MongoDB vs PostgreSQL: 8 Critical Differences Learn

In PostgreSQL, you can define relationships between tables using foreign keys. Using this system, you can perform complicated joins and form relationships between tables. This function is especially useful when you query data across multiple tables, using the relationships you define to connect data sets. Availability ensures that even during a server outage, there’s no data downtime.

  • PostgreSQL does this through a variety of strategies for indexing and concurrency.
  • PostgreSQL database has a monolithic architecture which means all its components are unified.
  • MongoDB also provides you with the option of schema validation to enforce data governance controls over every collection.
  • This means PostgreSQL structures data before them like other traditional RDMS and unlike MongoDB.
  • MongoDB can store and retrieve unstructured data like images, videos, and texts.

In our benchmarking, we saw a 10x index size reduction by enabling product quantization, reducing the index size from 7.92 GB to just 790 MB. We’ll delve deeper into why the algorithms used by this index are particularly advantageous in PostgreSQL. We are staunch proponents of offering diverse options to the PostgreSQL community, leading us to design an index distinct from those already available in the ecosystem. DEV Community — A constructive and inclusive social network for software developers.

MongoDB vs PostgreSQL: Relationships Among Tables

Changing structure after loading data is often very difficult, requiring multiple teams across development, DBA, and Ops to tightly coordinate changes. With 1,252.20 queries per second at 99% recall, Timescale Vector surpasses Qdrant’s 354 queries per second by more than 250%. In the future, we plan to test Timescale Vector against more specialized vector databases and benchmark the results. While this might entail a marginal decline in query accuracy, PostgreSQL already stores the full-scale vectors in the heap-table. This allows for correcting the diminished accuracy from the indexed data using heap data, refining the search results.

postgresql vs mongodb performance

PostgreSQL follows the ACID properties of atomicity, consistency, isolation, and durability. ACID principles enable PostgreSQL for storing data and running critical transactions safely. By clicking “Post Your Answer”, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct. Generate a REST API on any data source in seconds to power data products. Low-code ETL with 220+ data transformations to prepare your data for insights and reporting.

MongoDB find in command Complete tutorial in 2022

For each spatial distance three experiments are executed with different amount of timestamps and waypoints of a specific vessel’s trajectory. For example in case of 5 miles spatial proximity, for 100 different waypoints in the middle bar, the query finds all coordinates of vessels “close” to the specific waypoint for 100 different time intervals of equal size. Again the superiority of PostgreSQL is obvious as the sample grows and reduced almost at half. In case of PostgreSQL we used the fastest solution to find all vessels within some distance of a given point. The simplest way to perform this query is to use ST_DWithin with the PostGIS geography type, instead of geometry.

postgresql vs mongodb performance

PostgreSQL is a 100% free and open-source ORD (object-relational database) that dates back to 1987, making it significantly older than MongoDB. Instead of storing data like documents, the database stores it as structured objects. It offers several index types like B-tree, compound, text, geospatial, hashed, and clustered indexes. I did a POC between these a couple of years back and they were pretty neck in neck with synchronous JDBC drivers. When I leveraged the async JDBC driver with Mongo, then MongoDb performance well exceeded past PostGres by 8x. Honestly, these two are  really two different animals with different use cases.

MongoDB vs PostgreSQL: 8 Critical Differences

It is a traditional object-relational database that follows the syntax and schema of SQL databases. This means PostgreSQL structures data before them like other traditional RDMS and postgresql vs mongodb performance unlike MongoDB. This direct mapping helps software developers to easily use and query MongoDB. A Complex data structure can be easily stored in a document-based model of MongoDB.

MongoDB also offers an On-Premise pricing model with MongoDB Enterprise Advanced edition. In the modern world today, competition between companies is very common, especially when they are offering similar products. In the competitive field of Data Analytics, offering efficient products and services and having a majority customer share in the market does help determine the profit of the company. When it comes to the field of Database Management, the choice of MongoDB vs PostgreSQL is a relatively tough one. Companies like Samsung, Airbus, NEC, and startups rely on us to build great online products. We can help you too, by enabling you to hire and effortlessly manage expert developers.

What’s the Difference Between MongoDB and PostgreSQL?

Instead of storing data like documents, PostgreSQL stores it as Structured objects. If you require a modern database to process data from various sources and in various formats, then go for MongoDB. If SQL database structure suits your application needs, PostgreSQL is a better choice. 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.

postgresql vs mongodb performance

The larger this buffer, the greater the number of candidates required for loop completion, rendering the search process slower but ensuring higher accuracy. At query time, you set the buffer size using the tsv.query_search_list_size GUC. Let’s break down how the timescale-vector index works, focusing on the search and construction algorithms. We’ll begin with the search, as it plays a pivotal role in the construction process. Moreover, the Timescale Vector team is working on decreasing the index build time by using parallelism in upcoming releases.

Difference between Mongodb vsPostgreSQL

It also offers Atlas Search powered by Lucene, and with features that support data lakes built on cloud object storage. PostgreSQL has a full range of security features including many types of encryption. While it is all the same database, operational and developer tooling varies by cloud vendor, which makes migrations between different clouds more complex. MongoDB Atlas runs in the same way across all three major cloud providers, simplifying migration and multi-cloud deployment. MongoDB allows you to store data in almost any structure, and each field – even those deeply nested in subdocuments and arrays – can be indexed and efficiently searched. MongoDB stores data as documents in a binary representation called BSON (Binary JSON).

postgresql vs mongodb performance

I also discuss the installation process of MongoDB and explain how easy it is to install the operator using two of the most popular ways, Helm, and Kustomize. The video also provides several examples of how to define the database resource in Kubernetes, which is relatively simple and easy to do. This way, PostgreSQL can update both records at the same time, thus reducing the number of errors and maintaining a complete and accurate backup as well. PostgreSQL, on the other hand, uses the GROUP_BY to process and run queries. A Foreign Key is a column or a group of columns of one table that references another column (generally the primary key) of another table and establishes a relationship between them. MongoDB does not support Foreign Keys whereas PostgreSQL does support them.

PostgreSQL vs. MongoDB Scalability

There are other benefits of using when choosing between MongoDB vs. PostgreSQL. The platform has a unique pricing model that charges you for the number of connectors you use and not the data you consume. Plus, you can access world-class support and benefit from over 100 out-of-the-box connectors that move data between relational databases, transactional databases, customer relationship management (CRM) systems, and more.

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