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  • Learning Apache Cassandra
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Learning Apache Cassandra

Learning Apache Cassandra

By : Brown
4.4 (13)
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Learning Apache Cassandra

Learning Apache Cassandra

4.4 (13)
By: Brown

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
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Getting Up and Running with Cassandra
1. Getting Up and Running with Cassandra
What Cassandra offers, and what it doesn't
Installing Cassandra
Bootstrapping the project
Creating a keyspace
Summary
2
2. The First Table
2. The First Table
Creating the users table
Inserting data
Selecting data
Developing a mental model for Cassandra
Summary
3
3. Organizing Related Data
3. 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
4. Beyond Key-Value Lookup
4. Beyond Key-Value Lookup
Looking up rows by partition
Retrieving status updates for a specific time range
Paginating over rows in a partition
Reversing the order of rows
Paginating over multiple partitions
Building an autocomplete function
Summary
5
5. Establishing Relationships
5. Establishing Relationships
Modeling follow relationships
Storing follow relationships
Looking up follow relationships
Unfollowing users
Using secondary indexes to avoid denormalization
Summary
6
6. Denormalizing Data for Maximum Performance
6. Denormalizing Data for Maximum Performance
A normalized approach
Partial denormalization
Fully denormalizing the home timeline
Write complexity and data integrity
Summary
7
7. Expanding Your Data Model
7. Expanding Your Data Model
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 have a cost
Summary
8
8. Collections, Tuples, and User-defined Types
8. Collections, Tuples, and User-defined Types
The problem with concurrent updates
Collection columns and concurrent updates
Using lists for ordered, nonunique 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
Comparing data structures
Summary
9
9. Aggregating Time-Series Data
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9. Aggregating Time-Series Data
Recording discrete analytics observations
Recording aggregate analytics observations
Recording analytics observations
Summary
10
10. How Cassandra Distributes Data
10. How Cassandra Distributes Data
Data distribution in Cassandra
Data replication in Cassandra
Consistency
Handling conflicting data
Distributed deletion
Summary
11
A. Peeking Under the Hood
A. 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
12
B. Authentication and Authorization
B. Authentication and Authorization
Enabling authentication and authorization
Setting up a user
Controlling access
Authorization in action
Security beyond authentication and authorization
Summary
Wrapping up
13
Index
Index

Recording aggregate analytics observations

When we explore precomputed aggregate data storage, let's focus on the first question posed above: how many total views did status updates receive on each day of September?

More generally, we want to store the daily overall view counts in a structure that allows us to easily retrieve the counts for a given range of time. We don't need to store discrete information about every view event that happened; simply knowing how many views occurred per day is sufficient.

Let's create a new daily_status_update_views table that aggregates our analytics observations at just the right granularity:

CREATE TABLE "daily_status_update_views" (
  "year" int,
  "date" timestamp,
  "total_views" counter,
  "web_views" counter,
  "mobile_views" counter,
  "api_views" counter,
  PRIMARY KEY (("year"), "date")
);

Of course, the most striking thing about this table definition is the introduction of the counter column type; we'll dive into this a little later in this chapter...

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