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  • Book Overview & Buying Cassandra High Availability
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Cassandra High Availability

Cassandra High Availability

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

Cassandra High Availability

4.6 (14)
By: 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
6
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
7
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
8
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
9
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
10
Index
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Index

Failing over to a remote data center

The foundation of any robust load balancing strategy is DCAwareRoundRobinPolicy, because we'll assume you will be deploying to more than one data center. However, the implementation hides an interesting failover feature in the constructor overrides, which is worth a look.

In Chapter 4, Data Centers, we discussed several use cases for multiple data centers, with failover being one key scenario. If your desire is to fail over to a backup data center, and should replicas in your client's primary data center fail, you might be interested in the two additional parameters you can pass to the DCAwareRoundRobinPolicy constructor, which are mentioned here:

  • usedHostsPerRemoteDc: This vaguely named parameter allows you to specify a number of hosts in a remote data center that can be used by this client, should your local data center fail to satisfy the request. Note that by default, this will be ignored for LOCAL_ONE and LOCAL_QUORUM consistency levels.

  • allowRemoteDCsForLocalConsistencyLevel...

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