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Mastering Apache Cassandra - Second Edition
Mastering Apache Cassandra - Second Edition

Mastering Apache Cassandra - Second Edition: Build, manage, and configure high-performing, reliable NoSQL database for your application with Cassandra

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Mastering Apache Cassandra - Second Edition

Chapter 2. Cassandra Architecture

This chapter aims to set you into a perspective where you will be able to see the evolution of the NoSQL paradigm. It will start with a discussion of common problems that an average developer faces when the application starts to scale up and software components cannot keep up with it. Then, we'll see what can be assumed as a thumb rule in the NoSQL world: the CAP theorem that says to choose any two out of consistency, availability, and partition-tolerance. As we discuss this further, we will realize how much more important it is to serve the customers (availability), than to be correct (consistency) all the time. However, we cannot afford to be wrong (inconsistent) for a long time. The customers wouldn't like to see that the items are in stock, but that the checkout is failing. Cassandra comes into picture with its tunable consistency.

We will take a quick peep into all the actions that go on when a read or mutate happens. This leaves...

Problems in the RDBMS world

RDBMS is a great approach. It keeps data consistent, it's good for OLTP (http://en.wikipedia.org/wiki/Online_transaction_processing), it provides access to good grammar, and manipulates data supported by all the popular programming languages. It has been tremendously successful in the last 40 years (the relational data model was proposed in its first incarnation by Codd, E.F. (1970) in his research paper A Relational Model of Data for Large Shared Data Banks). However, in early 2000s, big companies such as Google and Amazon, which have a gigantic load on their databases to serve, started to feel bottlenecked with RDBMS, even with helper services such as Memcache on top of them. As a response to this, Google came up with BigTable (http://research.google.com/archive/bigtable.html), and Amazon with Dynamo (http://www.cs.ucsb.edu/~agrawal/fall2009/dynamo.pdf).

If you have ever used RDBMS for a complicated web application, you must have faced problems such as...

Enter NoSQL

NoSQL is a blanket term for the databases that solve the scalability issues which are common among relational databases. This term, in its modern meaning, was first coined by Eric Evans. It should not be confused with the database named NoSQL (http://www.strozzi.it/cgi-bin/CSA/tw7/I/en_US/nosql/Home%20Page). NoSQL solutions provide scalability and high availability, but may not guarantee ACID: atomicity, consistency, isolation, and durability in transactions. Many of the NoSQL solutions, including Cassandra, sit on the other extreme of ACID, named BASE, which stands for basically available, soft-state, eventual consistency.

Note

Wondering about where the name, NoSQL, came from? Read Eric Evans' blog at http://blog.sym-link.com/2009/10/30/nosql_whats_in_a_name.html.

The CAP theorem

In 2000, Eric Brewer (http://en.wikipedia.org/wiki/Eric_Brewer_%28scientist%29), in his keynote speech at the ACM Symposium, said, "A distributed system requiring always-on, highly-available...

Cassandra

Cassandra is a distributed, decentralized, fault tolerant, eventually consistent, linearly scalable, and column-oriented data store. This means that Cassandra is made to easily deploy over a cluster of machines located at geographically different places. There is no central master server, so no single point of failure, no bottleneck, data is replicated, and a faulty node can be replaced without any downtime. It's eventually consistent. It is linearly scalable, which means that with more nodes, the requests served per second per node will not go down. Also, the total throughput of the system will increase with each node being added. And finally, it's column oriented, much like a map (or better, a map of sorted maps) or a table with flexible columns where each column is essentially a key-value pair. So, you can add columns as you go, and each row can have a different set of columns (key-value pairs). It does not provide any relational integrity. It is up to the application...

Understanding the architecture of Cassandra

Cassandra is a relative latecomer in the distributed data-store war. It takes advantage of two proven and closely similar data-store mechanisms, namely Bigtable: A Distributed Storage System for Structured Data, 2006 (http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en//archive/bigtable-osdi06.pdf) and Amazon Dynamo: Amazon's Highly Available Key-value Store, 2007 (http://www.read.seas.harvard.edu/~kohler/class/cs239-w08/decandia07dynamo.pdf). The following diagram displays the read throughputs that show linear scaling of Cassandra:

Understanding the architecture of Cassandra

Like BigTable, it has a tabular data presentation. It is not tabular in the strictest sense. It is rather a dictionary-like structure where each entry holds another sorted dictionary/map. This model is more powerful than the usual key-value store and it is named a table, formerly known as a column family. The properties such as eventual consistency and decentralization are...

Summary

By now, you should be familiar with all the nuts and bolts of Cassandra. We have discussed how the pressure to make data stores to web scale inspired a rather not-so-common database mechanism to become mainstream, and how the CAP theorem governs the behavior of such databases. We have seen that Cassandra shines out among its peers. Then, we dipped our toes into the big picture of Cassandra read and write mechanisms. This left us with lots of fancy terms. Furthermore, we looked into the definition of these words, the components that drive Cassandra, and their influence on its behavior.

It is understandable that it may be a lot to take in for someone new to NoSQL systems. It is okay if you do not have complete clarity at this point. As you start working with Cassandra, tweaking it, experimenting with it, and going through the Cassandra mailing list discussions or talks, you will start to come across stuff that you have read in this chapter and it will start to make sense, and perhaps...

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Description

The book is aimed at intermediate developers with an understanding of core database concepts who want to become a master at implementing Cassandra for their application.

Who is this book for?

The book is aimed at intermediate developers with an understanding of core database concepts who want to become a master at implementing Cassandra for their application.
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Publication date : Mar 26, 2015
Length: 350 pages
Edition : 1st
Language : English
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Table of Contents

9 Chapters
1. Quick Start Chevron down icon Chevron up icon
2. Cassandra Architecture Chevron down icon Chevron up icon
3. Effective CQL Chevron down icon Chevron up icon
4. Deploying a Cluster Chevron down icon Chevron up icon
5. Performance Tuning Chevron down icon Chevron up icon
6. Managing a Cluster – Scaling, Node Repair, and Backup Chevron down icon Chevron up icon
7. Monitoring Chevron down icon Chevron up icon
8. Integration with Hadoop Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

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Full star icon Full star icon Full star icon Full star icon Half star icon 4.6
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manish arora Apr 01, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Good book
Amazon Verified review Amazon
Rajesh Ashtaputre Sep 05, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very well written book, provides just enough background compared to nonsensical philosophy many others offer.
Amazon Verified review Amazon
Glenn Wiorek Jun 10, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This second edition of Master Cassandra over comes the major complaint of the first edition as being out of date. It covers the latest versions of Cassandra and then some. All the first edition chapters have been updated, and CQL get a good introduction in the 3rd chapter now. This is my 3rd book on Cassandra and I have to say it is now my favorite as it has tons of internals information. It is well written and made many complex concepts very understandable for me. Glad to have in in my Library.One spot I disagree with the author though is on swap. In his experience he may prefer the database system go down from the OS's Out-of-Memory(OOM) kill from lack of swap space instead of having sluggish performance. Many places I have worked, every outage counted against your SLA and could lead to huge fines. Sluggish is not always consider down or count against your SLA and on the fly adjustments might be possible to get it back to acceptable levels. Other then that I enjoyed the book.
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Bob Mason Apr 29, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very good book that I'm using to teach a Cassandra course.
Amazon Verified review Amazon
Martin Friedemann Jan 15, 2016
Full star icon Full star icon Full star icon Full star icon Full star icon 5
It is refering to a recent version and it gives very good insides about the usage of Cassandra. I worked more than 1 year with Cassandra 2.0.x and 2.1.x and even after this time it helped me to get a better understanding of Cassandra.
Amazon Verified review Amazon
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