Key Value Store

A key value store or key value database, is a type of database that has gained popularity recently because of its ease of use, effectiveness, and adaptability. In this article at OpenGenus, we'll look at what a key value store is, its benefits, potential applications, and some well-known key value store examples.

Table of contents:

1. What is a key value store?
2. How key value stores work?
3. The Benefits of Key Value Stores
4. Use cases of key value stores
5. Popular Key Value Stores
6. Key value stores Vs other databases type
7. Conclusion

What is a key value store?

A Key Value Store is a database that stores data in the form of key-value pairs. Each key in the database uniquely identifies a value, and the keys are used to get the matching values. Values can be of any data type, such as strings, numbers, or objects, and are often stored in a manner that allows for quick retrieval.
The simplicity of a key value database is one of its distinguishing traits. A key value database, unlike typical relational databases, does not have a specified schema, which means it can store any type of data without first defining a schema. This makes it a very flexible manner of storing data because the data structure can change over time without requiring any changes to the database model.

How key value stores work?

Key-value stores work by storing data as a collection of key-value pairs, with each key corresponding to a value. To retrieve a value from the key-value store, simply enter the key that corresponds to the data you want, and the key-value store will return the appropriate value. Similarly, to update a value in the key-value store, you specify the key and the new value, and the key-value store replaces the old value with the new value associated with that key. Key-value stores are designed to get and update data quickly and efficiently, making them ideal for use in applications that require low-latency access to frequently requested data.

The Benefits of Key Value Stores

Key value stores have a number of advantages over standard relational databases, including the following:

• Simplicity: as previously said, key value stores are simple and adaptable, allowing for easy and quick data storing and retrieval.
• Speed: Because key value stores are built for high-speed data access, they are appropriate for use cases where quick retrieval is critical.
• Scalability: Since key value stores are extremely scalable, adding more capacity as needed without compromising performance is simple.
• Low latency: Key value stores have minimal latency for read and write operations, making them excellent for real-time data access use cases.
• Ease of Use: because they do not require complex SQL queries or joins, key value stores are frequently easier to use than relational databases.

Use cases of key value stores

Key-value store can be used in a variety of systems and applications. They are ideal for use cases in which quick data access, ease of usage, scalability, and customization are crucial. Here are a few scenarios in which key-value databases can be used:

• Caching: To increase efficiency and reduce database load, key-value stores are widely used for caching frequently requested data, such as website content or user session data.
• Session Management: Session data for web applications can be stored in key-value databases, making it simple to manage user sessions and maintain state between queries.
• Configuration Management: Key-value stores can be used to store application configuration data, making it simple to alter settings without changing the application code.
• Real-time Analytics: Key-value stores can be used for real-time analytics, such as tracking user behaviour on a website or real-time monitoring of system metrics.
• Distributed Systems: key-value stores are frequently used to maintain state and enable cooperation across different nodes in distributed systems.

Popular Key Value Stores

Several popular key values stores are available, including:

• Redis: Redis is a highly scalable in-memory key value store with a wide range of features such as caching, message queuing, and pub/sub messaging.
• Apache Cassandra: is a distributed key value store with great scalability and fault tolerance, making it suited for usage in large-scale applications.
• Riak: is a distributed key value store with high availability and fault tolerance, making it a popular candidate for usage in distributed systems.
• Amazon DynamoDB: DynamoDB is an Amazon Web Services managed key value store that is extremely scalable and provides low latency data access.

Key value stores Vs other databases type

In the table below we compare key value store to relational databases, document databases, graph databases and column-family databases.

Points	Key value databases	Relational databases	Document databases	Graph-databases	Column-family databases
Type	NOSQL	SQL	NOSQL	NOSQL	NOSQL
Functionality	* Simple data model based on key-value pairs * Very fast data access and retrieval * No support for complex queries or transactions * Limited support for data relationships * Limited support for data aggregation and analysis	* Structured data model with defined relationships between tables * Support for complex queries and transactions * Strong support for data relationships and consistency * Powerful data aggregation and analysis capabilities * Scalable for large datasets	* Flexible data model based on documents * Good support for complex queries and transactions * Limited support for data relationships and consistency * Highly scalable for large and frequently updated datasets * Ability to handle unstructured and semi-structured data	* Data model based on a graph structure with nodes and edges * Powerful support for complex queries and transactions * Very strong support for data relationships and consistency * Highly scalable for large and highly connected datasets * Ability to handle complex data models with high performance	* Highly efficient for read-heavy workloads * Good support for complex queries and transactions * Limited support for data relationships and consistency * Highly scalable for large datasets * Ability to handle highly structured data
Cost	Key-value stores are frequently open source, making them accessible for free. Commercial key-value stores, like Amazon DynamoDB, may demand a fee based on usage and storage, though.	Relational databases may be relatively costly, particularly for commercial solutions such as Oracle or Microsoft SQL Server, which require costly licensing. Furthermore, the hardware requirements for running a relational database can be substantial, especially for huge datasets.	Document databases like MongoDB may be free to use in small-scale applications, but enterprise-level functionality and support may need costly subscriptions. Due to the need for high-performance computation, running a document database can also have large hardware requirements.	Graph databases can be expensive, particularly for for-profit products like Neo4j that charge license fees depending on usage and storage. Due to the need for high-performance processing, running a graph database can also result in substantial hardware needs.	Column databases, like Apache Cassandra, can be used for free but may need expensive subscriptions for enterprise-level functionality and support. Due to the need for high-performance computation, running a column database might also have large hardware requirements.
Security	Key-value stores often provide fundamental security features like access control and authentication. To improve data security while in transit, some key-value stores, like Redis, additionally enable SSL and provide data encryption. Key-value stores may, however, lack sophisticated security features like auditing or role-based access control, making them less safe than other kinds of databases.	Relational databases provide robust security features including role-based access control, auditing, and encryption to guarantee the integrity and confidentiality of data. Relational databases are a common option for safe applications due to their support for fine-grained access control and defined security paradigm.	Document databases provide security features such as access control, authentication, and encryption to secure data confidentiality and integrity. Some document databases, such as MongoDB, also provide extra security features such as audits and role-based access control to guarantee sensitive data is safeguarded.	Graph databases include security features similar to relational databases, such as role-based access control, auditing, and encryption. However, due to the unique structure of graph databases, security systems must additionally evaluate the relationships between nodes and edges to maintain data privacy and integrity.	Column databases include security features such as access control, authentication, and encryption to maintain data confidentiality and integrity. However, because of their distributed nature, protecting data privacy and integrity can be difficult, and additional precautions such as data encryption and network security may be required.
Examples	Redis, Amazon DynamoDB, Riak	MySQL, PostgreSQL, Oracle Database, Microsoft SQL Server	MongoDB, Couchbase, Amazon DocumentDB, CouchDB, RavenDB	Neo4j, Amazon Neptune, Microsoft Azure Cosmos DB, OrientDB, ArangoDB	Apache Cassandra, HBase, Google Bigtable, ScyllaDB, Apache Accumulo

Conclusion

To summarize this article at OpenGenus, Key Value Stores are a simple, efficient, and adaptable method of storing and retrieving data. They outperform standard relational databases in terms of speed, scalability, and usability. Key value stores are used in a variety of use cases, from caching and session management to distributed systems and real-time analytics.