Hadoop vs ceph: What are the differences?

Introduction

Hadoop and Ceph are two popular technologies used in the field of big data and distributed storage. While both are designed to handle large volumes of data, they have key differences that set them apart.

1. Scalability: Hadoop is a distributed file system that can scale horizontally by adding more servers to the infrastructure. It uses the Hadoop Distributed File System (HDFS) to store data across multiple nodes. On the other hand, Ceph is a software-defined storage system that can scale both horizontally and vertically. It uses a dynamic clustering mechanism called CRUSH to distribute data across a cluster of storage nodes. Ceph's scalability is more flexible and can support larger volumes of data.

2. Data Placement: In Hadoop, data is replicated across multiple nodes to ensure fault tolerance. The replication factor can be set, with default usually being three copies. In Ceph, data is also replicated, but it uses a more sophisticated method called erasure coding. This technique enables Ceph to distribute data in smaller fragments across multiple storage nodes, resulting in more efficient storage utilization compared to Hadoop's replication.

3. Data Access: Hadoop provides a batch processing model, where data is stored and processed in a batch-oriented manner. It is well-suited for applications that require a high throughput of sequential data processing. On the other hand, Ceph provides a more versatile storage system that can be accessed in a variety of ways, including object storage, block storage, and file storage. Ceph's flexible access allows it to cater to different types of workloads.

4. Fault Tolerance: Both Hadoop and Ceph provide fault tolerance mechanisms. In Hadoop, data replication ensures that multiple copies of data are available in case of node failures. However, this replication can lead to higher storage overhead. In contrast, Ceph uses erasure coding to distribute data in smaller fragments across multiple nodes. This technique reduces storage overhead while still providing fault tolerance. Ceph also has built-in mechanisms to handle node failures and data recovery.

5. Data Consistency: Hadoop follows the eventual consistency model, where data consistency among replicas may take some time to achieve. This consistency model allows for higher write throughput but may result in temporary inconsistencies during data replication. Ceph, on the other hand, provides stronger consistency guarantees by default. It ensures that data is consistent across replicas before acknowledging the write operation. This stronger consistency model is beneficial for applications that require strong data consistency.

6. Community and Ecosystem: Both Hadoop and Ceph have large and active communities. Hadoop has been widely adopted in the industry and has a mature ecosystem with support for various tools and frameworks. Ceph, on the other hand, has gained popularity more recently and is known for its integration with OpenStack, a popular cloud computing platform. Ceph's community is growing and actively contributes to its development and integration with other technologies.

In summary, Hadoop and Ceph differ in terms of scalability, data placement, data access, fault tolerance, data consistency, and their respective communities and ecosystems. Hadoop focuses on batch processing and offers scalability through replication, while Ceph provides a more versatile storage system with flexible access and scalability through erasure coding.