What Is a Distributed Database and How Does It Work?

In today’s digital age, the amount of data created, copied, captured, and consumed every day has reached unprecedented levels. As a result, companies are seeking scalable data storage solutions that can accommodate both structured and unstructured data with flexibility. 

Additionally, with multiple users accessing data from mobile devices, enterprises need a solution that maintains a high degree of data integrity, security, and availability. Distributed databases are growing in popularity for these reasons. These collections of interconnected databases are spread across various locations and are ideal for global, data-intensive applications. In this overview, we’ll explore the components, benefits, and real-world applications of distributed databases. 

Defining distributed databases

A distributed database stores data in multiple locations while still appearing as a single database to users. Instead of housing all data on one computer or server, data is stored on multiple servers or on a computer cluster with individual nodes. These nodes may be virtual machines within a cloud database or physical computers. 

Unlike centralized databases, distributed databases spread data across multiple physical or virtual locations. By sharding data, these databases improve data availability and resiliency and support horizontal scaling. There are two types of distributed databases: homogenous and heterogeneous. 

Homogeneous 

In a homogenous distributed database, the machines, nodes, servers, or sites use the same data model. They also house the same data and use the same operating system. Additionally, they share either the same distributed database management system (DDBMS) or different DDBMSes from the same vendor. Homogenous distributed databases have similar nodes, ensuring redundancy. This redundancy offers significant data protection and simplified management. 

Heterogeneous

In a heterogeneous distributed database, machines, servers, nodes, and sites may house separate data sets. They may also operate using different systems and contain discrete data sets. For the nodes to communicate with each other, this system requires specific software. While they are complex to manage, they offer more flexibility with the schema choices, data models, and data types they can store. 

What is a distributed database used for?

Distributed databases exist everywhere and power the most visited websites, including Facebook and Google. They are also responsible for significant technological changes and the rise of cloud computing. Developed in response to the advent of mobile technology, they can accommodate the increased mobility, usage, and uptime requirements of modern applications. 

Distribute data geographically 

Laptops, mobile devices, and remote work allow those who access your servers to be anywhere in the world. Local access to data ensures that applications can operate quickly and efficiently. With auto-routing, a globally distributed database maintains transactional consistency. Routing to the closest cloud region ensures the lowest latencies. 

Scalability 

Traditional databases have limited scalability, which clashes with the needs of modern apps with thousands of users. If you anticipate rapid growth in data volume or user load, a distributed database can help you scale horizontally. You can add as many nodes as you need to handle high-volume workloads. Adding more nodes is more cost-effective and accessible than vertically scaling one server. 

Data resilience and high availability 

High availability and data resilience ensure that applications are functional even when parts of the system fail. They also protect data from being lost when a single server is corrupted. Distributed databases replicate data across multiple locations and nodes. This data replication ensures that if any node suffers a hardware failure, outage, or network issue, the system can continue to function. 

If one node fails, your system will redirect queries to other nodes that have the replica data. This failover process is automatic, which minimizes downtime, data loss, and end-user impact. 

Distributed database configurations

The main goal of a distributed database is to ensure that the data is always available. Distributed databases replicate the data across multiple instances, and there are a few ways you can configure the architectural setups for these replicas: 

Active-passive

This is the simplest configuration, particularly if you’re manually adapting a relational database to a distributed deployment. In an active-passive configuration, all write operations are directed to one “active” primary node, while read operations can be served by either the primary or passive replica nodes. The passive replicas continuously receive data updates from the active node to stay synchronized. For example, in a three-node deployment, all writes go to the active primary node one, which then replicates changes to passive replica nodes two and three. If the primary node fails, one of the passive nodes is promoted to become the new active primary.

Active-active 

In an active-active configuration, multiple nodes can accept both read and write operations simultaneously. Your system can route traffic to any available node, and changes are synchronized across all active replicas through conflict resolution mechanisms. This architecture provides better load distribution and reduces the impact of a node failure since other active nodes can immediately handle traffic without requiring failover promotion. However, these systems are more complex to configure due to sophisticated conflict resolution protocols, particularly when concurrent writes to the same data occur across different nodes.

How do distributed database systems work?

Distributed databases function by leveraging numerous key components. Data within a distributed system is stored across multiple servers, enabling the system to scale horizontally. This data distribution provides higher availability and fault tolerance. Core mechanisms that drive these systems include: 

Data distribution

Data distribution, also known as data partitioning, is essential for user access, efficiency, and security. There are two ways to distribute data in a distributed database, and the resulting datasets are referred to as shards. 

• Horizontal partitioning: This refers to splitting data tables into rows.
• Vertical partitioning: This involves splitting tables into columns.

Communication

In a distributed database system, nodes can function on their own. However, they need to communicate with other nodes because they do not share the same datasets or physical components. There are three ways distributed database systems communicate, including: 

• Broadcast: One node sends a message to all other nodes.
• Multicast: One message is sent to some but not all nodes.
• Unicast: One node sends a message to another node. 

Transaction management

Distributed databases utilize atomicity, consistency, isolation, and durability (ACID) principles to support distributed transactions. These transactions involve more than one node. The key ACID principles are: 

• Atomicity: A transaction is treated as a single unit. Data integrity is maintained by either storing a complete transaction or rejecting it as an error.
• Consistency: Distributed database systems maintain consistency by enforcing predefined data constraints and rules. If any part of a transaction violates the rule, it won’t be stored in the system.
• Isolation: Each transaction is separated from other transactions to prevent data conflicts and maintain data integrity. This helps large-scale enterprises manage multiple distributed data records across multiple sites.
• Durability: This ensures that stored data isn’t lost if the distributed database fails. 

A distributed database management system accomplishes durability through fault tolerance. Fault tolerance processes ensure reliable access and effective operations with key processes such as data replication, backup protocols, continuous failure detection, data checksums, load balancing, and query optimization. 

Data replication

This process copies data across different nodes, servers, or sites. There are several different replication schema databases used, including: 

• Full replication: The system sends a complete, functional copy of the entire database to all nodes on a routine schedule.
• Transactional: The system sends a complete database copy to each node. As transactions are processed, data changes are updated to the copies in real time.
• Partial: Certain nodes require only specific parts of the database. This defined portion is replicated to a select group of nodes.
• Merge: This process merges two databases into one and is the most complex of the replication types. 

Backup protocols

Automated data backups ensure data integrity and availability without overburdening your employees. With a full backup, the entire database is copied and stored. In a differential backup, only the changes made since the last full backup are copied and stored. Finally, incremental backups save any change to your data since the last backup was conducted. 

Continuous failure detection

Distributed database systems need to be continuously monitored for system failures. There are a few ways systems employ monitoring, including:

• Heartbeating: Each node sends out a signal to other nodes. If a signal isn’t received, the system creates a failure message.
• Watchdog timers: Individual nodes have timers focused on a specific activity. If the activity isn’t completed before the time expires, the system generates a failure message. 

Load balancing

Load balancing ensures that user requests and queries are evenly distributed across nodes. This process improves performance and prevents overload on a system if one node fails. 

Query optimization

Distributed databases utilize query optimization to distribute queries across nodes. These techniques help minimize data transfer traffic between nodes. This is usually accomplished through cost-based query optimization. Cost-based query optimization is considered the most efficient method to execute a query to ensure it is answered promptly.

Distributed database examples in practice

Distributed databases store and maintain data for scalability, locality, and reliability. These features make them ideal for use in telecommunications, gaming, the Internet of Things (IoT), and financial institutions. 

Global banks use distributed databases to distribute data across centers in New York, Tokyo, and London. These key financial hubs handle missions of daily transactions. If the London data center goes offline, the distributed database system will automatically route traffic to New York and Tokyo without manual intervention. Customers will not notice this switch. They will still be able to make transactions and check their balances without interruption. 

E-commerce platforms also utilize distributed databases. These databases offer faster data access and enhance the user experience. Data is read from the nearest replica for a quick and efficient load time. Additionally, a distributed database helps e-commerce industries manage their global inventory. 

Advantages and considerations of distributed databases

As with any database solution, there are both benefits and challenges.

Benefits

A distributed database can benefit your organization in many ways, including:

• Flexibility: Distributed databases can handle a variety of data asset types and processing requirements.
• Resiliency: Distributed databases store data across multiple nodes. By spreading data out, the risk of a significant failure is reduced.
• Scalability: You can easily adjust the number of nodes in a database. This scalability is ideal for growing organizations.
• Improved performance: Load balancing and query optimization techniques reduce wait times, improving performance and user experiences.
• High availability: Through data replication and fault tolerance processes, distributed databases are always available. 

Challenges

While distributed databases can benefit your organization, you also need to consider the challenges to make an informed decision about their practicality for your enterprise. Challenges include: 

• Complexity: The number of moving parts in a distributed database can make them difficult to design and manage.
• Latency: If your database is not effectively managed, users querying data from multiple nodes may experience latency.
• Data consistency: Distributed databases use multiple data schemas and structures. Therefore, maintaining data consistency requires more effort. Furthermore, if there is a network or hardware failure, the restoration process is more involved.
• Cost: The added complexity and flexibility make distributed databases more expensive than traditional ones. There may also be additional networking costs to accommodate more hardware and sites. 

Enhancing distributed databases with EnterpriseDB (EDB)

In a competitive business environment, distributed databases offer enterprises scalability and flexibility. For companies with high-volume transactions and geographically dispersed users, distributed databases are an ideal solution to help you meet growing user demands. 

EDB Postgres® AI High Availability (HA) offers several key features that help your organization stand out from others. With 5x throughput efficiency and up to 99.999% availability, EDB ensures that your applications won’t go down. Our system supports geo-distributed apps and promises security and data integrity, whether on the cloud, onsite, or both. 

Additionally, we enable you to process thousands of transactions per second so you can meet your customer expectations while adhering to data sovereignty and regulatory requirements. EDB Postgres AI HA utilizes multi-initiator replication so that each node maintains a complete, up-to-date copy of your databases, reducing the number of data conflicts. 

With fewer conflicts and high availability, your users can work efficiently and productively. Enhance your distributed database with EDB and contact us today.