Hire a MongoDB developer to fix the layer that is actually slow
Soham Dang designs and optimises MongoDB data layers — schemas shaped by real read patterns, aggregation pipelines for anything computed, and compound indexes justified by explain plans rather than guesses.
When a MERN application gets slow, the framework is almost never the reason. It is a collection that grew past the point where the original schema made sense, and nobody noticed because it happened gradually.
This page is about that layer specifically.
The four things that usually cause it
Relational modelling in a document store
Documents split across collections the way tables would be, so every read needs three lookups that a single well-shaped document would not.
Missing compound indexes
An index on each field individually does not serve a query that filters on two and sorts on a third. The explain plan says so; nobody reads it.
Rollups in application code
Ten thousand documents fetched into Node to be summed. The same work as an aggregation pipeline, done in the slowest possible place.
Unbounded arrays
Subdocuments appended forever inside a parent, until a single document approaches the size limit and every read of it is expensive.
How the work is approached
Measure first
Real explain plans on real query shapes. Optimising from intuition is how you end up with six indexes and no improvement.
Index what the queries need
Compound indexes ordered for the actual filter-sort combinations, including the tenant key where the system is multi-tenant.
Move computation into the database
Aggregation pipelines for dashboards, reporting and rollups, so the data never leaves the database to be counted.
Reshape only where it pays
Schema changes are migrations. They happen where the measurement justifies the cost, not everywhere at once.
Where this has been done
Avani Business OS runs KPI dashboards, payroll rollups and performance analytics on aggregation pipelines across a multi-tenant dataset. Payroll generates salary slips directly from attendance and leave data rather than from an exported spreadsheet.
At MS Infracon the work included MongoDB schema design and aggregation pipelines tuned for high-traffic reads, behind Node.js and Express services running on Nginx routing.
Common questions
What MongoDB experience does Soham Dang have?
MongoDB has been his primary database across all three professional roles since July 2023, covering schema design, Mongoose models, aggregation pipelines, compound indexing and query optimisation under concurrent multi-tenant load.
Why is my MongoDB query slow?
Most commonly a missing compound index for the actual filter-and-sort combination, documents modelled relationally so each read needs several lookups, or aggregation being done in application code instead of a pipeline. The explain plan will usually tell you which.
Can MongoDB handle multi-tenant applications?
Yes, provided tenant scoping is enforced in middleware rather than per query and compound indexes are led by the tenant key. Done that way, scoping helps performance rather than costing it.
Should I use MongoDB or a SQL database?
MongoDB suits document-shaped data and schemas that are still moving. If most of your queries join across many entities, or you need cross-entity transactional guarantees as the normal case, a relational database is the better foundation.