This post compares the two common types of NoSQL databases: document databases and key-value databases. The comparison criteria is based on data model, querying and use cases.
What is Document Database?
A document database is a type of NoSQL database that stores data in the form of documents, rather than in tables with rows and columns like a traditional relational database. These documents can be in a variety of formats, such as JSON, BSON, or XML. They often include nested data structures, which can make it easier to store and query complex data. Document databases are well suited for storing semi-structured data and are often used in web and mobile applications. They offer a flexible schema and high performance, but may have limited querying capabilities compared to relational databases and difficulties with data consistency and validation.
What is Key Value Database?
A key-value database, also known as a key-value store, is a type of NoSQL database that stores data as a collection of key-value pairs. Each key is a unique identifier that is used to retrieve the corresponding value. The value can be any type of data, such as a string, number, or object.
Key-value databases are designed for high performance and scalability, and are often used in situations where the data does not require complex relationships or joins. They are well suited for storing data that can be easily partitioned, such as caching data or session data. Key-value databases are simple and easy to use, but they may not be as suitable for complex queries or data relationships as other types of databases such as document or relational databases.
Document Database VS Key Value
Document databases and key-value databases are both types of NoSQL databases, but they have some key differences:
- Data Storage:
- A document database stores data in the form of documents, which can include nested data structures. Each document can have a unique structure and can contain different fields.
- A key-value database stores data as a collection of key-value pairs, where each key is a unique identifier and the value can be any type of data.
- Document databases support more advanced querying capabilities, and often have built-in support for indexing and searching.
- Key-value databases typically have more limited querying capabilities and may not support advanced search or indexing features.
- Data Modeling:
- Document databases are more flexible in terms of data modeling, and allow for more complex data structures and relationships.
- Key-value databases have a simple data model that is based on key-value pairs and may not support complex data structures or relationships.
- Use cases:
- Document databases are well suited for storing semi-structured or unstructured data, and nested data structures, such as JSON or XML documents. They are also well suited for complex queries and data relationships.
- Key-value databases are well suited for storing data that can be easily partitioned, such as caching data or session data. They are simple and easy to use, but they may not be as suitable for complex queries or data relationships as other types of databases.
More to Read
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- Relational Vs Non Relational Database
- Data Warehouse vs Database
- Dataset vs Database
- Database vs DataFrame
- Postgres Schema vs Database
- Primary Key vs Foreign Key
- Primary Key vs Candidate Key
- 13 Examples of Relational Database
- Relational Database Vs. Object-Oriented Database
- 9 Types of Databases
- Distributed Database
- Operational Database
- Personal Database
- Graph Databases
- Centralized Database