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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

Using secondary indexes to avoid denormalization


So far, we've exclusively used primary key columns to look up rows—either the full primary key when we're looking for a specific row, or just the partition key when retrieving multiple rows in a single partition. We know that these kinds of lookups are very efficient because Cassandra can satisfy the query by accessing the single region of storage that holds the partition's data in order. This is the motivation for the denormalized follow structure we've built in this chapter: whether we want to answer the question Who does alice follow? or the question Who follows alice?, we can construct a query that only needs to access a single partition. However, we're accepting additional complexity in the form of storing two versions of the same information in user_inbound_follows and user_outbound_follows.

As it happens, Cassandra does provide us with a way to answer both questions in a reasonably efficient way using a single table with a single representation...

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