Types of NoSQL Databases Explained: Document-Based Databases
Scientists are problem solvers. There are technological advancements that happened by chance and those that happened through research. No matter how hard you try to find an answer, those answers are always a means to an end, solutions to a problem.
NoSQL databases were created in response to the need to overcome some limitations of relational databases. These limitations usually result from the need to break free from the tight table schema of relational databases.
A major advantage of NoSQL databases is their performance and scalability. They are usually quick to process the stored data. Finally, another limitation overcome with NoSQL is the variety of data types that can be stored.
What are document databases?
A document-based database, also known as a document store, stores information in CML, YAML, JSON, or binary documents such as BSON. To organize these documents into a whole, a specific key is assigned to each document. This feature makes document stores similar to key-value stores.
Even though document stores do not have a unified schema, they are usually organized in order to easily use and eventually analyze data. This means that they are structured, to some degree. Since each object is usually stored in a single document, there is no need to define relationships between documents.
These documents are nothing like tables in a relational database; they don’t have a set number of fields, slots, etc. and there are no blank spaces – missing information is simply omitted rather than a blank spot remaining for it. Data can be added, modified, deleted and queried.
The keys assigned to each document are unique identifiers needed to access data in the database, usually a path, string, or uniform resource identifier. Credentials tend to be indexed in the database to speed up data retrieval.
The contents of documents in a document store are classified using metadata. With this feature, the database “understands” what category of information it contains – whether a field contains addresses, phone numbers, or social security numbers, for example. For improved efficiency and user experience, document stores feature a query language, which allows documents to be queried based on metadata or document content. This allows you to retrieve all documents that contain the same value in a certain field.
Amazon has provided the following terminology comparison between SQL and a document database, MongoDB. The following list makes it possible to establish a parallel between the two types of databases:
- SQL: Table, Line, Column, Primary key, Index, To see, Table or nested object, Deploy
- MongoDB: Collection, Document, Field, ObjectId, Index, View, Embedded Document, Table
One of the main priorities of any business is to make the most of the time and resources allocated, increasing overall efficiency. Choosing the right database according to its purpose and the type of data collected is a crucial step. The following features of a document store might make it the right choice for you:
- Flexibility. Document stores have the famous advantage of all NoSQL databases, which is a flexible structure. As mentioned earlier, documents in a database do not require consistency. They don’t need to be the same type, or structured the same way.
- Easy to update. Any new information, when added to a relational database, must be added to all datasets to maintain the unified structure in a table in a relational database. With document stores, you can easily add new information without having to add it to all existing datasets.
- Improved performance. Rather than pulling data from multiple linked tables, you can find everything you need in a single document. With everything kept in one place, it’s much faster to reach and retrieve data.
Regardless of the size of a database, because they are only semi-structured, NoSQL databases are simple compared to relational databases. If you compromise the simplicity of a document store, you also compromise the improved performance mentioned earlier. You can create references between documents in a document store by interconnecting them, but this can create complex systems and rob you of fast performance.
If you have a large volume database and want to create a network of mutually referenced data, you better find a way to map it and integrate it into a relational database.
Document database use cases
A document store is a simple way to store and retrieve information contained in documents and related to a single object. For this reason, this type of database is handy for user profiles where you have information about a single user and the user chooses what to provide and how to do it.
Similarly, for some other types of NoSQL databases, it is also handy for content management and user-generated content such as blogs.
Most popular document databases
Some of the most popular document stores are already mentioned in this article. Others include MongoDB Atlas, Amazon DynamoDB, Google Cloud Firestore, and Couchbase Server.
All NoSQL databases have something in common. They are usually designed to meet specific purposes and meet demands that a relational database would struggle to meet. Now you can get the most out of your data by avoiding wasting time putting it into a table in a relational database and mapping the relationships between the tables. Simply store it from the existing data source and retrieve it more efficiently.