The driver provided a simple and intuitive API that supported a wide range of MongoDB features, including querying, indexing, and aggregation. Today, MongoDB
still is the de-facto choice for full-stack developers because of its ease of use. It is imperative for the research community to contribute to the clarification of the purposes and highlight the pros and cons of certain distributed database platforms. MongoDB also supports database transactions across many documents, so chunks of related changes can be committed or rolled back as a group. With its multi-document transactions capability, MongoDB is one of the few databases to combine the ACID guarantees of traditional relational databases with the speed, flexibility, and power of the document model.

Other than these few differences, both databases are equally strong in terms of performance and will work well with any organization, customer, or business needs. Hevo Data, a No-code Data Pipeline helps to load data from any data source such as Databases, SaaS applications, Cloud Storage, SDK,s, and Streaming Services and simplifies the ETL process. Hevo not only loads the data onto the desired Data Warehouse but also enriches the data and transforms it into an analysis-ready form without having to write a single line of code. It is a source-available cross-platform document-oriented database program that uses JSON (JavaScript Object Notation)-like documents and optional schemas to store your data. 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.

DATABASES

Benchmarking databases that follow different approaches (relational vs document) is harder still. Despite the different data models that MongoDB and PostgreSQL expose, many organizations face the challenge of picking either technology. PostgreSQL shines when data integrity, complex querying, and strong SQL capabilities are paramount. It is an excellent choice for applications involving financial transactions, data warehousing, and complex reporting.

The object portion of this database relates to the varied extensions allowing it to incorporate alternative types of data, including JSON data objects, XML, and key/value stores. Growing databases are supported by an ecosystem made up of many services, partners, integrations, and other relevant products. The database is at the core of the MongoDB ecosystem, though there are numerous layers bringing users extra value and problem-solving capabilities. In PostgreSQL, you’ll find a comprehensive portfolio of security features, with a number of encryption types to choose from. This database is available in the cloud on every major cloud provider.

Support & Community

Document/schema free/lite DBs data stores are easier to get up and running on but are not as scalable (generally) as NoSQL flavors that require a more rigid data model like ScyllaDB. If your data volumes are going to be 10s of TB and transactions per sec 10s of 1000s (or more), look at Scylla. We have something called lightweight transactions (LWT) that can get you consistency. 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.

• One disadvantage of PostgreSQL when compared to MongoDB is its reliance on relational data models that are unfriendly to data structures that developers use in code.
• Depending on the user application, either option can be utilized to work on.
• Hevo Data, a No-code Data Pipeline helps to load data from any data source such as Databases, SaaS applications, Cloud Storage, SDK,s, and Streaming Services and simplifies the ETL process.
• MongoDB has a single master in a replica set that can accept reads and writes, and the secondaries can be configured for reading.

However, there is a difference between these two database products when it comes to the capability of catering to complex, real-time data search requirements. Really easy to startup since it doesn’t require you to install any additional software since is self-contained. MySQL could be next, sonce it’s easier to learn at first and has more resources available. PostgreSQL is less widespread, more challenging and has the fewer resorces, but once you have some experience with MySQL is really easy to learn as well.

Challenges of Using MongoDB & PostgreSQL

Simple queries to hit tables can be made to complete faster by creating B-TREE Indexes. The JSON data support in PostgreSQL is much more advanced compared to MySQL. There are several JSON-specific operators and functions, making data searches in JSON documents very efficient. The JSONB feature from PostgreSQL-9.4, which stores JSON in a binary format, started supporting Full-Text Indexing (known as GIN Indexing), and this really makes Full-Text searching on JSON documents much faster.

Wisconsin, AS3AP and SetQuery measure the performance of the system in general but other benchmarks such as SEQUOIA 2000 and PGS-DBMS are specially designed to evaluate the spatiotemporal capabilities of databases. However only three queries from SEQUOIA 2000 and one query of PGS-DBMS include the temporal component. Furthermore, Jackpine’s micro- and macro benchmarking consist only of spatial queries. On the other hand, the 3-Dimension spatiotemporal benchmark expands the aforementioned benchmarks and includes the time component. Nevertheless, the examined spatio-temporal queries have been designed specifically for the maritime domain and its specific applications. Thus, none of the above benchmarks are suitable for the evaluation.

PostgreSQL’s fit to purpose

Postgres open-source version supports SSL-based connections and can also be integrated with external authentication systems, including LDAP, Kerberos, and PAM, which are efficient and reliable. MySQL also supports SSL-based connections over the network and provides security based on SE-Linux modules. Integration with external authentication systems, such as LDAP and PAM, is part of the MySQL enterprise edition. Database security plays a vital role in securing data from unauthorized access. Secured access is implemented at various levels in the database, including the object level and connection level. Also, MySQL does not support standard SQL clauses such as FULL OUTER JOINS, INTERSECT, and EXCEPT, which are commonly used.

This coordinator node manages the overall cluster and coordinates queries and data distribution among other nodes. PostgreSQL triggers are advanced; the supported triggering events are AFTER, BEFORE, and INSTEAD OF triggers for INSERT, UPDATE, and DELETE events. If you need to execute a complex SQL when the trigger gets invoked, you can do this using functions. Yes, the triggers in PostgreSQL can also execute functions dynamically. MySQL supports Triggers for AFTER and BEFORE events for INSERT, UPDATE, and DELETE statements. Triggers cannot execute dynamic SQL statements or stored procedures.

Why do we need databases?

A user could further define the document’s structure and undertake some development by introducing new fields, reworking data, or developing it whenever they see fit. Relational databases often store mongodb postgresql information about tables, databases, columns, etc. in system catalogs. These “data dictionaries” appear to the user as tables, but they do have information stored internally by the database system.

If your application involves geospatial data, MongoDB’s native support for geospatial indexing and queries can simplify location-based services and mapping applications. MongoDB is an excellent choice when your application deals with data that doesn’t have a fixed or predefined structure. It allows you to store data as JSON-like documents, making it easy to accommodate changes and additions to your data model without affecting existing records. The performance is measured in terms of response time in a 5-node cluster and the results show that PostgreSQL outperforms MongoDB in almost all cases.

Stored Procedures

It will help simplify the ETL and management process of both the data sources and destinations. Load data from MongoDB or PostgreSQL or a source of your choice to your desired destination in real-time using Hevo. Most companies use databases to support their internal infrastructure, both Postgres and MongoDB permit this usage. When reviewing a product, users are asked to assess the product’s overall quality, which includes assigning specific ratings for ease of use, value for money, customer support, and functionality. Additionally, across all benchmark types, OnGres found that as the datasets becomes bigger than the available memory capacity, the Postgres performance advantage grows over MongoDB.