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Cassandra 3.x High Availability
Cassandra 3.x High Availability

Cassandra 3.x High Availability: Achieve scalability and high availability without compromising on performance , Second Edition

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Cassandra 3.x High Availability

Chapter 2.  Data Distribution

Cassandra's peer-to-peer architecture and scalability characteristics are directly tied to its data placement scheme. Cassandra employs a distributed hash table data structure that allows for data to be stored and retrieved by key quickly and efficiently. Consistent hashing is at the core of this strategy, as it enables all nodes to understand where data exists in the cluster without complicated coordination mechanisms.

In this chapter, we'll cover the following topics:

  • The fundamentals of distributed hash tables
  • Cassandra's consistent hashing mechanism
  • Token assignment, both manual and using vnodes
  • The implications of Cassandra's partitioner implementations
  • How hotspots form in the cluster 

By the time you finish this chapter, you should have a deep understanding of these concepts. Let us begin with some basics about hash tables in general, and then we can delve deeper into Cassandra's distributed hash table implementation...

Hash table fundamentals

Most developers have experience with hash tables in some form, as nearly all programming languages include hash table implementations. Hash tables store data by applying a hash function to the object, which determines its placement in an underlying array.

While a detailed description of hashing algorithms is out of the scope of this book, it is sufficient for you to understand that a hash function simply maps any input data object (which may be any size) to some expected output. While the input may be large, the output of the hash function will be a fixed number of bits.

In a typical hash table design, the result of the hash function is divided by the number of array slots; the remainder then becomes the assigned slot number. Thus, the slot can be computed using hash(o) % n , where o is the object and n is the number of slots. Consider the following hash table, with names as keys and addresses as values:

Hash table fundamentals
The values in the table on the left represent keys, which are...

Consistent hashing

The solution is consistent hashing. Introduced as a term in 1997, consistent hashing was originally used as a means of routing requests among a large number of web servers. It's easy to see how the web could 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.

How it works

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

Token value

Range end

Next token value -1

Tip

The following examples assume the default Murmur3Partitioner is used. For more information on this partitioner, take a look at the documentation, which can be found here: http...

Token assignment

In Cassandra terminology, the start of the hash range is called a token, and until version 1.2, each node was assigned a single token, in the manner discussed in the previous section. Version 1.2 introduced the option to use virtual nodes, or vnodes as the feature is officially termed. Vnodes became the default option in the 2.0 release.

Cassandra determines where to place data by using the tokens assigned to each node. Nodes learn about these token assignments via gossip. Additional replicas are then placed based on the configured replication strategy and snitch. More details about replica placement can be found in Chapter 3, Replication.

Manually assigned tokens

If you have chosen not to use vnodes, you have the requirement to assign tokens manually. This is accomplished by setting the initial_token setting in cassandra.yaml.

Manual token assignment introduces a number of potential issues:

  • Adding and removing nodes:When the size of the ring changes, all tokens must be recomputed...

Partitioners

You may 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 (org.apache.cassandra.dht.Murmur3Partitioner): Produces an even distribution of data across the cluster using the Murmur3 hash algorithm. This is the default as of version 1.2, and should not be changed as it is measurably faster than the RandomPartitioner.
  • RandomPartitioner (org.apache.cassandra.dht.RandomPartitioner): Similar to the Murmur3Partitioner, except that it computes an MD5 hash. This was the default prior to version 1.2.
  • ByteOrderedPartitioner (org.apache.cassandra.dht.ByteOrderedPartitioner): Places keys in byte order (lexically) around the ring. This partitioner should generally be avoided for reasons explained...

Summary

At this point, you should have a strong grasp of Cassandra's data distribution architecture, including consistent hashing, tokens, vnodes, and partitioners, as well as some of the causes of data hotspots. Your understanding of these fundamentals should help you to make sound design decisions that enable you to scale your cluster effectively and get the most out of your infrastructure investment.

In this chapter and the previous one, we've made reference a number of times to replication and its related concepts. In our next chapter, we'll discuss replication in depth, as replication is very important in determining the availability of data.

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Key benefits

  • See how to get 100 percent uptime with your Cassandra applications using this easy-follow guide
  • Learn how to avoid common and not-so-common mistakes while working with Cassandra using this highly practical guide
  • Get familiar with the intricacies of working with Cassandra for high availability in your work environment with this go-to-guide

Description

Apache Cassandra is a massively scalable, peer-to-peer database designed for 100 percent uptime, with deployments in the tens of thousands of nodes, all supporting petabytes of data. This book offers a practical insight into building highly available, real-world applications using Apache Cassandra. The book starts with the fundamentals, helping you to understand how Apache Cassandra’s architecture allows it to achieve 100 percent uptime when other systems struggle to do so. You’ll get an excellent understanding of data distribution, replication, and Cassandra’s highly tunable consistency model. Then we take an in-depth look at Cassandra's robust support for multiple data centers, and you’ll see how to scale out a cluster. Next, the book explores the domain of application design, with chapters discussing the native driver and data modeling. Lastly, you’ll find out how to steer clear of common anti-patterns and take advantage of Cassandra’s ability to fail gracefully.

Who is this book for?

If you are a developer or DevOps engineer who has basic familiarity with Cassandra and you want to become an expert at creating highly available, fault tolerant systems using Cassandra, this book is for you.

What you will learn

  • Understand how the core architecture of Cassandra enables highly available applications
  • Use replication and tunable consistency levels to balance consistency, availability, and performance
  • Set up multiple data centers to enable failover, load balancing, and geographic distribution
  • Add capacity to your cluster with zero downtime
  • Take advantage of high availability features in the native driver
  • Create data models that scale well and maximize availability
  • Understand common anti-patterns so you can avoid them
  • Keep your system working well even during failure scenarios
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Length: 196 pages
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Language : English
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Table of Contents

9 Chapters
1. Cassandras Approach to High Availability Chevron down icon Chevron up icon
2. Data Distribution Chevron down icon Chevron up icon
3. Replication Chevron down icon Chevron up icon
4. Data Centers Chevron down icon Chevron up icon
5. Scaling Out Chevron down icon Chevron up icon
6. High Availability Features in the Native Java Client Chevron down icon Chevron up icon
7. Modeling for Availability Chevron down icon Chevron up icon
8. Anti-Patterns Chevron down icon Chevron up icon
9. Failing Gracefully Chevron down icon Chevron up icon

Customer reviews

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(6 Ratings)
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4 star 16.7%
3 star 16.7%
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1 star 16.7%
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BJORN Apr 17, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I just started learning about Cassandra, and this book was a great help, I have about 10 years experience in networkings and linux and thought this was a good source of information to help you start designing and building your next Cassandra cluster. It also gives a good overview of how things work internally and how to setup clusters in multiple datacenters.
Amazon Verified review Amazon
Amazon Customer Sep 29, 2016
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I am about to deploy a production Cassandra cluster to AWS to back a mobile app and this book helps tremendously.
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Paul Rafferty Nov 23, 2016
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Must have reference for Cassandra 3.0 high availability.
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Rahul Dec 01, 2016
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Good
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Amazon Customer Oct 15, 2017
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
Not bad, but not good. Definitive guide include more information. This book has less
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