MySQL or NoSQL, where to use what – Confusion resolved

For any dynamic business databases are the foundations. In current, technology oriented world, where we encounter new and revolutionary applications and tools, databases plays as the foundation platform, whether it is software-as-a-service (SAAS), e-commerce or any other services oriented tool databases are the key drive of all the data. Here’s a quick look on the differences between Relational databases and non-relational databases.

There are two types of databases relational databases and non-relational databases and the most popular of them are MySQL and NoSQL respectively.

MySQL is the world’s most widely used open source relational database. With its ease of use, superior speed and reliability, it becomes the preferred choice of SaaS, ISV, Web, Web 2.0, Tele-communication companies and innovative IT application corporates as it enables and eliminates the key problems associated with administration, maintenance and downtime for innovative, State-of-the-art applications. Many organizations and fastest growing corporates across the globe use MySQL to reduce cost and save time fuelling their critical business systems, high volume of web portals and packaged applications including renowned industry leaders namely yahoo!, Nokia, YouTube and many fortune 500 organizations.

NoSQL database, also called Not Only SQL, is a method to data management and database design that’s beneficial for very large sets of distributed data. NoSQL, which includes a wide range of technologies and architectures, seeks to solve performance and scalability issues of big data performance that relational databases weren’t designed to address. NoSQL is specifically useful when an enterprise wants to access and analyze huge amounts of unstructured data or the data that is stored remotely on multiple virtual servers in the cloud.

Data Representation
MySQL represents data in tables and rows, each table contains a primary key, indicating the unique identification for each record. Tables that link to other tables do so with a primary and foreign key field.

NoSQL represents data as collections of JSON documents. A JSON document is very much like you are working with in the application layer. If you are working on javascript, it’s exactly what you’re working with. If you’re using PHP, it’s just like an associative array. If you’re using python, its just like a dictionary object.

One of the most important and best things about MySQL and relational databases in general is JOIN operation. This allows us to accomplish queries across multiple tables. NoSQL (NoSQL) does not support joins, infact we can perform multi-dimensional data types such as arrays and even other documents. Placing one document inside another is referred to as embedding. For example, if you were to create a blog using MySQL, you would have a table for posts and a table for comments. In NoSQL you might have a single collection of posts, and an array of comments within each post.

Another great thing about MySQL is its support for atomic transactions. The ability to contain multiple operations within a transaction and roll back the whole thing as if it were a single operation.
NoSQL does not support transactions, but single operations are atomic.

Schema Definition
MySQL requires you to define your tables and columns before you can store anything, and every row in a table must have the same columns.

One of my favorite things about NoSQL is that you don’t define the schema. You just drop in documents, and two documents within a collection don’t even need to have the same fields.

Schema Design and Normalization
In MySQL there is really isn’t much flexibility in how you structure your data if you follow normalization standards. The idea is not to prefer any specific application pattern.

In NoSQL, you have to use embedding and linking instead of joins and you don’t have transactions. This means you have to optimize your schema based on how your application will access the data. This is probably pretty scary to MySQL experts, but if you continue reading, you’ll see there is a place for both MySQL and NoSQL.

MySQL often gets blamed for poor performance. Well if you are using an ORM, performance will likely suffer. If you are using a simple database wrapper and you’ve indexed your data correctly, you’ll get good performance.

By sacrificing things like joins and providing excellent tools for performance analysis, NoSQL can perform much better than a relational database. You still need to index your data and the truth is that the vast majority applications out there don’t have enough data to notice the difference.

When should you use MySQL?
If your data structure fits nicely into tables and rows, MySQL will offer you robust and easy interaction with your data. If it’s performance that is your concern, there is a good chance you don’t really need NoSQL. Most likely, you just need to index your data properly. If you require SQL or transactions, you’ll have to stick with MySQL.

When should you use NoSQL?
If your data seems complex to model in a relational database system, or if you find yourself de-normalizing your database schema or coding around performance issues you should consider using NoSQL. If you find yourself trying to store serialized arrays or JSON objects, that’s a good sign that you are better off NoSQL. If you can’t pre-define your schema or you want to store records in the same collection that have different fields, that’s another good reason.

Cautious consideration should be taken when determining which database platform is more appropriate for your business model. MySQL is a relational database that is impeccable for structured data. NoSQL is a newer database structure that is more flexible than relational databases, making it more suitable for large data stores. With the growing significance of processing data, developers are progressively frustrated with the “impedance mismatch” between the object-oriented approach they use to write applications and the schema-based structure of a relational database.

NoSQL provides a much more flexible, schema-less data model that better maps to an application’s data organization and simplifies the interaction between the application and the database, resulting in less code to write, debug, and maintain.


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