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Cassandra High Availability

Cassandra High Availability

By : Strickland
4.6 (14)
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Cassandra High Availability

Cassandra High Availability

4.6 (14)
By: Strickland

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
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. Cassandra's Approach to High Availability
1. Cassandra's Approach to High Availability
ACID
The monolithic architecture
The master-slave architecture
Cassandra's solution
Cassandra's architecture
Summary
2
2. Data Distribution
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2. Data Distribution
Hash table fundamentals
Consistent hashing
Token assignment
Partitioners
Summary
3
3. Replication
3. Replication
The replication factor
Snitches
Consistency conflicts
Balancing the replication factor with consistency
Summary
4
4. Data Centers
4. Data Centers
Use cases for multiple data centers
Data center setup
Replication across data centers
Summary
5
5. Scaling Out
5. Scaling Out
Choosing the right hardware configuration
Scaling out versus scaling up
Growing your cluster
How to scale out
How to scale up
Removing nodes
Other data migration scenarios
Snitch changes
Summary
6
6. High Availability Features in the Native Java Client
6. High Availability Features in the Native Java Client
Thrift versus the native protocol
Setting up the environment
Connecting to the cluster
Executing statements
Handling asynchronous requests
Load balancing
Failing over to a remote data center
Tying it all together
Summary
7
7. Modeling for High Availability
7. Modeling for High Availability
How Cassandra stores data
Understanding compaction
CQL under the hood
Understanding queries
How collections are stored
Working with time-series data
Designing for immutability
Working with geospatial data
Summary
8
8. Antipatterns
8. Antipatterns
Multikey queries
Secondary indices
Distributed joins
Deleting data
Unbounded row growth
Summary
9
9. Failing Gracefully
9. Failing Gracefully
Knowledge is power
Logging
Monitoring node metrics
When a node goes down
Backing up data
Summary
10
Index
Index

Consistent hashing

To solve the problem of locating a key in a distributed hash table, we use a technique called consistent hashing. Introduced as a term in 1997, consistent hashing was originally used as a means of routing requests among large numbers of web servers. It's easy to see how the Web can benefit from a hash mechanism that allows any node in the network to efficiently determine the location of an object, in spite of the constant shifting of nodes in and out of the network. This is the fundamental objective of consistent hashing.

The mechanics of consistent hashing

With consistent hashing, the buckets are arranged in a ring with a predefined range; the exact range depends on the partitioner being used. Keys are then hashed to produce a value that lies somewhere along the ring. Nodes are assigned a range, which is computed as follows:

Range start

Range end

Token value

Next token value - 1

Note

The following examples assume that the default Murmur3Partitioner is used. For...

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