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As programming language will be used GOlang (truly believe it’s super fast, and just perfect for such tasks). And in this article I will shed some light on reasons how was made decision to use certain database. Like PostgreSQL, MongoDB has a thriving community that includes many resources such as forums, user groups, documentation, and drivers. Aside from that, The MongoDB Enterprise support consists of a comprehensive knowledge repository, which includes tutorials, user guides, and best practices. That’s not the case with MongoDB, released under the Server Side Public License (SSPL) – a restrictive license.

mongodb vs postgresql performance

You may have to check the database continuously if something doesn’t go as planned to avoid noticing a failure when it’s too late. The tight rules governing the structure of the database allow PostgreSQL to be a very secure database, hence it can be reliable to be used for banking systems. Developers can choose what’s essential in the application and make the changes required.

PostgreSQL is an object-relational database

There are challenges in managing and querying the massive scale of spatial data such as the high computation complexity of spatial queries and the efficient handling the big data nature of them. There is a need for an interactive performance in terms of response time and a scalable architecture. Benchmarks play a crucial role in evaluating the performance and functionality of spatial databases both for commercial users and developers.

mongodb vs postgresql performance

PostgreSQL outperforms MongoDB while bigger fluctuations are presented as the sample grows. The set of queries Q7 and Q8 is not performed for all the values of vessels and timestamps. We excluded the sample 1000 because the response time was significant high. The reason for this behavior is that the query itself is really complex. Choosing between MongoDB and PostgreSQL depends on the specific data requirements and expertise of your team. MongoDB shines in scenarios where flexibility, agility, and unstructured data storage are essential.

ACID transactions for changing large numbers of documents

This makes it ideal for situations where data needs to be real-time or near real-time, thereby enabling companies to get a holistic view of their business in real-time and improve data optimization. Plenty of BI and data management tools depend on SQL and create complex SQL statements to gather the right assortment of data from the database. PostgreSQL performs brilliantly in situations like these, as it’s a strong, enterprise-grade implementation that most developers understand. As well as its mature query planner and optimizer, PostgreSQL provides such performance optimizations as table partitioning, read query parallelization, and JIT (just in time) expression compilations.

If you want a database that delivers fast performance and lower latency, then you will never go wrong withPostgreSQL. MongoDB and PostgreSQL are the most popular open-source databases that almost all companies use. This blog will see a detailed comparison between MongoDB mongodb vs postgresql performance vs PostgreSQL. Ultimately, the decision between PostgreSQL and MongoDB depends on the specific needs of your application. It’s important to carefully evaluate the strengths and weaknesses of each system and choose the one that best meets your requirements.


MongoDB uses multi-document transactions, which allows you to perform transactions on multiple documents in a single operation. PostgreSQL, on the other hand, supports traditional SQL transactions, which are more familiar to developers. Choosing between MongoDB and PostgreSQL comes down to the needs of your application. Mission-critical applications with high data integrity and accuracy requirements may find PostgreSQL more suitable. At the same time, MongoDB is ideal for semi-structured data applications requiring high scalability and performance for quick and easy updates. A notable security feature is client-side field-level encryption, which encrypts data before sending it to the server.

MongoDB has implemented a modern suite of cybersecurity controls and integrations both for its on-premise and cloud versions. MongoDB is an open-source, document-oriented database NoSQL database that uses JSON-like documents with dynamic schemas instead of tables. It is designed for scalability, high availability, and performance, allowing data to be kept in a distributed system and making it more secure and reliable.

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For Q1 a regular BTree index is created in both systems for attribute “ship_id”. The index size varies between the two database systems even for the same attribute that performed. The two systems store data differently and the concept of “index” is different too. As an example the size of the index on attribute “ship_id” in MongoDB is about 6 GB while in PostgreSQL the size is 3,1 GB.

  • PostgreSQL uses a streaming replication method where changes made to the primary server are sent to replica servers through WAL files in real time.
  • As the Stack Overflow survey shows, though Postgres is the most popular database among all respondents, MongoDB
    is more welcomed by the new learners.
  • If you make incorrect assumptions about the data structures, queries, or the data volume ( as we did at the beginning), the performance will suffer.
  • Common use cases for MongoDB include customer analytics, content management, business transactions, and product data.
  • This flexibility is hugely useful when consolidating information from diverse sources or accommodating variations in documents over time, especially as new application functionality is continuously deployed.

MongoDB was known to be less reliable because it didn’t support ACID transaction semantics in the early days. This has changed since they acquired WiredTiger and use its WiredTiger storage engine. Today, from the transaction perspective, MongoDB is as solid as Postgres. The 2023 Stack Overflow survey shows that Postgres has become the most admired, desired database. 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. Both PostgreSQL and MongoDB have strong communities of developers and consultants who are ready to help.

Both databases are awesome, but what is your need?

It is a NoSQL (Not only SQL) and its syntax is different from the traditional relational database management systems. With relational databases like PostgreSQL, altering your table is necessary to make any changes. You might be able to alter a table later on, but this may lead to database downtime and bugs in your application. PostgreSQL databases can use foreign keys which explicitly link data between tables and are used to keep the data normalized. Users can access the data and make changes or updates to the schema as needed, unlike with the SQL database model where users can only access and store data once it has been processed and properly formatted. SPEC, BAPco and TPC benchmarks are not suitable for large database environments and they cannot be applied for spatiotemporal data.

mongodb vs postgresql performance

The main considerations for data partitioning is to avoid high density partitioned tasks and to handle properly boundary intersecting objects. Hadoop-GIS takes advantage of spatial access methods for query processing and provides a real time spatial query engine (RESQUE) which supports an in-memory indexing on demand approach. There are challenges in managing and querying the massive scale of spatial data such as the high computation complexity of spatial queries and the handling of the big data nature of them. There is a need for queries that their response time is in a reasonable time and a scalable architecture such as cluster or cloud environment. Hadoop fits well in that case as it can handle large scale data and support big data computations and analytics through MapReduce and some declarative query interfaces such as Hive [17], Pig [18] and Scope [19]. The main challenges in spatial partitioning are the spatial data skew problem which can result in bad response time through load imbalance and boundary objects problem which can lead to incorrect query results.

MongoDB vs. Postgres Benchmarks

And as both databases are heading upward, the choice will only become harder ????‍♂️. Most companies use databases to support their internal infrastructure, both Postgres and MongoDB permit this usage. Lots of data management and BI tools rely on SQL and programatically generate complex SQL statements to get just the right collection of data from the database. PostgreSQL does very well in such contexts because it is a robust, enterprise-grade implementation that is understood by many developers. The database complies with a wide range of security standards and has numerous features to support reliability, backup, and disaster recovery, usually through third party tooling. As any fundamental technology like a database grows, it is supported by a platform ecosystem of services, integrations, partners, and related products.

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