Scalability vs. Consistency in Databases: An Overview

Photo of author
Written By Naomi Porter

Naomi Porter is a dedicated writer with a passion for technology and a knack for unraveling complex concepts. With a keen interest in data scaling and its impact on personal and professional growth.

Technology advancements have revolutionized the amount of data we generate and process. We demand more processing power and data handling capabilities than ever before. Databases serve as essential tools in managing the massive amounts of data we produce. As our applications grow, our databases must keep pace, handling increasingly higher traffic volumes and providing optimal performance. The challenge comes in achieving scalability and consistency in database design. Achieving both scalability and consistency is a difficult task, as optimizing one comes at the expense of the other. This article provides an overview of the trade-off between scalability and consistency, the CAP theorem, and algorithms and architectures to achieve high scalability and consistency.


Database design poses significant challenges, including managing increasing amounts of data and traffic while maintaining data accuracy and uniformity. One of the critical trade-offs in database design is scalability vs. consistency. Achieving both requires compromising on one. There are tradeoffs and compromises to be made in database design. In this article, we explore these tradeoffs and ways to balance the need for scalability and consistency. We will introduce the CAP theorem, acid compliant and base model databases, Materialize’s virtual time, distributed consensus algorithms, and database configurations.

Scalability and Consistency in Databases

Scalability refers to the ability of the system to handle more data, traffic, or users while consistency refers to keeping the data uniform and reliable across the system. Achieving both scalability and consistency is difficult. As data volumes increase, horizontal scaling offers a solution, allowing developers to add more nodes to handle requests. Distributed databases provide a scalable solution by offering multiple machines to manage and store data. However, distributed databases can be challenging to manage, and consistency can be challenging to achieve across multiple machines.

In acid compliant databases, consistency is a priority. These databases guarantee that all transactions are atomic, consistent, isolated, and durable. The acid-compliant guarantees ensure that transactions are reliable and uniform across the system. Base model databases prioritize scalability and eventual consistency. These databases prioritize availability and partition tolerance while sacrificing immediate consistency for scalability. The base model ensures that data is eventually consistent, meaning updated data may not be uniform across the system.##The CAP Theorem

The CAP theorem states that distributed databases can provide either consistency or availability in the event of a network failure, but not both. Partition tolerance is essential, allowing the system to continue operating despite network partitions. The CAP theorem is instrumental in guiding developers’ tradeoffs between consistency, availability, and partition tolerance in distributed database designs.

Maximizing combinations of consistency and availability can overcome CAP’s historical limitations. Consistent databases ensure accuracy, while available databases focus on service. Traditional databases aim to be consistent with their transactions by waiting for data confirmation before moving on to the next request. NoSQL databases prioritize availability or consistency. The system designer must choose the best trade-off for their priorities, budgets, and system needs based on the customers’ value proposition.

Materials’ Virtual Time

Distributed databases handle conflicting writes differently, with some issuing an error upon conflict and others proceeding with either the first or the last write. Materialize has introduced a unique methodology to handle potential conflicts through its virtual time architecture. The virtual time assigns time-stamps to events in distributed systems, providing explicit histories of data collections. The technique, based on differential dataflow, ensures consistency by assign timestamps for unrelated sources, enhancing scalability and determinism.

Virtual time creates a deterministic guarantee by ensuring that precisely the same sequence of changes will be applied, even if out-of-order things happen asynchronously (such as network delays, retries, or local clock drift). In addition, cheap virtual timestamp assignment enhances scalability by keeping additional metadata cost-efficient.

Strong Consistency and Consensus Algorithms

Tailoring database systems to specific requirements involves a time-consuming and complex process that requires thoughtful planning. As nodes grow, the need for strong consistency grows as well, to ensure data objects are updated consistently across all clients. Consensus algorithms are used for transactional and replica consistency, achieving the most impactful data consistency through a combination of serializability and linearizability models.

Distributed consensus algorithms, such as Raft and Paxos, provide ways to ensure fault tolerance to maintain replica consistency through atomic broadcast, total order broadcast, or replicated state machines. Distributed transactions can be managed through a classic two-phase commit algorithm but have weaknesses in handling coordinator failures. In comparison, consensus algorithms focus on participant failure recovery, creating a more resilient distributed database system. Overall, the developer has trade-off decisions to make to get higher consistency and availability.

Database Configurations

Distributed databases can have different configurations to improve reliability or reduce latency, but a trade-off between the two always exists. Single-master, multiple-slave, and multi-master configurations enhance reliability but increase latency. Write replicas, for example, are usually placed in different regions that are geographically close enough for low latency. A high replication factor increases fault tolerance and handles the scale-out, but it also comes with high overhead.

Sharding reduces latency by distributing writes to different master databases, but it could complicate operations if data commits in various shards are required. Cassandra offers configurable consistency levels to balance data accuracy and availability when reading or writing data. By understanding and choosing the right configuration, distributed database applications can handle more data and traffic while maintaining data uniformity and accuracy.


Achieving high scalability and consistency in database design requires trade-offs and compromises. Developers must choose the best trade-off for their priorities, budgets, and application needs. A deep understanding of the concepts of consistency, scalability, and the CAP theorem can guide that choice. Architects must also consider advanced algorithms such as consensus algorithms, architectures such as Materialize’s virtual time, and database configurations such as sharding and single-master, multiple-slave, and multi-master configurations to enhance scalability, consistency, reliability, and latency. Accurate and reliable information is key, and database administrators hold the keys to make it happen.