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Home > Programming > Design Patterns > Cassandra Design Patterns
Cassandra Design Patterns
Cassandra Design Patterns

Cassandra Design Patterns: Build real-world, industry-strength data storage solutions with time-tested design methodologies using Cassandra , Second Edition

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Cassandra Design Patterns

Chapter 2. RDBMS Migration Patterns

 

"The secret of change is to focus all of your energy, not on fighting the old, but on building the new"

 
 --Dan Millman

A big bang approach to any kind of technology migration is not advisable. A series of deliberations have to take place before the eventual and complete change over. Migration from RDBMS to Cassandra is no different. Any new technology replacing an old one must coexist harmoniously with the old technology, at least for a short period of time. This gives a lot of confidence in the new technology to the stakeholders.

Many technology pundits provide you with various approaches for the RDBMS to NoSQL migration strategies. Many such guidelines are specific to particular NoSQL data stores, paying attention to specific areas, and most of the time that will end up paying attention to the process rather than the technology. Here, the point of discussion is not the process of the migration. All the technical, economical...

A brief overview

The concepts such as collection data types, counters, and TTL in Cassandra are very powerful. But before using them to talk about the design patterns, it will be a good idea to discuss some of the basic and general aspects outside the world of Cassandra.

Lists as an abstract idea, occur in our daily life, such as a list of things to buy, a list of tasks to be completed, a list of steps to follow, and so on and so forth. Each of the items in a list has a unique position. Referring to the items in a list by their position happens quite often in our daily conversations, for example, "tell me the first task to be completed", "tell me the high priority item from the list of things to buy", and so on. The items in a list may repeat. For example, the list of steps to follow when driving from point A to point B may have the instruction "Turn Right" repeated many times. However, the position of that step in the list matters. The characteristics of List...

List pattern

Whenever a list appears in a real-world use case, use the Cassandra data type the List pattern in the column family. Whenever the list in the real-world use case mandates the order of items, the List data type in Cassandra is ideal. Whenever there is a need to store the same value or object multiple times in the data store, the List data type in Cassandra is the best type to go with.

Motivations/solutions

In the normalized RDBMS tables, whenever there is a need to have a list of things associated with an entity, the easiest, no-brainer approach is to have a one-to-many relationship of two tables. When reports or other business processes need all these pieces of data together, a multi-table join is used. The joins on multiple tables always perform badly when the number of records in the tables are huge. This affects the read performance as well as the write performance. The performance is affected because of two reasons. One, as mentioned earlier, is the need to do table joins...

Set pattern

Whenever there is a set in a real-world use case, use the Cassandra data type Set in the column family. Whenever the set in the real-world use case does not mandate the order of items, and if the items don't repeat, the Set data type in Cassandra is ideal. In the applications, even if the duplicate check is relaxed, since the Set data type cannot handle duplicate items, it will not take duplicate items.

Motivations/solutions

In the normalized RDBMS tables, whenever there is a need to have a set of things associated with an entity, the easiest and the most straightforward approach is to have a one-to-many relationship of two tables. When the reports or other business processes need all these pieces of data together, a multi-table join is used. The joins on multiple tables always perform badly when the number of records on the tables is very large. This affects the read performance as well as the write performance. The performance is affected because of two reasons. One, as...

Map pattern

Whenever there is a need to provide a mapping of keys to values as part of a record, use the Cassandra data type Map in the column family. The "as part of a record" part of preceding sentence is very important. This must not be misunderstood as any general lookup service of records such as the master tables in the RDBMS world.

Motivations/solutions

In the normalized RDBMS tables, whenever there is a need to provide a key to value mapping for an entity, the easiest and most straightforward approach is to create a small lookup table with the key as the primary key of the lookup table and the value as the non-primary key. Just like all the other patterns discussed in this chapter, the joins of the tables are required to get all the pieces of data together even here. All the perils of the table joins are applicable here too. Cassandra comes with a data type Map that can be used in the column families to solve this problem. This avoids the need to have joins and indexes to...

Distributed Counter pattern

Whenever there is a need to maintain counters in applications that need to be persisted and distributed, use the Cassandra Counter data type in the column families. The distributed counter value is 64-bit long, supporting only two operations, namely increment and decrement. This is much better than storing the counter values in RDBMS tables, caches, log files, text files, and so on. In the latest version of Cassandra, the performance of the Counter data type has been improved a lot, and many issues have been fixed to allow it to support very powerful use cases.

Motivations/solutions

The need to use counters exists in most applications. If the application and the database lives in just one node, there is no issue and everything is hunky-dory. The moment any one of these components gets distributed across multiple nodes, the pain begins. Then the need for synchronization arises, and the external synchronization tools such as Zookeeper come into the solution stack...

A brief overview

The concepts such as collection data types, counters, and TTL in Cassandra are very powerful. But before using them to talk about the design patterns, it will be a good idea to discuss some of the basic and general aspects outside the world of Cassandra.

Lists as an abstract idea, occur in our daily life, such as a list of things to buy, a list of tasks to be completed, a list of steps to follow, and so on and so forth. Each of the items in a list has a unique position. Referring to the items in a list by their position happens quite often in our daily conversations, for example, "tell me the first task to be completed", "tell me the high priority item from the list of things to buy", and so on. The items in a list may repeat. For example, the list of steps to follow when driving from point A to point B may have the instruction "Turn Right" repeated many times. However, the position of that step in the list matters. The characteristics of List as an abstract concept are very...

List pattern

Whenever a list appears in a real-world use case, use the Cassandra data type the List pattern in the column family. Whenever the list in the real-world use case mandates the order of items, the List data type in Cassandra is ideal. Whenever there is a need to store the same value or object multiple times in the data store, the List data type in Cassandra is the best type to go with.

Motivations/solutions

In the normalized RDBMS tables, whenever there is a need to have a list of things associated with an entity, the easiest, no-brainer approach is to have a one-to-many relationship of two tables. When reports or other business processes need all these pieces of data together, a multi-table join is used. The joins on multiple tables always perform badly when the number of records in the tables are huge. This affects the read performance as well as the write performance. The performance is affected because of two reasons. One, as mentioned earlier, is the need to do table joins...

Set pattern

Whenever there is a set in a real-world use case, use the Cassandra data type Set in the column family. Whenever the set in the real-world use case does not mandate the order of items, and if the items don't repeat, the Set data type in Cassandra is ideal. In the applications, even if the duplicate check is relaxed, since the Set data type cannot handle duplicate items, it will not take duplicate items.

Motivations/solutions

In the normalized RDBMS tables, whenever there is a need to have a set of things associated with an entity, the easiest and the most straightforward approach is to have a one-to-many relationship of two tables. When the reports or other business processes need all these pieces of data together, a multi-table join is used. The joins on multiple tables always perform badly when the number of records on the tables is very large. This affects the read performance as well as the write performance. The performance is affected because of two reasons. One, as mentioned...

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Key benefits

  • Explore design patterns which co-exist with legacy data stores, migration from RDBMS, and caching technologies with Cassandra
  • Learn about design patterns and use Cassandra to provide consistency, availability, and partition tolerance guarantees for applications
  • Handle temporal data for analytical purposes

Description

If you are new to Cassandra but well-versed in RDBMS modeling and design, then it is natural to model data in the same way in Cassandra, resulting in poorly performing applications and losing the real purpose of Cassandra. If you want to learn to make the most of Cassandra, this book is for you. This book starts with strategies to integrate Cassandra with other legacy data stores and progresses to the ways in which a migration from RDBMS to Cassandra can be accomplished. The journey continues with ideas to migrate data from cache solutions to Cassandra. With this, the stage is set and the book moves on to some of the most commonly seen problems in applications when dealing with consistency, availability, and partition tolerance guarantees. Cassandra is exceptionally good at dealing with temporal data and patterns such as the time-series pattern and log pattern, which are covered next. Many NoSQL data stores fail miserably when a huge amount of data is read for analytical purposes, but Cassandra is different in this regard. Keeping analytical needs in mind, you’ll walk through different and interesting design patterns. No theoretical discussions are complete without a good set of use cases to which the knowledge gained can be applied, so the book concludes with a set of use cases you can apply the patterns you’ve learned.

Who is this book for?

This book is intended for big data developers who are familiar with the basics of Cassandra and wish to understand and utilize Cassandra design patterns to develop real-world big data solutions. Prior knowledge of RDBMS solutions is assumed.

What you will learn

  • Enable Cassandra to co-exist with RDBMS and other legacy data stores
  • Explore various design patterns to build effective and robust storage solutions
  • Migrate from RDBMS-based data stores and caching solutions to Cassandra
  • Understand the behaviour of Cassandra when trying to balance the needs of consistency, availability, and partition tolerance
  • Deal with time stamps related to data effectively
  • See how Cassandra can be used in analytical use cases
  • Apply the design patterns covered in this book in real-world use cases

Product Details

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Publication date : Nov 04, 2015
Length: 168 pages
Edition : 2nd
Language : English
ISBN-13 : 9781783988488
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SQL
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Publication date : Nov 04, 2015
Length: 168 pages
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Language : English
ISBN-13 : 9781783988488
Vendor :
Apache
Category :
Programming
Languages :
SQL
Concepts :
Design Patterns
Tools :
Cassandra

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Frequently bought together

Learning Apache Cassandra
Learning Apache Cassandra
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Mastering Apache Cassandra - Second Edition
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Cassandra Design Patterns
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Table of Contents

8 Chapters
1. Co-existence Patterns Chevron down icon Chevron up icon
1. Co-existence Patterns
A brief overview of Cassandra
Denormalization pattern
Reporting pattern
Aggregation pattern
References
Summary
2. RDBMS Migration Patterns Chevron down icon Chevron up icon
2. RDBMS Migration Patterns
A brief overview
List pattern
Set pattern
Map pattern
Distributed Counter pattern
Purge pattern
References
Summary
3. Cache Migration Patterns Chevron down icon Chevron up icon
3. Cache Migration Patterns
A brief overview
Cache to NoSQL pattern
References
Summary
4. CAP Patterns Chevron down icon Chevron up icon
4. CAP Patterns
A brief overview
Write-heavy pattern
Read-heavy pattern
Read-write balanced pattern
References
Summary
5. Temporal Patterns Chevron down icon Chevron up icon
5. Temporal Patterns
A brief overview
Time series pattern
Log pattern
Conversation pattern
References
Summary
6. Analytics Patterns Chevron down icon Chevron up icon
6. Analytics Patterns
Processing big data
A brief overview
Map/Reduce pattern
Transformation pattern
References
Summary
7. Designing Applications Chevron down icon Chevron up icon
7. Designing Applications
A brief overview
Application design and use cases
Service management and use cases
References
Summary
Index Chevron down icon Chevron up icon
Index

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Frank Dec 28, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
[Disclosure: I am the Technical Reviewer of this book]Cassandra Design Patterns is a superb introduction to Cassandra, for anyone approaching the subject from a traditional RDBMS background. All the important fundamental concepts are explained clearly and concisely, and in RDBMS terms.The book provides historical context surrounding the application design, which helps the reader to grasp the reasons behind Cassandra design decisions. I think this results in a more complete understanding of the subject for the reader. The author does this without getting bogged down or including irrelevant details.Particular attention is paid throughout the book to use cases where the application is deployed as a replacement for, or in operation with, existing non-distributed storage systems. The author methodically works over a number of categories of real-world uses, and works out each example completely. The task of applying these examples is discussed in each case.My favourite aspect of the book is the approach to the use cases: the author begins by discussing the relevant practical application, and then carefully deconstructs it down into a Cassandra setup. He makes sure to mention any obscure 'gotchas' or tweaks to the setup that may be necessary in certain circumstances.
Amazon Verified review Amazon
Christian Sep 25, 2016
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
At times unintillegible, non of the examples seem to be great examples really. This felt like a weekend project made it into a book. Seriously the worst technical book I've bought in years, I even requested a refund which I had never done before. stay away from this book. Even the english in the book feels awkward as hell, did anyone even review a draft of this garbage?
Amazon Verified review Amazon

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About the author

Profile icon Rajanarayanan Thottuvaikkatumana
Rajanarayanan Thottuvaikkatumana
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Rajanarayanan Thottuvaikkatumana, Raj, is a seasoned technologist with more than 23 years of software development experience at various multinational companies. He has lived and worked in India, Singapore, and the USA, and is presently based out of the UK. His experience includes architecting, designing, and developing software applications. He has worked on various technologies including major databases, application development platforms, web technologies, and big data technologies. Since 2000, he has been working mainly in Java related technologies, and does heavy-duty server-side programming in Java and Scala. He has worked on very highly concurrent, highly distributed, and high transaction volume systems. Currently he is building a next generation Hadoop YARN-based data processing platform and an application suite built with Spark using Scala. Raj holds one master's degree in Mathematics, one master's degree in Computer Information Systems and has many certifications in ITIL and cloud computing to his credit. Raj is the author of Cassandra Design Patterns - Second Edition, published by Packt. When not working on the assignments his day job demands, Raj is an avid listener to classical music and watches a lot of tennis.
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