NoSQL vs SQL Architecture: Differences & Similarities

When and how to migrate data from SQL to NoSQL are matters of much debate. It can certainly be a daunting task, but when your SQL systems hit architectural limits or your cloud provider expenses skyrocket, it’s probably time to consider a move.

Vertical Scaling in SQL Databases (Scaling Up)

Database administrators add capacity to RDBMS and NoSQL databases in very different ways. Typically, the only way to add capacity in a relational system is to add expensive hardware, faster CPUs, more RAM, and more advanced networking components. This is often referred to as ‘vertical’ scale, or ‘scaling up’ (SQL vertical scaling).

NoSQL Databases Enable Horizontal Scaling (Scaling Out)

In contrast, NoSQL databases are designed for low latency and high resilience, being built from the ground up to run across clusters of distributed nodes. This architecture is often referred to as ‘horizontal scale,’ or ‘scaling out’ (NoSQL database horizontal scaling). To add capacity to a NoSQL database, administrators simply add more nodes, a very simple process in modern cloud environments. In a NoSQL cluster, nodes are easy to add and remove according to demand, providing ‘elastic’ capacity. This feature enables organizations to align their data footprint with the needs of the business while maintaining availability even in the face of seasonal demand spikes, node failures, and network outages.

What Are The Tradeoffs of NoSQL Database Horizontal Scaling?

Horizontal scaling NoSQL brings tradeoffs of its own. Adding commodity hardware to a cluster can be cheap in terms of software licenses and subscriptions. However, as more and more nodes are added in the pursuit of higher throughput and lower latency, operational overhead and administrative costs spike. Big clusters of small instances demand more attention and generate more alerts than small clusters of large instances.

Notably, some next-generation NoSQL databases like ScyllaDB are able to overcome this tradeoff, scaling out in a way that can take advantage of the powerful hardware in modern servers and ultimately running in smaller, though still distributed, clusters of fewer nodes.

How Does Data Replication Work for SQL Databases?

Replicating data across multiple nodes allows databases to achieve higher levels of resilience. In the SQL world, it’s not trivial to replicate data across multiple instances. Relational databases do not support replication. Instead, they rely on external tools to extract and update copies of datasets. These tools run batch processes that often take hours to complete. As a result, with SQL data replication, there is no way to ensure real-time synchronization of data among the copies of data.

How Does Data Replication Work for NoSQL Databases?

While non-relational databases provide native support for data replication, they follow three basic models: multi-master databases, such as DynamoDB, master-slave architectures, such as MongoDB, and masterless, such as ScyllaDB, the fastest NoSQL Database. Given their reliance on master nodes, both multimaster and master-slave architecture introduce a point of failure. When a master goes down, the process of electing a new master introduces a brief downtime. Even though the delay may be minimal, measured in milliseconds, that delay can still cause SLA violations.

A masterless architecture addresses this limitation. In these databases, data is replicated across multiple nodes, all of which are equal. In a masterless architecture, no single node can bring down an entire cluster. A typical masterless topology involves three or more replicas for each dataset. Adopting a NoSQL database that implements a masterless architecture provides yet another layer of resilience for high-volume, low-latency applications.

Data Modeling Approaches for NoSQL vs SQL Database Development

The rise in popularity of NoSQL databases paralleled the adoption of agile development and DevOps practices. Unlike SQL databases, NoSQL databases encourage ‘application-first’ or API-first development patterns. Following these models, developers first consider queries that support the functionality specific to an application, rather than considering the data models and entities. This developer-friendly architecture paved the path to the success of the first generation of NoSQL databases.

In contrast, relational databases impose fairly rigid, schema-based structures to data models; tables consisting of columns and rows, which can be joined to enable ‘relations’ among entities. Each table typically defines an entity. Each row in a table holds one entry, and each column contains a specific piece of information for that record. The relationships among tables are clearly defined and usually enforced by schemas and database rules.

Uniformity in Data Models for NoSQL vs SQL Databases

Relational data models enforce uniformity, whereas non-relational models do not. NoSQL databases permit multiple ‘shapes’ of data objects to coexist, which is more flexible but can also be more error prone. In the world of relational databases, the schemas that support uniformity are usually managed by database administrators. This can sometimes introduce friction between administrators and development teams, resulting in long, non-agile application development lifecycles. Such highly structured data requires normalization to reduce redundancy. Since the data model is based on the entity being represented; query patterns are a secondary consideration.

NoSQL inverts this approach, placing more power in the hands of the developer and often decentralizing control over data structures. Nonrelational data models are flexible, and schema management is often delegated to application developers, who are relatively free to adapt data models independently. Such a decentralized approach can accelerate development cycles and provide a more agile approach to addressing user requirements.