Distributed Databases
Data is the soul of your business — which is why you need a database at the center of it all. However, not all databases are capable of meeting today’s enterprises’ rising data requirements. You’ll need a distributed database system in particular, which will allow you to easily innovate and evolve. In this blog, we’ll go over what distributed databases are, how they work at a high level, and the primary commercial benefits of using them [1]. We’ll also evaluate many distributed database systems on the market today so you know what to look for when choosing your next database. So, let’s get started…
A distributed database is not limited to a single system and is spread over various locations, such as multiple computers or a network. A distributed database system is made up of several sites with no physical components in common. If a database has to be seen by a huge number of individuals all over the world, this may be essential. It needs to be managed in such a way that users see it as a single database.
A distributed database consists of two or more files kept in several locations, either on the same network or on separate networks. The database is spread across numerous database nodes, and processing is done across multiple database nodes as well.
A centralized distributed database management system (DDBMS) links data in such a way that it can be managed as if it were all in one location. The DDBMS synchronizes all of the data regularly, ensuring that data changes and deletions made in one area are reflected in the data stored elsewhere. A centralized database, on the other hand, is made up of a single database file that is stored at a single location and is accessed over a single network [2].
Here are 5 main differences between distributed databases and centralized databases: -
How do distributed databases work: -
A distributed system is a group of connected computers that appear to function as one. A distributed database management system (DBMS) often manages numerous “sites,” which seem like a single logical database stored at one location. Applications do not need to know the actual site location where the data is kept since distributed databases allow location transparency. When a query is conducted on a distributed database, it is answered by a group of sites from several data centers working together [4].
Types of distributed database environments: -
Homogeneous and heterogeneous distributed database environments are two types of distributed database environments [5].
Homogeneous Database: A homogeneous database stores data in the same way across all sites. All of the sites use the same operating system, database management system, and data structures. As a result, they’re simple to handle.
● Heterogeneous Database: Different locations in a heterogeneous distributed database may utilize different schema and software, which might cause issues with query processing and transactions. Furthermore, a site may be utterly uninformed of the existence of other sites. A separate operating system and database program may be used on various PCs. They may even employ separate database data models. As a result, translations are necessary for communication across various sites.
Data can be kept on various sites in one of two ways: -
● Replication — In this method, the complete relationship is duplicated across two or more locations. It is a completely redundant database if the entire database is available at all sites. As a result, in replication, systems keep copies of data. This is useful since it enhances data availability across several places. Query queries may now be executed in parallel as well. It does, however, have certain downsides. Data must be updated regularly. Any change made at one site must be reflected at all other sites where the relationship is saved, otherwise, inconsistency would arise. This is a significant amount of overhead. Concurrent access must now be verified across several sites, making concurrency control much more difficult [2].
● Fragmentation — In fragmentation, the relations are broken (i.e., separated into smaller portions) in this manner, and each of the fragments is kept in multiple locations as needed. It must be ensured that the fragments can be utilized to recreate the original relationship (i.e., that no data is lost). Fragmentation is useful since it avoids the creation of duplicate data, and consistency is not an issue [2].
Relationships can be fragmented in two ways:
❖ Horizontal fragmentation — Splitting by rows — Each tuple is allocated to at least one fragment once the relation is broken into groups of tuples.
❖ Vertical fragmentation — Splitting by columns — The relation’s schema is broken into smaller schemas. To achieve a lossless join, each fragment must have a shared candidate key.
In other circumstances, a hybrid strategy of fragmentation and replication is utilized [2].
What are the advantages of a distributed database architecture?
Distributed databases are at the center of any organization’s data architecture since data has become an indispensable part of our lives. End-users interacting with a web service or mobile application may not notice a distributed database in operation, but it is the distributed database working hard behind the scenes that power many of these use-cases. Here are a few instances of the important advantages that distributed databases provide [7].
● Improved Performance:
Users are expressing their dissatisfaction. Your boss is irritated, and it’s time to address the sluggish program on which everyone relies. Where should you begin your search? A bottleneck with your centralized database is often the cause of performance slowdowns. You may disperse data across countries and bring it closer to your consumers with distributed databases; effective data access and transfer lead to faster application response times. Distributed databases also enable you to better exploit parallel processing across commodity servers, obviating the need for expensive or bespoke hardware.
● Enabling Massive Scalability:
When your system’s scalability is boosted with additional resources, you’ll get more out of it. Who doesn’t desire a system that can expand by company needs and at any time? Distributed databases are built to be flexible and may be readily expanded as needed. Distributed databases, in contrast to centralized databases, which can only expand vertically by adding more resources (CPU, RAM, and disc), may scale both vertically and horizontally (by adding more servers). This gives you even more freedom when it comes to scaling your infrastructure. Due to the pandemic, for example, many customers switched to internet purchasing possibilities. If you’re an online store, you’ll need to increase your data infrastructure rapidly to keep up with the flood of new customers.
● Delivering Round-The-Clock Reliability:
For today’s digital enterprises, staying online 24 hours a day, seven days a week is essential. This implies that if a database is down, data consumers — such as applications, customers, and business users — won’t be able to access vital information needed to keep the business running. Distributed databases enable data redundancy by automatically copying data across several locations. This arrangement enables quick failover to the replica site in the event of a breakdown, ensuring that data access is not disrupted. Businesses can’t afford downtime, therefore it’s critical to fail quickly, recover quickly, and minimize the severity of the failure. Distributed databases are the saving grace for many commercial systems, ensuring company continuity.
These are just a few of the benefits of using a distributed database. With so many alternatives to choose from, it’s critical to understand what features to search for and how they compare among the many databases available. Keep an eye out for part 2 of this series, in which we’ll go over what these features are and why they’re important.
What are the drawbacks of typical distributed database management systems?
Distributed databases have gone a long way in the previous several decades. They do, however, face a few significant problems that are worth highlighting [7] -
● Performance limitations at internet scale:
It’s difficult to allow writes over a geo-distributed database with millions of users. However, with today’s current programs, such as IoT, e-commerce, and social networks, this is a popular use case. Many classic distributed databases have addressed this by establishing a single core region in charge of organizing writes and bringing local data closer to users, but only for reads and not for updates. This design might have a significant impact on a system’s performance.
● Scaling is complex:
Partitioning data isn’t a panacea, and choosing a partitioning key is an art form in and of itself. If you choose the improper partitioning key, you may disrupt data load balancing, causing certain partitions to become hotter than others. This diminishes the partitioning’s efficacy and makes database management and maintenance more difficult.
● Database model! = programming model:
Traditional distributed database systems have only one data model, which does not sit well with today’s current applications in most circumstances. An ‘impedance mismatch’ occurs when the application data types are incompatible with the database model’s capabilities. This necessitates the use of extra programming language bindings as well as a database update anytime the app is updated.
● Decentralized data governance and security:
The inherent absence of centralized knowledge of the complete database is a key issue in developing and operating a distributed database. In a distributed database, this also relates to data governance and security. A lack of consistency in both of these areas creates dangers, and any data breach may swiftly ruin an organization’s reputation and be costly.
● High TCO, needing a dedicated operational team:
Distributed databases are complicated, necessitating the hiring of a full-time operational team to oversee your data architecture. The costs of administering a distributed database, including hardware purchase, maintenance, and personnel costs across several countries, quickly mount up to make it more expensive than a traditional database management system.
Top 25 Distributed Databases: -
Distributed databases make it easier to store and query data securely and reliably. This article compares and contrasts the best open source and commercial distributed databases to help you fulfill your growing data storage needs.
Businesses generate petabytes of data every day. However, not all databases offer the flexibility, availability, and scalability required to meet the increased demand for data storage and access.
A distributed database is a type of database that stores files and data in multiple physical locations on the same or distinct networks. Scalability allows distributed database systems to let you innovate and cope with expanding data needs.
Instead of relying on a single system for data storage and transaction processing, a distributed database makes use of numerous machines in different locations. As a result, performance, data recovery, and overall user satisfaction improve.
This article discusses some of the best databases for distributed data storage.
● YugabyteDB
YugabyteDB is a distributed data management open-source relational database. It can store a large amount of data across different availability zones, allowing for quick querying and minimal latency. It’s a cloud-native distributed database that builds on PostgreSQL’s continuous availability and horizontal scalability characteristics [8].
● CondensationDB
CondensationDB is a Cryptography-based immutable distributed data storage system. To guarantee excellent data security, availability, and dependability, it employs a zero-trust architecture. It’s cloud-ready and perfect for storing sensitive information and configurations [8].
● Citus
Citus is an open-source plugin for PostgreSQL that allows you to create a distributed database system. It distributes large amounts of data to PostgreSQL across numerous nodes in a distributed, high-performance, and scalable manner. It’s open-source, managed, and uses all of PostgreSQL’s features [8].
● Trino
Trino is a high-performance distributed SQL query engine that allows you to query data from many databases such as Cassandra and MongoDB. It was previously known as PrestoSQL. It’s built to scale and be highly available while serving low-latency data. It can be used in Big Data and other analytical applications [8].
● CrateDB
CrateDB is a distributed SQL database that is open source and well efficient. It has a hybrid data storage strategy and a shared-nothing system architecture. Its most common uses are operational analytics and IoT data processing. It’s a commercial database service with a community version that’s available for free [8].
● EventQL
EventQL is a distributed SQL database for storing and analyzing massive amounts of data. It’s a managed, cloud-native storage system for analytics data storage and retrieval. It provides strong data availability and scalability thanks to its column-oriented storage design [9].
● GhostDB
GhostDB is a distributed in-memory database that allows you to store and query data at scale. It is intended for high-speed data delivery in dynamic applications. To ensure low latency retrievals, it stores a large amount of data in key-value pairs and duplicates it over various availability zones [9].
● Rqlite
Rqlite is an SQLite-based lightweight distributed relational database. It’s a fully replicated storage system that may be used as a central storage location for essential relational data, with node-to-node encryption for production-grade SQL data security [9].
● Hibari
Hibari is a distributed NoSQL key-value data storage system with strong consistency. It’s a high-consistency, high-availability database that’s suitable for production. It is written in Erlang and is intended for rapid and dependable data querying, with replications ensuring data persistence in the event of a system failure [9].
● HerdDB
HerdDB is a Java-based embeddable SQL distributed database. It’s built to provide scalability, robustness, and data replication while maintaining consistent data availability and fast throughput with low latency [9].
● Nebula Graph
Nebula Graph is a free and open-source distributed database with low latency, high throughput, and fast read and write speeds. It’s a SQL-like database that can handle enormous amounts of data while retaining security, availability, and speed [9].
● Apache Ignite
Apache Ignite is an open-source project that offers a full-featured distributed database with in-memory speed and stellar results. It is well-known for its application in data caching, and it provides scalable SQL support as well as durable, highly available, and consistent data persistence. It’s a distributed database solution that’s quick and easy to use, with complete support for external databases like Cassandra [9].
● AWS SimpleDB
SimpleDB is an Amazon web service distributed database that connects with other AWS services such as EC2 and Amazon S3. High availability, flexibility, efficiency, scalability, and, like other AWS services, cost-effectiveness are some of its benefits. It reduces administrative overhead by removing operational complexity and utilizing a simple API for data access and storage that is automatically indexed [9].
However, when compared to other distributed storage services, it has a lower consistency and storage limit.
It’s great for storing data from online games, indexing Amazon S3 object references, and keeping track of audits and analysis metrics.
● Apache Cassandra
Cassandra is a NoSQL distributed database that was created by Facebook that provides highly available and performant data storage. Large tech businesses like Netflix, eBay, and Uber employ it since it’s a scalable solution.
It’s a platform-agnostic operating system with high availability, security, and resilience that helps it handle requests quickly. It is an open-source utility that can also be purchased from third-party providers who provide commercial support [9].
● Justin DB
Justin DB is a NoSQL key-value database that is open source, distributed, and consistent. It ensures data availability. It’s an upgraded Amazon DynamoDB implementation with fault tolerance and resilience. It’s based on Aka and takes advantage of the platform’s load balancing, location transparency, and self-maintenance features [9].
● ZanredisDB
ZanredisDB is a fault-tolerant, Redis-compatible distributed key-value database system. It has a high level of data consistency, scalability, and availability [10].
● Apache HBase
The HBase-non-relational database option for Apache Hadoop is another Apache distributed database service. It’s a project inspired by Google’s Bigtable that aims to store massive datasets in a scalable, consistent, and highly accessible way [10].
● Couchbase Server
Couchbase is a distributed NoSQL database designed for enterprise use. It’s an open-source key-value database with the scalability and flexibility that distributed cloud and edge systems demand. It is designed to be extremely fast, making it excellent for cloud, mobile, and edge computing applications [10].
● Clusterpoint
Clusterpoint is a distributed data schema-free integrated database system. It’s a scalable, high-availability, and cost-effective data storage and querying solution with flexibility and scalability. Financial services, healthcare, telecommunications, and other data-intensive businesses benefit from it [10].
● FoundationDB
FoundationDB is a distributed NoSQL database with a multi-model data storage design that is free source, allowing diverse data types to be stored in a single database. For both basic and large workloads, it is fault-tolerant and highly scalable with great performance. FoundationDB is appropriate for a variety of scenarios, including cloud and edge applications, because of its multi-model data storage [10].
● ETCD
ETCD is a large-scale distributed systems information storage solution based on open-source key-value data storage. It consistently and highly available saves the configurations, state, and metadata for distributed systems like Kubernetes. The CNCF project provides a simple interface that allows conventional tools like curl to do reads and writes. It’s perfect for storing sensitive data in production systems like container schedulers, service discovery services, and Kubernetes [10].
● TiDB
TiDB is a MySQL-compatible open-source database for distributed systems. It enables horizontal scalability, excellent consistency, and high availability for Hybrid Transactional and Analytical Processing workloads. It’s an open-source cloud-native database that’s utilized by companies like Xiaomi and Lenovo to store SQL data at scale [10].
● CockroachDB
CockroachDB is a commercial cloud-native distributed database from Cockroach Labs. It’s a distributed SQL database built for transactional and consistent key-value stores with big dataset performance and scalability. It has a high level of compatibility with cloud-native apps. SpaceX’s operating data is stored in CockroachDB, a database that excels in low latency, robust storage in global applications [10].
● Shardingsphere
Shardingsphere is a multi-component Apache open-source database project that offers distributed transaction, distributed governance, and accessible data scalability for a wide range of use cases. It’s a flexible and adaptable database system that works with plugins to add advanced capabilities like data sharding, replica queries, and database protocols [10].
Summary:
Almost every program now has a database at its core. It is quite difficult for a developer to store and retrieve data without the aid of a database. Distributed databases have major importance in the modern era. When contrasted to centralized databases, distributed databases provide significant advantages.
This article mainly discusses some basics of distributed databases, how it works, the top 25 distributed databases, etc. which can help a user to choose a database wisely.
References:
[4] https://www.tutorialspoint.com/distributed_dbms/distributed_dbms_databases.htm
[6] https://www.tutorialride.com/distributed-databases/distributed-databases-tutorial.htm
[7] https://www.ques10.com/p/17591/what-are-the-advantages-and-disadvantages-of-distr/?
[8] https://www.infoworld.com/article/3406458/the-best-distributed-relational-databases.html
[9] https://analyticsindiamag.com/10-most-used-databases-by-developers-in-2020/
[10] https://www.predictiveanalyticstoday.com/newsql-databases/
Authors: Tejas Kachare, Om Deshpande, Ayush Chandak, Naman Chandak, Yash Oswal.
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