SQL vs NoSQL or MongoDB vs MySQL? Which database is better?

SQL vs NoSQL or MongoDB vs MySQL? Which database is better?

In this post, we will understand the difference between NoSQL vs SQL, MySQL vs MongoDB Database. Which is better SQL or NoSQL?

When it comes to choosing a database, one of the biggest decisions is picking a relational (SQL) or non-relational (NoSQL) data structure. While both are viable options, there are certain key differences between the two that users must keep in mind when making a decision.

Here, we break down the most important distinctions and discuss two of the key players in the relational vs non-relational debate: MySQL and MongoDB.

SQL vs NoSQL: Differences Between SQL and NoSQL

  • SQL databases are primarily called as Relational Databases (RDBMS); whereas NoSQL database are primarily called as non-relational or distributed database.

  • SQL databases are table based databases whereas NoSQL databases are document based, key-value pairs, graph databases or wide-column stores. This means that SQL databases represent data in form of tables which consists of n number of rows of data whereas NoSQL databases are the collection of key-value pair, documents, graph databases or wide-column stores which do not have standard schema definitions which it needs to adhered to.

  • SQL databases have predefined schema whereas NoSQL databases have dynamic schema for unstructured data.

  • SQL databases are vertically scalable whereas the NoSQL databases are horizontally scalable. SQL databases are scaled by increasing the horse-power of the hardware. NoSQL databases are scaled by increasing the databases servers in the pool of resources to reduce the load.

  • SQL databases uses SQL ( structured query language ) for defining and manipulating the data, which is very powerful. In NoSQL database, queries are focused on collection of documents. Sometimes it is also called as UnQL (Unstructured Query Language). The syntax of using UnQL varies from database to database.

  • SQL database examples: MySql, Oracle, Sqlite, Postgres and MS-SQL. NoSQL database examples: MongoDB, BigTable, Redis, RavenDb, Cassandra, Hbase, Neo4j and CouchDb

  • For complex queries: SQL databases are good fit for the complex query intensive environment whereas NoSQL databases are not good fit for complex queries. On a high-level, NoSQL don’t have standard interfaces to perform complex queries, and the queries themselves in NoSQL are not as powerful as SQL query language.

  • For the type of data to be stored: SQL databases are not best fit for hierarchical data storage. But, NoSQL database fits better for the hierarchical data storage as it follows the key-value pair way of storing data similar to JSON data. NoSQL database are highly preferred for large data set (i.e for big data). Hbase is an example for this purpose.

  • For scalability: In most typical situations, SQL databases are vertically scalable. You can manage increasing load by increasing the CPU, RAM, SSD, etc, on a single server. On the other hand, NoSQL databases are horizontally scalable. You can just add few more servers easily in your NoSQL database infrastructure to handle the large traffic.

  • For high transactional based application: SQL databases are best fit for heavy duty transactional type applications, as it is more stable and promises the atomicity as well as integrity of the data. While you can use NoSQL for transactions purpose, it is still not comparable and sable enough in high load and for complex transactional applications.

  • For support: Excellent support are available for all SQL database from their vendors. There are also lot of independent consultations who can help you with SQL database for a very large scale deployments. For some NoSQL database you still have to rely on community support, and only limited outside experts are available for you to setup and deploy your large scale NoSQL deployments.

  • For properties: SQL databases emphasizes on ACID properties ( Atomicity, Consistency, Isolation and Durability) whereas the NoSQL database follows the Brewers CAP theorem ( Consistency, Availability and Partition tolerance )

  • For DB types: On a high-level, we can classify SQL databases as either open-source or close-sourced from commercial vendors. NoSQL databases can be classified on the basis of way of storing data as graph databases, key-value store databases, document store databases, column store database and XML databases.

MySQL and MongoDB: Which database is better?

MongoDB

The following are some of the benefits and strengths of MongoDB:

  • Free to use: Since October 2018, MongoDB's updates have been published under the Server Side Public License (SSPL) v1, and the database is free to use.

  • Dynamic schema: As mentioned, this gives you the flexibility to change your data schema without modifying any of your existing data.

  • Scalability: MongoDB is horizontally scalable, which helps reduce the workload and scale your business with ease.

  • Manageability: The database doesn’t require a database administrator. Since it is fairly user-friendly in this way, it can be used by both developers and administrators.

  • Speed: It’s high-performing for simple queries.

  • Flexibility: You can add new columns or fields on MongoDB without affecting existing rows or application performance.

  • ACID Transactions: MongoDB v.4 is finally getting support for multi-document ACID (atomicity, consistency, isolation, durability) transactions. That’s something the MongoDB community has been asking for for years and MongoDB Inc, the company behind the project, is now about to make this a reality.

  • MongoDB Atlas (a new feature): MongoDB recently added MongoDB Atlas global cloud database technology to its offerings. This feature allows you to deploy fully-managed MongoDB via AWS, Azure, or GCP. MongoDB Atlas lets you use drivers, integrations, and tools to reduce the time required to manage your database. Here's the pricing information from Atlas.

  • Who Should Use It? MongoDB is a good choice for businesses that have rapid growth or databases with no clear schema definitions (i.e., you have a lot of unstructured data). If you cannot define a schema for your database, if you find yourself denormalizing data schemas, or if your data requirements and schemas are constantly evolving - as is often the case with mobile apps, real-time analytics, content management systems, etc. - MongoDB can be a strong choice for you.

MySQL

Here are some MySQL benefits and strengths:

  • Owned by Oracle: Although MySQL is free and open-source, the database system is owned and managed by Oracle.

  • Maturity: MySQL is an extremely established database, meaning that there’s a huge community, extensive testing and quite a bit of stability.

  • Compatibility: MySQL is available for all major platforms, including Linux, Windows, Mac, BSD, and Solaris. It also has connectors to languages like Node.js, Ruby, C#, C++, Java, Perl, Python, and PHP, meaning that it’s not limited to SQL query language.

  • Cost-effective: The database is open-source and free.

  • Replicable: The MySQL database can be replicated across multiple nodes, meaning that the workload can be reduced and the scalability and availability of the application can be increased.

  • Sharding: While sharding cannot be done on most SQL databases, it can be done on MySQL servers. This is both cost-effective and good for business.

  • Who Should Use It? MySQL is a strong choice for any business that will benefit from its pre-defined structure and set schemas. For example, applications that require multi-row transactions - like accounting systems or systems that monitor inventory - or that run on legacy systems will thrive with the MySQL structure.

Which is better SQL or NoSQL?

In fact, every database has its unique advantages. No database offers the best solution, only the most suitable option for each project.

nosql mongodb sql mysql mssql

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SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.

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SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.

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