JuiceFS: A Cloud‑Native Distributed File System for Data Lake and Lakehouse

This article introduces JuiceFS, a cloud‑native distributed file system designed to address the limitations of traditional HDFS and object storage in modern data‑lake and lakehouse architectures.

It first explains the concepts of Data Lake and Lakehouse, their origins, and why storage‑compute separation has become essential for large‑scale data platforms.

Then it compares HDFS and object storage across dimensions such as scalability, consistency, capacity management, rename semantics, listing performance, and operational complexity, highlighting their respective “Achilles’ heels”.

Next, the design of JuiceFS is presented, covering its metadata engine, data persistence engine, and client layer that provide POSIX, HDFS, and S3 compatibility, as well as plug‑in support for various metadata back‑ends.

Performance evaluations show JuiceFS’s advantages in throughput, latency (especially rename), metadata operations, and cache‑accelerated reads compared with HDFS and raw object storage.

Several user case studies (a K12 education platform and Douban) illustrate how JuiceFS reduces ETL latency and enables seamless migration to the cloud.

The article concludes with a recap of JuiceFS’s features, its compatibility with lakehouse file‑system requirements, and its role in supporting BI‑to‑AI data pipelines.