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

Large monitor databases


Ceph monitors use leveldb to store all of the required monitor data for your cluster. This includes things such as the monitor map, OSD map, and PG map, which OSDs and clients pull from the monitors to be able to locate objects in the RADOS cluster. One particular feature that one should be aware of is that during a period where the health of the cluster doesn’t equal HEALTH_OK, the monitors do not discard any of the older cluster maps from its database. If the cluster is in a degraded state for an extended period of time and/or the cluster has a large number of OSDs, the monitor database can grow very large.

In normal operating conditions, the monitors are very lightweight on resource consumption; because of this, it’s quite common for smaller disk sizes to be used for the monitors. In the scenario where a degraded condition continues for an extended period, it’s possible for the disk holding the monitor database to fill up, which, if it occurs across all your monitor...