Have you ever used a JavaScript object or a Python dictionary to store data? If yes, you have already used a key-value database, at least in concept. A key-value database is a database that stores data as pairs of keys and values, where each key is unique and contains a value associated with it. In this post, we will explore what key-value databases are, how they work, and when to use them.

What is a key-value database?

A key-value database is one of the simplest and fastest database paradigms, as it does not require any complex data modeling or querying. The database itself is kind of like a giant hash table, where every key is mapped to a value. The value can be anything, such as a string, a number, a boolean, a list, a set, a map, a binary blob, or even another key-value pair. For example, we can store the bio of a user with the key user:23:bio and the value "I like turtles".

The key-value database does not care about the structure or the value type, as long as it can store it and retrieve it by the key. The key is usually a string, but it can also be a number, a binary, or a composite key. The key is the only way to access the value, as there is no other attribute or index to query by. Therefore, the key should be chosen carefully, as it should be unique, descriptive, and easy to remember.

How does a key-value database work?

Data Storage in Main Memory:

A key-value database works by storing the data in the main memory, unlike other database paradigms that use secondary memory. This makes the key-value database very fast, as it can access the data in nanoseconds, rather than milliseconds or seconds. However, this also limits the data the key-value database can store, as the main memory is usually smaller and more expensive than the secondary memory.

Data Distribution and Compression Techniques:

To overcome this limitation, some key-value databases use techniques such as replication, sharding, and compression to store more data and ensure availability and durability. Replication means creating copies of the data across multiple nodes so that if one node fails, the data can still be accessed from another node. Sharding means splitting the data into smaller chunks and distributing them across multiple nodes so that each node only stores a subset of the data. Compression means reducing the size of the data by removing redundant or unnecessary information, such as whitespace or metadata.

Data Management Features:

Some key-value databases also support features such as expiration, eviction, and persistence to manage the data more efficiently. Expiration means setting a time limit for the data so that it is automatically deleted after a certain period. Eviction means removing the data from the main memory when it is full, according to some policy, such as least recently used (LRU) or least frequently used (LFU). Persistence means saving the data to the secondary memory periodically or on demand so that it is not lost in case of a power outage or a system crash.

When to use a key-value database?

Caching Purpose:

• A key-value database is commonly used for caching.
• It allows rapid storage and retrieval of data, enhancing application performance and scalability.
• Caching involves storing frequently accessed or computationally expensive data in main memory, reducing the need to query the database or network repeatedly.
• Examples of cached data include web search results, user session data, and application configuration settings.

Storage of Simple Data:

• Key-value databases are suitable for storing simple or transient data.
• This includes items like counters, flags, queues, stacks, and sets within an application.

Limitations:

• Key-value databases are not suitable for complex or structured data.
• They lack the features necessary for handling operations like joins, aggregations, transactions, and constraints.
• For instance, storing e-commerce application data such as orders, products, customers, or invoices is not feasible using a key-value database.

Conclusion

A key-value database is a simple and fast way to store data as pairs of keys and values. It is good for caching and storing simple or transient data, but not for storing complex or structured data. Some popular key-value databases include Redis, Memcached, Etc.