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Learning Apache Cassandra

You're reading from   Learning Apache Cassandra Managing fault-tolerant, scalable data with high performance

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Product type Paperback
Published in Apr 2017
Publisher
ISBN-13 9781787127296
Length 360 pages
Edition 2nd Edition
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Author (1):
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Sandeep Yarabarla Sandeep Yarabarla
Author Profile Icon Sandeep Yarabarla
Sandeep Yarabarla
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Table of Contents (15) Chapters Close

Preface 1. Getting Up and Running with Cassandra FREE CHAPTER 2. The First Table 3. Organizing Related Data 4. Beyond Key-Value Lookup 5. Establishing Relationships 6. Denormalizing Data for Maximum Performance 7. Expanding Your Data Model 8. Collections, Tuples, and User-Defined Types 9. Aggregating Time-Series Data 10. How Cassandra Distributes Data 11. Cassandra Multi-Node Cluster 12. Application Development Using the Java Driver 13. Peeking under the Hood 14. Authentication and Authorization

Data distribution in Cassandra

In a traditional relational database such as MySQL or PostgreSQL, the entire contents of the database reside on a single machine. At a certain scale, the hardware capacity of the server running the database becomes a constraint: simply migrating to more powerful hardware will lead to diminishing returns.

Let's imagine ourselves in this scenario, where we have an application running on a single-machine database that has reached the limits of its capacity to vertically scale. In that case, we'll want to split the data between multiple machines, a process known as sharding or federation. Assuming we want to stick with the same underlying tool, we'll end up with multiple database instances, each of which holds a subset of our total data. Crucially, in this scenario, the different database instances have no knowledge of each other; as far as each instance is concerned, it...

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