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Cassandra 3.x High Availability
Cassandra 3.x High Availability

Cassandra 3.x High Availability: Achieve scalability and high availability without compromising on performance , Second Edition

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Cassandra 3.x High Availability

Chapter 1.  Cassandras Approach to High Availability

What does it mean for a data store to be highly available? When designing or configuring a system for high availability, architects typically hope to offer some guarantee of uptime even in the presence of failure. Historically, it has been sufficient for the vast majority of systems to be available for less than 100 percent of the time, with some attempting to achieve the five nines, or 99.999, percent uptime.

The exact definition of high availability depends on the requirements of the application. This concept has gained increasing significance in the context of web applications, real-time systems, and other use cases that cannot afford any downtime. Database systems must not only guarantee system uptime, the ability to fulfill requests, but also ensure that the data itself remains available.

Traditionally, it has been difficult to make databases highly available, especially the relational database systems that have dominated the scene for the last couple of decades. These systems are most often designed to run on a single large machine, making it challenging to scale out to multiple machines.

Let's examine some of the reasons why many popular database systems have difficulty being deployed in high availability configurations, as this will allow us to have a greater understanding of the improvements that Cassandra offers. Exploring these reasons can help us to put aside previous assumptions that simply don't translate to the Cassandra model.

Therefore, in this chapter, we'll cover the following topics:

  • The Atomicity Consistency Isolation Durability (ACID) properties
  • Monolithic architecture
  • Master-slave architecture, covering sharding and leader election
  • Cassandra's approach to achieving high availability

Introducing the ACID properties

One of the most significant obstacles that prevents traditional databases from achieving high availability is that they attempt to strongly guarantee the ACID properties:

  • Atomicity: This guarantees that database updates associated with a transaction occur in an all-or-nothing manner. If some part of the transaction fails, the state of the database remains unchanged.
  • Consistency: This assures that the integrity of data will be preserved across all instances of that data. Changes to a value in one location will definitely be reflected in all other locations.
  • Isolation: This attempts to ensure that concurrent transactions that manipulate the same data do so in a controlled manner, essentially isolating in-process changes from other clients. Most traditional relational database systems provide various levels of isolation with different guarantees at each level.
  • Durability: This ensures that all writes are preserved in nonvolatile storage, most commonly on disk.

Database designers most commonly achieve these properties via write masters, locks, elaborate storage area networks, and the like – all of which tend to sacrifice availability. As a result, achieving some semblance of high availability frequently involves bolt-on components, log shipping, leader election, sharding, and other such strategies that attempt to preserve the original design.

Monolithic simplicity

The simplest design approach to guarantee ACID properties is to implement a monolithic architecture where all functions reside on a single machine. Since no coordination among nodes is required, the task of enforcing all the system rules is relatively straightforward.

Increasing availability in such architectures typically involves hardware layer improvements, such as RAID arrays, multiple network interfaces, and hot-swappable drives. However, the fact remains that even the most robust database server acts as a single point of failure. This means that if the server fails, the application becomes unavailable. This architecture can be illustrated with the following diagram:

Monolithic simplicity

A common means of increasing capacity to handle requests on a monolithic architecture is to move the storage layer to a shared component such as a storage area network (SAN) or network attached storage (NAS). Such devices are usually quite robust, with large numbers of disks and high-speed network interfaces. This approach is shown in a modification of the previous diagram, which depicts two database servers using a single NAS.

Monolithic simplicity

You'll notice that while this architecture increases the overall request-handling capacity of the system, it simply moves the single failure point from the database server to the storage layer. As a result, there is no real improvement from an availability perspective.

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Key benefits

  • See how to get 100 percent uptime with your Cassandra applications using this easy-follow guide
  • Learn how to avoid common and not-so-common mistakes while working with Cassandra using this highly practical guide
  • Get familiar with the intricacies of working with Cassandra for high availability in your work environment with this go-to-guide

Description

Apache Cassandra is a massively scalable, peer-to-peer database designed for 100 percent uptime, with deployments in the tens of thousands of nodes, all supporting petabytes of data. This book offers a practical insight into building highly available, real-world applications using Apache Cassandra. The book starts with the fundamentals, helping you to understand how Apache Cassandra’s architecture allows it to achieve 100 percent uptime when other systems struggle to do so. You’ll get an excellent understanding of data distribution, replication, and Cassandra’s highly tunable consistency model. Then we take an in-depth look at Cassandra's robust support for multiple data centers, and you’ll see how to scale out a cluster. Next, the book explores the domain of application design, with chapters discussing the native driver and data modeling. Lastly, you’ll find out how to steer clear of common anti-patterns and take advantage of Cassandra’s ability to fail gracefully.

Who is this book for?

If you are a developer or DevOps engineer who has basic familiarity with Cassandra and you want to become an expert at creating highly available, fault tolerant systems using Cassandra, this book is for you.

What you will learn

  • Understand how the core architecture of Cassandra enables highly available applications
  • Use replication and tunable consistency levels to balance consistency, availability, and performance
  • Set up multiple data centers to enable failover, load balancing, and geographic distribution
  • Add capacity to your cluster with zero downtime
  • Take advantage of high availability features in the native driver
  • Create data models that scale well and maximize availability
  • Understand common anti-patterns so you can avoid them
  • Keep your system working well even during failure scenarios

Product Details

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Publication date : Aug 29, 2016
Length: 196 pages
Edition : 2nd
Language : English
ISBN-13 : 9781786462107
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Apple
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Product Details

Publication date : Aug 29, 2016
Length: 196 pages
Edition : 2nd
Language : English
ISBN-13 : 9781786462107
Vendor :
Apple
Category :
Languages :
Tools :

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Table of Contents

9 Chapters
1. Cassandras Approach to High Availability Chevron down icon Chevron up icon
2. Data Distribution Chevron down icon Chevron up icon
3. Replication Chevron down icon Chevron up icon
4. Data Centers Chevron down icon Chevron up icon
5. Scaling Out Chevron down icon Chevron up icon
6. High Availability Features in the Native Java Client Chevron down icon Chevron up icon
7. Modeling for Availability Chevron down icon Chevron up icon
8. Anti-Patterns Chevron down icon Chevron up icon
9. Failing Gracefully Chevron down icon Chevron up icon

Customer reviews

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Full star icon Full star icon Full star icon Half star icon Empty star icon 3.8
(6 Ratings)
5 star 50%
4 star 16.7%
3 star 16.7%
2 star 0%
1 star 16.7%
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BJORN Apr 17, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I just started learning about Cassandra, and this book was a great help, I have about 10 years experience in networkings and linux and thought this was a good source of information to help you start designing and building your next Cassandra cluster. It also gives a good overview of how things work internally and how to setup clusters in multiple datacenters.
Amazon Verified review Amazon
Amazon Customer Sep 29, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I am about to deploy a production Cassandra cluster to AWS to back a mobile app and this book helps tremendously.
Amazon Verified review Amazon
Paul Rafferty Nov 23, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Must have reference for Cassandra 3.0 high availability.
Amazon Verified review Amazon
Rahul Dec 01, 2016
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Good
Amazon Verified review Amazon
Amazon Customer Oct 15, 2017
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
Not bad, but not good. Definitive guide include more information. This book has less
Amazon Verified review Amazon
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