Choosing the Right Database: A Comparison of SQL and NoSQL

Picking the right database is like choosing the foundation for a skyscraper. If built on shaky ground, even the most ambitious designs can crumble. Startups, companies, and projects have fallen not due to lack of vision, or efficient code, but because they underestimated the critical role of their database, or simply made the wrong decision of which database to utilize for their application.

Choosing the wrong database can lead to:

- Painful Scalability: Your app suddenly goes viral, but your database can’t handle the surge in traffic. (In example, Twitter’s Fail Whale).

- Data Integrity Issues: Inconsistent data and lost updates make your application unreliable. (Imagine a stock trading app/website that has data inaccuracies and displays wrong prices).

- Frustrating Development: Rigid database structures slow down development and stifle innovation. (Much of the banking sector is based on decades old software which is pretty difficult to change. Decades old databases are even harder to change and work with).

In this post, we will examine two of the options that are offered in the world of databases. SQL and NoSQL, each with different strengths and weaknesses.

Major differences in short

The Foundation: Understanding Data Organization

SQL: (RDBMS)

• Tables: Picture SQL databases as collections of interconnected tables. Each table resembles a spreadsheet with rows (representing individual records) and columns (representing data attributes).

• Keys: Keys play a pivotal role in SQL databases.

• Primary Keys: Uniquely identify each row within a table (e.g., user ID, customer ID, order number).

• Foreign Keys: Create relationships between tables and interconnect them. For instance, an ‘order’ table might have a foreign key referencing a customer ID in the ‘customers’ table.

Let’s play around with the customers and orders example to showcase the true nature of SQL in a simple manner.

Here we create a table with 4 columns (customer_id, name, email, address). The unique primary key is customer_id, this key will be used as our main identifier for our customers.

Next, we need to create the Orders table and somehow interconnect it to the Customers table that we created earlier. To do that, we will add a foreign key, that references the (unique) primary key of our Customers table. This way, we can correlate one or more orders to a specific customer.

On the very last line, we correlate our Orders customer_id column to the Customers customer_id column and this way we interconnect these 2 tables together.

NoSQL: Unstructured Dynamic

• Beyond Tables: NoSQL doesn't follow the rigid row/columns structure. Instead, it’s more like flexible containers. The way it stores data, differs based on data type. Here are some common NoSQL data types:

• Document Stores: Data is stored as documents (often in JSON format). These documents can have nested structures, similar to a folder containing different file types and subfolders.

Document Store in JSON(Example for a Blog post regarding SQL vs NoSQL)

• Key-Value Stores: Like dictionaries, each data item has a unique key and its associated value.

Storing user preferences with NoSQL key-value

• Graph Databases: Ideal for representing networks of relationships. Data is stored as nodes (entities) and edges (the connections between them).

NoSQL graphs are mostly used in Social Networks, as shown in the example

• Wide-Column Stores: These offer a table-like feel but with more flexibility in column organization (think of it as rows with dynamically changing sets/numbers of columns)

Weather sensor example

Key Takeaway: NoSQL doesn’t have a universal equivalent to SQL tables, rows, or keys. The way data is stored and accessed depends on the specific type of NoSQL database you choose. Will brings us to the point, that the main strength of NoSQL is its flexibility. On the other hand, SQL is perfect for strict relationships between data and complex queries.

Scaling: Vertical vs Horizontal

Vertical Scaling (SQL): Vertical Scaling is upgrading the hardware of the server that is running the database. It can be very effective but also expensive.

Horizontal Scaling (NoSQL): Horizontal Scaling is about adding more machines (computers/servers) to distribute (share) the load. NoSQL servers, most of the time are designed in a way to easily distribute data across servers, thus, rendering it more cost-effective when it comes to scaling to meet high traffic demands.

ACID (SQL) vs BASE (NoSQL)

When comparing SQL and NoSQL databases, one of the fundamental differences lies in how they handle transactional properties. In SQL databases, the ACID (Atomicity, Consistency, Isolation, Durability) properties provide a strong foundation for ensuring data integrity and reliability in transactions. On the other hand, NoSQL databases often adhere to the BASE (Basically Available, Soft state, Eventually consistent) principles, which prioritize availability and partition tolerance over strict consistency.

ACID transactions make sure that database transactions are executed reliably and consistently. They ensure that either all operations within a transaction are successfully completed, or none of them are, maintaining data consistency and integrity. Moreover, ACID properties ensure isolation between concurrent transactions and durability of committed changes, even in the event of system failures.

In contrast to ACID, BASE principles ease up on some of the constraints of ACID in favor of scalability and fault tolerance. Available that the system remains operational and responsive even in the face of partial failures, prioritizing availability over consistency. Soft state means that data may temporarily be in an inconsistent or tentative state, allowing for flexibility in distributed environments. Eventually consistent means that while data may not be immediately consistent (soft state) across all nodes, it will eventually change to a consistent state over time, given no further updates.

While ACID provides strong consistency guarantees suitable for applications with strict transactional requirements, BASE principles offer greater flexibility and scalability, making them well-suited for distributed systems and applications where high availability is crucial. Developers must carefully consider the trade-offs between consistency, availability, and partition tolerance when choosing between SQL and NoSQL databases based on their specific application needs and requirements.

Use Cases: Picking the Correct Tool for the Desired End Goal

• SQL (When Structure Matters):

• Financial Systems: Transactions need to be pin-point accurate, and data consistency is non-negotiable.

• Applications with Complex Reporting: SQL’s powerful query language excels at analysis and generating complex reports.

• NoSQL (When Flexibility is Key):

• High-Traffic Web Apps: Handling massive user bases and sudden traffic spikes often requires the scaling ease of NoSQL. (I.e. Social Media)

• Evolving Data: Applications where the data structure may change frequently (e.g., user profiles on a social platform adding new fields).

Hybrid Solutions: The Best of Both Worlds

Increasingly, modern applications don’t rely on just one type of database. A combination of SQL and NoSQL might be the perfect solution, utilizing each for their strengths:

• Example: An e-commerce site might use:

• SQL to manage product inventory and order transactions.

• NoSQL to store user reviews and recommendations with flexible data structures.

Conclusion

The decision between SQL and NoSQL databases depends on the specific requirements and goals of the project at hand. SQL databases offer robustness, ACID compliance, and structured data management, making them ideal for applications with complex transactions and relational data. On the other hand, NoSQL databases follow BASE principles that provide flexibility, scalability, and high performance, making them suitable for handling large volumes of unstructured or semi-structured data in distributed environments. By carefully evaluating factors such as data structure, scalability, consistency, and query requirements, developers can make an informed choice that aligns with their project’s needs, ensuring optimal performance and scalability in the long run.