SQL vs NoSQL Databases

Introduction

Have you ever been in a dilemma while choosing the most appropriate storage type for your application? What could be the most viable database type that could meet the business expectations and offer efficient services? If so, then this blog will give you critical insights to choose your database (SQL or NoSQL) and make your application work efficiently.

Nowadays, most businesses heavily rely on processing and managing large amounts of data. The core decision behind choosing any type of database depends on the business logic of the application, the critical functionalities of the system, the structure of the data, and its requirements.

The primary factor behind selecting a database is the data structure and the kind of operations associated with it. But before diving deep into selecting a suitable database, it is necessary to have a clear understanding of the structure and functionalities of each kind of database. Databases are broadly categorized into Relational (SQL) and Non-Relational (NoSQL) databases.

SQL Database

SQL (Structured Query Language) is a programming language specifically used for relational databases, which are highly structured in nature. These databases are mainly composed of tables, with each table consisting of rows and columns.

Relational databases have a predefined schema, which establishes the relationship between tables and field types. This is an essential tool for keeping data in synchronization and minimizing redundancy.

Relational databases are ACID-compliant, which makes them highly suitable for transaction-oriented systems and storing financial data. ACID compliance ensures error-free services, even in the event of failures, which is essential for transaction validity. Some examples of SQL databases include MySQL, Oracle, PostgreSQL, and MariaDB.

NoSQL Database

NoSQL is a non-relational database system that is highly flexible and scalable. NoSQL databases offer dynamic schemas and are most suitable for storing unstructured data such as article content, video, social media posts, and various types of unstructured data. There are various types of NoSQL databases such as Key-Value Stores, Document Oriented Database, Column Oriented Databases, and Graph databases.

While NoSQL databases are query inefficient, their highly distributed and flexible nature makes them highly suited to modern frameworks such as Hadoop, and they are suitable for big data and real-time processing services.

NoSQL databases offer BASE (Basically Available Soft state Eventual) compliance, which provides high flexibility. NoSQL is highly suitable for storing hierarchical data and solving data availability problems. Some examples of NoSQL databases are MongoDB, Redis, Neo4j, Cassandra, and Hbase.

Types of NoSQL Database

Key-Values Stores

The Key/Value model is the most basic and straightforward database model to apply. The primary idea is to use a hash table with a unique key and a pointer to a specific piece of information. However, it can be inefficient when you only want to query or update a portion of data.

Data is stored in key-value databases as a hash table, with each key being unique and the value being in formats such as JSON, BLOB, text, or others. Examples of key-value pair storage databases include Redis, Voldemort, Oracle BDB, Amazon SimpleDB, and Riak.

You can explore this blog: Design key-value store.

Column-Oriented Stores

Column stores were designed to store and analyze enormous volumes of data across multiple devices. The columns are grouped according to their column family. Because the data is readily available in a column, they give good performance on aggregate queries like SUM, COUNT, AVG, MIN, etc. Data warehouses, corporate intelligence, CRM, and library card catalogs all use column-based NoSQL databases. Examples: Cassandra, HBase, Hypertable, etc.

Document Databases

Document databases are the next step up from Key/Value databases, allowing for nested values to be associated with each key. More efficient querying is possible with document databases. Examples: CouchDB, MongoDB, Amazon SimpleDB, etc.

Graph Databases

Graph databases offer a flexible model that can scale across multiple machines. A graph database is multi-relational and is mainly used for social networks, logistics, spatial data. Examples: Neo4J, InfoGrid, Infinite Graph, OrientDB, etc.

When to Use SQL Databases?

One should prefer SQL databases when:

1. Data is highly structured and requires fewer updates.
2. Data Integrity is essential.
3. Service requires a large number of complex queries.
4. ACID compliance is very necessary.

When to Use NoSQL Databases?

One should prefer NoSQL databases when:

1. Flexibility, scalability, and speed are the key requirement of the system.
2. The size of the data is huge, and data needs flexible schemas.
3. ACID compliance is not necessary.
4. The service requires distributed architecture and cloud computing.
5. The system requires rapid development.

Scalability

To handle the increasing amount of traffic efficiently, it is necessary to scale the system. Scalability improves the throughput of the system and minimizes latency. However, SQL and NoSQL databases differ significantly in the way they are scaled.

Horizontal scaling involves adding more machines to the system, while vertical scaling involves increasing the capacity of the same machine to handle a larger amount of traffic. NoSQL databases support horizontal scaling, while SQL databases support vertical scaling.

In SQL or relational databases, data is present in a highly structured manner, and hence while scaling, it is essential to maintain the data structure. To maintain the integrity of the service, SQL databases are designed to run using a single server.

On the other hand, in NoSQL databases, there is no need to maintain the data structure, and each entity is independent of the other. So, such systems can be easily scaled using horizontal scaling, by adding more servers.

Combining the best of two

As systems become more complex, it can be useful to use both types of databases for different tasks associated with the service. For instance, YouTube system stores video content in NoSQL databases but user metadata and other information in relational databases. By leveraging each database’s best features, it’s possible to meet business requirements by integrating both types of databases.

Some databases offer both types of services. For example, MySQL Document Store provides the structured nature of an SQL database and the flexibility of a NoSQL database. Similarly, MongoDB, a NoSQL database, also offers ACID transactions.

Conclusion

The choice of a database depends entirely on the type of application and the services it offers. However, there is no single database that fits all conditions. Various aspects of businesses require either relational or non-relational databases, or sometimes a combination of both.

There are applications where data structure and integrity are necessary, and schemas are well-defined. For such applications, relational databases are suitable. While some applications focus more on speed and scalability, NoSQL databases are a better fit for them.

With newer technologies, real-time processing, and highly distributed systems, NoSQL databases are gaining popularity. However, the final decision on choosing the type of storage depends on the specific application. One needs to consider all the edge cases when choosing a particular type of architectural design.

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