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What are KV Stores, and why do they matter?
As cloud costs continue to rise and consumers expect highly responsive experiences as standard, organizations are increasingly turning to edge computing to reduce latency and lower public cloud spending.
However, traditional databases are often far too complex and resource-intensive to be viable for applications at the edge. For edge processing solutions to deliver maximum value, applications need to become less reliant on queries to the central cloud or origin server.
This is where KV stores come in. KV stores are an alternative to relational databases and offer speed, scalability and low-latency. Keep reading to learn more about how this type of data store supports edge computing and helps developers create amazing experiences for their customers.
What are key-value stores?
Key-value stores are a type of specialized NOSQL database that allows you to store data in the form of key-value pairs instead of in a table-based structure. This data management paradigm is also referred to in computer science as an “associative array” or “dictionary”.
Applications retrieve records by providing unique identifiers known as “keys”. These keys typically come as unique strings of numbers or letters. Values may contain one or many fields, as shown in the graphic below:
The structure of KV data stores uses less memory than relational databases and is also more flexible, with no requirements for predefined schemas or structured tables. As seen in the graph above, these data containers have the freedom to store diverse data types in a single value. This minimalism and the lack of a need for complex SQL queries allow for blazing-fast reads and writes, making KV stores perfect for high-performance applications that need efficient scalability.
Why do KV stores matter for your business?
KV stores are vital because they allow you to significantly improve application responsiveness, reduce the burden on your origin servers, and simplify data storage and management. This approach to data expands the horizons for developers to build unique solutions for their customers that exceed their expectations. Here are some of the top reasons why organizations are increasingly incorporating KV stores into their data strategies:
Turbocharge application performance
Applications can retrieve data extremely quickly, supporting rapid response times even during high traffic.
Scale effortlessly
Sharding is supported by default, and data can be easily distributed across multiple servers or nodes. This allows for rapid, horizontal scaling without any loss in performance.
Declutter data models
Less complexity in database design makes maintenance and development faster and more affordable.
Boost availability
Data is replicated across multiple servers to ensure continuous access and prevent downtime.
Enable real-time applications
Rapid data updates and retrievals allow for real-time processing at the edge.
Optimize resource usage
The lack of placeholder values, rigid schemas, and resource-intensive table joins allows for much lower system usage, reducing hardware and operational costs.
Take advantage of flexible schema
Flexible schemas support structured and unstructured data, including images, videos, and XML documents, adapting to evolving data needs without disrupting existing frameworks.
Enhance caching mechanisms
Activates fast, in-memory data access for frequently used information, enhancing performance.
KV stores vs. traditional databases
It’s important to understand the differences between KV stores and traditional relational databases to know which approach is right for your use case. Each type of database has distinct advantages and limitations. Below is a table covering these defining factors:
Feature | Key-Value Stores | Traditional Databases |
Definition | Non-relational database that stores data in key-value pairs | Structured, relational database based on tables and relationships |
Data Model | Data is stored in values and associated with unique identifiers called keys | Tabulated data with rows, columns, and relationships |
Schema | No fixed schema, adaptable to data variations | Predefined schema |
Scalability | Supports strong horizontal scaling through additional nodes | Scales better vertically (upgrading single servers) |
Performance | High-speed read/writes, low overhead | Tends to have longer latencies, but offers greater control |
Consistency | Typically provides eventual consistency (delayed but guaranteed consistency) | Supports strong consistency |
Query Capabilities | Limited to key-based lookups and basic commands like get, put, and delete. Filtering is limited. | Offers sophisticated query functionality, including table joins |
Top 7 use cases of KV stores
KV stores are ideal for applications that need speed, scalability, and simplicity, such as:
Caching systems: Extremely high read speeds (with latencies as low as 6ms) make them perfect for read-heavy applications like caching. KV stores retain frequently requested data at the edge, reducing database load and enhancing response speed.
Session management: Maintaining user session information for web applications requiring low latency.
Real-time analytics: Processing and storing high-velocity data streams for immediate analysis and reporting.
Internet of Things (IoT) data storage: Managing large volumes of sensor data from connected devices with rapid read/write operations.
User preference storage: Storing and retrieving user settings and preferences for personalized experiences.
Distributed locking: Implementing distributed locks to synchronize access to shared resources within distributed systems.
High-speed logging: Capturing and storing log data at high velocities for system monitoring and troubleshooting.
Why Fastly's KV store solution stands out
KV stores are a dynamic data management solution that offers incredible speeds, advanced scalability, and a flexible structure. By arranging data into values that can be accessed via unique keys, data models can be simplified, and requests can be streamlined.