Book Image

Seven NoSQL Databases in a Week

By : Sudarshan Kadambi, Xun (Brian) Wu
Book Image

Seven NoSQL Databases in a Week

By: Sudarshan Kadambi, Xun (Brian) Wu

Overview of this book

This is the golden age of open source NoSQL databases. With enterprises having to work with large amounts of unstructured data and moving away from expensive monolithic architecture, the adoption of NoSQL databases is rapidly increasing. Being familiar with the popular NoSQL databases and knowing how to use them is a must for budding DBAs and developers. This book introduces you to the different types of NoSQL databases and gets you started with seven of the most popular NoSQL databases used by enterprises today. We start off with a brief overview of what NoSQL databases are, followed by an explanation of why and when to use them. The book then covers the seven most popular databases in each of these categories: MongoDB, Amazon DynamoDB, Redis, HBase, Cassandra, In?uxDB, and Neo4j. The book doesn't go into too much detail about each database but teaches you enough to get started with them. By the end of this book, you will have a thorough understanding of the different NoSQL databases and their functionalities, empowering you to select and use the right database according to your needs.
Table of Contents (16 chapters)
Title Page
Copyright and Credits
Dedication
Packt Upsell
Contributors
Preface
Index

Cassandra anti-patterns


Cassandra is a great tool for solving specific problems, but it is not a general-purpose data store. Considering the prior section where we discussed the read and write paths, there are some obvious scenarios in which Cassandra is not the correct choice of the data store. These are important to remember, and we will discuss them in this section:

Cassandra reconciles data returned from both memory, disk, and read-time.

Frequently updated data

Primary keys in Cassandra are unique. Therefore there is no difference between an insert and an update in Cassandra; they are both treated as a write operation. Given that its underlying data files are immutable, it is possible that multiple writes for the same key will store different data in multiple files. The overwritten data doesn't automatically go away. It becomes obsolete (due to its timestamp).

When Cassandra processes a read request, it checks for the requested data from both memory and disk. If the requested data was written...