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Seven NoSQL Databases in a Week

Seven NoSQL Databases in a Week

By : Sudarshan Kadambi, Xun (Brian) Wu
3.5 (6)
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Seven NoSQL Databases in a Week

Seven NoSQL Databases in a Week

3.5 (6)
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 (10 chapters)
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How does Neo4j work?

Neo4j stores nodes, edges, and properties on disk in stores that are specific to each type—for example, nodes are stored in the node store.[5, s.11] They are also stored in two types of caches—the file system (FS) and the node/relationship caches. The FS cache is divided into regions for each type of store, and data is evicted on a least-frequently-used (LFU) policy.

Data is written in transactions assembled from commands and sorted to obtain a predictable update order. Commands are sorted at the time of creation, with the aim of preserving consistency. Writes are added to the transaction log and either marked as committed or rolled back (in the event of a failure). Changes are then applied (in sorted order) to the store files on disk.

It is important to note that transactions in Neo4j dictate the state and are therefore idempotent by nature...

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