MongoDB vs PostgreSQL: What to consider when choosing a database

Similar to traditional SQL, there are 4 types of joins in PostgreSQL- Inner, Left, Right, and Full Join. If you want all the data from both tables into a single table, you can use a Full Join. MongoDB is a schema-free NoSQL database that supports a distributed architecture. MongoDB uses collections to enforce different rules and triggers to maintain the relationship between different attributes in the database.

In case of write requests the queries are forwarder only to the primary node. Instead, it has its own query language – MongoDB Query Language (MQL). MQL is built to interact specifically with MongoDB databases and match similar features and flexibility as in SQL databases. With MQL, you can query any field, embedded documents, mongodb postgresql or nested arrays in the MongoDB database. MongoDB is a user-friendly, schema-free, proprietary NoSQL database written as JSON-like (BSON) documents for storing data. It is a document database that uses BSON (stands for binary JSON), which offers more data types than regular JSON data, such as floating point, date, etc.

DATABASES

It is built on a distributed, scale-out architecture and offers a comprehensive cloud-based platform for managing and delivering data to applications. MongoDB handles transactional, operational, and analytical workloads at scale. If your concerns are time to market, developer productivity, supporting DevOps and agile methodologies, and building stuff that scales without operational gymnastics, MongoDB is the way to go.

Partitioning of larger tables can improve performance, but partition tables in MySQL cannot be placed in general tablespaces, which is a serious showstopper for I/O balancing. Table segments growing bigger in size can pose performance problems, and queries hitting those tables take more resources and time to complete. While views created with simple SQLs can be updated, the views created with complex SQLs cannot be updated.

Horses ready! What was faster, MongoDB or PostgreSQL?

We plan to evaluate and compare the two types of clusters and draw conclusions of which system is best for different cases. Figure 6 illustrates the average response time concerning the set of queries Q1, Q2 and Q3 in five node cluster between MongoDB and PostgreSQL. It’s quite clear that PostgreSQL outperforms MongoDB in all queries.

Even better, MongoDB provides drivers for many popular programming languages such as Python, Java, and JavaScript, enabling users to interact with data directly from their application’s native code. This integration is achieved by using MongoDB libraries, eliminating the need to introduce complex languages like SQL to the application. These use a standard SQL interface to link to other databases or streams. Within a relational database, data would be modeled across distinct parent-child tables in a tabular schema. As a result, a transaction would be necessary to update every record at once. Determining the most suitable database management system for a specific use case is crucial in maximizing the efficiency and functionality of an application.

Other key differences: MongoDB vs. PostgreSQL

Unlike MongoDB, FerretDB’s open source nature means you can freely use, modify, and contribute to the codebase. Regarding data models, MongoDB and PostgreSQL follow different approaches, with MongoDB using a document-based data model, while PostgreSQL uses a relational data model. Some everyday use cases for PostgreSQL include web applications requiring high reliability and stability, such as bank systems, process management applications, analytics, geospatial data, and many more. PostgreSQL users have to be prepared for the difficulties of scalability when an application is launched.

• This feature is particularly useful when you’re working with distributed queries in a Citus cluster.
• One or more fields may be written in a single operation, including updates to multiple subdocuments and elements of an array.
• MongoDB and PostgreSQL are both different types of databases, and both serve different purposes.
• MongoDB gives us the flexibility to change the data schema at any time.MongoDB can handle operational, transactional, and analytical workloads easily.
• It’s also common that Postgres and MongoDB co-exist inside an organization.
• Finally, polygons relating to the intersection in Q9 were also uniformly selected within Mediterranean Sea and each polygon’s area from every group is of equal size.

By adding this extra layer of security, you can protect your data from data breaches and unauthorized access. Additionally, MongoDB offers a range of other security features, such as access controls, encryption at rest, and network isolation. These features can be configured to meet the specific needs of different organizations and use cases, providing a flexible and robust security framework for MongoDB deployments. According to a 2022 Statista survey, PostgreSQL and MongoDB are two of the most popular databases today, ranking 4th and 5th, respectively. Both databases share a few similarities and striking differences in terms of architecture, data model, commands, use cases, security, and more. They have also highlighted that, at present, there are no relational databases that fully conform to that standard.

MongoDB

PostgreSQL stores the information about the columns, and tables, along with information regarding the data types, functions, and access methods present. By storing data in fields such as nested subdocuments and arrays, related information in JSON documents can be stored together for quick query access through the MongoDB query language. In PostgreSQL, you can define relationships between tables using foreign keys. Using this system, you can perform complicated joins and form relationships between tables.

MySQL also supports pluggable storage engines, which can help the database meet specific storage requirements for various types of applications such as OLTP, Data Warehousing, etc. This is one of the biggest advantages of using MySQL as a database. The pluggable storage feature can be enabled by installing plugins, and although configuring pluggable storage involves complexity, applications are completely oblivious to this. MongoDB doesn’t enforce schema upfront and has an easy learning curve.

Scalability, resilience, and security

Contrast this with PostgreSQL, which requires a defined database schema and is used to store data in traditional table format with predefined columns and data types. PostgreSQL is more suitable for applications that need to maintain relationships among different data types, such as e-commerce sites or financial applications. Furthermore, if you’re working with a tabular data model that’s unlikely to change on a regular basis and has no need to scale-out, SQL and relational databases can be a terrific option.

PostgreSQL defaults to the read committed isolation level, and allows users to tune that up to the serializable isolation level. PostgreSQL has become a very popular NoSQL choice for developers in the last three years and has enormous JSON capabilities. With JSON and JSONB data types, JSON-based data operations can be significantly faster and more efficient. JSON data can also be Indexed with B-Tree and GIN for improved searches, and XML and HSTORE data types can handle XML formats and other complex text-format data as well. With the support for spatial data types, PostgreSQL is no doubt a complete multi-model database. What makes PostgreSQL stand out is its support for a wider range of data types, such as ARRAYs, NETWORK types, and Geometric data types (including advanced spatial data functions) to store and process spatial data.

Features of mongodb

In comparison, the maximum BSON document size is 16 megabytes in MongoDB, and in PostgreSQL the maximum row size is 1.6 terabytes. PostgreSQL has a numerous selection of data types which include Boolean, Character, Numeric, Temporal, UUID, Array, JSON, key-value pairs, and special types such as network address and geospatial data. Foreign keys allow us to keep our data normalized by referencing an object from one table in another so the second table has access to the first table’s keys and values.