, Apache Spark, and Flink.
• Machine Learning and AI: Facilitates the storage and processing of large datasets required for training machine learning models, with fast access and high throughput.
• Cloud-Native Applications: Integrates seamlessly with Kubernetes, providing shared storage for containerized workloads.
• Backup and Archiving: Leverages object storage for long-term data retention, disaster recovery, and archiving purposes.

Community and Support

JuiceFS benefits from an active and growing community, offering multiple avenues for support and engagement:

• Documentation: Comprehensive guides, tutorials, and installation instructions are available on the official JuiceFS website.
• Forums: Users can participate in community forums to ask questions, share experiences, and seek assistance.
• GitHub: The project is hosted on GitHub, where users can access the source code, report issues, and contribute to its development.

History

JuiceFS was created in 2020 by Juicedata, a company dedicated to developing cloud-native storage solutions. The project emerged to meet the demand for a distributed file system that combines the scalability and cost-effectiveness of object storage with the performance and POSIX compatibility required by modern applications, particularly in big data, artificial intelligence (AI), and cloud-native ecosystems. Its initial vision was to provide a seamless integration of cloud storage with a high-performance metadata engine, addressing challenges in managing large-scale data efficiently.

The first public release of JuiceFS occurred in January 2021, introducing key features such as full POSIX compatibility, support for Redis as its metadata engine, and integration with Amazon S3 for data storage. This marked the project's debut as an open-source solution, aimed at delivering a flexible and scalable file system for cloud-based workloads.

A major milestone was achieved with the release of JuiceFS 1.0 in March 2022. This version introduced significant enhancements, including performance optimizations, expanded metadata engine options (e.g., MySQL and PostgreSQL), and the addition of a Kubernetes CSI driver, enabling seamless use in containerized environments. The 1.0 release established JuiceFS as a mature and dependable storage solution, broadening its appeal across diverse use cases.

Since its inception, JuiceFS has evolved through continuous updates and feature additions. Key integrations include compatibility with Hadoop and Apache Spark, enhancing its utility in big data processing, and support for TiKV as a metadata engine, improving scalability for large-scale deployments. These developments have positioned JuiceFS as a versatile tool for both traditional and cloud-native applications.

The JuiceFS community has seen steady growth, with contributions from over 50 developers and widespread adoption in fields like AI, machine learning, and cloud computing. By 2025, the project had earned over 10,000 stars on GitHub, reflecting its rising popularity and the increasing need for high-performance, scalable storage solutions. Notable deployments include its use as shared storage for machine learning model training and as a file system for Kubernetes clusters.