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Key-value database in Operational Database

Learn what is a key-value database, why do we use a key-value database, how do key-value databases work, most usable key-value databases, differences between key-value and relational databases.
Submitted by IncludeHelp, on March 06, 2022

1. What is a key-value database?

1.1 Definition:

The key-value database, also known as a key-value store, is a type of database in which data is stored in a "key-value" format and is optimized for both reading and writing that data. Using a single unique key or a group of unique keys, the data is fetched to retrieve the value associated with each key. Simple data types such as strings and numbers, as well as complex objects, can be used as values.

1.2 Key-value database - an introduction

Known as a key-value database, this type of non-relational database stores information using a straightforward key-value method, a key-value database is a type of database that stores data as a collection of key-value pairs, where the key serves as a unique identifier for each pair. Neither the keys nor the values need to be anything in particular; they can be anything from simple objects to complex compound objects. As a result of their high partitionability, key-value databases are capable of horizontal scaling at scales that other types of databases are unable to achieve. For example, if an existing partition reaches its maximum capacity and additional storage space is required, Amazon DynamoDB will allocate additional partitions to the table.

In each key-value pair, the key is represented by an arbitrary string, such as a filename, URI, or hash, while the value is represented by a number. An image, user preference file, or document is all examples of data that can be used as the value. The value is stored as a blob, so there is no need to do any data modelling or schema definition beforehand.

The use of a blob to store the value eliminates the need to index the data to improve performance. However, because the value is opaque, you are unable to filter or control what is returned as a result of a request based on the value.

In general, key-value stores do not support any kind of query language. Data is stored, retrieved, and updated with the help of simple get, put, and delete commands; the path to retrieve data is a direct request to the object in memory or on disc; the path to update data is a direct request to the object on disc. Key-value stores benefit from the simplicity of this model because they are quick, simple to use, scalable, portable, and flexible.

Over time, database systems have progressed from traditional relational databases that stored data in rows and columns to NoSQL distributed databases that allow for a solution tailored to each individual use case. Key-value pair stores are not a new concept, and they have been around for several decades at the time of writing. It is well-known that the old Windows Registry, which allows the system and its applications to store data in a "key-value" structure, where a key can be represented as a unique identification number or as a unique path to the value, is one of the known stores.

Data is written (added, updated, and deleted) and queried based on the key to store and retrieve the value of the data stored. The key value database is illustrated in the following table.

Key 1"Value" 1
Key 2"Value" 2
Key 3"Value" 3

Key-value databases are frequently regarded as the most straightforward of the NoSQL databases. It is because of the simplicity of this model that key-value stores and databases are quick, user-friendly and portable while also being scalable and flexible. The original key-value systems, on the other hand, were not intended to allow researchers to filter or control the data that was returned in response to a request – they did not include a search engine, for example.

2. Why do we use a key-value database?

It is defined as a key-value database by the fact that it allows programs or users of programs to retrieve information by using keys, which are essentially names or identifiers that point to some stored value. Because key-value databases are defined so simply but can be extended and optimized in a variety of ways, there is no comprehensive list of their features; however, the following are some of the most common:

2.1 Scalability and dependability of the system

To scale-out, key-value stores must implement partitioning (storing data on more than one node), replication, and auto-recovery mechanisms. Using RAM to keep the database in memory, they can scale up while minimizing the effects of ACID guarantees (the guarantee that committed transactions will persist somewhere) by avoiding locks, latches, and low-overhead server calls.

2.2 Use Cases and Real-World Applications

Key-value stores are capable of dealing with large data sets and processing a continuous stream of reading/write operations with low latency, making them ideal for applications such as session management at a large scale.

2.3 Preference and profile databases for users

  • Product recommendations; the most recently viewed items on a retailer's website are used to determine future customer product suggestions.
  • Ad servicing; customer shopping habits are used to create personalized ads, coupons, and other offers for each customer in real-time.
  • Can effectively serve as a cache for frequently accessed but rarely updated information.
  • Key-value databases can also be used for massive multi-player online games, managing each player's session.

2.4 Session management

Provide users with the option to save and restore sessions as part of the session management feature. Session management in web applications is frequently accomplished through the use of key-value databases. In terms of managing the session information for all of the new user apps on smartphones and other devices, they do an excellent job.

2.5 Product recommendations

Customized items that a customer might find interesting are covered in They are excellent at managing shopping carts for online buyers – at least until it is time to make a payment. A relational database facilitates the processing of payment transactions as well as the posting of revenue.

2.6 Coupons and personalized advertisements

Customers can adapt and view the content in real-time. In addition to being among the simplest NoSQL databases, key-value databases have the advantage of being able to scale easily for big data research while serving multiple users at the same time.

2.7 Implementation

Key-value stores differ in their implementation; some, such as Berkeley DB, and MemcacheDB, support ordering of keys, while others, such as Redis, maintain data in memory (RAM), and some, such as Aerospike, are built natively to support both RAM and solid-state drives (SSDs). Another type of server, such as Couchbase Server, stores data in RAM but also supports rotating discs as a backup.

2.8 Modern applications

Modern applications will almost certainly require more features than what is listed above, but this is the bare minimum for a key-value store to function properly.

3. How do key-value databases work?

A key-value database or key-value store associates a value (which can be anything from some simple string to a complex object) with a key, which is used to track the object's location. In its most basic form, a key-value store is similar to a dictionary, array, or map object, as they are found in most programming paradigms, but it is stored persistently and managed by a Database Management System.

To locate a value by its key quickly and reliably, key-value databases make use of compact, efficient index structures. This makes key-value databases particularly well suited for systems that must be able to find and retrieve data in near real-time. The key-value database Redis, for example, is designed specifically for tracking relatively simple data structures (primitive types, lists, heaps, and maps) in a persistent database while maintaining high performance. Having a small number of value types allows Redis to expose an extremely simple interface for querying and manipulating them, which, when configured properly, results in a high throughput when performing complex operations.

3.1 When should a key-value database be used?

Key-value store approaches are optimal in some scenarios, including but not limited to the following ones:

  • When your application is required to handle a large number of small continuous reads and writes, it may be referred to as volatile. Key-value databases provide access to data that is stored in memory quickly.
  • When storing basic information, such as customer details; when storing webpages with the URL as the key and the webpage as the value; when storing shopping-cart contents, product categories, and e-commerce product details; when storing shopping-cart contents, product categories, and e-commerce product details
  • Applications that do not require frequent updates or that do not need to support complex queries should be considered.
  • For example, real-time random data access in an online application such as gaming or finance; caching mechanism for frequently used data or configuration based on keys; and applications designed around simple key-based queries are all examples of what is meant by key-based queries.
  • The simplicity of key-value stores is the key to their speed, which is why they are so popular. The path to retrieve data is a direct request to the object in memory or on disc, which is the most efficient method. When using a query language, there is no need for the relationship between data to be calculated; there is also no need for optimization to be performed. They can exist on distributed systems and do not have to worry about where to store indexes, how much data is stored on each system, or the speed of a network within a distributed system; they simply work and do not require any additional resources.
  • In addition to using SSDs or flash storage and implementing secondary indexes, some key-value stores, such as Aerospike, take advantage of other characteristics to improve performance, such as using SSDs or flash storage and implementing secondary indexes, to continue to push the limits of today's technology to places we haven't yet imagined.

4. Most usable key-value databases

The most usable Key-value databases are as -

  • MongoDB:
    MongoDB supports a broad range of database examples and use cases, including key-value pair data concepts, which are supported by MongoDB. MongoDB is a compelling store for "key-value" data because of its flexible schema and powerful query language, which includes secondary indexes.
    Collecting data in MongoDB is done through collections, which are a collection of BSON (Binary JSON) documents, with each document being built primarily from a field-value structure. In addition to its ability to store flexible schema documents efficiently and perform an index on any of the additional fields for random seeks, MongoDB is a compelling key-value store due to its ability to perform indexes on any of the additional fields for random seeks.
  • Amazon DynamoDB - a database service provided by Amazon:
    At any scale, Amazon DynamoDB is a non-relational database that provides consistent and reliable performance. DB2 is a fully managed database that is divided into multiple regions and managed by multiple masters. It has consistent single-digit millisecond latency and includes built-in security features such as backup and restore as well as in-memory caching. An Item in DynamoDB is made up of a primary or composite key and a variable number of attributes that can be customised. Even though there is no explicit limit on the number of attributes that can be associated with a single item, the aggregate size of an item, which includes all of the attribute names and attribute values, cannot be greater than 400 KB. Just as a table in a relational database is composed of rows, a table in a non-relational database is composed of data items. Each table can contain an essentially limitless number of data items.
  • Aerospike:
    Aerospike is an open-source NoSQL database that makes use of flash-optimized in-memory architecture.
  • Apache Cassandra: Apache Cassandra is a NoSQL database management system that is distributed, free, and open-source, and has a large number of columns.
  • Berkeley DB:
    Berkeley DB is a database storage library that is simple, high-performance, embedded, and open-source.
  • Couchbase:
    Designed for mission-critical applications and heavily modified, Couchbase offers full-text searches, SQL-based querying, and analytics capabilities.
  • Memcached:
    Memcached is a web application that speeds up websites by caching data and objects in RAM, reducing the number of times an external data source must be queried for information. It is completely free and open-sourced.
  • Riak:
    Riak is a fast, flexible, and scalable database and application development platform that is well-suited for collaborating with other databases and applications.
  • Redis:
    Redis is a database, message broker, and memory cache all in one package. It can handle hashes, strings, lists, bitmaps, and HyperLogLog data structures.

5. Differences between key-value and relational databases

The most common differences between key-value and relational databases are as follows -

  1. Simplicity is a keyword associated with key-value databases, which are databases in which everything is straightforward. Because there are no tables, unlike relational databases, there are no features associated with tables, such as columns and constraints on columns, in NoSQL databases.
  2. There is no need for joins if there are no tables in the database. As a result, foreign keys do not exist in key-value databases, and as a result, key-value databases do not support a powerful query language such as SQL.
  3. Buckets, also known as collections, are the only additional feature supported by some key-value databases. They are used to create separate namespaces within a database, which we will discuss later. In this way, we can use the same keys for more than one namespace because keys from one namespace do not conflict with keys from another. This can be used to implement something similar to a relational schema in a non-relational environment.
  4. In contrast to relational databases, which use meaningless keys, key-value databases use meaningful keys to identify the data they contain.
  5. While duplicating data is discouraged in relational databases, it is a common practice in key-value (and NoSQL) databases in general.

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