Modern Distributed Database Architectures - Part 1

Introduction

In the era of big data and cloud computing, the demand for scalable, high-performance, and resilient data management systems has never been greater. Enter distributed databases—a modern solution designed to handle the challenges of massive data volumes, global accessibility, and fault tolerance. But what exactly is a distributed database?

At its core, a distributed database is a data management system that spreads data across multiple servers or nodes. While not a hard requirement, these codes can be located in different physical or geographic locations. This is achieved through two key mechanisms: sharding and replication.

• Sharding splits data into smaller, more manageable pieces (called shards) and distributes them across multiple nodes. This enables horizontal scalability, allowing the system to handle increasing workloads by simply adding more nodes.
• Replication involves creating multiple copies of data across different nodes, ensuring redundancy and fault tolerance. Depending on the design and configuration, as long as the number of failed nodes remains below a certain threshold, the system can continue operating using the replicated data from the remaining nodes.

By combining these two patterns, distributed databases achieve high horizontal scalability—sometimes theoretically infinite—and higher throughput, making them ideal for applications that require massive scale and performance. However, these benefits come with tradeoffs. One of the most notable is potentially increased latency. To maintain a certain level of data consistency across distributed nodes, the system often incurs additional communication and coordination overhead required by consensus algorithms, which can lead to higher average latency compared to traditional standalone databases.

In this series of blog posts, we’ll explore different modern distributed database architectures, examine a few popular designs and their tradeoffs, and break down some design decisions related to Dragonfly and Dragonfly Cluster. Let’s dive in!

Primary-Replica Architecture

The primary-replica architecture is one of the earliest and most straightforward approaches to building distributed database systems. In this model, there is a single primary node that handles all write operations and some read operations, while one or more replica nodes replicate data from the primary, often asynchronously, and serve more read requests. In my opinion, this architecture can be only considered “semi-distributed” because it introduces replication for redundancy, read scalability, and high availability (HA). However, it lacks sharding. Without sharding, the system cannot distribute write workloads across multiple nodes, limiting its ability to scale effectively for modern, data-intensive applications.

Advantages

• Ease of Implementation: The primary-replica model leverages existing mature database technologies. This made it an attractive option when distributed systems were still evolving.
• Read Scalability: By offloading read queries to replica nodes, the primary node can focus more on handling write operations, improving overall system throughput for read-heavy workloads.
• Fault Tolerance: If the primary node fails, one of the replicas can be promoted to take its place, ensuring high availability.

Disadvantages

• No Sharding: Since data is not sharded across nodes, the system lacks horizontal scalability for write operations. This limits its ability to handle massive datasets or extremely high write throughput.
• Limited Scalability: Adding more replica nodes only improves read scalability. The primary node remains a bottleneck for write operations, as it must handle all writes and coordinate replication to the replicas.
• Replication Lag: Asynchronous replication can lead to delays between the primary and replica nodes. This means that reads from replicas might return stale data, which can be problematic depending on application requirements. During a failover operation, data loss is also possible.

The primary-replica architecture is widely used in modern database systems, including Amazon RDS, Google Cloud SQL, MongoDB Replica Sets, and Amazon Aurora DB Clusters. RDS and Cloud SQL services normally provide replication support for databases like MySQL and PostgreSQL, allowing read replicas to offload queries from the primary node. MongoDB Replica Sets enhance this model by allowing clients to specify write concerns, enabling control over how many replicas must acknowledge a write operation before it is considered successful. Aurora DB Clusters improve upon traditional RDS by using a cluster volume—a virtual storage layer that spans multiple availability zones (AZs). Each AZ maintains a copy of the data, enhancing durability and availability while reducing the possibility of data loss.

Amazon Aurora DB Cluster Architecture

The primary-replica architecture serves as a foundational concept in distributed systems and is still relevant today, especially for applications that prioritize simplicity and read scalability over full horizontal scalability. As you can see from the examples above, these systems highlight the evolution of primary-replica architectures to meet modern scalability and fault-tolerance needs. However, as data demands grow both in terms of volume and consistency, more advanced architectures have emerged to address its limitations.

Shared-Nothing Architecture

The shared-nothing architecture is a fully distributed database design where each node in the cluster operates independently. Each node is homogeneous, meaning they are identical in terms of functionality, and there is no special component in charge of special tasks such as cluster metadata management. Instead, metadata and coordination are distributed across the nodes themselves, often using techniques like consistent hashing to manage data distribution.

Advantages

• Homogeneous Nodes: Since every node in the cluster is identical, the system is easier to develop, deploy, and maintain. There’s no need to manage specialized nodes or roles, simplifying operations.
• High Scalability: The shared-nothing design allows the system to scale horizontally by adding more nodes. Each node operates independently, so the system can handle massive workloads by distributing data and processing across many nodes.
• Fault Tolerance: Data is replicated across multiple nodes, ensuring that the system remains operational even if some nodes fail. This makes shared-nothing architectures highly resilient.

Disadvantages

• Metadata Management Overhead: Since there’s no central metadata manager, each node must maintain and synchronize metadata about the cluster’s state, often via the Gossip protocol. This can introduce coordination overhead and complexity for individual nodes.
• Uneven Data Distribution: Shared-nothing systems often rely on consistent hashing to distribute data across nodes. While this approach is generally effective, it can sometimes lead to uneven data distribution, resulting in hotspots where certain nodes handle much more data or traffic than others.
• Complexity in Adding New Nodes: While the architecture is highly scalable, adding new nodes can be tedious. Again, since there’s no node with special roles in the cluster, the system normally follows a predefined unsophisticated process upon a new node joining. The system would then stream necessary data over to the new node, which can be resource-intensive and may temporarily impact the overall performance of the cluster.

Cassandra, ScyllaDB, and VictoriaMetrics are prominent examples of shared-nothing architectures. Cassandra, a distributed NoSQL database, uses consistent hashing for data distribution and offers tunable consistency levels, making it ideal for high availability and scalability. ScyllaDB, a high-performance alternative to Cassandra and DynamoDB, leverages the same shared-nothing design but is optimized for low latency and extremely high throughput. VictoriaMetrics, a time-series database, adopts this architecture to efficiently handle large volumes of time-series data, ensuring high scalability and performance. These systems exemplify the shared-nothing approach, balancing scalability, fault tolerance, and operational simplicity.

ScyllaDB Shared-Nothing Ring Architecture

The shared-nothing architecture is widely adopted in modern distributed databases because it balances scalability, fault tolerance, and simplicity. By eliminating shared resources and central coordination, it avoids bottlenecks and single points of failure, making it ideal for large-scale, high-performance applications. However, the tradeoffs—such as metadata management overhead and potential data distribution challenges—must be carefully considered when designing and operating such systems.

Conclusion

In this first part of our exploration into modern distributed database architectures, we’ve examined two foundational designs: the primary-replica model and the shared-nothing architecture. While the primary-replica approach offers simplicity and read scalability, it falls short in handling massive write workloads due to its lack of sharding. On the other hand, the shared-nothing architecture excels in horizontal scalability and fault tolerance but come with challenges like metadata propagation and uneven data distribution.

These architectures represent the evolution of distributed systems, each addressing specific tradeoffs between scalability, consistency, and operational complexity. In the next part, we’ll explore additional architectures that tackle the challenges of modern data management in unique ways, highlighting their strengths and the inevitable tradeoffs they bring. We will also revisit Dragonfly and Dragonfly Cluster's design decisions and see why they make the most sense accomplishing our design goals. Stay tuned!