Christian Amor Kvalheim - The Little Mongo DB Schema Design Book
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The Little MongoDB Schema Design Book, covers the fundamentals off Schema design with MongoDB, as well as several useful Schema design patters for your applications.
I wrote this book to be a helpful and concise guide to MongoDB Schema design, as well as a repository to look up specific MongoDB Schema patterns. This book came around, due to my experiences teaching people about using MongoDB for application development. It tries to cover essential information that you can apply to your own applications.
We cover a lot of different aspects of Schema Design in this book. These include.
- Schema Basics including one to one, one to many and many to many relationships
- Embedding versus linking
- Bucketing Strategy
- Understanding the MongoDB MMAP and WiredTiger storage engine
- MongoDB Indexes
- The Metadata Schema Pattern
- Time Series Schema Pattern
- Queues Schema Pattern
- Nested Categories Schema Pattern
- Account Transactions Schema Pattern
- Shopping Cart Schema Pattern with and without product reservation
- A Theater Ticket Reservation Schema Pattern
- An Embedded Array Cache Schema Pattern
- An Internationalization Schema Pattern
- Sharding
The book aims to provide developers with a deep but concise understanding of how to efficiently work with MongoDB.
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This book is for sale at http://leanpub.com/mongodbschemadesign
This version was published on 2015-10-09
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This is a Leanpub book. Leanpub empowers authors and publishers with the Lean Publishing process. Lean Publishing is the act of publishing an in-progress ebook using lightweight tools and many iterations to get reader feedback, pivot until you have the right book and build traction once you do.
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ISBN for EPUB version: 978-1-943846-77-1
ISBN for MOBI version: 978-1-943846-77-1
One of the questions that comes up very often from new users of MongoDB and document databases is how to do schema design. The concepts of embedding and linking documents combined with many years of working with relational models means there is a learning process involved in moving to a document database.
As the product has matured over time, some patterns of schema design have emerged. This book is an attempt to distill that knowledge into actionable information you can use for your own applications.
We will cover basics of MongoDB schema design, how MongoDB works under the covers and look at a series of schema design patterns that aim to solve specific issues that you might run into while working on your application.
That said, this is not the end all of schema design for MongoDB. If you come up with other brilliant schema design patterns feel free to drop me an email at christkv@gmail.com or send me a tweet to@christkv
I hope you enjoy the book and find the information useful.
Christian Amor Kvalheim
Before exploring the more advanced schemas in this book its important to revisit schema basics. In this chapter we will explore the basic relationships from traditional relational databases and how they relate to the document model in MongoDB.
We will start with a look at the One-To-One (1:1) relationship then moving on to the One-To-Many (1:N) and finally the Many-To-Many (N:M).
The 1:1 relationship describes a relationship between two entities. In this case the Author has a single Address relationship where an Author lives at a single Address and an Address only contains a single Author.
A One to One Relational Example The 1:1 relationship can be modeled in two ways using MongoDB. The first is to embed the relationship as a document, the second is as a link to a document in a separate collection. Lets look at both ways of modeling the one to one relationship using the following two documents:
1{2name:"Peter Wilkinson",3age:274}1{2street:"100 some road",3city:"Nevermore"4}The first approach is simply to embed the Address document as an embedded document in the User document.
An example User document with Embedded Address1{2name:"Peter Wilkinson",3age:27,4address:{5street:"100 some road",6city:"Nevermore"7}8}The strength of embedding the Address document directly in the User document is that we can retrieve the user and its addresses in a single read operation versus having to first read the user document and then the address documents for that specific user. Since addresses have a strong affinity to the user document the embedding makes sense here.
The second approach is to link the address and user document using a foreign key.
1{2_id:1,3name:"Peter Wilkinson",4age:275}1{2user_id:1,3street:"100 some road",4city:"Nevermore"5}This is similar to how traditional relational databases would store the data. It is important to note that MongoDB does not enforce any foreign key constraints so the relation only exists as part of the application level schema.
Embedding Preferred
In the one to one relationship Embedding is the preferred way to model the relationship as its more efficient to retrieve the document.
The 1:N relationship describes a relationship where one side can have more than one relationship while the reverse relationship can only be single sided. An example is a Blog where a blog might have many Comments but a Comment is only related to a single Blog.
A One to Many Relational Example The 1:N relationship can be modeled in several different ways using MongoDB. In this chapter we will explore three different ways of modeling the 1:N relationship. The first is embedding, the second is linking and the third is a bucketing strategy that is useful for cases like time series. Lets use the model of a Blog Post and its Comments.
1{2title:"An awesome blog",3url:"http://awesomeblog.com",4text:"This is an awesome blog we have just started"5}A Blog Post is a single document that describes one specific blog post.
Some example Comment documents1{2name:"Peter Critic",3created_on:ISODate("2014-01-01T10:01:22Z"),4comment:"Awesome blog post"5}67Font size:
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