Various Data Types You Can Use in MongoDB

To truly master MongoDB, it’s essential to get familiar with the various data types it supports, as they form the backbone of effective database design.While MongoDB is used by over 70% of those developers for its flexible schema and scalability, it is interesting that so many struggle to unlock its full potential because they don't understand the underlying data types themselves. Failure to grasp the nature of the basic data types can lead to significant performance bottlenecks, bad data, and poorly architecture application that undermines the reason for using a NoSQL database in the first place.

In this article, you will learn:

1. The basic definition of BSON and how it's different from JSON.
2. A thorough breakdown of the most common MongoDB data types, including use cases for benefits.
3. Coding examples illustrating how to use different data types to enhance data modeling and query.
4. Help with choosing the right data type in order to preserve data integrity and performance.
5. How to avoid common problems arising out of choosing the wrong data types in MongoDB.

The Foundation of MongoDB: Understanding BSON

While working with MongoDB, you are not actually working with JSON, you are working with BSON (Binary JSON). BSON is what MongoDB uses to store and represent your data in its documents. BSON keeps the expressiveness of JSON, but it allows additional data types that do not exist in JSON. This is an important distinction that allows you to use the full power of a flexible schema with MongoDB.

BSON is a binary-encoded serialization of JSON-like documents. The binary format allows BSON to be faster to parse, and for the serialization to be much smaller than if it were stored as text representation of JSON. This is important for a high-performance database. BSON also provides another level of type safety and structure to your data, which allows you to be more specific and varied with the type of data you represent. BSON provides certain types for dates, 64-bit integers, and binary data that do not exist in JSON.

Common MongoDB Data Types and Their Applications

Selecting the right data type is key to a well-designed MongoDB schema. Let's get into some examples of the most commonly used MongoDB data types, and when they are the most appropriate.

String Data Type

The string data type is likely the most common data type you will see in any database, and MongoDB is no exception. It represents text strings. In BSON, strings are stored as UTF-8. They are very flexible and can hold a wide variety of information, including names and addresses and descriptions and user generated content. When you think of a MongoDB document many of the fields (username, email, product_name, for example) will be strings.

Appropriately using collation settings can have a big impact on how string comparisons and sorting is handled, especially in different languages. If you think of SEO, for example, a string might be the perfect data type for a blog post's slug or meta description.

Numeric Data Types

MongoDB provides multiple numeric types to meet various precision requirements. The most commonly utilized are Double, Int32, and Int64.

Double: The default numeric type within MongoDB. This is a 64-bit floating-point number that is appropriate for storing decimal values such as price, coordinates, or measurements.

Int32: A 32-bit integer type, making it a great choice for small whole numbers such as counts, ages, or status codes. It is more memory-efficient than a Double for this use case.

Int64: A 64-bit integer type for whole numbers that may exceed the range of Int32 values (like timestamps or unique identifiers from other systems).

Your decision on whether to use these types should depend entirely on the data. For example, if you are recording something that will never exceed the Int32 range then it is an easy way to save space and memory on data types.

Boolean Data Type

Using boolean type is very basic and represents logical states: either true or false. This type would be perfect for flags to run code application logic, e.g. isPublished, isActive, or isPaid. A boolean is much more efficient to use than storing, say, a string of "true" or "false", and the same goes with queries and indexing.

Array Data Type

Perhaps one of the most compelling features of MongoDB is its support for the Array data type, which allows a list of values to be stored in a single field. These values can be any type of data, including other documents. This feature is very powerful when modeling "has many" relationships without the use of separate tables (e.g. a list of tags for a blog post, or a list of ingredients for a recipe). In fact, arrays are a core part of denormalized schema design in MongoDB, which we will discuss later and can substantially reduce the number of queries required to retrieve related data.

Object Data Type (Embedded Documents)

In addition to primitive types, MongoDB supports rich, hierarchical data structures. The Object data type, or embedded document, allows you to nest one document within another. This is the foundation of a flexible schema design. For example, in a user profile, you might embed an address document with fields for street, city, and zip. This is a great way to maintain structure for related information, which may provide a performance advantage with MongoDB data types, especially in read-heavy applications.

Date Data Type

It is a common error to store dates as a String. MongoDB has a proper Date data type that stores dates as milliseconds from the Unix epoch. This is the proper way to store dates for a number of reasons: it is consistent, it will allow you to perform date queries accurately (greater than less than), and it provides native date aggregation. You should always use the Date type for any temporal data.

Practical Data Modeling and Type Selection

The impact data types can have on the performance of your application and scalability is direct. Here is how to think about it practically.

When to Use Strings: A blog post title, a product description, a user's name—this is the default text value.

When to Use Arrays: A list of tags for an article, a list of skills for a resume, a history of status changes for a purchase order.

When to Use Embedded Documents: A user's address, a product's specifications, a sensor's readings, where a collection of fields relates to a single concept.

When to Use Numbers and Dates: Any numeric value that will be used in calculations or range queries (price, quantity, age) and any date-based data (created date, last updated date).

A real benefit of MongoDB is the flexibility of its design, but with that flexibility comes the responsibility to make a reasonable decision on your schema design. You can store data anywhere in theory, but by choosing a MongoDB data type intentionally at the start, you can save headaches going forward. For example, by storing a Double when, for your purposes, an Int32 would do, it would waste memory for every record you stored, and slow down certain operations, especially as your dataset grows larger and larger.

Avoiding Common Data Type Pitfalls

Having guidance around being schemaless does not mean there are no errors to avoid or best practices to consider while creating your database.

Here are a few common pitfalls to avoid while using MongoDB:Inconsistency of Data Type - Storing a field as a Steve in one document and a number in others will make querying a nightmare. It is possible, however. Once again you violate the implicit schema and if you're now writing code to process each application level query, you'll need to consider this, implications in repairs, or else.

Use a String for Dates and Numbers - Once again, one of the biggest sins. Not only will it eventually catch up to you in sorting but also any series of logic within queries is limited. Simply put, when you store a date as a String it is stored lexicographically instead of chronologically, if you are attempting any type of sorting or logic you may have number or temporal functions to consider.

Over Normalization versus Embedding - This is particularly a risk for those who come from relational database backgrounds and promote relational processes to access data in MongoDB. Having a high number of queries when assembling a single view of data through a number of child queries defeats the purpose of Document databases altogether. Having embedded documents and arrays where applicable means leverage MongoDB when needed and stop thinking in tables.

The experience to correctly use data types are as oftentimes what separates a novice user of MongoDB from a professional, who is building a performant and scalable system. This is a skill as it relates to systems that are fundamental, regardless of being front-end/back-end, that will impact every layer of an application. Time spent here is really technical debt prevention.

Conclusion

Having an understanding of the different data types that MongoDB offers is not only a technical skill; it's actually a fundamental skill in building an efficient, scalable, and maintainable application. From the binary level of BSON, to the pragmatic use of types like String, Array, Date and more - every decision you make about a single data item impacts not just a data model's operational performance, but even worse, it can have serious ramifications for a data model's future state. Meaning that if you thoughtfully choose the data type for every piece of information in documents, then you can guarantee data integrity, better handle queries, and create a system that can last. A good schema that is responsible and utilizes a strong knowledge of these types, is the true secret to using MongoDB effectively.

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Frequently Asked Questions

1. What is the difference between BSON and JSON?
   BSON, or Binary JSON, is a binary-encoded serialization of JSON-like documents that MongoDB uses. Unlike text-based JSON, BSON is faster to parse, more compact, and includes additional data types like Date and Int64 that are not natively supported in JSON.
   
2. Why should I not store a date as a String in MongoDB?
   Storing dates as a String is discouraged because it prevents MongoDB from performing native date-based queries and sorting correctly. It also requires more storage and can lead to data inconsistencies. Using the dedicated Date data types ensures chronological sorting and enables powerful date-specific aggregation operations.
   
3. Can a field in a MongoDB collection have different data types in different documents?
   Yes, a key benefit of MongoDB's flexible schema is that a field can have different data types in various documents within the same collection. For example, the price field might be a Double in one document and an Int32 in another. However, for consistency and easier querying, it is generally recommended to use a single type for a given field across a collection.