System Design : SQL vs NoSQL databases

UpdatedAugust 13, 2024

I am Nandan, And you probably know me as a "Software Engineer who "hacked" an Airline to retrieve his luggage".

I am a full-time Software Engineer, Tech Speaker, and mentor. I enjoy talking about Web Development, Machine Learning, Natural language Processing, Machine learning Accelerated Mobile Pages, Progressive Web Apps, Cybersecurity, Chatbots, etc.

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Vinod Sharma1y ago

Excellent article! I have solid experience in SQL but much less information with NoSQL, and this post was incredibly helpful.

Could you explain what you mean by SQL databases being able to scale vertically while NoSQL databases can scale horizontally?

Nandan Kumar1y ago

Thanks, Vinod Sir for your kind comment.

To answer your question:

SQL databases typically scale vertically, which means that to handle an increased load, you add more resources (like CPU, RAM, or storage) to a single server. This approach is often referred to as "scaling up." While it can improve performance, it has its limits and can become costly as you need more powerful hardware.

On the other hand, NoSQL databases are designed to scale horizontally. This means that instead of adding more resources to a single server, you add more servers to handle the increased load. This approach is known as "scaling out." It allows for better distribution of data and can handle larger amounts of traffic by spreading the load across multiple servers.

In summary, vertical scaling (SQL) involves enhancing the capacity of a single machine, while horizontal scaling (NoSQL) involves adding more machines to distribute the load.

I hope this clarifies the difference! If you have any more questions, feel free to ask.

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In the realm of databases, two primary types of solutions exist, SQL (relational) and NoSQL (non-relational) databases. These two categories differ significantly in their construction, the nature of the data they store, and their storage methods. Relational databases are structured with predefined schemas, while non-relational databases are unstructured, distributed, and feature dynamic schemas.

High-level differences

Here are some high-level differences between SQL and NoSQL:

Storage

SQL stores data in tables where each row represents an entity, and each column represents a data point related to that entity.

NoSQL databases utilize various data storage models, including key-value, graph, and document-oriented approaches.

Schema

In SQL, each record follows a fixed schema, which means that the columns must be determined and chosen before data entry, and each row must contain data for each column. The schema can be modified later, but this involves modifying the database using migrations.

NoSQL schemas are dynamic. Fields can be added on the fly, and each record doesn’t have to contain data for each field.

Querying

SQL databases use Structured Query Language (SQL) to define and manipulate data, which is highly powerful.

In a NoSQL database, queries focus on a collection of documents, with different databases having different syntax for querying.

Scalability

In most cases, SQL databases can be scaled vertically, which can become very costly. While it's feasible to scale a relational database across multiple servers, it's a difficult and time-consuming process.

NoSQL databases are horizontally scalable, which means it's easy to add more servers to handle large traffic. They can be hosted on inexpensive hardware or cloud instances, making them cost-effective compared to vertical scaling. Many NoSQL technologies also distribute data across servers automatically.

Reliability

The majority of relational databases are ACID compliant. Therefore, SQL databases are still the best choice for data reliability and transaction safety.

Many NoSQL solutions prioritize performance and scalability over ACID compliance.

Reasons

As always we should always pick the technology that fits the requirements better. So, let’s look at some reasons for picking SQL or NoSQL based database:

For SQL

Structured data with strict schema
Relational data
Need for complex joins
Transactions
Lookups by index are very fast

For NoSQL

Dynamic or flexible schema
Non-relational data
No need for complex joins
Very data-intensive workload
Very high throughput for IOPS

That's all folks.

Feel free to comment on how you like my blog on the system design series or shoot me an email at connect@nandan.dev If you have any queries I will try to answer them.

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