Learn MQL with Real Code Examples

MQL is designed for querying JSON-style documents stored in MongoDB collections.

Supports CRUD operations, complex filters, aggregation pipelines, indexing, and data manipulation.

Widely used for backend development, analytics, and building scalable, flexible data-driven applications.

find(), insert(), update(), delete() operations

Aggregation framework (match, group, project, sort)

Index management (createIndex, dropIndex)

Update operators ($set, $unset, $inc, $push)

Query operators ($eq, $gt, $in, $regex)

Documents and collections

CRUD operations

Query operators ($eq, $ne, $in, $regex)

Update operators ($set, $inc, $push)

Aggregation stages ($match, $group, $sort)

Indexes and performance considerations

Database -> Collections -> Documents

Index definitions per collection

Views for read-only transformations

Embedded documents and arrays

Collections for related entities

Design document schema

Create collections and indexes

Perform CRUD operations using MQL

Aggregate and filter data using pipelines

Monitor and optimize queries with explain()

Beginner: Simple find() queries

Intermediate: Aggregation pipelines

Advanced: Sharded collections and replication

Expert: Complex analytics and real-time reporting

More flexible than SQL for schema-less design

Aggregation pipelines more intuitive than raw SQL joins

Faster for document-oriented workloads

Less suitable for highly relational transactional systems

MongoDB 1.x – Initial releases (2009)

MongoDB 2.x – Replication and indexing improvements

MongoDB 3.x – Aggregation framework introduced

MongoDB 4.x – Multi-document transactions

MongoDB 5.x – Time-series collections and improved analytics

MongoDB 6.x – Enhanced aggregation, cluster-wide transactions, improved sharding

Document: JSON-like data object

Collection: Group of documents

Index: Optimizes query performance

Aggregation: Pipeline to transform or analyze data