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.