Which database is best? MongoDB vs SQL

MongoDB vs SQL are two completely different approaches to storing and managing data. While the NoSQL solution MongoDB impresses, above all, with its flexibility, SQL is the robust, tried-and-tested choice.

SQL: The standard for decades

It’s worth taking a look at the history and development of MongoDB and SQL before we compare them. SQL is essentially the database language that is required if you’re working with relational databases. On the one hand, SQL makes it possible to insert, change and delete data. On the other hand, the language is also used to query or request certain data. SQL goes back to the SEQUEL query language, which was developed in the 1970s. Today, SQL is the standard language for all relational database management systems. The best-known systems include MySQL, Microsoft SQL Server, Oracle, PostgreSQL and Sybase.

MongoDB: The SQL alternative

When comparing MongoDB and SQL, MongoDB is the much younger of the two. It was developed in 2009 by the company 10gen (now MongoDB Inc.) and is now one of the most popular and most frequently used databases in the world. The name of the NoSQL system comes from the term ‘huMongous’ and is intended to describe the fact that MongoDB is designed in particular for managing large and diverse data deposits. Two features of the database management system that contribute to this goal are its enormous flexibility and high scalability. MongoDB can grow with increasing requirements and can, in principle, be scaled horizontally endlessly. The system is also open source.

Functionality

MongoDB and SQL are both used to store data. Aside from this, they share little in common. The significant difference between the two systems is the type of storage they offer, as this not only determines all further differences, but is also a deciding factor in finding the approach best suited to your needs.

SQL works relationally, which means that all data is stored in the form of a table. This table consists of rows and columns in which all relevant information can be found. Common SQL database management systems store different databases which can connect to each other. SQL is used via a server to query the stored data. To do this, the process runs through several components:

1. The parser checks an instruction for rule conformity and the necessary authorisations.
2. The query processor schedules the retrievals, write processes or updates.
3. The storage engine executes the request, reads the data and sends the result back to the client.

Compared to SQL, MongoDB works non-relationally. This means that the data is not stored in the form of a table, but is instead stored in documents. These documents in the binary JSON format BSON are bundled in collections. MongoDB and comparable NoSQL systems can all store and manage completely different types of data in this way. These can be structured, semi-structured or unstructured. MongoDB also relies on key-value pairs. While the key is always a character string, the value can be files, other documents or Boolean values. The query language is the MongoDB Query Language (MQL).

Scaling and availability

Both representatives can be scaled in the ‘MongoDB vs SQL’ comparison. However, this is much easier and more effective with the NoSQL solution. MongoDB uses a technique called sharding to scale databases horizontally. This involves storing data on different nodes and thus distributing the load across several shoulders. This approach is not only comparatively inexpensive, it also increases the query speed and security of the data. Replicas make it possible to react immediately to a server failure. An automatic failover then selects a new node as the primary node. This approach is particularly promising for large data volumes and different data types.

Databases that rely on SQL, on the other hand, can only be scaled vertically. This means that additional storage space, larger capacities or higher computing power are required for larger data volumes. This approach is often associated with immense costs, which is why the method is recommended in most cases for either constant or only marginally growing data volumes. However, there is also a high level of availability in the comparison MongoDB vs SQL. SQL relies on various nodes in order to be able to react immediately to failures and still make the data available in the event of disruptions.

Query language

When comparing MongoDB and SQL, the language deemed to be more user-friendly depends primarily on the user’s own personal taste and prior knowledge. SQL has been a standard for decades. It is easy to learn and at least partially based on the English language. Once you have successfully used an SQL solution, you will find it easier to use the next similar system. The language is very well documented and widely used. At the same time, however, it is not very dynamic and relies on fixed schemas, such as the table form. Although SQL has many dialects, the basis remains identical.

The MongoDB Query Language is much more dynamic and more flexible than SQL and thus, in the majority of cases, meets the requirements that can arise from different data types and queries. It is designed for working with BSON documents and offers the option of changing fields by deleting or adding them. However, the language is only designed for MongoDB. Other NoSQL systems or SQL databases, on the other hand, use languages that are partially or completely different from MQL.

MongoDB vs SQL: ACID and CAP

While SQL relies on relational tables, the non-relational MongoDB uses documents in collections. The two systems are based on different approaches. SQL is based on the classic ACID properties. ACID stands for Atomicity, Consistency, Isolation and Durability. This means that transactions are executed completely or not at all, remain consistent and are protected from internal or external influences. The focus is therefore clearly on the security of the data and the reliability of the data transfer.

In contrast, MongoDB relies on the CAP theorem. CAP stands for Consistency, Availability and Partition or consistency, availability and partition tolerance. This theorem states that only two of the three factors are possible at any one time. MongoDB is one of the CP data stores that guarantee consistency and partition tolerance, but make compromises when it comes to availability. MongoDB relies on a primary node which all queries run through. Its secondary nodes are only used if this fails.

Intended use

The requirements placed on MongoDB or SQL are also very different. Which solution you choose for your data, for example, depends on what data you want to store and manage. SQL is suitable for structured data that can be modelled in tabular form. If you want to focus on consistency, the classic approach is still recommended. SQL scores highly in areas such as e-commerce and other transaction systems, in the accounting of small companies or for processes that require strict data integrity. SQL is considered a very secure and robust solution.

The big advantage of MongoDB, on the other hand, is its flexibility. This applies to the data types taken into account, which can be diverse, structured, semi-structured or unstructured. MongoDB’s flexibility is evident again when it comes to scaling. This is done horizontally so that your system can grow with your requirements. MongoDB’s speed is also a convincing argument in favour of the NoSQL approach. It is also used in e-commerce, for content management systems, cloud solutions or for applications that require analysis functions in real time.

Which companies use MongoDB and SQL databases?

Numerous companies rely on one of the two approaches to store and manage their data. The following companies, among others, have opted for MongoDB:

• Adobe
• Amadeus
• AppScale
• Craftbase
• Disney
• Etsy
• Foursquare
• Lyft
• MTV
• The New York Times
• Via Varejo

These companies and platforms rely on an SQL solution:

• Adobe
• Apple
• Dell
• IMDB
• LinkedIn
• Microsoft
• Reddit
• Seagate
• Skype
• Spotify
• Twitch

MongoDB and SQL can also be used together

Based on this list, you might notice that there are companies for whom it’s not an ‘either, or’, when it comes to MongoDB vs SQL but rather a ‘both’! It is possible to combine SQL and NoSQL solutions within an application architecture. This allows you to benefit from the strengths of both approaches. This may not make sense for every user, but it can certainly lead to getting the best of both worlds under the right circumstances.