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MongoDB Data Modeling, by Wilson da Rocha Franca
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Focus on data usage and better design schemas with the help of MongoDB
About This Book- Create reliable, scalable data models with MongoDB
- Optimize the schema design process to support applications of all kinds
- Use this comprehensive guide to implement advanced schema designs
This book is intended for database professionals, software developers, and architects who have some previous experience with MongoDB and now want to shift their focus to the concepts of data modeling. If you wish to develop better schema designs for MongoDB-based applications, this book is ideal for you.
What You Will Learn- Optimize your models based on priorities
- Discover the schemaless document-oriented database design
- Improve the process of querying a document with the help of indexing
- Set up a sharded environment with replication
- Configure the read preference in accordance with your application's requirements
- Create effective indexes that will fit your application needs
- Understand how to analyze the performance of your queries to avoid performance issues
This book covers the basic concepts in data modeling and also provides you with the tools to design better schemas. With a focus on data usage, this book will cover how queries and indexes can influence the way we design schemas, with thorough examples and detailed code.
The book begins with a brief discussion of data models, drawing a parallel between relational databases, NoSQL, and consequently MongoDB. Next, the book explains the most basic MongoDB concepts, such as read and write operations, indexing, and how to design schemas by knowing how applications will use the data. Finally, we will talk about best practices that will help you optimize and manage your database, presenting you with a real-life example of data modeling on a real-time logging analytics application.
- Sales Rank: #915377 in Books
- Published on: 2015-06-30
- Released on: 2015-06-22
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .46" w x 7.50" l, .78 pounds
- Binding: Paperback
- 176 pages
About the Author
Wilson da Rocha Franca
Wilson da Rocha Franca is a system architect at the leading online retail company in Latin America. An IT professional, passionate about computer science, and an open source enthusiast, he graduated with a university degree from Centro Federal de Educacao Tecnologica Celso Suckow da Fonseca, Rio de Janeiro, Brazil, in 2005 and holds a master's degree in Business Administration from Universidade Federal de Rio de Janeiro, gained in 2010. Passionate about e-commerce and the Web, he has had the opportunity to work not only in online retail but in other markets such as comparison shopping and online classifieds. He has dedicated most of his time to being a Java web developer. He worked as a reviewer on Instant Varnish Cache How-to and Arduino Development Cookbook, both by Packt Publishing.
Most helpful customer reviews
4 of 4 people found the following review helpful.
Fairly good exploration of MongoDB
By Patrick William Ackerman
Fairly good exploration of MongoDB. Rocha Franca exposes both the low level (designing schema, creating databases and collections, queries) and higher level (indexing, node replication, and sharding) capabilities of Mongo from the perspective of someone who understands Mongo in the context of the DBMS market place. The title "MongoDB Data Modeling" is a little bit misleading, though, as this is a more well-rounded approach to MongoDB as a whole rather than specifically focusing on data modeling. In fact, there is a much more in-depth discussion on the topic of data modeling in Mongo's official documentation. The book does culminate in a useful "capstone" chapter that incorporates many of the ideas introduced earlier, showing the decision making process from deciding on the schema of a particular collection, implementing the queries in a simple Node.js application, and examining the results in the stored data.
Complaints:
-There is a brief foray into using the Nginx platform (for half a chapter or so), which is quickly abandoned for Node.js. I found this a bit confusing and question its purpose.
-Even for such a new book, some of the examples of command results are seemingly already out-of-date. Particularly, the section where the author has you run "explain()" functions on your queries. The function still operates as you would expect and returns more-or-less the same data as shown in the book, however the labeling of said data and the format/structure of the result are almost completely different which means you will have to do some decoding/mapping to figure out the correspondence. Also, the way he formats the "explain()" function, by chaining it before "find()" did not work for me, I always had to take "explain()" out and append it to be after "find()" to get it to run.
-Like most of Packt's books, several awkwardly constructed sentences, inexplicably informal word choice (why is the word "nowadays" so prevalent in Packt's non-native speakers' books?), and typos that should have been caught by the reviewers and editors. I don't blame this on Rocha Franca (or the other Packt authors), in fact, I'm often amazed at how well non-native English speakers can communicate their ideas.
0 of 0 people found the following review helpful.
The right book to get introduced to MongoDB general concepts
By Carlos F. Enguix
Although the book is divided in 8 chapters conceptually it is divided into two parts one generic that covers general concepts, that is Part I and Part II that covers examples of using MongoDB:
Part I:
Chapter1 Introducing Data Modeling it is just an introductory chapter. It gives a brief explanation of what is the MongoDB document database and NoSQL databases, a brief overview of the ANSI/SPARC architecture and so on. Although some authors state that some NoSQL databases are schemaless it is not true for MongoDB given that it is used for representing semi-structured and structured data.
Chapter 2 Data Modeling with MongoDB is the core chapter for a MongoDB beginner that truly fulfils the book's title. The chapter starts with a brief introduction to JSON and BSON and how MongoDB uses JSON for document notation and BSON to serialize documents. After this it is explained how flexible is MongoDB to define different document structures for the same document represented, how MongoDB uses the default _id field to define the document object id and how we can define sequence fields to assign a value although it is recommended for scalability purposes to use MongoDB default object id generation. Common document patterns are introduced for both embedded and referenced documents depending on the type of document relationship explaining when to embed or to use references.
Chapter 3 Querying Documents is at the MongoDB beginner's level. It starts telling us how to specify queries with the collection's find operation, query criteria and query field projection parameters similar as done in SQL queries that return cursors for selected documents. Results will be presented as documents in JSON format. An introduction to comparison operators such as $gte, $lte and so on is presented. Also logical operators such as $and, $or, $not are presented with some examples. A small section on element operators is presented later. The $exists operator tests if a given field is defined in a document. What follows next is an example of a $regex query and array operators. This was just an introduction to MongoDB query operators because MongoDB includes a very rich set. An introduction to write operations such as insert, update and remove is presented next. Also a good explanation of write concerns or the levels of guaranteeing a successful write operations is included. Write concerns are classified into unacknowledged, the default acknowledged, journaled and replica acknowledged.
Chapter 4 Indexing. This chapter for the RDBMS fellows is at the beginners level. MongoDB internal data structure for indexes uses a B-tree data structure. With the createIndex method we can create either indexes ordered ascendently or descendently. Also we can create single field indexes, multiple field indexes, multikey indexes that index arrays and finally text indexes that index string or arrays of string fields. Text indexes are more sophisticated given that string tokenization, word stemming and stop word removal is produced. The chapter ends up with TTL indexes based upon date fields, unique and sparse indexes. Sparse indexes are also special given that the index will not contain documents that do not have the indexed field.
Chapter 5 Optimizing Queries. This chapter is devoted to DBAs and it delves into MongoDB query optimizer's query plans which are explained by the cursor's explain method. Three types of parameters for the explain method namely queryPlanner, executionStats and allPlansExecution provide different levels of details. By using the explain method you would be surprised to find out that a declared index is not being used and that instead an entire collection scan is being produced and so on. What follows next is an explanation of how to cover queries by projecting only the fields that are part of an index, retrieving directly values from the index. In MongoDB we can force the query optimizer to generate a query plan that uses a given index with the hint method. The chapter ends up with an explanation of read preferences for replica sets. Although it is not specified where does MongoDB stand in the CAP theorem one can imagine it according to read preference options.
Chapter 6 Managing the Data. In this chapter it is explained how we can use tag sets to define read preferences and custom write concerns in replica sets. Not only we can use tag sets to declare configurations to separate MongoDB applications by application operation type but we can also define configurations to declare MongoDB applications separated geographically. What follows next is the explanation of capped collections and when to use them. Capped collections are fixed size collections with high read and write throughput in natural order, that is how they are stored on disk, without requiring the overhead of indexes. This chapter ends up by showing how to use TTL indexes to remove data after a given set of time and the shortcomings of using them.
Chapter 7 Scaling: section Scaling MongoDB with sharding. MongoDB scales horizontally with the sharding technique by partitioning data and distributing it among many servers. There is a compromise between availability and consistency but the main issue about sharding is to obtain high throughput operation. MongoDB offers mainly range-based and hash-based sharding with respect to the shard key. In the choosing the shard key subsection an example of range-based sharding and shard key shortcomings is presented. Choosing the right shard key is critical to database performance. Generally speaking, shard keys must be composed of fields with high cardinality enabling data chunks to be highly distributed suited for write operations. In the case of read operations, performance throughput will be better when retrieving data from a single shard.
Part II
Chapter 7 Scaling: section Scaling a social inbox schema design and Chapter 8 Logging and Real-time Analytics with MongoDB contain examples of using sharding and shard keys for a distributed social inbox and how to use MongoDB to process custom log files generated by an Nginx web server respectively.
Summary:
The first 6 chapters and the first part of chapter 7 covers general MongoDB concepts and it is intended for beginners and intermediate readers. The second part of chapter 7 and chapter 8 are devoted to intermediate and advanced readers. Readers of this second part must set up the right environment to grasp the concepts being covered.
0 of 0 people found the following review helpful.
Fine way to start one's adventure with MongoDB
By chris k
Rocha Franca's MongoDB Data Modeling is a fine way to start one's adventure with MongoDB. Before diving into technical details author takes time to introduce reader to brief history of NoSQL databases and basic concepts of architecture. From there reader smoothly continues to MongoDB's way of storing data, gets feet wet with JSON (and BSON) format and finally finds what the title promised - how the data can be modeled and tips when to use each pattern. Data modelling chapter is followed by practical introduction to MongoDB with read and write operations and indexing, everything backed by easy to understand examples. Later reader enters more advanced topics like query optimization, gets acquainted with query plans and optimizer to finally take a look at how Mongo handles replications, write concerns and finally scaling with sharding using real life example. The book finishes with a good chapter summarizing everything reader has learnt so far - author gives us concrete problem to solve and leads our way in finding not only good, but better solution.
Why not 5 stars? I believe that aggregation pipeline deserved at least its own chapter highlighting concepts and possibilities. Also somehow I expected more data modeling case studies than MongoDB basics. Overally we are given a good book, just bit below my maybe too high expectations.
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