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  • Book Overview & Buying Cassandra High Availability
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Cassandra High Availability

Cassandra High Availability

By : Robbie Strickland
4.6 (14)
Buy this Book
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Cassandra High Availability

Cassandra High Availability

4.6 (14)
By: Robbie Strickland
Buy this Book

Overview of this book

If you are a developer or DevOps engineer who understands the basics of Cassandra and are ready to take your knowledge to the next level, then this book is for you. An understanding of the essentials of Cassandra is needed.
Table of Contents (11 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. Cassandra's Approach to High Availability
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1. Cassandra's Approach to High Availability
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ACID
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The monolithic architecture
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The master-slave architecture
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Cassandra's solution
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Cassandra's architecture
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Summary
2
2. Data Distribution
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2. Data Distribution
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Hash table fundamentals
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Consistent hashing
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Token assignment
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Partitioners
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Summary
3
3. Replication
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3. Replication
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The replication factor
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Snitches
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Consistency conflicts
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Balancing the replication factor with consistency
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Summary
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4. Data Centers
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4. Data Centers
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Use cases for multiple data centers
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Data center setup
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Replication across data centers
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Summary
5
5. Scaling Out
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5. Scaling Out
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Choosing the right hardware configuration
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Scaling out versus scaling up
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Growing your cluster
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How to scale out
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How to scale up
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Removing nodes
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Other data migration scenarios
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Snitch changes
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Summary
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6. High Availability Features in the Native Java Client
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6. High Availability Features in the Native Java Client
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Thrift versus the native protocol
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Setting up the environment
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Connecting to the cluster
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Executing statements
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Handling asynchronous requests
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Load balancing
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Failing over to a remote data center
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Tying it all together
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Summary
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7. Modeling for High Availability
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7. Modeling for High Availability
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How Cassandra stores data
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Understanding compaction
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CQL under the hood
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Understanding queries
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How collections are stored
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Working with time-series data
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Designing for immutability
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Working with geospatial data
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Summary
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8. Antipatterns
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8. Antipatterns
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Multikey queries
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Secondary indices
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Distributed joins
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Deleting data
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Unbounded row growth
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Summary
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9. Failing Gracefully
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9. Failing Gracefully
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Knowledge is power
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Logging
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Monitoring node metrics
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When a node goes down
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Backing up data
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Summary
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Index
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Index

Use cases for multiple data centers

There are several key use cases that involve deploying Cassandra across multiple data centers, including the obvious failover and load balancing scenarios. Let's examine a few of these cases.

Live backup

Traditional database backups involve taking periodic snapshots of the data and storing them offsite in case the system fails. In such a case, there will be downtime as a new system is brought up and the data is restored. This strategy also inevitably leads to data loss for the time period between the last backup and the point of failure.

Cassandra supports these types of backups, and we will discuss this in greater depth in Chapter 9, Failing Gracefully. While snapshot backups are still useful to protect against data corruption or accidental updates, Cassandra's data center support can be used to provide a current backup for cases such as hardware failures.

The basic idea involves setting up a second data center that maintains a current set of...

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