A key-value database is a NoSQL non-relational storage strategy where records are stored as a series of keys (the identifier) and values (the value for the identifier). Key-value pairs can be a simple number or string, or a value could be an object such as an image or geospatial data. High-volume applications requiring fast responses benefit from key-value databases.
What Are Key-value Stores?
Relational databases store data in tables, but a key-value relationship is a series of keys and values. The key is the unique identifier used to query the database for the right value. For example, the price for a product could be stored with “price” as the key and “12.00” as the value. Some applications use key-value stores for metadata that can then be used to query additional information in larger relational or other non-relational databases.
Storing a series of key-value pairs makes querying faster, so applications that need quick responses and support millions of users can benefit from using a key-value database. Key-value storage is also beneficial when businesses need to store simple metadata to link to additional larger data silos. Some businesses use key-value pairs for simple data sets where millions of requests hit the database but only a few values must be returned per query.
Key-value Stores in Action
Applications querying for small data sets can benefit from the speed of a key-value store. As an example, suppose you have a social media feed used by millions of users. Users constantly refresh their feed, and the application shows the top five threads from their feed. A key-value pair with the latest post and link to the post could be stored.
Real-time applications also benefit from key-value stores in action. Because key-value stores return data quickly, applications with tickers or second-by-second updates use them. For example, a stock ticker displaying the latest value of a particular stock requires display updates every second. A key-value database can be used for these queries.
Examples of Key-value Databases
Key-value Stores vs. Relational Databases
Traditional relational databases still have their purpose in conventional applications. A relational database stores values in a table-like strategy. A single record has several columns, and each column has a value assigned to it. Developers assign a unique identifier to each record, so information can be associated with the right record. For example, a unique customer identifier links the customer’s name and address. Name and address in this example are two columns, and the customer’s information including the unique identifier, name, and address is a single record.
Key-value stores associate a single unique identifier key with a value. The value can be more than a simple number or string. Key-value stores can assign an object to a single key identifier, so developers aren’t limited to basic data. A combination of keys can also be used to retrieve specific data.
Choosing the Right Key-value Store
Applications using key-value stores need fast responses, so look for a database that offers in-memory caching. Frequently used data can be cached in memory, which means that the database engine does not need to search data on a disk, retrieve it, and then transfer it to the requesting application. Because key-value stores perform continuous rapid read and write actions, caching is optimal for performance.
Large data storage requirements can benefit from data partitioning and distribution. Horizontal distribution allows administrators to improve performance by adding nodes to the database cluster. Several partitioning strategies are available including hashing and sharding. Choose a strategy that fits your infrastructure and administrator preferences.
Best Practices for Key-value Store Implementation
Whether you decide to implement a key-value store on premises or in the cloud, you should follow a few best practices to preserve performance. You might not notice performance issues at first, but they often present themselves after the database grows and the number of application users increases. A few best practices:
Use compound primary keys to optimize ranged queries.
Use a sorting key for ranged queries.
Use a sharding key for distributed storage.
In the examples above, best practices are necessary for performance. Performance is critical for a key-value database so that it can support real-time applications. Caching data will also improve performance, so choose a database engine that supports it.
Conclusion
If you have a high-volume application that requests data that can be stored as a key-value pair, a key-value store might be the best choice. Speed is a critical factor in these databases, so choose one that caches data in memory and offers a distributed hashing or sharding method. You can work with on-premises or cloud databases, but if you’re looking for a solution, Pure Storage offers storage solutions that fit your business.
