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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
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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
10
Index
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Index

Partitioners

You might recall from the earlier discussion of distributed hash tables that keys are mapped to nodes via an implementation-specific hash function. In Cassandra's architecture, this function is determined by the partitioner you choose. This is a cluster-wide setting specified in cassandra.yaml. As of version 1.2, there are three options:

  • Murmur3Partitioner: This produces an even distribution of data across the cluster using the MurmurHash algorithm. This is the default as of version 1.2, and should not be changed as it is measurably faster than the RandomPartitioner.
  • RandomPartitioner: This is similar to the Murmur3Partitioner, except that it computes an MD5 hash. This was the default prior to version 1.2.
  • ByteOrderedPartitioner: This places keys in byte order (lexically) around the ring. This partitioner should generally be avoided for reasons explained in this section.

The only reason to switch from the default Murmur3Partitioner to ByteOrderedPartitioner would be to enable...

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