MongoDB vs PostgreSQL: What to consider when choosing a database

Finally, we conducted experiments that consider a combination of different sets of vessels and timestamps. For each experiment we gather metrics concerning average response time and volume of data returned. In a relational database, relationships between data in different tables can be achieved through joins, and within hierarchical databases, relationships across nodes are impossible.

This post isn’t about picking one or either apart — our aim is to help you get a firm grasp of each database’s character and understand which use cases both databases serve best. When choosing between MongoDB and PostgreSQL, consider your project’s needs and the benefits of each database engine. Both MongoDB and PostgreSQL have their own set of features and challenges. Ultimately, the decision comes down to the business use case you are working with, and its needs. MongoDB uses sharding, read scalability, and automatic data balancing to offer horizontal scalability. PostgreSQL also provides various index types, including B-tree, hash, GIN, GiST, and Sp-GiST.

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Finally, it highlights a few challenges you might face when you use these databases. Read along how you can choose the right database for your organization. The advantage of MongoDB’s horizontal scaling is that it can handle unlimited data and traffic by adding more nodes to the cluster. On the other hand, PostgreSQL’s vertical scaling is more limited by the resources available on a single instance. Learn about the 9 key differences between MongoDB and PostgreSQL so you can choose the right database for your project. So far, we discussed PostgreSQL and MongoDB from their very basics and definition, their security, and their use cases.

  • Again, we repeated a number of experiments for 10, 100 and 1000 sets of intervals of the same duration.
  • Finally in [20] is presented a system called Hadoop-GIS, a scalable and high performance spatial data warehousing system which can efficiently perform large scale spatial queries on Hadoop.
  • It is built on a distributed, scale-out architecture and offers a comprehensive cloud-based platform for managing and delivering data to applications.
  • Partitioning and sharding are essentially about breaking up large datasets into smaller subsets.
  • Its indexing strategies include multicolumn, B-tree, parial, and expressions.

It’s equivalent to user-defined functions (UDF) which allow users of relational databases (like PostgreSQL) to extend SQL statements. 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. The important thing to note here is that transactions allow various changes to a database to either be made or rolled back in a group.

Postgresql vs MongoDB Overview

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. As time goes on, the schema can be changed with no consequence to the database. The frontend developer would just need to perform some error handling if null values are present in the
API calls. With PostgreSQL 9.2, query results can be returned as JSON data types.

MongoDB and PostgreSQL Database Technologies

The query returns coordinates of vessels in proximity up to different spatial distances (2, 5, 10 miles) and transmitted within a 5 minutes time period from different waypoints of a specific vessel’s trajectory. For each spatial distance three experiments are executed with different amount of timestamps and waypoints of a specific vessel’s trajectory. 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. The geography type is intended to be used with latitude/longitude coordinates on the earth’s surface, and performs accurate spheroid distance calculations in meters.

ACID transactions for changes to many documents

PostgreSQL scales vertically by adding more resources to a single instance, such as CPU, memory, and storage. Another cool thing about MongoDB is its ability to scale horizontally. With its distributed architecture, you can easily add more nodes to your cluster as your data grows without sacrificing performance.

This section will compare two of the most popular databases available in the market to assist you in identifying the best one for your needs. MongoDB also supports vertical scaling which is an easier way to scale MongoDB. You simply add more resources like RAM, CPU, or hard disk to cushion the effect of increasing load.

Query language

The object part of PostgreSQL relates to the many extensions that enable it to include other data types such as JSON data objects, key/value stores, and XML. PostgreSQL, like Linux, is an example of a well-managed open source project. One of the most broadly adopted relational databases, PostgreSQL came out of the POSTGRES project at the University of California at Berkeley starting in 1986 and it has evolved with the times.

MongoDB and PostgreSQL Database Technologies

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.

Key-value stores

PostgreSQL, on the other hand, is a free, open-source RDBMS (Relational Database Management System) that was developed at the University of California, Berkley. Both these technologies are leveraged by organizations of all scales, both big & small, and depending on the situation, one can dominate over the other. Indexing is creating data structures that allow for quick and efficient data retrieval. Both MongoDB and PostgreSQL support indexing, but they do it differently. MongoDB uses automatic indexing, which automatically creates indexes for frequently used queries.

MongoDB and PostgreSQL Database Technologies

Since version 5.0, MongoDB has included a “live” resharding feature that comes as a major time-saver since you only need to set a policy. The database can mongodb vs postgresql automatically redistribute the data when the time comes. Data can be distributed across different regions with ease via the MongoDB Atlas cloud service.

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For example, a primary key constraint ensures that each row in a table has a unique identifier, while a foreign key constraint ensures that a value in one table references a valid value in another table. The reason for this behavior is that the data stored in MongoDB are in GeoJson format and each record consist of many extra characters and a unique auto created id called ObjectId. Thus, each record is significant bigger in size than it was in its original CSV format. On the other hand, in PostgreSQL the data ingested in database as CSV, with the addition of the_geom column that contains the POINT geometries of each latitude and longitude. There are many different types of database available, and each has advantages and disadvantages.

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