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Learning Apache Cassandra
Learning Apache Cassandra

Learning Apache Cassandra: Managing fault-tolerant, scalable data with high performance , Second Edition

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Learning Apache Cassandra

Inserting data


For our status-sharing application, the first thing we'll want any user to do is to create an account. We'll ask them to choose a username column, enter their email column, and pick a password column; our business logic will be responsible for ensuring that the entries are valid and for encrypting the password appropriately. At that point, we'll be ready to insert the account information as a new row in the users table:

 INSERT INTO "users"
 ("username", "email", "encrypted_password")
 VALUES (
   'alice',
   'alice@gmail.com',
   0x8914977ed729792e403da53024c6069a9158b8c4
 );

 

In the previous statement, which should be familiar to anyone who has used an SQL database, we provide the following information:

  • We want to add a row to the users table
  • We'll be adding data to three columns in that row: username, email, and encrypted_password
  • Finally, we provide the values to insert into those columns in the same order that the column names were listed previously

Note

Does whitespace matter...

Selecting data


We now know how to retrieve data from the database, but that isn't much good unless we can get it back again. Let's say we now want to build an account settings page for MyStatus; we've got the user's username stored in a persistent session, but we will retrieve the other profile fields from the database to display in the settings form:

    SELECT * FROM "users"
    WHERE "username" = 'alice';

This query tells Cassandra we want to retrieve the rows where the value for username (the primary key) is alice. The * wildcard simply says we would like all the columns in that row, saving us from having to type them all out. You'll see the rows we requested nicely formatted in the CQL shell as follows:

In other scenarios, we don't need all the columns. When a user tries to log in to MyStatus, we want to retrieve their password and compare it to the one the user provided us with, but we don't care about the email. Avoiding unnecessary columns reduces the amount of data that needs to be...

Developing a mental model for Cassandra


Any time you learn a new tool, you will naturally begin to develop a mental model for how that tool works. So far, our model for how Cassandra tables works is fairly simple; we will expand upon it throughout the book.

For now, we can think of a Cassandra table as a collection of keys, each of which points to a row. Each row contains data in some subset of its columns.

We also know that, at least in the users table, rows are stored non-contiguously; accessing each row requires Cassandra to seek a different place in storage. So we may imagine the current state of our users table to look something like this:

Essentially, we can think of our Cassandra table as a key-value store, where each value consists of one or more predefined columns containing data of a predefined type.

Summary


In this chapter, you're off to a running start with your MyStatus application, having created our first table, inserted data into it, and learned a few ways to retrieve that data. We developed a model for how Cassandra tables are structured and took a deep dive into Cassandra's type system.

We now have experience with the INSERT and SELECT statements, two of the core operations in CQL. You learned that primary keys, at least the ones we've seen so far, determine the order in which rows are returned, but that ordering is opaque to an application. We can only rely on it being consistent. We saw some of the limitations of simple tables in Cassandra, and we know that the types of multirow retrieval we know so far aren't the most efficient.In Chapter 3, Organizing Related Data, we'll introduce a new way to structure a table that lets us overcome many of the limitations we encountered here.

Selecting data

We now know how to retrieve data from the database, but that isn't much good unless we can get it back again. Let's say we now want to build an account settings page for MyStatus; we've got the user's username stored in a persistent session, but we will retrieve the other profile fields from the database to display in the settings form:

    SELECT * FROM "users"
WHERE "username" = 'alice';

This query tells Cassandra we want to retrieve the rows where the value for username (the primary key) is alice. The * wildcard simply says we would like all the columns in that row, saving us from having to type them all out. You'll see the rows we requested nicely formatted in the CQL shell as follows:

In other scenarios, we don't need all the columns. When a user tries to log in to MyStatus, we want to retrieve their password and compare it to the...

Developing a mental model for Cassandra

Any time you learn a new tool, you will naturally begin to develop a mental model for how that tool works. So far, our model for how Cassandra tables works is fairly simple; we will expand upon it throughout the book.

For now, we can think of a Cassandra table as a collection of keys, each of which points to a row. Each row contains data in some subset of its columns.

We also know that, at least in the users table, rows are stored non-contiguously; accessing each row requires Cassandra to seek a different place in storage. So we may imagine the current state of our users table to look something like this:

Essentially, we can think of our Cassandra table as a key-value store, where each value consists of one or more predefined columns containing data of a predefined type.

Summary

In this chapter, you're off to a running start with your MyStatus application, having created our first table, inserted data into it, and learned a few ways to retrieve that data. We developed a model for how Cassandra tables are structured and took a deep dive into Cassandra's type system.

We now have experience with the INSERT and SELECT statements, two of the core operations in CQL. You learned that primary keys, at least the ones we've seen so far, determine the order in which rows are returned, but that ordering is opaque to an application. We can only rely on it being consistent. We saw some of the limitations of simple tables in Cassandra, and we know that the types of multirow retrieval we know so far aren't the most efficient.In Chapter 3, Organizing Related Data, we'll introduce a new way to structure a table that lets us overcome many of the limitations we encountered...

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Key benefits

  • • Install Cassandra and set up multi-node clusters
  • • Design rich schemas that capture the relationships between different data types
  • • Master the advanced features available in Cassandra 3.x through a step-by-step tutorial and build a scalable, high performance database layer

Description

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.

Who is this book for?

If you are a NoSQL developer and new to Apache Cassandra who wants to learn its common as well as not-so-common features, this book is for you. Alternatively, a developer wanting to enter the world of NoSQL will find this book useful. It does not assume any prior experience in coding or any framework.

What you will learn

  • • Install Cassandra
  • • Create keyspaces and tables with multiple clustering columns to organize related data
  • • Use secondary indexes and materialized views to avoid denormalization of data
  • • Effortlessly handle concurrent updates with collection columns
  • • Ensure data integrity with lightweight transactions and logged batches
  • • Understand eventual consistency and use the right consistency level for your situation
  • • Understand data distribution with Cassandra
  • • Develop simple application using Java driver and implement application-level optimizations

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Length: 360 pages
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Table of Contents

14 Chapters
Getting Up and Running with Cassandra Chevron down icon Chevron up icon
The First Table Chevron down icon Chevron up icon
Organizing Related Data Chevron down icon Chevron up icon
Beyond Key-Value Lookup Chevron down icon Chevron up icon
Establishing Relationships Chevron down icon Chevron up icon
Denormalizing Data for Maximum Performance Chevron down icon Chevron up icon
Expanding Your Data Model Chevron down icon Chevron up icon
Collections, Tuples, and User-Defined Types Chevron down icon Chevron up icon
Aggregating Time-Series Data Chevron down icon Chevron up icon
How Cassandra Distributes Data Chevron down icon Chevron up icon
Cassandra Multi-Node Cluster Chevron down icon Chevron up icon
Application Development Using the Java Driver Chevron down icon Chevron up icon
Peeking under the Hood Chevron down icon Chevron up icon
Authentication and Authorization Chevron down icon Chevron up icon

Customer reviews

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(26 Ratings)
5 star 50%
4 star 23.1%
3 star 11.5%
2 star 7.7%
1 star 7.7%
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Vishnuprasad Ramasubramani Apr 25, 2021
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Udemy Verified review Udemy
Muhammad Rizal Nov 22, 2020
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Udemy Verified review Udemy
bubbapump Oct 17, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I've typically been a detractor of Cassandra because there are so many caveats to every feature. This book is excellent mostly because it's not a happy shiny description of how awesome cassandra is but instead talks about the features but then also tells you about the "buts". "Secondary indexes let you do this....but...". For that reason, this changed my mind about Cassandra and made me see how to use its strengths and how to know its limits. Great book!
Amazon Verified review Amazon
Sai Mishra Nov 16, 2020
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Udemy Verified review Udemy
Sujal Shelke Sep 07, 2020
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Each section was digestible as the sub topics were concise
Udemy Verified review Udemy
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