Distributed Databases Explained: How Exactly Do They Work

If you have a powerful and scalable app and your database seems to be lagging behind, that might mean it’s time to consider a distributed database approach. Particularly valuable for work in the cloud, a distributed database is a great alternative to a traditional centralized database that often lacks the needed capacity and cannot effectively scale up and down upon request.

So what is a distributed database, exactly, and how do they work? Read below.

What is a distributed database?

As the name implies, a distributed database system is present in multiple locations; thus, it is not limited to one computer system. In other words, this database runs on multiple computers and hence, is much more scalable, effective, and powerful than a traditional database. Distributed databases have become especially popular in the cloud since cloud apps require a lot of resources and a very high level of flexibility. Obviously, you’ll need a distributed database management system to support this system.

A distributed database in DBMS can be:

• Homogenous: all physical locations of the database run on the same hardware and have the same operating systems – this facilitates their management and configuration.
• Heterogeneous: the hardware and operating systems of physical locations differ, which adds extra complexity and requires more resources and time.

In this way, the most commonly met distributed databases are homogenous. 

To gain an even better understanding of a distributed database and how it differs from a centralized database, let’s look at its main features:

• Location-independent: since there are several machines involved, they are most often located at different sites, which adds to accessibility;
• Data access: available to a ery big number of users at the same time;
• Robustness: the system keeps functioning and is available even if one server fails.

As for distributed database examples, they include MongoDB, Azure Cosmos DB, Apache Cassandra, Couchbase Server, Amazon SimpleDB, and many others.

Benefits and challenges of distributed databases

In order to better understand the reason behind the popularity of distributed databases, let’s look at their main benefits – but also, at their biggest cons, so you know what kind of issues you might face. 

Benefits

As you can see, distributed databases offer several advantages over centralized ones. If we round them up, the main benefits are:

Scalability

By adding as many machines as you need, you achieve near-endless scalability and can easily scale the system up and down based on your current needs. This contributes to saving costs since you will allocate the resources precisely where and when you need them. 

High availability 

With distributed databases in DBMS, files are usually delivered to users from the nearest locations, meaning higher response time. And due to the independence from operating systems and hardware, a distributed system is much more available than a centralized database.

Fault tolerance 

As already mentioned, the data is distributed among several machines. So if one fails, it won’t lead to the whole system collapsing and the app will remain functioning. This is a significant advantage, especially for mission-critical applications requiring 24/7 availability.

Performance improvement

Being fault-tolerant and highly reliable, a distributed database ensures seamless app functioning and higher responsiveness. Hence, the performance is optimized significantly, leading to a better user experience and minimized risks.

Challenges

Despite being a highly effective solution to an overloaded database, data distribution also brings certain challenges to an organization that decides to implement it. Below, we list the main things to consider in advance if you want to adopt a distributed database.

Complexity

With great reliability and performance comes great complexity. Being a network with many independently functioning machines (that do not even share the same hardware), a distributed database calls for rigorous configuration and management. 

Cost

The more complex a software solution is, the more expensive it usually tends to be, and distributed databases are no exception. Numerous costs are associated with this database type, including maintenance, hardware, procurement, network, labor, and many more.

Data consistency

Since the data is distributed among numerous nodes, it becomes more difficult to maintain its consistency and integrity. When a small change is applied to one node, other nodes should be synchronized in order to reflect this change properly. Needless to say, data inconsistency results in data corruption and incorrect results, which is unacceptable, especially for mission-critical apps.

Security

The complex nature of distributed databases brings in one more consideration, aka security concerns. Even the smallest error in configuration can lead to major data leaks or unauthorized access. Hence, it is important to invest in robust security and ensure that every node is safeguarded properly.

Network latency

Though distributed databases claim to bring more efficiency to the database performance, they might also have network latency issues. Since data is transferred among nodes via network communication, this might result in network latency. This, in turn, might negatively impact the performance, so it’s critical to monitor and manage the speed and quality of network connections.

Types of distributed database architectures 

The distributed database architecture comes in various types:

Client-server architecture

In this architecture type, a centralized server manages all transactions, distributes load, manages data storage, and provides access control. Hence, when a client (user application) sends a request, it is received by this server, which then directs the query to the most available database machine. This approach is relatively simple regarding setup and configuration due to a two-level architecture.

Peer-to-peer architecture

In a peer-to-peer architecture, each node acts both as a client and a server. Thus, a node can act independently, including processing and storing the data as well as organizing communication with other nodes. Such an approach brings in a very high level of fault tolerance since the failure of a single load would not critically affect the performance of the whole system. 

Federated architecture

This approach to the distributed database architecture is a bit more complex than the ones mentioned above. In a federated architecture, there are several independent databases. But these heterogeneous databases are integrated via a middleware layer into a single meta−database, providing a common interface for the clients regarding data access and querying, so that they function as a single database system. 

Shared nothing architecture

A shared-nothing architecture implies that each node is responsible for a particular portion of the data – unlike in the federated architecture, where each node contains its own database. Hence, all nodes run independently, and it’s easy to add more nodes and thus scale the system. This approach is especially common with large-scale systems, like analytics platforms for big data.

How do distributed databases work? Ways of data distribution

We’ve talked about the architecture types of distributed systems – now let’s discuss how the data can be distributed across such a database. The method that you choose will depend on your business goals and needs, so we recommend studying all available options in advance.

Sharding

What is sharding? It is a horizontal scaling method that implies distributing the data across nodes (shards). The main goal of sharding is to make the load more manageable and add efficiency to the database performance. So, one way to do so is vertical scaling when the database is moved to a more powerful machine, and sharding is a horizontal distribution when there are simply more machines added to the cluster. In this way, you obtain near-endless scaling opportunities and greatly improve the reliability of your database. 

Note though, that sharding is most often used when you need to increase the read/write capacity. Also, there are various sharding types, so you’ll have to choose the one depending on the data that you store and process and what exactly you want to achieve. 

Replication

Unlike sharding, replication means that each machine contains a replica of a dataset (not part of it). This approach is mostly used for read-focused workloads, providing great data availability and effective load balancing. 

There are several approaches to data replication:

• Full replication: exact copies of the dataset are stored on all machines, but this approach is quite costly in terms of storage and resource expenses.
• Partial replication: only certain data pieces are replicated, depending on their relevance or on access patterns.
• Multi-master replication: nodes accept read and write functions, which makes the database much faster and more fault-tolerant.

Specialized services

One more way to distribute the data in your database is by delegating part of the workload to a third-party provider. And while this approach helps improve the database efficiency, it also poses certain security risks since you won’t be in 100% control of your data.

Pros and cons of distributed databases

Now, let’s quickly wrap up the main pros and cons of data distribution:

Summing up

What is a distributed database? Now we know the main strengths and weaknesses of this approach. If you have a complex application that demands scaling or need your database to catch up with the rest of the app, then database distribution might be the best solution. However, do not forget that before implementing it, a lot of work has to be done in advance so all your data remains secure and integral and that your app does not experience any downtime during the database transfer.