Database: Why did NoSQL emerge when SQL already existed?

Recently, I had the opportunity to participate in the development and construction of a large-scale product, designed to serve hundreds of thousands, even millions, of users daily. As a result, the core architecture was built on the CQRS Pattern, a model that separates data reading and writing operations. This approach has allowed us to optimize the system’s performance and scalability while minimizing risks when handling large volumes of data. Through these experiences, I’ve been able to answer a question that has puzzled me for a long time: ‘Why did NoSQL emerge when SQL already existed?’ In this article, I’ll share with you the origins and development reasons behind SQL and NoSQL, as well as the challenges each of these database systems faces. Let’s dive in!

The History and Development of SQL

SQL (Structured Query Language) is a relational database query language created by IBM in the 1970s. It was later developed and standardized by ANSI (American National Standards Institute) and ISO (International Organization for Standardization). Originally, SQL was designed to support relational database management systems (RDBMS) and quickly became the standard for interacting with databases. One of the first systems to use SQL was IBM’s System R, an experimental product that paved the way for future systems.

Over the decades, SQL has evolved with various versions and standards, from SQL-86 and SQL-92 to more modern versions like SQL:1999, SQL:2003, and SQL:2016. Each of these versions introduced new features, making SQL increasingly flexible and powerful in data processing. However, SQL comes with certain constraints that can make scaling and expanding systems challenging, especially when dealing with large and complex data sets.

How long will SQL continue to exist?

Absolutely, SQL has been, is, and will continue to address most database management challenges. It has proven its strengths over the past half-century, serving as the foundation for massive and critical systems across various sectors like banking, insurance, healthcare, and more. SQL is also the technology that has given rise to modern database management standards and models such as ACID and CAP.

With its ability to handle complex queries and manage data efficiently, SQL has become an indispensable tool in building and maintaining information systems.

SQL Is Not a Solution for Every Problem

Although SQL has proven its value over many decades, the rapid development of technology and the increasing demand for storing and processing large volumes of data have led to the emergence of new database technologies, notably NoSQL. NoSQL (Not Only SQL) encompasses a variety of non-relational database technologies designed to handle large, unstructured data and provide higher speed compared to traditional relational systems.

First and foremost, it’s important to understand that SQL possesses certain characteristics that make it less suitable for some of today’s modern system architectures.

ACID and the Challenges of SQL

Let’s start with one of the most fundamental and critical features of SQL: the ACID properties. When discussing SQL, ACID is a core element that ensures data integrity during transactions. ACID stands for four key principles:

• Atomicity: A transaction must be fully completed or not executed at all—there is no in-between state (All or nothing).
• Consistency: Data must always be in a valid state after a transaction is completed.
• Isolation: Transactions must be independent and not interfere with each other.
• Durability: Once a transaction is committed, the data must be preserved and cannot be lost.

These properties form the foundation that enables SQL to maintain data consistency and integrity. However, implementing ACID in systems that require high availability, distribution, and scalability presents significant challenges:

Atomicity and Isolation

Ensuring atomicity and isolation in a distributed system often requires trade-offs in performance and availability. To guarantee that all parts of a transaction are either fully completed or not executed at all, the system may need to employ complex mechanisms such as resource locking or synchronization, which can reduce its load-handling capacity.

Consistency

Strong consistency in ACID demands that every node in the system instantly updates data after each transaction. In a distributed environment, especially when nodes are geographically distant or there are network issues, this requirement can significantly slow down system operations.

Durability

Ensuring durability in a distributed system requires data to be synchronized across multiple nodes before a transaction is confirmed. This process can slow down system response times and affect fault tolerance.

Data Normalization

Next, let’s discuss one of the key principles in SQL: data normalization. This is a crucial database design principle that helps you organize data optimally, reducing unnecessary redundancy and enhancing data integrity. Essentially, normalization involves breaking down large tables into smaller ones and linking them together using foreign keys. Common levels of normalization you may encounter include First Normal Form (1NF), Second Normal Form (2NF), Third Normal Form (3NF), and even the more advanced Boyce-Codd Normal Form (BCNF).

However, data normalization isn’t always a walk in the park. In some cases, especially when dealing with complex queries, normalization can decrease performance and increase system complexity. This becomes particularly significant when working with large and complex datasets, where query speed is crucial. Let’s dive into some specific challenges:

Increased Number of Join Queries

When data is normalized, information is often spread across multiple tables. This means that whenever you need to retrieve data, you’ll have to perform complex join queries to link these tables together. In distributed systems, performing joins can result in high latency, especially when data resides on different nodes across the network. This leads to data being transmitted between nodes, which can significantly slow down the system.

Reduced Performance in Distributed Environments

With normalized data, each piece of information may be stored on different nodes in a distributed system. This not only increases latency when retrieving or updating data but also reduces overall performance. Each operation may require multiple data transfers between nodes, which is far from ideal for your system.

Scalability Challenges

Normalization can cause data fragmentation, which makes scaling the system more complex. When you need to scale your system, maintaining data consistency and integrity across a large distributed environment becomes a challenging task. And of course, the more nodes and linked tables you have, the more challenges you’ll face.

Availability

In systems that require high availability, maintaining the links between normalized tables can become a weak point. If a table or node containing data goes down, you might face significant issues when trying to access or update data in related tables. This not only reduces the system’s availability but can also impact the overall user experience.

In summary, while data normalization is a golden rule in database design, it’s not always the best choice for every situation. Carefully weigh its benefits against its limitations to make the right decision for your system!

The Emergence and Development of NoSQL

Now that we’ve covered the fundamental principles of SQL and the challenges of data normalization, you might be wondering, ‘Is there another way to manage data without facing these barriers?’ The answer is NoSQL!

NoSQL emerged as a natural response to the limitations of traditional relational database management systems (RDBMS). As web applications, social networks, and cloud technologies began to explode, major tech companies like Google, Amazon, and Facebook realized that traditional RDBMS could not meet their demands for speed, availability, and scalability. As a result, NoSQL was quickly developed to address these issues.

Why Did NoSQL Emerge?

In the era of Big Data, where applications need to handle millions, even billions, of requests daily, traditional SQL began to encounter serious limitations. RDBMS require strong consistency, which can make them slow and difficult to scale as data volumes increase rapidly.

Additionally, as applications grew more complex and data became more unstructured—such as data from social networks, IoT sensors, or text documents—storing this data in relational tables became cumbersome and inflexible. This is where NoSQL stepped in.

Types of NoSQL database systems

NoSQL is not a single concept but rather encompasses a variety of database management systems, each designed to address specific challenges:

• Document Stores: Such as MongoDB and CouchDB, allow you to store data as documents with flexible structures, making them ideal for unstructured data.
• Key-Value Stores: Like Redis and Riak, are well-suited for applications that require fast data access with simple requests, thanks to their key-value data structure.
• Column-Family Stores: Examples include Cassandra and HBase, which are commonly used in systems that need to store massive amounts of data and support distributing data across multiple nodes.
• Graph Databases: Like Neo4j, are powerful for managing complex relationships between entities, such as in social networks or supply chain management systems.
• Time Series Databases: Such as InfluxDB, support storing and querying time-series data, making them suitable for IoT applications, monitoring, and data analytics.
• Search Engines: Like Elasticsearch and Solr, help you quickly search and retrieve data from various data sources.

The evolution of NoSQL

Since its inception, NoSQL has grown rapidly and become the preferred choice for many large technology companies. The advantage of NoSQL lies in its ability to scale horizontally, allowing systems to easily expand as demand grows. Moreover, NoSQL offers high flexibility in handling unstructured and semi-structured data, making it easier for developers to design applications.

The purpose of using NoSQL is to flatten data. This is similar to designing a schema to meet the query needs of an application, rather than designing a schema to ensure data normalization as in SQL. This enables faster and more efficient queries, avoiding the need for complex join queries as in SQL.

Although NoSQL does not completely replace SQL, it has become an indispensable part of the modern database ecosystem. Today, many companies use a combined model of SQL and NoSQL to maximize the benefits of both, depending on the specific needs of the application.

In conclusion, NoSQL has and continues to change the way we think about databases. If you are looking for a flexible, scalable solution, and especially if you are working with large and unstructured data, NoSQL is definitely a viable option.

Comparison between SQL and NoSQL

To help you better understand the differences between SQL and NoSQL, I will summarize some of the most important points between these two database systems through the comparison table below:

Real-world use cases of SQL and NoSQL

To further illustrate the practical applications of SQL and NoSQL, let’s explore some common use cases for each type of database:

SQL (Relational Databases)

• Customer Relationship Management (CRM) systems: SQL is frequently used in CRM systems where data consistency and integrity are paramount.
• Financial management systems: SQL is well-suited for financial management systems that require complex transaction processing and data accuracy.
• Product and inventory management systems: SQL effectively manages product information, from storing product details to managing orders and inventory.

NoSQL (Non-Relational Databases)

• Social networks: Learn how NoSQL helps social media platforms manage and analyze massive amounts of user data.
• Big data management systems: NoSQL enables the handling of large datasets and enhances system scalability, making it ideal for applications demanding high scalability.
• Internet of Things (IoT): Discover why NoSQL is the preferred choice for processing and storing sensor data from IoT devices.

Combining SQL and NoSQL in a system

To leverage the best of both SQL and NoSQL, many large companies have opted to combine both in a system through the following models:

• Command Query Responsibility Segregation (CQRS): This model separates read and write operations, optimizing system performance and scalability.
• Event Sourcing: This model stores all events that occur in the system, allowing for flexible tracking and restoration of data state.
• Polyglot Persistence: This model enables the use of multiple database types within a single system, leveraging the advantages of both SQL and NoSQL.

The future of SQL and NoSQL

When discussing the future of SQL and NoSQL, we cannot ignore the rapid development of technology and new trends in data management. Both types of databases have their own strengths, and in many cases, they complement each other rather than compete. So, what does the future hold for SQL and NoSQL?

Continued parallel development

It is clear that SQL and NoSQL will continue to coexist and develop in parallel. This is because each type of database serves different needs in data management. SQL, with its strong consistency and complex transactions, will remain the top choice for applications requiring high accuracy, such as finance, banking, and customer information management.

Meanwhile, NoSQL will continue to grow and expand in areas requiring large-scale, unstructured data processing and flexible scalability, such as e-commerce, social networks, and IoT. With the explosion of unstructured data from sources like videos, sensors, and user-generated content, the demand for NoSQL will only increase.

Polyglot persistence: Combining SQL and NoSQL

The future will see an increase in “Polyglot Persistence” systems – that is, the simultaneous use of multiple database types to meet the diverse requirements of a single application. Instead of trying to choose between SQL and NoSQL, many businesses will opt for both, using SQL for operations requiring consistency and NoSQL for tasks requiring speed and scalability.

For example, an e-commerce application could use SQL to manage payment transactions and customer information, while NoSQL would be used to process product views and analyze real-time user data.

Tips for choosing between SQL and NoSQL

Before making a decision, carefully consider the following factors:

Understand Your Data Type

• Structured data: If you’re working with well-defined structured data like customer information, orders, or financial data, SQL is the ideal choice. SQL is designed to handle structured data models with complex relationships between tables.
• Unstructured or semi-structured data: If your data doesn’t fit neatly into tables, such as text documents, images, videos, or social media data, NoSQL is a better fit. NoSQL can flexibly and efficiently store data in formats like JSON, XML, or other document-based formats.

Determine scalability and performance requirements

• Scalability: If your application is expected to handle large volumes of data at high speeds and requires horizontal scaling (adding more servers to increase capacity), NoSQL is a suitable choice. NoSQL databases like Cassandra or MongoDB are designed to scale easily without performance issues.
• Consistency and accuracy: If your application requires high data accuracy and complex transactions, such as in financial or banking systems, SQL is the better option. SQL provides strong consistency with ACID transaction support, ensuring that your data is always accurate.

Consider query complexity

• Complex queries: If your application requires complex queries with joins, calculations, and data analysis, SQL is the optimal choice. SQL databases offer a powerful query language and query optimization tools to handle these scenarios.
• Simple queries and high speed: If your requirements are simple data retrieval by key or basic CRUD operations, NoSQL will provide better performance. NoSQL databases like Redis or DynamoDB excel at fast queries with simple data structures.

Evaluate cost and complexity

• Deployment and maintenance cost: SQL can require more effort to set up and maintain, especially when managing complex relational schemas and handling transactions. However, SQL offers stability and a rich ecosystem of tools.
• Simplicity and flexibility: NoSQL can be easier to deploy in some cases, especially for web applications or systems that require rapid development. NoSQL databases often have simpler structures and are easier to integrate into modern applications.

Conclusion

“In today’s increasingly complex data landscape, SQL and NoSQL have proven to be indispensable, each addressing different specific needs. SQL provides stability and accuracy for systems requiring high consistency, while NoSQL offers flexibility and scalability in unstructured and large-scale data environments.

However, rather than choosing one over the other, the current trend is to combine both SQL and NoSQL to maximize the benefits of each. This not only helps meet diverse requirements but also opens up the possibility of building more robust, flexible, and sustainable data systems.

Ultimately, the choice between SQL and NoSQL is not merely a technical decision but a strategic consideration, based on the specific needs of the project and long-term vision. Choose and use them wisely to optimize your ability to manage and exploit data in the future.

I hope this article has helped you better understand SQL and NoSQL, as well as how to effectively use them in practice. If you have any questions or comments, please leave them below for further discussion.