Challenges and pain points
- The tight coupling of traditional Hadoop systems is not designed for the elastic scalability for the cloud
- Deploying HDFS on the cloud disks is about 3 times that of deploying HDFS on bare metal. If deploying on virtual machines on public clouds, the same O&M challenges in IDC persist in the cloud too.
- The HDFS NameNode that manages a single namespace cannot support massive data. HDFS Federation addresses this limitation by supporting multiple Namenodes/namespaces to HDFS but it brings the high O&M cost.
- Directly accessing data for big data analytics in the object storage would result in problems like poor performance, lack of strong consistency guarantee, etc., which would greatly impact efficiency, stability, and accuracy.
- JuiceFS is fully compatible with HDFS API and all the Hadoop ecological components (Hadoop 2.x or 3.x), and compatible with mainstream Hadoop distributions;
- JuiceFS is based on object storage on the cloud, which can not only achieve elastic scaling of storage space, but also greatly reduce storage costs;
- The JuiceFS single namespace can support tens of billions of files and hundreds of PiB data storage, significantly reducing O&M costs;
- JuiceFS provides strong consistency guarantee, and has better metadata and data read and write performance than object storage;
- JuiceFS is also fully compatible with POSIX and S3 APIs and can easily integrate various types of applications (such as AI) into the big data platform.
- JuiceFS can be a drop-in replacement of HDFS as the storage base for the entire big data platform.
- Archive the warm and cold data to JuiceFS from HDFS, OLAP to JuiceFS to scale the storage capacity and lower the cost.
- Combine JuiceFS with data lake components to build Lakehouse and real-time data warehouse architecture.
- You can easily and quickly build a Hadoop-like data platform on public clouds with the same experience and feature guarantee as HDFS in traditional datacenters, reducing migration costs;
- You can make full use of the elastic scalability of public cloud resources, flexibly operate and manage the entire data platform, and greatly reduce costs;
- You can upgrade from the "storage-computing coupling" architecture to the "storage-computing separation" architecture to build a next-generation big data platform;
- You can easily build the Lakehouse platform by integrating various types of application data (structured, semi-structured, unstructured) into the big data platform.