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  • Book Overview & Buying Learning Apache Cassandra
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Learning Apache Cassandra

Learning Apache Cassandra

By : Brown
4.4 (12)
Buy this Book
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Learning Apache Cassandra

Learning Apache Cassandra

4.4 (12)
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
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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
2
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
4
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
5
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
6
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
8
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
10
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
11
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
12
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

Beyond two columns

We've now seen a table with two columns in its primary key: a partition key, and a clustering column. As it turns out, neither of these roles is limited to a single column. A table can define one or more partition key columns, and zero or more clustering columns.

For instance, in our status application, we might want to allow users to reply to other users' status updates. In this case, each status update would have a stream of replies; replies would be partitioned by the full primary key of the original status update, and each reply would get its own timestamped UUID:

CREATE TABLE "status_update_replies" (
  "status_update_username" text,
  "status_update_id" timeuuid,
  "id" timeuuid,
  "author_username" text,
  "body" text,
  PRIMARY KEY (
    ("status_update_username", "status_update_id"),
    "id"
  )
);

Note the extra set of parentheses around the status_update_username...

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Learning Apache Cassandra
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