What Is a Distributed Database?

Not long after commercial databases became available, companies started looking for ways to elegantly distribute a single database across multiple machines while keeping their data perfectly synchronized. By doing so, they could combine the compute power of several nodes to share the work of recording transactions and running batch analytics. A distributed model also meant that when a computer went down, a database—and the applications that relied on it—could keep working. As a result, database administrators would finally get to sleep through the night and enjoy their weekends.

Turns out, this was a thorny computer science task. When the first commercial distributed database solution arrived in 2001, it set the stage for the contemporary flowering of shared-architecture cloud databases. Let’s explore how distributed databases work and how they’re being used today.

What Is a Distributed Database?

A distributed database is a database that stores data across multiple physical locations to improve the reliability, scalability, and performance of the overall system. These collections of servers, also called instances or nodes, that comprise a distributed database might reside in a single data center or in different data centers. They might even be in different geographical regions or be hosted by different cloud providers.

When a database is distributed, it can scale horizontally to take advantage of the compute power and storage resources of multiple machines. That architecture vastly increases data availability—if one node goes down, the database can just access the data it needs from another node and keep functioning. Distributed databases offer horizontal scalability, data durability, and high availability. Because of this, they’re increasingly popular in contemporary application designs and architectures that serve globally distributed applications and cloud-based infrastructures, as well as compute-hungry generative AI services.

Distributed vs Centralized Databases

As their names suggest, the key difference between a distributed and a centralized database is the number of nodes. A centralized database stores all data in a single location, typically on a central server, while a distributed database spreads data across multiple servers. The key benefits of a centralized architecture are that it’s easier and less expensive to manage a single database instance, and it takes much less effort to maintain data integrity. The downside, of course, is that a single server can become a single point of failure or a performance bottleneck.

A distributed database, by contrast, can make use of many machines to handle a given workload, improving query or transaction performance. And because servers can be dispersed across many locations, and even globally distributed sites, availability and fault tolerance increase. Issues with distributed databases include the complexity of keeping data synchronized across servers and the potential for latency as packets travel between servers. Distributed database administrators can make this distance a strength, however, by placing frequently used data in servers that are geographically closer to users, lowering latency and improving performance while maintaining the benefits of a distributed architecture.

Key Takeaways

• Distributed databases store and access data on a collection of networked servers, also called nodes or instances.
• By using the power of multiple machines to break up database workloads, distributed database systems can improve application performance while maintaining data integrity.
• Distributed databases are a cornerstone technology of high availability architectures, disaster recovery systems, and fault-tolerant applications.

Distributed Database Explained

Distributed databases are a cornerstone technology of high-throughput and high availability database systems. The primary architectural feature of these databases is a collection of networked servers, also referred to as instances or nodes, that store, update, and balance data among themselves.

Depending on the use case, a node may store a complete copy of the data or only a portion of it. Distributed systems that store a complete copy on each node are typically used for disaster recovery. The more common technique is to partition, or shard, portions of the overall data set on different nodes and then share and balance the workloads through the network. This partitioning can be done horizontally (by rows) or vertically (by columns), depending on whether the system is designed primarily for transactions or for batch analytics.

Any coordination and communication among nodes is orchestrated by a distributed database management system (DDBMS) that facilitates data consistency, handles transactions, and provides a unified view of data to users.

How Does a Distributed Database Work?

A distributed database stores data across multiple physical locations that are connected by a network. Each location, sometimes called an instance or a node, might store a small portion of the data or a more complete copy, depending on the needs of the application. These nodes can be in different data centers, regions, or countries. The distributed nature of the database enables it to handle large volumes of data and high user traffic levels by dividing the load across multiple nodes. These nodes are orchestrated to work in parallel and keep changes to the data in sync.

This setup has the following benefits:

• It enhances performance by using the compute power of many servers to power a single database.
• It reduces latency by allowing users to access data from the node closest to their locations
• It provides redundancy and thus improves fault tolerance and reliability.

What enables a distributed database to work is a sophisticated management system that coordinates nodes to help ensure consistency and integrity across all database instances. The system uses a combination of technologies and techniques including data replication, where multiple copies of data are stored to ensure availability and fault tolerance; sharding, which partitions data into smaller, more manageable pieces to help break up the processing work; and data locality controls, which help optimize data access and reduce network latency.

For data synchronization and conflict resolution across nodes, the full system relies on sophisticated methods such as quorum-based algorithms that ensure data redundancy and eventual consistency or consensus protocols that enable distributed machines to work as a coherent group.

What Are Distributed Databases Used For?

Distributed database technology is the backbone of modern, global applications and cloud services. Some use cases rely on NoSQL document-type databases, which are well suited to highly scalable web applications. These databases offer BASE-type (basic availability, soft state, and eventual consistency) data consistency and allow for fast, scalable transactions with eventual consistency. Other applications, such as those run by global financial services or online retailers, for example, rely on relational databases that provide ACID (atomicity, consistency, isolation, and durability) compliance. This helps them provide the most immediately accurate transactions and data processing to clients and inventory control systems.

Both database architectures allow for a database to be distributed across multiple servers. BASE databases trade immediate consistency for higher availability and scalability. Meanwhile, ACID supports data integrity and reliability and is suitable for applications with functions that require strict data consistency, such as financial transactions.

Here are five of the most common examples:

1. Scalable web applications

Large-scale web applications that handle high volumes of user traffic and data depend on distributed databases. These applications, often built on document-style database architectures, include social media platforms, ecommerce sites, and content management systems. They distribute database workloads across multiple nodes to accommodate peaks in traffic or market growth without performance degradation.

2. Big data analytics

In big data analytics systems, large data sets often need to be processed and analyzed in real time. Think of huge data warehouses, business intelligence applications, and machine learning systems, where a distributed database is needed to efficiently handle storage and processing by distributing workloads dynamically across multiple nodes.

3. Geographically distributed operations

Distributed databases can help ensure that data is accessible and consistent across many locations, even for organizations that have operations spread across the globe. The distributed database system lets administrators reduce network latency, improve performance, and help address local data residency requirements by storing data closer to the teams or applications that use it.

4. High availability and fault tolerance

Distributed databases provide high availability and fault tolerance by replicating data across multiple nodes. This allows vital applications to continue to operate consistently even if some nodes fail. Backup and disaster recovery systems are dependent on this type of database architecture.

5. Real-time data processing

Beyond disaster recovery, distributed databases are often used to enable high performance, real-time data processing. This is where data needs to be processed and analyzed as it’s generated. By breaking up the workload between many different machines, distributed databases help make real-time analytics, IoT systems, and data streaming platforms possible.

Distributed Database Features

Distributed databases rely on a host of technologies and techniques to increase the speed, usefulness, and availability of the database while abstracting complexity and controlling access. These include:

• Data distribution. In a distributed database, IT teams must decide where to store data fragments and replicas. This data allocation strategy considers the physical locations of nodes, the frequency of data access, and the network topology. The right allocation strategy can improve performance by reducing data transfer times and minimizing network latency. For example, frequently accessed data might be allocated to nodes that are closer to end users, while less frequently accessed data can be stored on more distant nodes. In another key distribution technology—data fragmentation, commonly known as sharding—a distributed database breaks data into smaller, more manageable fragments and stores these fragments across different nodes in the network. This allows for more efficient querying and reduces the load on individual nodes. Challenges with data fragmentation include increased difficulty keeping data consistent and running analytics.
• Data replication. With data replication, a distributed database creates multiple copies of data and stores the copies on different nodes. This improves data availability and fault tolerance because data can still be accessed from another node if one goes down. Replication can also improve read performance by allowing users to access data from the nearest or least busy node. The challenge with data replication is in maintaining data consistency across all replicas.
• Data transparency. Even though the database is distributed, users and applications should be able to interact with it as if it were a single, unified system. Data transparency means that the database management system (DBMS) handles the complexities of data fragmentation, replication, and allocation, providing a unified and consistent view of the data.
• Centralized administration. The intricate architectures of distributed databases require centralized administration, that lets administrators configure and monitor the system so it can operate cohesively as a single logical entity. Through the central interface, administrators can set up and configure the system, scale nodes up or down, run backups, access monitoring metrics, configure security settings, and perform many other functions.

Other key features include:

• Authorization. Authorization means granting or denying access to system resources or data based on user roles and permissions. This access can be granted through lists of authorized personnel, or, more commonly, role-based access control (RBAC) systems.
• Identity management. Identity management is the sum of the business processes and technologies used to manage digital identities, allowing for effective role- or list-based authorization. Identity management encompasses user credentials, identity verification, and user authentication.
• Fault tolerance. Fault tolerance is one of the core reasons to maintain a distributed database. It’s the ability of the system to continue operating correctly in the event of hardware or software failure. When a node within the distributed system fails, the database can automatically switch to a backup node to maintain continuous service.
• Data locality controls. This is simply placing data in servers that are closer to the users or applications that need that data most. The goal is to reduce network latency and improve performance, so data fragments and replicas are stored to optimize for geographic location, network topology, and access patterns. In addition, data locality controls are often used to help comply with data sovereignty regulations.
• Security architecture. This is a combination of measures taken at the server, network, and software layers to protect a distributed database against unauthorized access, breaches, and other security threats. These measures can include encryption of data at rest and in transit, secure communication protocols, and robust control mechanisms to prevent unauthorized access.

Advantages of Distributed Databases

Today’s always-on, data-intensive world is characterized by increasing demands on an organization’s data management system. Distributed databases are key to effectively using data to power applications, gain insights, and remain competitive in the digital age.

Specific advantages of distributed database systems include the following:

• Availability. By replicating data across multiple nodes, a distributed database system can improve data accessibility—even if nodes fail. Another common technique, called load balancing, distributes queries or transactional workloads across multiple nodes to maintain responsiveness even during peak usage.
• Cost-effectiveness. Distributed databases are a great way to get the most out of commodity hardware and cloud services. By distributing data across multiple nodes and automatically scaling as needed, these systems can be more cost-effective than traditional monolithic database solutions.
• Flexibility. The modular nature of distributed databases allows administrators, or automated systems, to add and remove nodes without disrupting the entire system. This flexibility means organizations can tailor their distributed databases to meet specific requirements or use cases, including different data models, storage strategies, and deployment methods.
• Performance. There are several ways a distributed database can improve performance. These include techniques such as sharding, replication, and locality controls. By breaking data down into smaller fragments and storing them across multiple nodes, the system can handle queries more efficiently. Meanwhile, data replication and locality controls allow administrators to store data closer to the people who use it most often, improving response times.
• Reliability. Distributed database architectures can vastly increase reliability through features such as data replication and fault tolerance. Data replication creates multiple copies of data on different nodes. If one node fails, the system will automatic failover to another node, providing operational continuity.
• Scalability. A distributed database can be designed to scale up and down as needed to match changes in user traffic or data volumes—adding nodes to the network to accommodate growth and, in many cloud services, reducing the number of nodes dynamically to save on infrastructure costs.

Challenges of Distributed Databases

While sharding and distributed databases offer significant benefits in terms of performance, scalability, and availability, there are attendant challenges too. These need to be carefully considered and addressed during the design and implementation phases.

• Complexity. Managing the many physical or virtual servers in a distributed database is a more complex task than managing a single centralized database. Sophisticated algorithms and protocols are required to handle data distribution, replication, and consistency. Cloud providers now offer fully managed databases to help more organizations take advantage of distributed database architectures.
• Data consistency. It can be difficult to ensure that all copies of data are consistent across the network of servers that make up a distributed database, especially in the event of network failures or node crashes. Data constancy can be strictly maintained in an ACID-compliant database or eventually consistent in a BASE-type database, which can be more cost-effective for large-scale web applications.
• Network delays (latency). The number of switches and routers between nodes in a distributed database can contribute to time delays, or latency, in the communication between them. Excess latency can affect the performance of the system itself, particularly for transactions that require immediate consistency.
• Security risks. Distributed databases may be more vulnerable to security threats due to the increased number of access points and the complexity of the network.

Types of Distributed Databases

A distributed database can take many different forms. The best option for an organization depends on its primary use case. The forms a distributed database can take include the following:

• Homogenous. This refers to a distributed database in which all nodes in the network use the same database and database management system (DBMS) and share the same data structure.
• Heterogenous. Here, the nodes in the network might use different database management systems and have different data structures that require an application to translate and integrate data from various systems. A heterogeneous database system can result from adding new functionality to a legacy database or quickly integrating different systems, as when one organization acquires another.
• Federated. A federated database allows multiple databases to work together as a single database. Each database maintains its autonomy but can share data and cooperate with all databases in the federated system.
• Partitioned. In a partitioned database, data is divided into smaller, more manageable parts called partitions, and each partition is stored on a different node in the network.
• Hybrid. A hybrid distributed database combines elements of federated and partitioned databases. It can include both autonomous databases that cooperate and partitioned data that is distributed across nodes.
• Replicated. In a replicated database, copies of the data are stored on multiple nodes to enable high availability and fault tolerance. To maintain consistency, changes to data are immediately propagated to backup replicas.

Distributed Database Architecture

The primary components of a distributed database architecture include the physical or virtual servers where data is stored and processed and the network that facilitates communication and data transfer between them. The servers, also known as nodes or instances, store portions of the data. The network’s job is to enable communication across nodes such that queries and transactions are executed correctly across the entire distributed system.

Next, the DDBMS handles tasks such as data replication, data partitioning, data sharding, and load balancing to enable the database to operate efficiently and remain consistent. From there, middleware or cloud infrastructure services provide interfaces for system monitoring, performance management, tuning, and other administrative tasks like fleet diagnostics and troubleshooting.

Applications of Distributed Database

Organizations choose to run distributed databases for many reasons. The main overarching benefit is load balancing—allowing the system to automatically divide heavy demand from workloads, whether transactions or analytics, across multiple instances to prevent any single server from becoming a bottleneck.

Here are other ways that these systems can help address the many challenges and requirements faced by organizations today.

• Global data distribution. For applications with a global reach, a distributed database is essential. First, it allows the system to store data closer to users, reducing latency for both reads and writes. This improves the user experience, especially for applications designed to deliver real-time performance. Resilience is also a huge consideration. If a data center or server in one region experiences an outage, users in other regions can still access the data and the application itself from local operational nodes.
• Efficient scaling. Distributed databases allow for quick, horizontal scaling by allowing administrators to add more nodes to the system in response to increased data volume, more complex requests, and more concurrent users. This scaling can also be set up to happen autonomously.
• Data locality and compliance. For organizations with stringent data governance rules or regional data residency requirements, distributed databases allow administrators to store data in the geographic location where it’s generated, enabling these companies to comply with local mandates.
• High availability and fault tolerance. A distributed database is vital for mission-critical workloads because, by replicating data across multiple nodes, data remains available even if certain nodes fail. Most disaster recovery architectures are based on a distributed database model—following a catastrophic event, data can be quickly recovered from a distant node or region.
• Performance. With its data and workloads distributed across multiple servers, a database system can improve performance by reducing the load on any single node. This can lead to faster query responses and better overall system performance.

Examples of Distributed Databases

The distributed database model excels in providing increased processing power through horizontal scalability, data locality, and fault tolerance. The specific database an organization chooses, however, will often come down to the needs and limitations of the company’s applications. Some use cases require the absolute consistency of an ACID-compliant relational database, while others are better suited to a BASE-type database that offers eventual consistency. Let’s look at some examples of each.

Examples of ACID-compliant systems include:

• Oracle Database. Oracle Database facilitates globally distributed, linearly scalable, multimodel databases that support structured and unstructured data. Through unique multicloud agreements with Microsoft Azure, AWS, and Google Cloud, Oracle’s distributed database service runs in these hyperscalers’ data centers to unify management and operations and help eliminate network latency and the cost of moving data across clouds.
• Google Cloud Spanner. Cloud Spanner is a globally distributed, horizontally scalable database service that runs on Google Cloud. The service provides global distribution, strong consistency, and automatic sharding.
• CockroachDB. This distributed SQL database features ACID transactions, distributed architecture, and multiregion deployments.
• YugabyteDB. An open-source distributed SQL database, YugabyteDB combines features of relational databases with the scalability and resilience of NoSQL databases.

Examples of BASE-type systems include:

• Oracle Database. Oracle’s multimodal database supports schemaless application development using the JSON data model. This allows for a hybrid development approach, combining the application development of NoSQL document stores with the features of an enterprise-class relational database.
• Apache Cassandra. Cassandra is a distributed NoSQL database designed to handle large amounts of data across commodity servers. It provides high availability at a minimal cost.
• Couchbase. A distributed NoSQL database, Couchbase features document-oriented storage, in-memory caching, and automatic sharding, as well as multi-data center replication.
• MongoDB. MongoDB is a popular NoSQL database that uses a document-oriented data model. It’s designed for high performance and easy scalability and features flexible schemas, automatic sharding, and replica sets for high availability.
• Redis. This in-memory, NoSQL data store can be used as a database, cache, and/or message broker. It supports a wide variety of data structures, including strings, hashes, lists, and sets. In its enterprise offering, Redis attempts to offer both BASE- and ACID-compliant systems.

Streamline Application Development and Operations with Oracle

Let Oracle simplify your application architecture with a truly global distributed database system that runs on premises, in the cloud, across a multicloud network, or in a hybrid architecture. With Oracle Database, you’ll get a surprisingly cost-effective, globally distributed, linearly scalable multimodal database that requires no specialized hardware or software. This is the database run by many of the world’s largest and most successful organizations, but priced so that even smaller organizations can gain its advantages. Explore the Oracle Globally Distributed Database, where you’ll find strong consistency, the full power of SQL, native support for structured and unstructured data, and the Oracle Database ecosystem.

Distributed database systems are now a core technology underpinning applications across retail, finance, streaming, and business applications and, increasingly, their AI agents. As a source of database flexibility, scalability, performance, and fault tolerance, distributed database architecture is poised to remain popular—and to continue to evolve to address the needs of the most demanding, globe-spanning applications.

Distributed Database FAQs

When should we use a distributed database?

Use a distributed database if your application experiences changes in usage patterns over time, or if your organization needs applications that require your database to remain operational without downtime. Distributed databases are popular for large, web-scale applications such as social media sites and high performance transactional sites used by online retailers and financial services.