MongoDB vs PostgreSQL for E-Commerce: Which Database Should Your Store Run On?

Selecting a database for an e-commerce platform is more complicated than selecting between two database options. An online store has a number of workloads, including management of product catalogs, customer sessions, shopping carts, orders, payments, and inventory. Additionally, there are workloads for locating and reporting on data (e.g. sales, customers, orders, etc.).

For a comparison of PostgreSQL and MongoDB, let’s look at an online store that sells a number of different products, processes a number of orders each day, takes payments, and manages inventory. Additionally, the store needs to generate and manage reports about the business. For a store that has the type of workload described, it would be better to use PostgreSQL to manage the orders and inventory and generate reports. MongoDB would be a better choice to manage a highly variable product catalog and other app data.

The following table shows the rationale for each workload:

What is MongoDB?

MongoDB is a document database. Unlike traditional databases that require all data to be modeled using the same field and record structure, MongoDB allows data to be modeled using different document structures.

MongoDB can be used for ecommerce to model product catalogs where each product may contain a different set of attributes. MongoDB's website provides an example of product catalogs to illustrate how flexible its database model is.

It is important to understand that when MongoDB states “no schema,” they mean that the user need not specify a strict schema in their application code. The MongoDB database still enforces constraints. For example, the user can state that a certain field of a document cannot be null.

What is PostgreSQL?

PostgreSQL also supports storing and retrieving JSON. So, it could also be used to model product catalogs like MongoDB. Like MongoDB, PostgreSQL also offers many ways to define constraints. For example, PostgreSQL also supports defining relations and foreign keys.

In addition, PostgreSQL also supports defining different transaction isolation levels like MongoDB, so that if one transaction needs to read data in a serializable manner, it can.

If a business wants to implement a product catalog, they will probably want to find a way to define relations between data, define transaction rules, and use PostgreSQL. For example, they would probably want to define rules about how product prices change over time.

Is MongoDB or PostgreSQL Better for E-Commerce? The Best Database for Ecommerce Depends on the Workload

PostgreSQL can be a better option for representing eCommerce applications, as it supports transactions better, and MongoDB can be complicated to represent and control transactions.

When it comes to applications with catalogs that change often and have a great deal of variety, MongoDB is a good alternative, since it supports schemas with flexible, nested documents.

Of course, PostgreSQL can still be a good option for full stack development services. Let’s say there is a business that sells a wide variety of products and also offers customizable products. This business could also have another product with completely different data attributes. Each of the business’s data models would probably be different from a business selling generic products.

With the release of PostgreSQL 18, it added support for handling some of the e-commerce application needs, such as JSON, and also provided improvements to search and transaction controls. MongoDB 8.2 also provided features to support e-commerce application modeling. Because of this, both MongoDB and PostgreSQL can be used to support e-commerce applications.

How the Two Databases Actually Differ: MongoDB vs PostgreSQL Performance

MongoDB and PostgreSQL support different means of data and relationship modeling. Because of this, they both have different design philosophies. For example, PostgreSQL supports relationships between tables and supports constraints, whereas MongoDB does not.

In general, both MongoDB and PostgreSQL are fully adaptable to support most e-commerce use cases. However, each case should be evaluated on an individual basis.

For that reason, this comparison will not be using generic figures for MongoDB versus PostgreSQL throughput. An assertion of this type would require an equal comparison of the remaining aspects of the benchmark and the presence of an e-commerce sample workload. The benchmark should disclose the complete details of its methodology, the data and hardware it employed.

A more constructive comparison is to detail how each database handling system maps to the six e-commerce workloads.

The Product Catalogue: Product Catalog Database Design for E-Commerce

PostgreSQL 18 also allows users to model documents in a fashion similar to MongoDB. Defining how data is structured is up to the user, and PostgreSQL enforces constraints similar to MongoDB. In some use cases, it might be easier to use MongoDB over PostgreSQL. For example, if modeling product documents, MongoDB might be easier to use. In other cases, PostgreSQL 18 might give users more ways to define constraints than MongoDB.

Product catalogs lend themselves to a great deal of flexibility in the organization of data. For any given product, a catalog may contain numerous pieces of information. For example, there may be one or more product identifiers:

MongoDB can be a good choice for this type of database. MongoDB 8.2 states that a good example of polymorphic data, in which a collection contains documents with varying fields and data types, is a good candidate for that database. PostgreSQL can also accommodate this type of design by using JSONB to represent variable attributes of a given product.

In PostgreSQL 18, JSONB makes a good case for choosing PostgreSQL over MongoDB for a database to represent a product catalog. It should be noted, however, that MongoDB is document-oriented while PostgreSQL is relational, so there are other design constraints in MongoDB that are not present in PostgreSQL.

Does PostgreSQL JSONB solve this?

PostgreSQL 18 provides GIN indexes for JSONB. This provides support for JSONB operations, including containment and existence checks, and JSON path queries. The jsonb_path_ops operator class provides path queries but may provide better performance.

(Add a picture of PostgreSQL JSONB code path)

There are architectural limitations. Representing data as JSONB may lead to loss of clarity and rigidness in data models. Data representing product variants may be modeled as JSONB, but data representing product relationships that require frequent joins and/or constraints, and data representing product attributes that are subject to detailed reports may be modeled as relations.

Orders, Payments and Transactional Correctness: PostgreSQL for Ecommerce

PostgreSQL 18 provides features to model E-commerce data. Transactions and related data (e.g. payments, orders) require boundaries to ensure consistency. MongoDB may also be used to model transactions. However, the data and transaction models must be designed appropriately. The data models for transactions may lead to increased atomicity and consistency. E.g. An order may be modeled as a document that represents a customer as well as related documents that represent product line items and/or prices, and/or discounts, payment and shipping information, and inventory.

A relational model represents relationships between things with links.

PostgreSQL 18 now supports embedded transactions across related commands. As noted in the MongoDB docs, distributed transactions should not be used to replace proper schema design. For a system like a Mongo-based e-commerce site, this is an important consideration.

Inventory and Concurrency: MongoDB vs PostgreSQL Write Performance

Although MongoDB and PostgreSQL have different designs, with respect to concurrency and stock management, MongoDB can be preferred due to the nature of e-commerce systems.

PostgreSQL 18 offers features like transaction controls and locking mechanisms to manage concurrency. It has the default transaction isolation level of Read Committed. It offers other isolation levels like Repeatable Read and Serializable.

MongoDB 8.2 supports transactional isolation for operations that involve multiple documents or collections. It provides single-document atomic operations. Its document provides patterns for handling concurrent updates and states that performing distributed transactions comes at the expense of writing multiple single documents.

If one record represents the available quantity for a SKU, the record can be configured to manage concurrency. Duplicating the quantity across multiple records increases the complexity of the system.

Search: Database for Online Store Search

PostgreSQL 18 and MongoDB 8.2 can facilitate full-text search for an online store. The two databases can support an e-commerce store that has search features like auto-completion and relevance ranking. However, in the long run, it may be more cost-effective to use a dedicated search service.

Full-text search is supported by both MongoDB and PostgreSQL. PostgreSQL comes with text search features with dictionary, ranking and Other bells and Whistles. It also provides text search indexes. MongoDB offers text indexes and states that for performing full-text search, it's better to use the MongoDB Search service, which is offered by Atlas.

If the e-commerce store is small, MongoDB and PostgreSQL can be used to facilitate full-text search.

Sometimes, the search functions required to effectively interrogate a catalog can outgrow the data store that underlies an application. Some of the search functions might include:

Reporting and Analytics: Relational vs Document Database Ecommerce

Of the various databases that can underlie an application, PostgreSQL and MongoDB can both satisfy many of the requirements. However, it is best to treat the search layer as a separate layer from the application.

PostgreSQL, in particular, is very strong for support reporting and analytics. However, MongoDB can also be used for reporting if the documents in MongoDB are designed with reports in mind.

If orders have been placed, many reports can be written to support analysis and questioning by management.

The analytics requests for an e-commerce website typically include:

• Analysis by Product Category

• Average Order Value

• Percentage of Customer Purchases

• Rate of Inventory Turnover

• Best/Worst Selling Items

• Best/Worst Selling Coupons

• Sales by Geographical Area

• Payment and Refund Transactions

Reports of this nature often drive business decisions and should be supported by the database design prior to locking in the database schema.

As the Volume of Orders Increases, How Do the Two Systems Scale? MongoDB vs PostgreSQL

MongoDB's sharding mechanism allows for distribution of collection-level data across a set of shards. PostgreSQL 18 provides support for large tables using partitioning. Whether an application requires sharding or partitioning really depends on the nature of the workloads and the distribution of the data.

MongoDB relies on the shard key to evenly distribute data across shards. MongoDB documents provide several examples of poor shard key choice and the impact this has on workload balance.

When would PostgreSQL 18 and MongoDB 8.2 be required?

When a large volume of orders needs to be managed

MongoDB would rely on sharding and partitioning of collections, and PostgreSQL would rely on partitioning of large tables.

When read operations outnumber write operations

MongoDB would rely on distributed collections, and PostgreSQL would rely on replication.

When the volume of data managed by an application increases

MongoDB would rely on sharding and partitioning, and PostgreSQL would rely on vertical scaling.

When the nature of the workload managed by the application demands that data be managed by several entities

PostgreSQL would rely on distribution of related data, and MongoDB would rely on native sharding.

When would an indexing strategy fail?

When data is partitioned, sharded, or both, in an unbalanced manner.

Can an E-commerce Site Use Both MongoDB and PostgreSQL?

Yes, an e-commerce site can use both MongoDB and PostgreSQL. If different business processes are best modeled using different databases, an e-commerce site can deploy both and maintain its processes. The site should have a good reason for using both, as the deployment architecture can become complex.

One example is a site using PostgreSQL to model its customers and orders and to manage its inventory and payments. The site can use MongoDB to store and manage product documents and content.

An example that does not involve a model for processing payments is using PostgreSQL to model orders and to manage inventory, and using a separate system to model and manage the catalog. Using multiple databases increases the cost of deploying, backing up and managing the systems. There should be a good reason for using multiple systems

Xcentric services publicly available information states that it builds e-commerce sites and custom applications and integrations, but doesn't state a preference for MongoDB or PostgreSQL for e-commerce projects. For this reason, no preference is stated for Xcentric.

What It Costs to Migrate Later: MongoDB vs PostgreSQL

Costs to migrate MongoDB and PostgreSQL later are similar, as both are deeply integrated in business logic and processes. The cost is more related to the complexity of the system than the number of records in the system.

Migrations affect multiple layers of the system.

Other challenges include inconsistent behavior and unexpected results. Application behavior often depends on business logic and data as well as application code.

Product data changes over time. Some data is transient. Inventory may be distributed and synchronized with different locations.