What’s the Difference Between MongoDB and 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.

Indexing

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

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

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. 

Scalability

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. 

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.

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.