MongoDB vs Postgres Meaning and Differences with Infographics

MongoDB essentially uses JSON or BSON to store its data as documents. With MongoDB, you can store data as documents in a binary representation known as binary JSON (BSON). Fields can differ based on the document it is catering to, therefore, there’s no need to declare the structure of documents to the system — documents are self-describing. In the next section, we’ll elucidate the differences between MongoDB and PostgreSQL to help you make that decision easily. Our information is based on key factors like architecture, ACID compliance, extensibility, replication, security, and support to name a few. PostgreSQL can support replication but more advanced features such as automatic failover must be supported by third-party products developed independently of the database.

Five consecutive separate execution calls are conducted, in order to gather the experimental results and collect the average values concerning response times of the queries. We compare the response time in a 5-node cluster in MongoDB and PostgreSQL. Furthermore, we perform a small number of experiments in order to evaluate how the lack of indexes affects the response time of the examined queries.

Relational vs. Non-Relational Databases: Choosing the Right One for Your Project

Even the free version includes free cloud monitoring hosted on their site for your local installation. Ultimately, the best choice of a database depends on the specific needs of the project. Whether you go for MongoDB or PostgreSQL, Astera Centerprise provides a powerful code-free means for you to natively connect to the database of your choice and use it as part of a data integration pipeline. Q7i returns the haversine distances of vessels by calculating continuous distances of pairs of points and by summing these distances for every vessel passed in the query. This code is executed for a different set of ListOfTimestamps and ship_id. In this perspective it makes sense to focus on different subsets of a Mediterranean dataset rather than examining a very sparse dataset, e.g. in the Pacific or Atlantic ocean.

mongodb vs postgresql for big data

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. As mentioned above, the data are stored in MongoDB as GeoJSON and the $geometry field contains the coordinates values, latitude and longitude.

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Its indexing strategies include multicolumn, B-tree, parial, and expressions. But advanced techniques are available too, such as SP-Gist, GiST, GIN, KNN Gist, and BRIN, spanning indexes and bloom filters. As with MySQL and alternative open-source relational databases, PostgreSQL’s efficiency has been proven in the mix of demanding use cases spanning multiple areas of industry.

  • The index size varies between the two database systems even for the same attribute that performed.
  • This SQL database has approaches for boosting concurrency, handling indexing, introducing optimizations, and enhancing performance — such as table partitioning, advanced indexing, among additional functions.
  • Moreover, it doesn’t have revising tools or reporting instruments that could show the current condition of the database.
  • PostgreSQL also carries no licensing cost, eliminating the risk of over-deployment.
  • MongoDB also supports database transactions across multiple documents allowing bits of related changes to be rolled back or committed as a group.

In particular, the work conducted, set to identify the most efficient data store system in terms of response times, comparing two of the most representative of the two categories (NoSQL and relational), i.e. Furthermore, the average response time is radically reduced with the use of indexes, especially mongodb vs postgresql for big data in the case of MongoDB. A type of database system that does not necessarily use traditional structured query language (SQL) to query database systems. NoSQL databases are non-tabular, and they vary based on their different data models, such are document, wide-column, key-value, or graph.

Database as Code – the Good, the Bad and the Ugly

Using JSON allows you to change your schema on a whim without repercussion. 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.

If scaling is a chief requirement, then a database like MongoDB is a good option. If you need more consistent data, PostgreSQL will be worth considering. PostgreSQL uses load balancing, connection pooling tools, and partitioning to offer scalability. MongoDB uses currency control mechanisms, document-level atomicity, optimistic locking, and MVCC to offer concurrency. Beyond the core architectural and performance differences between MongoDB and PostgreSQL, there are other key differences. Keep up with the latest web development trends, frameworks, and languages.

Using JavaScript in the MongoDB shell

NoSQL, or non-relational, databases are designed for handling large volumes of data while being able to scale horizontally. In this section, we’ll take a look at some of the best big data databases. Big data databases rapidly ingest, prepare, and store large amounts of diverse data.

mongodb vs postgresql for big data

Their structure provides flexible schemas, and they can be scaled easily. MongoDB is an open-source software from MongoDB Inc used for non-relational database management systems. At the same time, PostgreSQL is developed and maintained by the PostgreSQL Development group, which is used for the relational database management system. Since PostgreSQL handles relational databases, it exhibits an object-oriented nature. In MongoDB, all the contents of the database are documents and files.

Best database for big data

Mongo RealmDB is available free of charge to all Atlas users for evaluation and light usage, enabling developers to build and release mobile applications. MongoDB has only recently (with version 4) started to support ACID transactions similar to SQL databases. Assessing the performance of two different database systems is challenging since both MongoDB and PostgreSQL have different ways of storing and retrieving the data. Replication is the process of creating a copy of the same dataset on more than one server. It enables database administrators to provide high data redundancy and high availability of data. 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.

You can nest fields in a data record, or add different fields to individual data records as and when you need. PostgreSQL users have to be prepared for the difficulties of scalability when an application is launched. PostgreSQL utilizes a scale-up strategy, so at one time or another in high-performance use cases, it’s possible to hit a wall. PostgreSQL’s design principles place a heavy focus on SQL and relational tables, and allow considerable extensibility. This database provides a wealth of ways to enhance its efficiency, though it utilizes a scale-up strategy at its core.


MongoDB is scalable because of partitioning data across instances within the cluster. It doesn’t split the documents into pieces as they are independent units making it easier to distribute them across various servers while data is locally preserved. Since there are no tables in MongoDB, there are no foreign keys in MongoDB either; hence no foreign key constraints. However, MongoDB does have a DBRef standard which helps standardize the creation of the references. Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.

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