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)

Recording discrete analytics observations


Let's say we want to keep very close track of how many times our user's status updates are viewed by someone else. Status updates may be viewed on the MyStatus website, or using our mobile app, or via a third-party app using our API. We'll want to track that, as well as which status update was viewed and when. To do this, let's create a table to store analytics observations:

CREATE TABLE "status_update_views" ( 
  "status_update_username" text, 
  "status_update_id" timeuuid, 
  "observed_at" timeuuid, 
  "client_type" text, 
  PRIMARY KEY ( 
    ("status_update_username", "status_update_id"), 
    "observed_at" 
  ) 
);

In this new table, we store a partition for each individual status update, with the full primary key of the status update serving as the partition key for our table. Each time someone views a status update, we'll store a new row in the table, generating a timestamp UUID (Universally unique identifier) for the row to populate the observed_at...