Book Image

Learning Apache Cassandra - Second Edition

Book Image

Learning Apache Cassandra - Second Edition

Overview of this book

Cassandra is a distributed database that stands out thanks to its robust feature set and intuitive interface, while providing high availability and scalability of a distributed data store. This book will introduce you to the rich feature set offered by Cassandra, and empower you to create and manage a highly scalable, performant and fault-tolerant database layer. The book starts by explaining the new features implemented in Cassandra 3.x and get you set up with Cassandra. Then you’ll walk through data modeling in Cassandra and the rich feature set available to design a flexible schema. Next you’ll learn to create tables with composite partition keys, collections and user-defined types and get to know different methods to avoid denormalization of data. You will then proceed to create user-defined functions and aggregates in Cassandra. Then, you will set up a multi node cluster and see how the dynamics of Cassandra change with it. Finally, you will implement some application-level optimizations using a Java client. By the end of this book, you'll be fully equipped to build powerful, scalable Cassandra database layers for your applications.
Table of Contents (14 chapters)

Building an autocomplete function


So far, we've been focused on storing users and their status updates, but we can use our knowledge of compound primary keys to make it a bit easier to write status updates too. Let's introduce a hashtagging function into the status update composition interface and then autocomplete hashtags as users type them.

First, we'll set up a table to store hashtags using the following query:

CREATE TABLE "hash_tags" ( "prefix" text, "remaining" text, "tag" text, PRIMARY KEY ("prefix", "remaining"));

The structure of our table is a bit unusual, but it will work very well for our purposes. The partition key is a prefix, which we'll use to store the first two letters of each hashtag. The clustering column, remaining, will store the remaining letters of the hashtag, and tag will contain the entire hashtag start to finish.

By partitioning the table this way, we'll make things easy for Cassandra by immediately narrowing down the list of possible autocomplete tags to those in...