Intro To Key-value Stores
When the internet gained popularity and relational databases could not cope with the tremendous variety of data types in the mid-1990s, non-relational databases or commonly referred to as NoSQL was developed and introduced by Carlo Strozzi. The acronym NoSQL was first used in 1998 when Carlo Strozzi gave a name to his lightweight and open-source relational database that did not use SQL.
Initially, NoSQL was developed as a response to web data, processing the unstructured data and the need for quicker processing time. NoSQL usually stores data in key-value storage, document storage, wide column storage as well as in graph databases. In this blog post, we will go through in detail one of the categories and perhaps the most popular solution which is a key-value store.
What Is Key-Value Store And How Does It Work?
A key-value store also known as a key-value database can be defined as a non-relational database and simple database that utilize an associative array as their underlying data model. This associative array like JSON can comprise anything from a number or string even to a complicated object that uses a key to keep track of the object. In its simplest form, a key-value store uses a simple key-value method to store data. Take a look at the example below:
For this type of database, to store unique keys together with the pointers to the corresponding data values, a hash table is implemented. Since the key-value store is within the NoSQL family, generally it has no query language, therefore key management is essential for stable operations. Nevertheless, a way to add or remove the key-value pairs is provided and only the key can be queried while this is not possible for values. The way the data could be retrieved and updated is using a simple command like delete, get, and put. In most cases, the key-value store offers better improvement in terms of performance and also speed.
A key-value pair on the other hand is a combination of two pieces of data that are associated with each other. One good example of a key-value pair is a telephone directory where the key is the person while the value is the phone number of that respective person.
The key-value store allows horizontal scaling which is something that other types of databases cannot achieve and is also highly partitionable. To be able to precisely and swiftly locate a value by its key, key-value stores use compact and efficient index structures. As a result, it makes key-value stores the right choice for systems that require finding and retrieving data in continuous time.
Redis for example is a key-value database/store that has been optimized for tracking simple data structures like primitive types, lists, heaps, as well as maps in a persistent database. With this approach (supporting a limited number of value types), it manages to reveal a remarkably simple interface during querying and manipulating the data, not to mention the capability of its throughput. Besides Redis, the following are the lists of some of the common and popular key-value stores/databases, it’s worth noting that not every key-value store/database are the same as they are using different techniques and approaches:
Aerospike: An open-source and using a flash-optimized in-memory
Apache Cassandra: Distributed, free and an open-source management system
Oracle Berkeley DB: Basic, high performance, embedded, open-source
AWS DynamoDB: Fully managed service
Couchbase Server: Suitable for business-critical applications
Riak: Fast, flexible and scalable
Key-value Store Advantages
When working with a large amount of data, it is essential to have remarkably efficient and swift retrieval techniques or processes to ensure a smooth operation: you might have heard of MongoDB (NoSQL), that is sometimes used for precisely this reason: managing large data sets. If you are wondering why that’s the case, here are four notable advantages of using key-value stores:
Performance & Speed
Key-value stores bypass the traditional restriction that requires indexes. Since key-value stores use keys instead of indexes, it gives unmatched performance and delivers high throughput for data-intensive applications.
Improved User Experience
Storing data for customer personalization is very typical in key-value stores. Once the data is pulled from the customer together with their behaviours and preferences, they will be used to customize the user experience.
There is no doubt that key-value stores are excellent when it comes to processing data consequently makes it infinitely scalable in a horizontal method. Improves capability to accommodate the reads/writes operation.
Considering the value can be anything from a number to string or even JSON, it makes key-value stores completely flexible.
The Use Cases For Key-Value Store
There are always questions when it comes to handling a high volume of read/write operations for relational databases while this is not the case for key-value stores. Due to its advantages, especially in terms of scalability, no matter how big the data is, they are easily handled by key-value stores. Moreover, key-value stores manage to handle data lost with their built-in redundancy capability. Considering all of those factors, here are the list of common use cases for key-value stores or when typically it’s being used:
In web applications to store user session details and preferences
Extensively used in real-time product recommendations and advertisement
Used as a data caching to increase application performance
As a data cache for the data that is not updated regularly
Used as a material for big data research
Limitations of Key-Value Stores
Even though key-value stores look promising, there are still some limitations and disadvantages. These limitations also could possibly lead to a problem in some situations. Here are some of the lists that should be taken into consideration. These are general limitations:
Key-value stores are not optimized and require a parser for multiple values. They are only optimized for a single key and value.
These kinds of stores cannot filter out the value fields.
Key-value stores are not optimized for lookup. Lookup will do the scanning of the whole collection which will affect performance.
Key-value stores are, obviously, not compatible with SQL.
Key-value stores do not give us the ability to do a rollback in the event of a failure to save a key.
There is no standard query language as opposed to SQL.
A key-value store is one of the categories in NoSQL other than document storage, wide column storage as well as a graph database. It’s a non-relational and simple database that utilizes an associative array as its underlying data model. There are a lot of advantages as well as some disadvantages of using this type of database. We also went through some of the use cases for key-value stores.
Hopefully, this blog post gives a better overview of key-value stores for some of you.
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