A distributed database is not restricted to a single system and is dispersed across numerous places, such as two or more computers or a network of computers. A distributed database or data management system is spread over several sites with no physical parts in common.
This can be required if a certain database needs to be accessible to many individuals across the globe.
Therefore, it must be administered so that it appears to consumers as a single database.
Distributed databases can be used for horizontal scalability and satisfying load needs without changing the database schema or vertically growing a single system.
Distributed databases address several concerns that might develop when utilizing a single system and a single database, including availability, fault tolerance, throughput, latency, scalability, and many more.
Distributed databases provide data location clarity while retaining local control. This implies that, even if apps don’t know which the data is, each site may govern data locally, manage security, log transactions, and recover when local website problems occur.
Even if connectivity to other sites breaks, autonomy is still available. This offers greater flexibility in situations where specialized data kept in specific locations may require additional security and compliance restrictions than other data.
For example: customer data maintained for retail clients in the EU area must comply with GDPR rules.
There are two different ways by which data can be stored on various sites so that it forms a distributed database.
The two ways are- Duplication and Fragmentation.
Database replication methods replicate data across many locations. However, an utterly redundant database is stored in many places. The benefit of database duplication is that it promotes data availability across several sites and enables parallel query processing.
However, database replication necessitates frequent updates and synchronization with other sites to maintain an exact database copy. Therefore, any modifications made on one side must be replicated on other sites to avoid discrepancies.
In addition, frequent updates increase server costs and complicate concurrency management by requiring many concurrent queries to be verified on all accessible sites.
Whenever it comes to a distributed database storing fragmentation, the relationships are fragmented, which indicates they are broken into smaller portions. Therefore, each piece is stored in a distinct location when needed.
Fragmentation requires that even the pieces can be rebuilt into the original relationship without data being lost. The benefit of fragmentation would be that no information duplicates are created, preventing data inconsistency.
Fragmentation can be classified into two types: Horizontal fragmentation entails dividing the relation schema into groups of rows, with each group (tuple) given to a different fragment. Vertical fragmentation entails fragmenting the related model into smaller schemas, with each element including a shared candidate key to ensure a lossless join.
The distributed database is mainly classified into two types that are heterogeneous and homogeneous distributed databases.
Homogeneous Distributed Database
All locations utilize the same DBMS and operating systems in a homogeneous distributed database. The sites employ software that is quite similar, as well as the same DBMS or DBMS from the same provider. In addition, each site is aware of the presence of many other sites and collaborates with them to execute users’ requests. In addition, the database is accessible through a single platform as if it were a single database.
Homogeneous databases are further divided into types that are autonomous and non-autonomous. Independent means that each database is self-contained and operates on its own. A managing program integrates them and uses message passing to communicate data changes.
Meanwhile, in non-autonomous, data is dispersed throughout the homogenous nodes, and changes are coordinated across the locations by a centralized or master DBMS.
Various locations in a heterogeneous distributed database include different operating systems, DBMS products, and data models. Multiple websites in it employ various schemas and technologies. For example, the system might have many relational, network, hierarchical, or object-oriented DBMSs. Another feature is that query execution is complicated owing to the disparity of schemas. Because of the discrepancy in software, transaction processing is complex. For example, because a site may be unaware of many other websites, there is limited coordination in processing user requests.
Federated and un-federated heterogeneous distributed systems are the other two categories. In federated databases, heterogeneous database systems are autonomous and connected because they work as a unitary database system. In contrast, the databases are accessible through a central coordinating unit in un-federated databases.
Distributed databases are the foundation of any organization’s information architecture as data becomes a more significant part of our daily lives.
For example, end-users engaging with a web server or a mobile phone app may not see a distributed database in operation in most circumstances – it is the distributed database working extremely hard in the background that powers many of these use-cases.
The essential advantages spread databases bring to the game are improved performance, massive scalability, and round-the-clock dependability.
Businesses create petabytes of data every day. However, it’s not like all databases provide the flexibility, availability, and scalability necessary to meet the increased demand for data storage and access.
A distributed database holds documents and data in several physical locations across the same or other networks. Scalability allows distributed database systems to let you adapt and meet expanding data demands. For example, a distributed database uses several machines at various locations instead of confining storage space and transaction processing to a single system. This improves speed, data recovery, and experience for customers.
One of the top databases available for distributed data storage is HarperDB.
HarperDB is a distributed data and application development platform that supports both SQL and NoSQL. It is wholly indexed, does not replicate data, and can be used on any system, from the edge to the cloud.
With Custom Functions and a Microservices Architecture, HarperDB is easy to use and easy to integrate. The data platform is helping organizations reduce costs on global infrastructure while delivering sub-10 millisecond latency.
HarperDB was designed to support both SQL & NoSQL use cases by combining the best features of both into one platform.
Furthermore, it features a unique clustering technique for replicating data between HarperDB nodes. It allows for table-level, pub-sub configuration, so you don’t need to migrate all data to all nodes. For example, certain portions of data, subsets, or tables can reside on an edge server where the cloud may contain everything. Then another edge node may have a different subset of the data. As a result, it is incredibly efficient and works with virtually any data structure you can think of.
To enable complicated SQL queries, most NoSQL databases use a multi-model architecture. Under the hood, the multi-model is equivalent to running two separate databases. However, HarperDB supports both SQL and NoSQL use cases out-of-the-box with a high-performance single model data store. With flexible user-defined APIs enabled by Custom Functions, and a simple HTTP/s interface, you can build your entire application in one place, and HarperDB scales with your application from proof of concept to production.
A couple of HarperDB’s features that make it an out-of-the-box distributed database solution:
Because its store engine stores the attributes independently according to an exploded model, the features (or columns) form an index on write. All characteristics were indexed at the time of writing.
The memory engine uses an “Exploded Model,” an out-of-the-box storage model. When it reads a record, it instantly divides it into distinct attributes, saving the features and associated values separately on the disc. In addition, it connects the characteristics using the needed hash value. This is what allows it to function as a NewSQL database.
With Custom Functions, you can define your own API endpoints within HarperDB. With a pre-configured Fastify server integrated into HarperDB, users can define their routes and handlers- removing the need for dependencies, configuration, and command line configuration. This eliminates the second connection from your API to the database in favor of a direct connection to the data layer, which can reduce round-trip API latency by 50%.
It does not save the data as a file or in a tabulated form; instead, it atomizes each record into its separate properties (or columns) and stores them discretely on a disc. If the search includes new attributes, the attributes are immediately defined for any table entries with a value of 0 with each new write. That is what Dynamic Schema is all about.
One benefit of utilizing HarperDB is that it is programmer friendly, built with a focus on developer experience. It was designed natively as a set of microservices, and you can conduct different database operations via an API request. Its design also allows you to deal with SQL and NoSQL queries. Because of its REST design for dealing with databases, any developer may quickly adapt to HarperDB. There are also many adapters/drivers/clients available for different languages, with which one may get started promptly.
Finally, you may have learned from this article that a database is an organized information collection.
Databases are widely grouped into two types: distributed databases and centralized databases. Distributed databases address several concerns that might develop when utilizing a single system and a single database, such as availability, fault tolerance, throughput, latency, scalability, and many more.
For example, a distributed database is a type that is made up of two or more files placed on multiple computers or locations on the same network or a completely different network. These locations share no physical components.
There are several benefits of using distributed databases. Availability, dependability, and faster reaction time are a few examples. Distributed databases like HarperDB are also reducing costs by reducing the amount of servers and systems needed, and removing the need for expensive maintenance upkeep. If you want to learn more about different database architectures and their use cases, check out this article.