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MongoDB: The Definitive Guide, 2nd Edition by Kristina Chodorow

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Chapter 1. Introduction

MongoDB is a powerful, flexible, and scalable general-purpose database. It combines the ability to scale out with features such as secondary indexes, range queries, sorting, aggregations, and geospatial indexes. This chapter covers the major design decisions that made MongoDB what it is.

Ease of Use

MongoDB is a document-oriented database, not a relational one. The primary reason for moving away from the relational model is to make scaling out easier, but there are some other advantages as well.

A document-oriented database replaces the concept of a “row” with a more flexible model, the “document.” By allowing embedded documents and arrays, the document-oriented approach makes it possible to represent complex hierarchical relationships with a single record. This fits naturally into the way developers in modern object-oriented languages think about their data.

There are also no predefined schemas: a document’s keys and values are not of fixed types or sizes. Without a fixed schema, adding or removing fields as needed becomes easier. Generally, this makes development faster as developers can quickly iterate. It is also easier to experiment. Developers can try dozens of models for the data and then choose the best one to pursue.

Easy Scaling

Data set sizes for applications are growing at an incredible pace. Increases in available bandwidth and cheap storage have created an environment where even small-scale applications need to store more data than many databases were meant to handle. A terabyte of data, once an unheard-of amount of information, is now commonplace.

As the amount of data that developers need to store grows, developers face a difficult decision: how should they scale their databases? Scaling a database comes down to the choice between scaling up (getting a bigger machine) or scaling out (partitioning data across more machines). Scaling up is often the path of least resistance, but it has drawbacks: large machines are often very expensive, and eventually a physical limit is reached where a more powerful machine cannot be purchased at any cost. The alternative is to scale out: to add storage space or increase performance, buy another commodity server and add it to your cluster. This is both cheaper and more scalable; however, it is more difficult to administer a thousand machines than it is to care for one.

MongoDB was designed to scale out. Its document-oriented data model makes it easier for it to split up data across multiple servers. MongoDB automatically takes care of balancing data and load across a cluster, redistributing documents automatically and routing user requests to the correct machines. This allows developers to focus on programming the application, not scaling it. When a cluster need more capacity, new machines can be added and MongoDB will figure out how the existing data should be spread to them.

Tons of Features…

MongoDB is intended to be a general-purpose database, so aside from creating, reading, updating, and deleting data, it provides an ever-growing list of unique features:


MongoDB supports generic secondary indexes, allowing a variety of fast queries, and provides unique, compound, geospatial, and full-text indexing capabilities as well.


MongoDB supports an “aggregation pipeline” that allows you to build complex aggregations from simple pieces and allow the database to optimize it.

Special collection types

MongoDB supports time-to-live collections for data that should expire at a certain time, such as sessions. It also supports fixed-size collections, which are useful for holding recent data, such as logs.

File storage

MongoDB supports an easy-to-use protocol for storing large files and file metadata.

Some features common to relational databases are not present in MongoDB, notably joins and complex multirow transactions. Omitting these was an architectural decision to allow for greater scalability, as both of those features are difficult to provide efficiently in a distributed system.

…Without Sacrificing Speed

Incredible performance is a major goal for MongoDB and has shaped much of its design. MongoDB adds dynamic padding to documents and preallocates data files to trade extra space usage for consistent performance. It uses as much of RAM as it can as its cache and attempts to automatically choose the correct indexes for queries. In short, almost every aspect of MongoDB was designed to maintain high performance.

Although MongoDB is powerful and attempts to keep many features from relational systems, it is not intended to do everything that a relational database does. Whenever possible, the database server offloads processing and logic to the client side (handled either by the drivers or by a user’s application code). Maintaining this streamlined design is one of the reasons MongoDB can achieve such high performance.

Let’s Get Started

Throughout the course of the book, we will take the time to note the reasoning or motivation behind particular decisions made in the development of MongoDB. Through those notes we hope to share the philosophy behind MongoDB. The best way to summarize the MongoDB project, however, is through its main focus—to create a full-featured data store that is scalable, flexible, and fast.

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