MongoDB

Why MongoDB

MongoDB solves a real problem. Relational databases force you to define your schema before you understand your domain. That’s backwards for early-stage products.

When you’re iterating on product-market fit, your data model changes weekly. Adding a column in PostgreSQL means migrations, deployments, and coordination. Adding a field in MongoDB means updating your application code. The database adapts to your product, not the other way around.

Document databases also model certain data naturally. A product with variants, attributes, and nested specifications. A user profile with preferences, settings, and activity history. These fit documents better than normalized tables. No joins. No object-relational impedance mismatch. Just data shaped like your application needs it.

The trade-off is clear: you sacrifice some query flexibility and transactional guarantees for schema flexibility and development speed. That’s often the right trade for startups.

What We Build With It

Content management systems are MongoDB’s sweet spot. Every content type has different fields. A blog post has an author and tags. A product page has specifications and variants. A landing page has sections and components. Modeling this in SQL means either a sparse table with hundreds of columns or a complex EAV pattern. MongoDB stores each content type naturally.

We’ve built product catalogs for e-commerce platforms where every category has different attributes. Electronics have technical specs. Clothing has sizes and colors. Books have ISBNs and page counts. MongoDB handles this without forcing a rigid schema.

Event logging and analytics pipelines work well too. When you’re capturing user behavior, the shape of events evolves constantly. New features mean new event types. MongoDB ingests them without schema changes.

Multi-tenant applications use MongoDB for configuration stores. Each tenant has different settings, integrations, and customizations. Documents capture this complexity cleanly.

We also use MongoDB for rapid prototyping. When you need to ship something in two weeks, schema flexibility removes friction.

Our Experience Level

We’ve run MongoDB in production across multiple projects. Not just CRUD operations. Real production workloads with performance requirements.

We understand the aggregation pipeline deeply. Map-reduce, lookups across collections, faceted search, and complex transformations. We write pipelines that perform and maintainable code that generates them.

Index design in MongoDB differs from relational databases. Compound indexes, partial indexes, text indexes, and geospatial indexes each have their use cases. We know how the query planner selects indexes and how to design for your access patterns.

Sharding is where MongoDB’s complexity lives. We’ve designed shard keys, managed chunk migrations, and debugged issues where queries scatter across shards. We know when sharding helps and when it adds operational burden without benefit.

We understand MongoDB’s consistency model. Write concerns, read preferences, and the implications of replica set topology. We design applications that handle these correctly.

When to Use It (And When Not To)

MongoDB fits when your data is:

• Hierarchical — Nested structures that would require multiple joins
• Polymorphic — Different documents have different fields
• Append-heavy — Logs, events, time-series data with simple reads
• Rapidly evolving — Schema changes happen weekly during development

Avoid MongoDB when you need:

• Complex queries across relationships — SQL handles this better
• Multi-document transactions everywhere — MongoDB supports them, but they’re slower
• Strong consistency guarantees — Replica set elections can cause brief unavailability
• Mature tooling for analytics — SQL still dominates for ad-hoc analysis

The honest answer: most applications work fine with either PostgreSQL or MongoDB. We’ve seen teams choose MongoDB for the wrong reasons and regret it. We’ve also seen teams force relational models onto document-shaped data. We help you pick correctly.

Common Challenges and How We Solve Them

Schema validation at scale. Flexibility becomes chaos without discipline. We implement JSON Schema validation at the database level and application-level validation that catches issues before they persist.

Unbounded array growth. Documents have a 16MB limit. We’ve seen applications store arrays that grow without bound. We design schemas with this limit in mind, splitting into separate collections when necessary.

Query performance degradation. Without proper indexes, MongoDB scans entire collections. We use explain plans to identify slow queries, design indexes for specific access patterns, and monitor query performance in production.

Data integrity across documents. MongoDB doesn’t have foreign keys. We implement referential integrity in application code, use change streams for denormalization, and design for eventual consistency where appropriate.

Migration between schema versions. Schema flexibility doesn’t mean no schema. When you do change structure, you need to migrate existing data. We build migration scripts that run safely on production data without downtime.

MongoDB is a tool. We know when to use it and how to use it well.