What’s the Difference Between MongoDB and PostgreSQL?

What’s the Difference Between MongoDB and PostgreSQL?

MongoDB and PostgreSQL are two different types of databases. MongoDB is a non-relational or NoSQL database with a flexible data model. You can store all types of data as JSON documents for fast retrieval, replication, and analysis. In contrast, PostgreSQL is an object-relational database management system that you can use to store data as tables with rows and columns. It offers flexibility in data types, scalability, concurrency, and data integrity for structured data.

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Data model differences: MongoDB vs. PostgreSQL

MongoDB and PostgreSQL are different types of databases that have distinct data models.


MongoDB is a document database that stores data as key-value pairs in JSON documents. Each document can hold various types of data, including arrays, Booleans, numbers, strings, and nested documents. By using Binary JSON (BSON), MongoDB holds additional data types and processes data efficiently. With the data storage flexibility in MongoDB, you can store unstructured, evolving, and dynamic data.

MongoDB organizes each document into collections, with each having a unique ObjectId, which you use to identify a document. The following table shows an example of customer data in MongoDB.



  customer_id: "1",

  name: “John Doe”,

  country: "United States"



  customer_id: "2",

  age: “35”

  email: "jane_doe@example.com"



In contrast, PostgreSQL is an object-relational database management system (ORDBMS) that combines object-oriented features with relational database capabilities. In a table, every row represents individual data points, and each column defines the type of information that you store there. PostgreSQL supports a range of data types, including dates, text, integers, and Booleans. 

Unlike MongoDB, PostgreSQL uses a predefined schema to store data. A schema allows for strong data consistency and integrity, as each column holds a specific data type. However, it’s less flexible. We share an example in the following table.







John Doe




Jane Doe



Architectural differences: MongoDB vs. PostgreSQL

MongoDB and PostgreSQL have several architectural differences.

Basic unit of storage

In MongoDB, the basic unit of storage is a serialized JSON document. A document is a JSON data structure that contains key-value pairs. In these pairs, keys are strings and the values are types of data. MongoDB support various data types including nested documents, arrays, strings, dates, Boolean values, and numbers.

Unlike in NoSQL databases, PostgreSQL’s basic storage unit is a row, called a tuple. Each tuple holds a single record under a specific data type that the column defines. Tuples can store integers, strings, dates, Booleans, and more. Alongside the data values, each tuple also contains metadata like the primary key, which identifies each tuple within a table.

Query language

MongoDB uses MongoDB Query Language (MQL) which allows you to interact with the document-oriented structure of MongoDB. MQL is rich in features and supports projection, aggregation frameworks, document querying, aggregation pipelines, geospatial queries, and text searches. 

PostgreSQL uses an SQL variant, called Postgres SQL, as its query language. Although similar to SQL, it has additional features like an extensible type system, functions, and inheritance. However, PostgreSQL is still compatible with standard SQL, so you can use SQL queries as well.


An index is a data structure that maps values of one or more columns to a physical location of the corresponding data on the disk. It increases the efficiency of database data retrieval operations.

MongoDB uses indexes to optimize query performance. It supports indexing at both the field and collection levels. It offers several index types like B-tree, compound, text, geospatial, hashed, and clustered indexes.

PostgreSQL also provides various index types, including B-tree, hash, GIN, GiST, and Sp-GiST. The create index command creates a B-tree index by default. 


Concurrency is the ability of a database system to manage multiple transactions at the same time. Concurrency allows multiple users to access and modify data without causing inconsistency issues or conflicts.

MongoDB has currency control mechanisms that use document-level atomicity and optimistic locking. It assumes there are no conflicts between most concurrency write operations, which allows people to modify data at the same time without acquiring locks. Every modification is atomic. This means that operations are either fully applied or not at all. It also creates a new revision ID for the document, which allows multiple documents with the same data to exist simultaneously. 

PostgreSQL also uses multi-version concurrency control (MVCC) to manage data and concurrent transactions. MVCC creates separate rows when users make data changes, which ensures no conflicts between transactions. It supports these isolation levels: read uncommitted, read committed, serializable, and repeatable read. PostgreSQL also uses write-ahead-logging (WAL), which logs any changes to a database before writing them to a disk. 


Availability ensures that even during a server outage, there’s no data downtime. MongoDB uses primary node replication, which duplicates data into replica sets. A singular primary node receives the writes, and secondary nodes then replicate this data. MongoDB automatically triggers a failover that elects a new primary node if a primary node becomes unavailable. These processes minimize MongoDB’s downtime.

In contrast, PostgreSQL uses logical and stream replication to ensure high availability. Logical replication selectively replicates specific tables or subsets of data. Streaming replication creates standby replicas that receive changes in the primary database. Additionally, PostgreSQL uses the PostgreSQL Automatic Failover (PAF) to allocate a new primary if there’s a failure event. 


Both PostgreSQL and MongoDB use a form of load balancing to evenly distribute read operations across multiple replicas while achieving a high degree of scalability. Their distributed architecture processes move data to improve performance. Data moves between replicas in PostgreSQL and between partitions in MongoDB. 

MongoDB also uses sharding and read scalability to ensure a high level of horizontal scalability. Sharding distributes data across multiple partitions, and each shard holds a subset of data. Sharding distributes the workload for high-traffic data sets across multiple servers. Secondary replicas can handle read operations, which helps to distribute the read workload and increase performance. 

PostgreSQL also offers partitioning, which splits large tables into smaller, more manageable parts. You can partition based on a hash, range, list, or another criterion. 

Other key differences: MongoDB vs. PostgreSQL

Beyond the core architectural and performance differences between MongoDB and PostgreSQL, there are other key differences.

ACID compliance

PostgreSQL ensures transactions are atomic, consistent, isolated, and durable (ACID). It promotes high levels of data consistency. As it’s a relational database management system, PostgreSQL can guarantee that transactions follow each property of ACID.

MongoDB introduced ACID-compliant transactions from version 4.0. However, you only use this in a few limited scenarios, while ACID compliance is a core part of PostgreSQL.

Data relationships

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.

MongoDB is a NoSQL database that does not use predefined relationships between collections. MongoDB uses denormalization, which embeds related data within documents. Denormalization helps to optimize read operations, as all the data you need for a query will be present within that document. This system minimizes the need to join data together.

Community support

PostgreSQL’s community has been growing since its launch in 1996. It has a strong open-source community with lots of PostgreSQL support libraries, tools, extensions, and general support available.

While MongoDB doesn’t have the same level of community maturity, it does offer drivers for many programming languages. There is lots of community and aid to help you interact with MongoDB using one of your preferred programming languages.

When to use MongoDB vs. PostgreSQL

Your data largely determines the choice between MongoDB and PostgreSQL.

MongoDB use cases

MongoDB is a NoSQL database with a flexible data model, high performance, and effective horizontal scaling. The following examples are use cases for MongoDB.

Content management systems 

MongoDB can store and retrieve unstructured data like images, videos, and texts. It can query and retrieve content rapidly and handle many concurrent read and write operations. This makes it a good choice for high-traffic content management applications.

Transactional data

MongoDB’s horizontal scalability and high availability mean it’s ideal for handling transactional data in financial systems. 

Stream analysis

High scalability, horizontal partitioning, and flexible schema make MongoDB useful for streaming data applications like Internet of Things (IoT) platforms and real-time analytics.

PostgreSQL use cases

PostgreSQL’s structured and feature-rich system helps support use cases like the following examples.

Data warehousing

PostgreSQL can handle complex joins, outline relationships, and rapidly query data. As it’s structured, it can process large volumes of data and rapidly provide insight and advanced analytics. These features also allow it to integrate well into business intelligence tools and work effectively as a data warehouse.

Ecommerce and web applications

As PostgreSQL is similar to SQL databases, it offers ACID compliance. It’s reliable for processing transactions and ensuring data consistency. PostgreSQL's complex queries and indexing give it high performance for companies that need to process orders, authenticate users, and manage inventory.

Flexible connections

PostgreSQL’s federated data hub allows it to connect to various data stores, including both non-relational and relational databases. PostgreSQL uses JSON support and foreign data wrappers to connect and access other database systems. These features make it able to work with a polyglot database environment, which means it’s good for complex industries that want to optimize their storage.

Summary of differences: MongoDB vs. PostgreSQL




Data modeling

MongoDB processes data as JSON-like documents in collections. 

PostgreSQL is an object-relational database management system that uses tables, rows, and columns to store data.

Basic unit of storage

Serialized JSON documents.

Rows, called tuples.


MongoDB indexes at the field and collection level and uses B-tree, compound, text, geospatial, hashed, and clustered indexes.

PostgreSQL supports B-tree, hash, GIN, GiST, and Sp-GiST index types.

Query language

MongoDB uses MongoDB Query Language (MQL).

PostgreSQL uses an SQL variant that is compatible with standard SQL queries.


MongoDB uses currency control mechanisms, document-level atomicity, optimistic locking, and MVCC to offer concurrency.

PostgreSQL uses MVCC, data snapshots, flexible isolation levels, and deadlock detection to provide concurrency.


MongoDB uses primary node replication and secondary nodes to offer availability. It can handle transactional workflows.

PostgreSQL uses logical and stream replication plus PAF to offer availability. It can process high data volume simultaneously. 


MongoDB uses sharding, read scalability, and automatic data balancing to offer horizontal scalability.

PostgreSQL uses load balancing, connection pooling tools, and partitioning to offer scalability.

How can AWS support your MongoDB and PostgreSQL requirements?

Amazon Relational Database Service (Amazon RDS) for PostgreSQL makes it easy to set up, operate, and scale PostgreSQL deployments in the cloud. It also manages complex and time-consuming administrative tasks such as PostgreSQL software upgrades, storage management, and backups for disaster recovery.

Amazon DocumentDB (with MongoDB compatibility) allows you to easily manage native JSON document databases in the cloud. It helps you scale to millions of document read and write requests. It improves productivity and offers enterprise capabilities like high availability and low latency.

Get started with MongoDB and PostgreSQL on Amazon Web Services (AWS) by creating an account today.