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  • Book Overview & Buying Learning Apache Cassandra
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Learning Apache Cassandra

Learning Apache Cassandra

By : Brown
4.4 (13)
Buy this Book
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Learning Apache Cassandra

Learning Apache Cassandra

4.4 (13)
By: Brown
Buy this Book

Overview of this book

If you're an application developer familiar with SQL databases such as MySQL or Postgres, and you want to explore distributed databases such as Cassandra, this is the perfect guide for you. Even if you've never worked with a distributed database before, Cassandra's intuitive programming interface coupled with the step-by-step examples in this book will have you building highly scalable persistence layers for your applications in no time.
Table of Contents (14 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
Free Chapter
1
1. Getting Up and Running with Cassandra
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1. Getting Up and Running with Cassandra
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What Cassandra offers, and what it doesn't
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Installing Cassandra
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Bootstrapping the project
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Creating a keyspace
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Summary
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2. The First Table
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2. The First Table
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Creating the users table
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Inserting data
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Selecting data
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Developing a mental model for Cassandra
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Summary
3
3. Organizing Related Data
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3. Organizing Related Data
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A table for status updates
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Working with status updates
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Anatomy of a compound primary key
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Beyond two columns
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Compound keys represent parent-child relationships
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Coupling parents and children using static columns
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Refining our mental model
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Summary
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4. Beyond Key-Value Lookup
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4. Beyond Key-Value Lookup
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Looking up rows by partition
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Retrieving status updates for a specific time range
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Paginating over rows in a partition
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Reversing the order of rows
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Paginating over multiple partitions
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Building an autocomplete function
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Summary
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5. Establishing Relationships
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5. Establishing Relationships
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Modeling follow relationships
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Storing follow relationships
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Looking up follow relationships
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Unfollowing users
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Using secondary indexes to avoid denormalization
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Summary
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6. Denormalizing Data for Maximum Performance
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6. Denormalizing Data for Maximum Performance
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A normalized approach
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Partial denormalization
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Fully denormalizing the home timeline
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Write complexity and data integrity
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Summary
7
7. Expanding Your Data Model
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7. Expanding Your Data Model
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Viewing a table schema in cqlsh
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Adding columns to tables
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Deleting columns
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Updating the existing rows
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Removing a value from a column
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Inserts, updates, and upserts
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Lightweight transactions have a cost
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Summary
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8. Collections, Tuples, and User-defined Types
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8. Collections, Tuples, and User-defined Types
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The problem with concurrent updates
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Collection columns and concurrent updates
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Using lists for ordered, nonunique values
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Using maps to store key-value pairs
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Collections in inserts
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Collections and secondary indexes
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The limitations of collections
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Working with tuples
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User-defined types
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Choosing between tuples and user-defined types
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Comparing data structures
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Summary
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9. Aggregating Time-Series Data
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9. Aggregating Time-Series Data
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Recording discrete analytics observations
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Recording aggregate analytics observations
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Recording analytics observations
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Summary
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10. How Cassandra Distributes Data
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10. How Cassandra Distributes Data
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Data distribution in Cassandra
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Data replication in Cassandra
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Consistency
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Handling conflicting data
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Distributed deletion
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Summary
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A. Peeking Under the Hood
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A. Peeking Under the Hood
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Using cassandra-cli
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The structure of a simple primary key table
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Compound primary keys in column families
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Collection columns in column families
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Summary
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B. Authentication and Authorization
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B. Authentication and Authorization
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Enabling authentication and authorization
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Setting up a user
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Controlling access
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Authorization in action
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Security beyond authentication and authorization
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Summary
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Wrapping up
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Index
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Index

The limitations of collections

As we saw, collection columns are a useful and versatile feature of CQL. In fact, we might suppose that collections would be a good way to model data we've modeled in other ways in previous chapters. For instance, why not model a user's status updates as a list of text values in the users table?

As it turns out, collection columns do have some limitations that circumscribe the cases in which they're the best solution. We'll explore the major limitations now.

Reading discrete values from collections

The most powerful feature of CQL collections is the ability to write discrete values to a collection. It's possible to append a single value to a list, update a single key-value pair in a map, remove a specific value from a set, and so on.

At read time, however, there is no special support for reading discrete values. For instance, we might want to be able to do something like the following to read a specific element from a list column:

SELECT "shared_by"[2]
FROM "user_status_updates...

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