Book Image

Ceph: Designing and Implementing Scalable Storage Systems

By : Michael Hackett, Vikhyat Umrao, Karan Singh, Nick Fisk, Anthony D'Atri, Vaibhav Bhembre
Book Image

Ceph: Designing and Implementing Scalable Storage Systems

By: Michael Hackett, Vikhyat Umrao, Karan Singh, Nick Fisk, Anthony D'Atri, Vaibhav Bhembre

Overview of this book

This Learning Path takes you through the basics of Ceph all the way to gaining in-depth understanding of its advanced features. You’ll gather skills to plan, deploy, and manage your Ceph cluster. After an introduction to the Ceph architecture and its core projects, you’ll be able to set up a Ceph cluster and learn how to monitor its health, improve its performance, and troubleshoot any issues. By following the step-by-step approach of this Learning Path, you’ll learn how Ceph integrates with OpenStack, Glance, Manila, Swift, and Cinder. With knowledge of federated architecture and CephFS, you’ll use Calamari and VSM to monitor the Ceph environment. In the upcoming chapters, you’ll study the key areas of Ceph, including BlueStore, erasure coding, and cache tiering. More specifically, you’ll discover what they can do for your storage system. In the concluding chapters, you will develop applications that use Librados and distributed computations with shared object classes, and see how Ceph and its supporting infrastructure can be optimized. By the end of this Learning Path, you'll have the practical knowledge of operating Ceph in a production environment. This Learning Path includes content from the following Packt products: • Ceph Cookbook by Michael Hackett, Vikhyat Umrao and Karan Singh • Mastering Ceph by Nick Fisk • Learning Ceph, Second Edition by Anthony D'Atri, Vaibhav Bhembre and Karan Singh
Table of Contents (27 chapters)
Title Page
About Packt
Contributors
Preface
Index

Ceph FS – a drop-in replacement for HDFS


Hadoop is a programming framework that supports the processing and storage of large data sets in a distributed computing environment. The Hadoop core includes the analytics MapReduce engine and the distributed file system known as Hadoop Distributed File System (HDFS), which has several weaknesses that are listed as follows:

  • It had a single point of failure until the recent versions of HDFS

  • It isn't POSIX compliant

  • It stores at least three copies of data

  • It has a centralized name server resulting in scalability challenges

The Apache Hadoop project and other software vendors are working independently to fix these gaps in HDFS.

The Ceph community has done some development in this space, and it has a filesystem plugin for Hadoop that possibly overcomes the limitations of HDFS and can be used as a drop-in replacement for it. There are three requirements for using Ceph FS with HDFS; they are as follows:

  • Running the Ceph cluster

  • Running the Hadoop cluster

  • Installing...