Syed Jafer K

Overview

The Inventory and Order Management System is an end-to-end MongoDB project focused on managing products, suppliers, and customer orders. It covers all aspects of database management, from CRUD and validation to indexing, replication, and disaster recovery. The goal is to simulate how an e-commerce backend or retail management system would handle real-world data consistency and durability challenges.

Database Design

The project consists of three primary collections — products, suppliers, and orders.
The products collection keeps details like product name, category, price, stock quantity, and update timestamp.
The suppliers collection stores information about supplier name, contact, and region.
The orders collection captures order details such as product ID, customer name, quantity, date, and order status.
Relationships are maintained through references between products and orders, allowing efficient lookups and analytics.

CRUD Operations

The CRUD layer enables administrators to add new products, update stock levels, record customer orders, and remove obsolete entries. For example, every time a new order is placed, the corresponding product’s stock count is automatically reduced. This step reinforces how MongoDB’s document model simplifies transactional workflows for real-world inventory management.

Schema Validation

The project enforces schema validation rules to maintain data quality. Constraints ensure that fields like price and stock are non-negative and that essential attributes like product name are always present. This introduces schema enforcement in a NoSQL database while retaining flexibility.

Indexing and Query Optimization

Indexing strategies are designed to improve query efficiency for frequent operations such as fetching products by category or filtering orders by date. Learners experiment with single-field, compound, and text indexes to understand their impact on performance and how the query planner chooses index paths.

Aggregation Framework

Aggregation pipelines are used to compute total sales per product, total revenue by category, and low-stock alerts. Learners practice building multi-stage pipelines that join collections, calculate totals, and return meaningful insights for business decisions.

Transactions and Consistency

The project showcases how MongoDB handles concurrent updates, ensuring that inventory and order data remain consistent. Transactions are demonstrated when multiple collections must be updated atomically — such as creating an order and adjusting stock at the same time.

Replication for High Availability

Replication is configured to protect against hardware or node failures. A three-node replica set demonstrates how MongoDB replicates data across nodes and automatically promotes a secondary node to primary if the main one fails. Learners observe how this architecture improves uptime and provides read scalability.

Backup and Restore

Backups are taken regularly using logical export tools. This demonstrates how to plan and test database recovery using backups. The restore process is tested by intentionally deleting and restoring data, ensuring a complete understanding of backup validation and restoration workflows.

Point-in-Time Recovery (PITR)

Using the replication oplog, learners implement point-in-time recovery to restore the system to a specific moment before data corruption or accidental deletion occurred. This hands-on exercise introduces disaster recovery planning and the concept of Recovery Time Objective (RTO) versus Recovery Point Objective (RPO).

Snapshot and Disaster Simulation

Filesystem or volume-level snapshots are used to capture instant backups of the MongoDB data directory. Students learn how snapshots differ from logical backups and how they can be used for faster restores during system failures or ransomware-like scenarios. A simulated disaster scenario is created to test full restoration using snapshots.

Monitoring and Profiling

To ensure smooth operation, the system uses monitoring tools that track performance, replication lag, and query execution times. Profiling is enabled to analyze slow queries and optimize index design. These tools collectively teach proactive database maintenance and performance tuning.

Optional Advanced: Sharding

As an optional step, the project can be scaled horizontally by implementing sharding on the orders collection. Sharding based on region or customer name demonstrates how MongoDB handles distributed data across multiple servers while maintaining query performance.

API Structure

1. Product Management APIs

Purpose	Endpoint	Method	Description
Add product	/api/products	POST	Create a new product with name, category, price, and stock.
Get all products	/api/products	GET	List all products, optionally filtered by category or price range.
Get product by ID	/api/products/{id}	GET	Fetch detailed product info.
Update product	/api/products/{id}	PUT	Update stock, price, or description.
Delete product	/api/products/{id}	DELETE	Permanently remove a product.

2. Supplier Management APIs

Purpose	Endpoint	Method	Description
Add supplier	/api/suppliers	POST	Create a new supplier with name, contact, and region.
Get suppliers	/api/suppliers	GET	Retrieve all suppliers.
Get supplier by ID	/api/suppliers/{id}	GET	Fetch supplier details and linked products.
Update supplier	/api/suppliers/{id}	PUT	Modify supplier information.
Delete supplier	/api/suppliers/{id}	DELETE	Remove a supplier record.

3. Order Management APIs

Purpose	Endpoint	Method	Description
Place new order	/api/orders	POST	Create a new order and automatically reduce stock.
Get all orders	/api/orders	GET	Retrieve all orders with optional filters (date, status, product).
Get single order	/api/orders/{id}	GET	View full order details.
Update order status	/api/orders/{id}	PUT	Modify status (e.g., pending, shipped, delivered).
Cancel order	/api/orders/{id}	DELETE	Delete or cancel an existing order.

4. Reports & Insights APIs

Purpose	Endpoint	Method	Description
Sales by product	/api/reports/sales-by-product	GET	Show total revenue per product.
Sales by category	/api/reports/sales-by-category	GET	Aggregate sales grouped by category.
Low stock alert	/api/reports/low-stock	GET	Identify products with critical inventory levels.
Supplier performance	/api/reports/supplier-performance	GET	Analyze suppliers based on delivery and product stats.

5. Backup, Restore & PITR APIs

Purpose	Endpoint	Method	Description
Initiate backup	/api/admin/backup	POST	Trigger data backup operation.
Restore data	/api/admin/restore	POST	Restore inventory system from the latest backup.
Create snapshot	/api/admin/snapshot	POST	Simulate snapshot capture.
Perform PITR	/api/admin/pitr	POST	Rollback data to a particular timestamp.
Backup logs	/api/admin/backup-logs	GET	Display backup history and restore events.

6. Replication & Monitoring APIs

Purpose	Endpoint	Method	Description
Replica set info	/api/admin/replica-status	GET	Show replication health and node roles.
Performance metrics	/api/admin/performance	GET	Return read/write performance statistics.
Health check	/api/health	GET	Simple readiness probe to confirm database availability.

7. Optional Sharding & Scaling APIs

Purpose	Endpoint	Method	Description
Shard status	/api/admin/shard-status	GET	Display current shard distribution.
Move chunk	/api/admin/shard-migrate	POST	Simulate shard migration between nodes.
Balancer control	/api/admin/shard-balancer	PUT	Enable or disable balancer for testing chunk movement.

By completing this project, learners develop a complete understanding of how MongoDB powers real-world business systems. They gain practical experience in designing schemas, implementing CRUD operations, performing aggregations, ensuring high availability, planning disaster recovery, and monitoring performance. The project mirrors production-grade design principles, giving a holistic view of MongoDB administration and data engineering.