By Robbie Strickland
Apache Cassandra is a hugely scalable, peer-to-peer database designed for 100% uptime, with deployments within the tens of millions of nodes helping petabytes of data. This e-book deals readers a realistic perception into construction hugely on hand, real-world purposes utilizing Apache Cassandra.
The e-book begins with the basics, aiding you to appreciate how the structure of Apache Cassandra permits it to accomplish one hundred pc uptime while different platforms fight to take action. you should have a very good realizing of information distribution, replication, and Cassandra's hugely tunable consistency version. this can be by means of an in-depth examine Cassandra's strong aid for a number of information facilities, and the way to scale out a cluster. subsequent, the ebook explores the area of software layout, with chapters discussing the local driving force and knowledge modeling. finally, you will discover out find out how to avoid universal antipatterns and benefit from Cassandra's skill to fail gracefully.
What you are going to learn:
- Understand how the middle structure of Cassandra allows hugely to be had applications
- Use replication and tunable consistency degrees to stability consistency, availability, and performance
- Set up a number of info facilities to allow failover, load balancing, and geographic distribution
- Add capability for your cluster with 0 down time
- Take good thing about excessive availability beneficial properties within the local driver
- Create facts versions that scale good and maximize availability
- Understand universal anti-patterns so that you can keep away from them
- Keep your method operating good even in the course of failure scenarios
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Extra resources for Cassandra High Availability
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. This means each individual node is doing less work than without vnodes, resulting in greater operational stability. This is especially problematic when adding or removing nodes, as it would become necessary to recompute the tokens to achieve a proper balance.
This value becomes the index in an array of street addresses. We can look up the street address of a given name by computing its hash, then accessing the resulting array index. There are additional complexities in hash table design, specifically around avoiding hash collisions, but the basic concept remains straightforward. Let’s examine the distributed hash table architecture and the means by which it solves this problem. Each node in the DHT must share the same hash function so that hash results on one node match the results on all others.
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. Tip Use vnodes whenever possible to avoid issues with topology changes, node rebuilds, hotspots, and heterogeneous clusters. It’s best to always assign your tokens to the nodes in the same order to avoid unnecessary shuffling of data.