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Apache Cassandra Essentials
Apache Cassandra Essentials

Apache Cassandra Essentials: Create your own massively scalable Cassandra database with highly responsive database queries

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Apache Cassandra Essentials

Chapter 2. An Architectural Overview

Now we know how to set up and run a Cassandra cluster, so let's take another step and discuss the architecture of Cassandra. Cassandra is a highly available, distributed NoSQL database. Cassandra clusters can be linearly scalable depending on our load requirements. Cassandra has no single point of failure, as it allows us to control the trade-off between consistency and latency based on our requirements. In a Cassandra cluster, there is no master or slave—node all nodes are equal. This way enables Cassandra to read from any node and write to any node unlike some traditional relational databases. Cassandra partitions data among all nodes in its cluster; this partitioned data can be looked up using a key called a partition key. Cassandra replicates data among its cluster nodes to become highly available. We can configure data replication based on our specific requirements. In this chapter, we'll discuss more about these features...

Background

Cassandra's design was based on Google's Bigtable and Amazon's DynamoDB, and it was developed at Facebook. Later on, it was open sourced to Apache Foundation. It is a NoSQL nonrelational database. While working with Cassandra, we need not worry about normalization of our tables, as Cassandra promotes denormalization of your database tables. In Cassandra, the database schema is designed based on the data that you would be reading from or writing to Cassandra.

Cassandra focuses on availability and partition tolerance among consistency, availability, and partition tolerance of the CAP theorem by Brewer. The CAP theorem states that, among consistency, availability, and partition tolerance, you can pick only two at a time. Here, consistency means when you read from or write to any node of the cluster, you should get the same up-to-date data. Availability refers to the fact that we should be able to access the cluster even if some node in the cluster is down, and partition...

Cassandra cluster overview

As mentioned earlier, Cassandra is a peer to peer cluster—no node is master or slave. Data is distributed among all nodes of the cluster using partitioners. This enables Cassandra to shard data transparently, and allows it to scale linearly. This means that Cassandra server performs the data sharding, and the application layer need not implement any additional logic for sharding. As the load grows, new nodes can be added to share the load as Cassandra will distribute the load among them automatically.

For example, let's imagine that we initially have three node cluster, which are capable of handling 3x transactions. Now, suppose that the load is increased to 4x, then we can add one additional node to the cluster without doing any change in our application layer. The following image shows the example where initially we have a three-node cluster with nodes A, B, and C capable of handling load 1x each; hence, the capacity of handling load of 1x + 1x +...

Data distribution

One of the key features of Cassandra is auto-sharding. Data is distributed among nodes in a cluster based on partition keys automatically. A partition key is a column or multiple columns, which are part of a primary key of a column family. Data is distributed based on the tokenized value calculated over the partition key. A partitioner determines how distribution tokens are calculated. Each node of Cassandra cluster its owns a range of tokens. A row is stored on the node that owns the respective token of the row's partition key.

A partitioner can be set using the configuration option partitioner in cassandra.yaml. The new cluster should go with Murmur3Partitioner, as it is a faster partitioner than older ones and also distributes data more efficiently. Other partitioners for backward compatibility are RandomPartitioner, ByteOrderedPartitioner, and OrderPreservingPartitioner.

Here is a brief description of all the listed partitioners:

  • Murmur3Partitioner: This is the default...

Replication

Cassandra is able to automatically keep multiple copies of data on multiple nodes, hence providing no single point of failure. The number of copies it will create is configurable and determined by a configuration option called a replication_factor. While defining a keyspace, we mention its replication_factor function. This configuration option is applied to all column families of that keyspace. Let's suppose that column family users, which we discussed in the section Data distribution, are part of the keyspace called cassandrademodb; and replication_factor of the keyspace is 3, then every row of the column family users will have three replicas copied to three different nodes. All replicas are the same and there is no primary or secondary replica. First, replica placement is decided by the partitioner and subsequent replicas are placed on consecutives nodes in clockwise order. Cassandra tries not to have two replicas on the same rack while doing so for rack-aware strategies...

SimpleStrategy

The SimpleStrategy is a basic replication strategy. It's used when using a single datacenter. This method is rack unaware. It places replicas on subsequent nodes in a clockwise order.

Here is a sample CQL command to create a keyspace with SimpleStrategy and a replication factor of 3, which can be fired from the cqlsh prompt:

cqlsh>  CREATE KEYSPACE cassandrademodb WITH REPLICATION = { 'class' : 'SimpleStrategy', 'replication_factor' : 3 };

Background


Cassandra's design was based on Google's Bigtable and Amazon's DynamoDB, and it was developed at Facebook. Later on, it was open sourced to Apache Foundation. It is a NoSQL nonrelational database. While working with Cassandra, we need not worry about normalization of our tables, as Cassandra promotes denormalization of your database tables. In Cassandra, the database schema is designed based on the data that you would be reading from or writing to Cassandra.

Cassandra focuses on availability and partition tolerance among consistency, availability, and partition tolerance of the CAP theorem by Brewer. The CAP theorem states that, among consistency, availability, and partition tolerance, you can pick only two at a time. Here, consistency means when you read from or write to any node of the cluster, you should get the same up-to-date data. Availability refers to the fact that we should be able to access the cluster even if some node in the cluster is down, and partition tolerance...

Cassandra cluster overview


As mentioned earlier, Cassandra is a peer to peer cluster—no node is master or slave. Data is distributed among all nodes of the cluster using partitioners. This enables Cassandra to shard data transparently, and allows it to scale linearly. This means that Cassandra server performs the data sharding, and the application layer need not implement any additional logic for sharding. As the load grows, new nodes can be added to share the load as Cassandra will distribute the load among them automatically.

For example, let's imagine that we initially have three node cluster, which are capable of handling 3x transactions. Now, suppose that the load is increased to 4x, then we can add one additional node to the cluster without doing any change in our application layer. The following image shows the example where initially we have a three-node cluster with nodes A, B, and C capable of handling load 1x each; hence, the capacity of handling load of 1x + 1x + 1x = 3x. However...

Data distribution


One of the key features of Cassandra is auto-sharding. Data is distributed among nodes in a cluster based on partition keys automatically. A partition key is a column or multiple columns, which are part of a primary key of a column family. Data is distributed based on the tokenized value calculated over the partition key. A partitioner determines how distribution tokens are calculated. Each node of Cassandra cluster its owns a range of tokens. A row is stored on the node that owns the respective token of the row's partition key.

A partitioner can be set using the configuration option partitioner in cassandra.yaml. The new cluster should go with Murmur3Partitioner, as it is a faster partitioner than older ones and also distributes data more efficiently. Other partitioners for backward compatibility are RandomPartitioner, ByteOrderedPartitioner, and OrderPreservingPartitioner.

Here is a brief description of all the listed partitioners:

  • Murmur3Partitioner: This is the default...

Replication


Cassandra is able to automatically keep multiple copies of data on multiple nodes, hence providing no single point of failure. The number of copies it will create is configurable and determined by a configuration option called a replication_factor. While defining a keyspace, we mention its replication_factor function. This configuration option is applied to all column families of that keyspace. Let's suppose that column family users, which we discussed in the section Data distribution, are part of the keyspace called cassandrademodb; and replication_factor of the keyspace is 3, then every row of the column family users will have three replicas copied to three different nodes. All replicas are the same and there is no primary or secondary replica. First, replica placement is decided by the partitioner and subsequent replicas are placed on consecutives nodes in clockwise order. Cassandra tries not to have two replicas on the same rack while doing so for rack-aware strategies, using...

SimpleStrategy


The SimpleStrategy is a basic replication strategy. It's used when using a single datacenter. This method is rack unaware. It places replicas on subsequent nodes in a clockwise order.

Here is a sample CQL command to create a keyspace with SimpleStrategy and a replication factor of 3, which can be fired from the cqlsh prompt:

cqlsh>  CREATE KEYSPACE cassandrademodb WITH REPLICATION = { 'class' : 'SimpleStrategy', 'replication_factor' : 3 };
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Key benefits

  • Create a Cassandra cluster and tweak its configuration to get the best performance based on your environment
  • Analyze the key concepts and architecture of Cassandra, which are essential to create highly responsive Cassandra databases
  • A fast-paced and step-by-step guide on handling huge amount of data and getting the best out of your database applications

Description

Apache Cassandra Essentials takes you step-by-step from from the basics of installation to advanced installation options and database design techniques. It gives you all the information you need to effectively design a well distributed and high performance database. You’ll get to know about the steps that are performed by a Cassandra node when you execute a read/write query, which is essential to properly maintain of a Cassandra cluster and to debug any issues. Next, you’ll discover how to integrate a Cassandra driver in your applications and perform read/write operations. Finally, you’ll learn about the various tools provided by Cassandra for serviceability aspects such as logging, metrics, backup, and recovery.

Who is this book for?

If you are a developer who is working with Cassandra and you want to deep dive into the core concepts and understand Cassandra’s non-relational nature, then this book is for you. A basic understanding of Cassandra is expected.

What you will learn

  • Install and set up your Cassandra Cluster using various installation types
  • Use Cassandra Query Language (CQL) to design Cassandra database and tables with various configuration options
  • Design your Cassandra database to be evenly loaded with the lowest read/write latencies
  • Employ the available Cassandra tools to monitor and maintain a Cassandra cluster
  • Debug CQL queries to discover why they are performing relatively slowly
  • Choose the best-suited compaction strategy for your database based on your usage pattern
  • Tune Cassandra based on your deployment operation system environment
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Table of Contents

8 Chapters
1. Getting Your Cassandra Cluster Ready Chevron down icon Chevron up icon
2. An Architectural Overview Chevron down icon Chevron up icon
3. Creating Database and Schema Chevron down icon Chevron up icon
4. Read and Write – Behind the Scenes Chevron down icon Chevron up icon
5. Writing Your Cassandra Client Chevron down icon Chevron up icon
6. Monitoring and Tuning a Cassandra Cluster Chevron down icon Chevron up icon
7. Backup and Restore Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5
(2 Ratings)
5 star 50%
4 star 50%
3 star 0%
2 star 0%
1 star 0%
Aman Mar 17, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Book contains sufficient information about the functionality of Cassandra and covers the technical portion with some very good real world examples
Amazon Verified review Amazon
Ian Stirk May 28, 2016
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Hi,I have written a detailed chapter-by-chapter review of this book on www DOT i-programmer DOT info, the first and last parts of this review are given here. For my review of all chapters, search i-programmer DOT info for STIRK together with the book's title.This book aims to explain the core concepts of Cassandra, how does it fare?The growth of Big Data has highlighted the scalability limits of relational databases. Several NoSQL databases have arisen to fill this niche, one of the more popular ones is Cassandra.The book is aimed at developers working with Cassandra wanting a more in-depth understanding. To get the most out of this book, a basic understanding of Cassandra is required, together with an appreciation of Java code and database systems.The book is relatively short, containing 146 working pages, spread over seven chapters.Below is a chapter-by-chapter exploration of the topics covered.Chapter 1 Getting Your Cassandra Cluster ReadyThe book opens with a comprehensive guide to installing Cassandra, covering the prerequisites (e.g. memory requirements), before downloading Cassandra source code, compiling and installing it. Instructions are also provided on installing a precompiled binary. The content of the various directories is outlined.The chapter next looks at the various configuration files, including: cluster, data partitioning, storage, client and security. The major content of each is briefly discussed.The chapter ends with details on running a Cassandra server, both on a single node and on a cluster of nodes. The Cassandra nodetool utility is used to check and monitor the cluster.This chapter provides a useful introduction to getting your Cassandra cluster up and running.The chapter is generally easy to read, with plenty of hands-on detail, useful tables and diagrams, and reusable scripts. There are occasional grammar problems, a recurring problem. Some terms are used without being defined (e.g. NoSQL). These traits apply to the whole of the book....ConclusionThis book aims to explain the core concepts of Cassandra, and generally succeeds. The book is easy to read, with plenty of hands-on detail, useful tables and diagrams, and helpful scripts.This is not a book for the complete Cassandra novice. Additionally, to get the most from the book, an understanding of Java code, and database systems is needed. Some knowledge of related terms is assumed (e.g. NoSQL, normalization), since these are not defined. Occasionally, there is bad grammar, but nothing too onerous. Sometimes the topics in a chapter are not introduced or linked together – making it difficult to make sense of.The book contains a mixture of both developer and administrator tasks, this seems to be the norm with NoSQL databases, whereas these areas are distinct in relational database environments.The book would benefit from a section on where to go next to get further information (e.g. websites, newsletters, forums etc).Overall, this is a useful overview of Cassandra, its internals and functionality.
Amazon Verified review Amazon
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