Key-value Store

Data Storage and Sources
Updated on:
July 11, 2024

What is a key-value store?

A key-value store is a type of NoSQL database optimized for high-speed storage and retrieval of data as simple key-value pairs. In a key-value store, data is modeled as an associative array where each key serves as a unique identifier that maps to an associated value. The keys follow conventions like being strings or numbers. The values can range from simple data types to complex serialized objects and blobs.

Unlike relational SQL databases which require predefined schema, key-value stores are schema-less and impose no strict formatting on the data. They allow applications to flexibly store, retrieve, update and delete values by their key. This singular focus on speed and flexibility for basic key-based data access makes key-value stores highly performant and scalable for simple workflows. However, they lack functionality like transactions, querying by attributes, and joins. Key-value stores tradeoff features for ultra-fast lookups, inserts, updates, and deletes keyed exclusively by a unique identifier.

Key-value stores are often used together with other NoSQL databases like document stores and graph databases to provide high-performance data access.

What does it do/how does it work?

A key-value store allows applications to store and retrieve values by their unique key like a dictionary. The simple put/get operations provide fast reads and writes for accessing data.

Keys have an associated value which can range from simple data types like strings to complex blobs and serialized objects. Some key-value stores support advanced operations like sorting.

Why is it important? Where is it used?

The speed and flexibility of key-value stores make them ideal for use cases like caching frequently accessed data, storing user sessions, shopping carts, user profiles.

They simplify application development by allowing storage of data without complex schema modeling. Key-value stores help scale applications by handling very high throughput for simple data.

FAQ

How are key-value stores different from other NoSQL databases?

Unlike other NoSQL databases, key-value stores function like fast, flexible dictionaries for accessing data using keys. They sacrifice advanced querying for high-speed lookups and writes.

  • Simple put/get by key operations, without complex querying.
  • Highly optimized for fast reads and writes of values by keys.
  • Flexible schemas, without tables, documents or graph models.
  • Limited data relationships and operations beyond basic CRUD.

When should you use a key-value store?

Key-value stores work well for very fast, simple lookups using keys and are ideal for:

  • Caching frequently used data like rendered web components.
  • Storing session information in stateless architectures.
  • User profiles, preferences and other simple data.
  • High-throughput, low-latency data access.

What are examples of key-value stores?

Some popular key-value database technologies include:

  • Redis - Open source in-memory key-value store.
  • Aerospike - High performance distributed key-value database.
  • Amazon DynamoDB - Fully managed key-value database.
  • Memcached - Distributed memory object caching system.
  • Hazelcast - In-memory key-value store and compute engine.

What are key challenges with key-value stores?

However, key-value stores also come with tradeoffs around querying, relationships and analytics:

  • Lack of secondary indexes or ability to query by value.
  • No ability to model relationships between data.
  • Overhead of managing keys, expirations manually.
  • Limited monitoring into data access patterns.
  • Scaling writes and reads as data volumes grow.

References:

Related Entries

Document Store

Document store database manages collections of JSON, XML, or other hierarchical document formats, providing querying and indexing on document contents.

Read more ->
Graph Database

A graph database stores data in a graph structure with nodes, edges and properties to represent and query relationships between connected data entities.

Read more ->

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