Which database is best? MongoDB vs Post­gr­eSQL

SQL or NoSQL? Although these two databases have aspects in common, there is an option more suited to you, depending on the intended use. The following com­par­is­on of MongoDB vs Post­gr­eSQL is focused primarily on speed and security factors.

MongoDB: Ho­ri­zont­al scaling and maximum flex­ib­il­ity

To un­der­stand the different methods involved in this com­par­is­on, we’ll give you a brief overview of the MongoDB and Post­gr­eSQL databases them­selves. MongoDB owes its name to the English term ‘humongous’. The system was published in 2009 by 10gen (now MongoDB Inc.). It is designed to enable users to manage huge amounts of data in a clear and concise way. So as to make this possible, the NoSQL database is par­tic­u­larly flexible and can be easily scaled. The struc­tured, semi-struc­tured or un­struc­tured data is stored in the JSON-like format BSON in the form of documents. MongoDB was written in C++ and is still dis­trib­uted worldwide under the open source SSPL.

Post­gr­eSQL: A supposed oldie with modern ad­vant­ages

In our MongoDB vs Post­gr­eSQL com­par­is­on, the second solution takes a com­pletely different approach. Post­gr­eSQL functions com­pletely re­la­tion­al and cross-platform, though non-re­la­tion­al data types are also supported. The system first appeared in 1996 and is based, at least in part, on databases that have been developed at the Uni­ver­sity of Berkeley since the 1980s. The system, which is main­tained by the Post­gr­eSQL Global De­vel­op­ment Group, is still open source today. Post­gr­eSQL claims to be the most advanced open-source database in the world. What is certain is that it is valued worldwide for its flex­ib­il­ity and stability. The man­age­ment system was written in C and is often simply called ‘Postgres’.

What are MongoDB and Post­gr­eSQL used for?

At first glance, our MongoDB vs Post­gr­eSQL com­par­is­on seems to suggest both databases could be used for similar purposes. Both solutions are well thought-out, highly func­tion­al and com­par­at­ively flexible databases that ensure order and overview even when dealing with large or in­creas­ing data volumes. A more detailed analysis reveals that companies need to decide according to their own re­quire­ments who has the edge in the MongoDB vs Post­gr­eSQL com­par­is­on and which database offers the response to their needs.

The NoSQL solution scores par­tic­u­larly well if you need a system that can grow along with your re­quire­ments. This applies not only to the sheer volume of data, but also to different data types. MongoDB has ho­ri­zont­al scalab­il­ity, which makes the system the ideal solution in the e-commerce sector, where trans­ac­tion data has to be trans­ferred quickly and securely. These ad­vant­ages, paired with its flex­ib­il­ity regarding data types, ensure that MongoDB is an excellent choice for content man­age­ment systems. If you require con­fig­ur­a­tion options and extensive analysis functions in real time, MongoDB is worth con­sid­er­ing.

Post­gr­eSQL is suitable for extensive web ap­plic­a­tions and provides valuable resources for e-commerce. The system is a good choice for ap­plic­a­tions in the Cloud and the Internet of Things. Post­gr­eSQL is also highly effective in co­oper­a­tion with other databases.

Func­tion­al­ity

The paths taken by MongoDB and Post­gr­eSQL to achieve their goals are very different. As a pure NoSQL solution, MongoDB com­pletely dispenses with rigid re­la­tion­al tables and instead works in a document-oriented manner. These binary JSON documents (called BSON) are then sum­mar­ised in col­lec­tions. The system relies on key-value pairs. The key here consists of a character string; the values can be other documents, Boolean values, numbers or com­pletely different file types. The structure of a JSON document can be easily changed by deleting or adding in­di­vidu­al fields. A text search is provided to identify specific documents. Struc­tured, semi-struc­tured and un­struc­tured data is taken into account.

In com­par­is­on, Post­gr­eSQL follows a re­la­tion­al approach. Although there are numerous NoSQL al­tern­at­ives, using a table-based system also has its ad­vant­ages. An important feature of Post­gr­eSQL is that the man­age­ment system is much more flexible than other SQL options and allows columns with sub values. The database man­age­ment system also relies on foreign keys and triggers. Queries are made using the classic client-server principle. Files and con­nec­tions are managed via the central server component ‘post­mas­ter’. Different clients then send their queries. Post­gr­eSQL supports numerous data types, although these must be struc­tured in advance.

Per­form­ance

MongoDB’s name suggests it can handle huge amounts of data without any major issues. The system ab­so­lutely lives up to this claim. The database is ho­ri­zont­ally scalable and not dependent on the computing power of an in­di­vidu­al machine. Thanks to the com­bin­a­tion of options with a wide range of hardware, there are the­or­et­ic­ally no limits to its per­form­ance and storage volume. Even when numerous users are accessing data all at the same time, the query speed remains high. Sharding dis­trib­utes the load across different computers. This not only con­trib­utes to better per­form­ance, but also offers the best possible pro­tec­tion against possible server failures.

Post­gr­eSQL, on the other hand, scales data ver­tic­ally and therefore cannot quite keep up with the per­form­ance of the NoSQL solution. Nev­er­the­less, the per­form­ance of the re­la­tion­al system is im­press­ive. It is possible, for example, to perform write and read op­er­a­tions sim­ul­tan­eously. Data au­then­tic­a­tion and in-depth, low-latency data analysis are also often better with Post­gr­eSQL than with many of its com­mer­cial com­pet­it­ors. The database works with complex data types and queries and can therefore also score points when it comes to big data. Ad­di­tion­al resources such as memory or CPUs can be added to meet in­creas­ing re­quire­ments. Features such as just-in-time com­pil­a­tion and table par­ti­tion­ing also help when pro­cessing large volumes of data.

Com­pat­ib­il­ity

Both solutions work across platforms and can therefore be used on Linux, macOS, Solaris and Windows. Post­gr­eSQL goes even further and also works on FreeBSD, HP-UX, NetBSD and OpenBSD. The SQL database is ACID-compliant by nature (Atomicity, Con­sist­ency, Isolation, Dur­ab­il­ity), while MongoDB at least offers this option. Both systems support numerous pro­gram­ming languages, whereby the choice in the MongoDB vs Post­gr­eSQL com­par­is­on is sig­ni­fic­antly greater with the younger system.

Security

One of the most important reasons why users opt for Post­gr­eSQL is the database’s strong security ar­chi­tec­ture. This includes the Light­weight Directory Access Protocol (LDAP) and a Pluggable Au­then­tic­a­tion Module (PAM) as well as host-based au­then­tic­a­tion, data en­cryp­tion and SSL cer­ti­fic­ates. The pre­defined database structure also ensures that your data is always protected in the best possible way. MongoDB also has numerous security features, including en­cryp­tion at field level and on the client side. The dis­tri­bu­tion to different servers also offers at least a high level of re­li­ab­il­ity and ensures that data is available again without major delays.

What are the various versions of MongoDB and Post­gr­eSQL?

One of the sim­il­ar­it­ies between MongoDB and Post­gr­eSQL is their open-source approach. Both systems are therefore not only open source, but also available free of charge, at least in their basic version. Although this also means that there is no pro­fes­sion­al support in this case, two dedicated com­munit­ies make up for this short­com­ing and are also happy to advise newcomers. The doc­u­ment­a­tion and expansion options are also somewhat more extensive due to the longer market maturity of Post­gr­eSQL. MongoDB also offers various Pro versions. The ‘En­ter­prise’ and ‘Atlas’ versions (for cloud use) are subject to a fee, but also have some ad­di­tion­al features and com­pre­hens­ive support.

Which companies use the two databases?

Even if the MongoDB vs Post­gr­eSQL com­par­is­on shows that the two solutions pursue two very different ap­proaches, they still have one thing in common, which is that numerous large companies rely entirely or partially on the services and ad­vant­ages that the two databases offer them.

The best-known companies that rely on MongoDB include the following:

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

Post­gr­eSQL is used by the following companies and platforms, among others:

• Apple
• IMDB
• Instagram
• Reddit
• Runkeeper
• Skype
• Spotify
• Twitch