Cassandra High Availability by Robbie Strickland

By Robbie Strickland

Apache Cassandra is a hugely scalable, peer-to-peer database designed for one hundred pc uptime, with deployments within the tens of millions of nodes helping petabytes of data. This booklet bargains readers a realistic perception into construction hugely to be had, real-world purposes utilizing Apache Cassandra. 

The booklet begins with the basics, supporting you to appreciate how the structure of Apache Cassandra permits it to accomplish one hundred pc uptime whilst different structures fight to take action. you will have a great figuring out of knowledge distribution, replication, and Cassandra's hugely tunable consistency version. this is often by means of an in-depth examine Cassandra's strong aid for a number of facts facilities, and the way to scale out a cluster. subsequent, the publication explores the area of software layout, with chapters discussing the local driving force and knowledge modeling. finally, you can find out the way to avoid universal antipatterns and benefit from Cassandra's skill to fail gracefully.

What you'll learn:

  • Understand how the middle structure of Cassandra allows hugely on hand applications
  • Use replication and tunable consistency degrees to stability consistency, availability, and performance
  • Set up a number of information facilities to let failover, load balancing, and geographic distribution
  • Add means on your cluster with 0 down time
  • Take good thing about excessive availability beneficial properties within the local driver
  • Create information versions that scale good and maximize availability
  • Understand universal anti-patterns so that you can stay away from them
  • Keep your process operating good even in the course of failure scenarios

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Additional info for Cassandra High Availability

Sample text

As a result, machines involved in the transfer end up under less load than without vnodes, thus increasing availability of those ranges. As a result, the ring becomes naturally balanced on its own. Cassandra provides a mechanism to automatically rebuild a failed node using replicated data. However, Cassandra will only use one replica in the rebuild operation. So in this case, a rebuild operation involves three nodes, placing a high load on all three. Even worse, token ranges A and B reside entirely on nodes that are being taxed by this process, which can result in overburdening the entire cluster due to slow response times for these operations.

Cassandra also includes a mechanism that maintains the replication factor during node failures. It also allows you to create separate data centers for online transactions and heavy analysis workloads, while allowing data written in one data center to be immediately reflected in others. Chapters 3, Replication, and Chapter 4, Data Centers, will provide a complete discussion of Cassandra’s extensive replication features. However, as previously discussed, consistency should be thought of as a continuum, not as an absolute.

This causes a significant amount of administrative overhead for a large cluster. We’ll discuss this in detail later in this chapter. Hotspots: In some cases, the relatively large range assigned to each node can cause hotspots if data is not evenly distributed. Attempting to subdivide ranges to deal with nodes of varying sizes is a difficult and error-prone task. For existing installations, migrating to vnodes will improve the performance, reliability, and administrative requirements of your cluster, especially during topology changes and failure scenarios.

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