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  • Learning Apache Cassandra
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Learning Apache Cassandra

Learning Apache Cassandra

By : Yarabarla
4 (26)
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Learning Apache Cassandra

Learning Apache Cassandra

4 (26)
By: Yarabarla

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 (15 chapters)
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Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
Getting Up and Running with Cassandra
Getting Up and Running with Cassandra
What is big data?
Challenges of modern applications
Why not relational databases?
How to handle big data
What is Cassandra and why Cassandra?
MapReduce and Spark
Installing Cassandra
Installing the JDK
Installing on Debian-based systems (Ubuntu)
Installing on RHEL-based systems
Installing on Windows
Installing on Mac OS X
Installing the binary tarball
Bootstrapping the project
CQL—the Cassandra Query Language
Interacting with Cassandra
Getting started with CQL
New features in Cassandra 2.2, 3.0, and 3.X
Summary
2
The First Table
The First Table
How to configure keyspaces
Creating the users table
Inserting data
Selecting data
Developing a mental model for Cassandra
Summary
3
Organizing Related Data
Organizing Related Data
A table for status updates
Working with status updates
Anatomy of a compound primary key
Beyond two columns
Compound keys represent parent-child relationships
Coupling parents and children using static columns
Refining our mental model
Summary
4
Beyond Key-Value Lookup
Beyond Key-Value Lookup
Looking up rows by partition
The limits of the WHERE keyword
Retrieving status updates for a specific time range
Paginating over rows in a partition
Reversing the order of rows
Paginating over multiple partitions
JSON support
Building an autocomplete function
Summary
5
Establishing Relationships
Establishing Relationships
Modeling follow relationships
Storing follow relationships
Cassandra data modelling
Denormalization
Looking up follow relationships
Unfollowing users
Using secondary indexes to avoid denormalization
Materialized views
Summary
6
Denormalizing Data for Maximum Performance
Denormalizing Data for Maximum Performance
A normalized approach
Partial denormalization
Fully denormalizing the home timeline
Write complexity and data integrity
Batching in Cassandra
Summary
7
Expanding Your Data Model
Expanding Your Data Model
Viewing a keyspace schema
Viewing a table schema in cqlsh
Adding columns to tables
Deleting columns
Updating the existing rows
Removing a value from a column
Inserts, updates, and upserts
Lightweight transactions and their cost
Summary
8
Collections, Tuples, and User-Defined Types
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Collections, Tuples, and User-Defined Types
The problem with concurrent updates
Collection columns and concurrent updates
Using lists for ordered, non-unique values
Using maps to store key-value pairs
Collections in inserts
Collections and secondary indexes
The limitations of collections
Working with tuples
User-defined types
Choosing between tuples and user-defined types
Nested collections
Nested tuples/UDTs
Comparing data structures
Summary
9
Aggregating Time-Series Data
Aggregating Time-Series Data
Recording discrete analytics observations
Recording aggregate analytics observations
Recording analytics observations
Cassandra configuration
User-defined functions
User-defined aggregate functions
Summary
10
How Cassandra Distributes Data
How Cassandra Distributes Data
Data distribution in Cassandra
Data replication in Cassandra
Gossip protocol
Multidata center cluster
Consistency
Handling conflicting data
Distributed deletion
Table configuration options
Summary
11
Cassandra Multi-Node Cluster
Cassandra Multi-Node Cluster
3 - node cluster
Consistency in action
Cassandra internals
Cassandra repair mechanisms
Summary
12
Application Development Using the Java Driver
Application Development Using the Java Driver
A simple query
Prepared statements
QueryBuilder API
Asynchronous querying
Driver policies
Summary
13
Peeking under the Hood
Peeking under the Hood
Using cassandra-cli
The structure of a simple primary key table
Compound primary keys in column families
Collection columns in column families
Summary
14
Authentication and Authorization
Authentication and Authorization
Enabling authentication and authorization
Setting up a user
Controlling access
Authorization in action
Security beyond authentication and authorization
Summary
Wrapping up

Choosing between tuples and user-defined types

As we saw, tuples and user-defined types have a lot in common. In both structures, a column contains a fixed, predefined set of fields, each of which can have its own type. Both structures are stored as frozen, meaning that Cassandra cannot perform discrete operations on their internal components. Both can be indexed and used in the WHERE clause of a query. So how do we decide which to use?

In most cases, a user-defined type is a better option. User-defined types give names to their fields, making it easier for application developers to reason about their usage. Also, user-defined types can be partially selected in queries, tuples cannot.

The only reason to use a tuple is convenience: a tuple does not need to be defined separately from its use in a column definition. So, for quick prototyping of schema structures, a tuple can be a better option. However, for a schema that's going into production, a user-defined type is nearly always going to be...

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