PostgreSQL vs MongoDB: Choosing the Right DBMS

Both these technologies are leveraged by organizations of all scales, both big & small, and depending on the situation, one can dominate over the other. Our future plan is to expand the comparison with more systems that support spatiotemporal functionality. Scalable and high performance systems which can efficiently perform large scale spatial queries such as Apache GeoSpark and Hadoop-GIS, constitute our main priority.

You can accelerate MongoDB’s query performance if you make indexes on fields in documents and sub documents. This database enables all document fields to be indexed and queried simply, mongodb vs postgresql as well as those that are deep within sub documents and arrays. And every field may be indexed and searched easily (even when they’re nested deep within sub documents and arrays).

Mongodb vs PostgreSQL performance

There is lots of community and aid to help you interact with MongoDB using one of your preferred programming languages. PostgreSQL’s community has been growing since its launch in 1996. It has a strong open-source community with lots of PostgreSQL support libraries, tools, extensions, and general support available. PostgreSQL also offers partitioning, which splits large tables into smaller, more manageable parts. You can partition based on a hash, range, list, or another criterion.

MongoDB vs PostgreSQL

It is an important feature of MongoDB as it makes queries faster. The serialization format stores documents and skips keys that are not required in the query, making it faster. Data is a fundamental component of every business process, and a database management system is an essential requirement to store this data with security and controlled accessibility. Even though it is popular, it still lacks in a few things compared to other databases.

An object relational database

PostgreSQL, also known as Postgres, is an open-source relational database management system that emphasizes extensibility and SQL compliance. Let’s look at the key features on Postgres to get a better sense of its uses. Using JSON allows you to change your schema on a whim without repercussion. Unlike relational databases, where altering your table is necessary to make any changes, MongoDB is a bit more flexible because it uses the JSON/BSON format. Individual entries are their own instance of the schema that was written.

PostgreSQL uses the relational database model that depends on storing data within tables and utilizing the structured query language (SQL) for database access. It has a large object facility, which provides stream-style access to user data that is stored in a special large-object structure. MongoDB is an open-source cross-platform document-based database. It is a non-relational database which means it can be used to store and process data in different formats. It is a NoSQL (Not only SQL) and its syntax is different from the traditional relational database management systems. MongoDB, though, supports a fast, iterative development cycle so effectively due to the way in which document databases transform data into code under developer control.

Rethink the way you share the data between micro-services with Change-Data-Capture

PostgreSQL complies with a wealth of security standards and includes various features for backup, reliability, and disaster recovery (typically via third-party tooling). PostgreSQL employs an engineering-centric approach to almost everything. The company has stated that it works to conform with the latest SQL standard when that doesn’t contradict conventional features or may contribute to ill-founded architectural choices. Below, we’ll explore how SQL and MongoDB approach querying data, with a few helpful examples.

MongoDB vs PostgreSQL

In Timescale particularly, max_connections is set at 25 in the smallest compute plan and rises to 500 as the number of CPUs grows. SQLite’s simplicity actually avoids most of these experiences, which is not helpful to you if that is what you hope to learn. I’m much more experienced with MySQL than any other database and I am having a hard time getting on board with noSQL entirely because it’s really hard to query complex data with relationships using noSQL.

PostgreSQL is an object-relational database

Although a number of other benchmarks limited to a specific database or application, Jackpine presents one important feature, portability in terms that can support any database (JDBC driver implementation). It supports micro benchmarking that is a number of spatial queries, analysis and loading functions with spatial relationships and macro benchmarking with queries which address real world problems. Also includes all vector queries from the SEQUOIA 2000 benchmark. Indexes are a type of data structure that can store a minimal amount of data in an easily readable form. They help you to resolve queries with greater efficiency by making the data simpler and thereby easier to scan. NoSQL databases are generally simpler by nature, so MongoDB is relatively easy to learn for those with any prior programming experience.

  • You can also implement list partitioning where the table is partitioned according to the key values specified.
  • Therefore, in a relational database, the data would be modeled across independent parent-child tables in a tabular schema.
  • Second solution proved to be 3,6 times faster comparing to the well-known ST_DWithin function.
  • MySQL could be next, sonce it’s easier to learn at first and has more resources available.
  • While it is possible to alter tables after their creation as an application’s needs change, this process can be complicated and error-prone.
  • Write to us your initial project specifications, and one of our account managers will get back to you for further assistance.
  • The support/default tooling (such as MySQL Query Workbench) certainly is a little more baked than what you’ll find for Postgres.

Like MySQL and other open source relational databases, PostgreSQL has been proven in the cauldron of demanding use cases across many industries. If data aligns with objects in application code, then it can be easily represented by documents. MongoDB is a good fit during development and in production, especially if you have to scale. PostgreSQL has a full range of security features including many types of encryption. PostgreSQL is available in the cloud on all major cloud providers.

What companies use MySQL?

MongoDB also supports database transactions across multiple documents allowing bits of related changes to be rolled back or committed as a group. Owing to its multi-document transactions capability, MongoDB is one of the few databases to coalesce the flexibility, speed, and power of the document model with the ACID guarantees of traditional databases. Despite the popularity of NoSQL databases, relational databases continue to be relevant for various applications because of their robustness and strong querying abilities.

This article aims to assist you in choosing the right type of database. We hope this discussion sheds some new light on which will better meet your needs. Giving up on SQL means walking away from a large ecosystem of technology that already uses SQL. That’s easier to do if you are working on a new application, or plan on modernizing an existing one. Because PostgreSQL is widely used, you can be pretty sure that most development tools and other systems have been tested with it and are compatible.

Head To Head Comparison Between MongoDB and Postgres (Infographics)

In SQL, a JOIN clause is used to combine rows from two or more tables, based on a common column, and there are three types of JOIN clauses for different needs. BASE is an acronym that means Basically Available, Soft State, Eventually Consistent. These types of databases don’t have the ACID guarantee, as they are eventually consistent. This means that there could be times where the database is not reliable, but over time it will reach consistency. So your data is basically in a fluid state until it becomes consistent.

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