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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

Developing a mental model for Cassandra


Any time you learn a new tool, you will naturally begin to develop a mental model for how that tool works. So far, our model for how Cassandra tables works is fairly simple; we will expand upon it throughout the book.

For now, we can think of a Cassandra table as a collection of keys, each of which points to a row. Each row contains data in some subset of its columns.

We also know that, at least in the users table, rows are stored non-contiguously; accessing each row requires Cassandra to seek a different place in storage. So we may imagine the current state of our users table to look something like this:

Essentially, we can think of our Cassandra table as a key-value store, where each value consists of one or more predefined columns containing data of a predefined type.

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