ADDIS ABABA UNIVERSITY

COLLEGE OF NATURAL AND COMPUTATIONAL

SCIENCES DEPARTMENT OF

COMPUTER SCIENCE

Selected Topics in Data Management Systems (CoSc6031)

Lab Project Report

Project Title: Implementation of a Scalable and Distributed E-commerce Database System

using MongoDB Compass

1. Solomon Zinabu GSE/2578/17

2. Sasebih Nega GSE/7037/17
3. Dawit Kebede GSE/4878/17
4. Minale Ejigu GSE/5402/17

Date: 12/04/2025
1. Introduction

This project demonstrates a comprehensive understanding and practical implementation

of key features in MongoDB, using Compass as the primary tool. The e-commerce
dataset comprises five collections: customers, orders, order_items, payments, and
products, each containing 38,279 documents. The objective is to apply and showcase the
following functionalities:

 CRUD operations (Create, Read, Update, Delete)

 Projections
 Indexing
 Sorting and Aggregations
 Replica Set (1 Primary + 2 Secondaries)
 Sharding (Scalable distribution)
 Advanced MongoDB features (Lookups, Array functions, Grouping, Conditional
logic)

2. Data Collections Overview

Customers Collection

{
"customer_id": "I74lXDOfoqsp",
"customer_zip_code_prefix": 6020,
"customer_city": "goiania",
"customer_state": "GO"
}

Orders Collection

{
"order_id": "u6rPMRAYIGig",
"customer_id": "I74lXDOfoqsp",
"order_purchase_timestamp": "2017-11-18 12:29:57",
"order_approved_at": "2017-11-18 12:46:08"
}

Order Items Collection

{
"order_id": "u6rPMRAYIGig",
"product_id": "1slxdgbgWFax",
"seller_id": "3jwvL6ihC45G",
 "price": 24.1,
"shipping_charges": 20.9
}

Payments Collection

{
"order_id": "u6rPMRAYIGig",
"payment_type": "credit_card",
"payment_installments": 2,
"payment_value": 155.77
}

Products Collection

{
"product_id": "1slxdgbgWFax",
"product_category_name": "toys",
"product_weight_g": 50,
"product_length_cm": 16,
"product_height_cm": 5,
"product_width_cm": 11
}
3. Functional Implementation Using MongoDB Compass

3.1 CRUD Operations

Each collection supports Create, Read, Update, and Delete operations performed directly
in Compass.

 Insert – Sample records added to each collection.

 Read – Filtered and projected queries to retrieve documents.
 Update – Updates on customer city/state.
 Delete – Removal based on customer_id, order_id, or product_id.

3.2 Projection

Used to limit fields during queries for performance:

{
"customer_id": 1,
"customer_city": 1,
"_id": 0
}
3.3 Indexing

Created indexes on:

 customer_id
 order_id
 product_id
 payment_type
 product_category_name

Improves query speed for searches and aggregations.

3.4 Aggregations

Customers: Cities with most customers

[ { "$group": { _id: "$customer_city", total: { "$sum": 1 } } }, { "$sort": { total: -1 } } ]

Orders: Monthly order count

[ { "$project": { month: { "$substr": ["$order_purchase_timestamp", 0, 7] } } }, { "$group":

{ _id: "$month", count: { "$sum": 1 } } } ]
Payments: Average by type

[ { "$group": { _id: "$payment_type", avg_payment: { "$avg": "$payment_value" } } } ]

3.5 Advanced Aggregation & Lookup

Join Orders with Customers:

[ { "$lookup": { from: "customers", localField: "customer_id", foreignField: "customer_id",

as: "customer_info" } } ]

Group products by category with average size:

[ { "$group": {
_id: "$product_category_name",
avg_weight: { "$avg": "$product_weight_g" },
avg_length: { "$avg": "$product_length_cm" }
}} ]

Find high-value orders:

[ { "$match": { payment_value: { "$gt": 1000 } } } ]

4. Replication & Sharding Setup

4.1 Replication

Replication in MongoDB provides high availability by creating copies of the same data
on multiple servers. For this project, we configured a Replica Set consisting of one
primary and two secondary nodes.

Objective: Ensure data redundancy and availability in case of hardware failure or server
crash.

Replica Set Configuration Steps:

1. Start MongoDB instances (in separate terminal windows or services):

2. mongod --replSet rs0 --port 27017 --dbpath /data/rs0 --bind_ip localhost
3. mongod --replSet rs0 --port 27018 --dbpath /data/rs1 --bind_ip localhost
mongod --replSet rs0 --port 27019 --dbpath /data/rs2 --bind_ip localhost

4. Initiate Replica Set from any node (done via mongosh):

5. rs.initiate({
6. _id: "rs0",
7. members: [
8. { _id: 0, host: "localhost:27017" },
9. { _id: 1, host: "localhost:27018" },
10. { _id: 2, host: "localhost:27019" }
11. ]
})

12. Check status using:

rs.status()

13. Read/Write Setup in Compass:

o Use connection string:
mongodb://localhost:27017,localhost:27018,localhost:27019/?replicaSet=rs
0
o Read Preference: Primary or Secondary
o Write always routes to Primary

Benefits Observed:

 Reads from secondaries for load balancing.

 Automatic failover if primary is unavailable.
 No data loss during network partitions.
4.2 Sharding

Sharding enables horizontal scaling by distributing data across multiple machines.

MongoDB uses mongos as a query router and distributes collections based on a shard
key.

Sharding Configuration Steps:

1. Start Config Server Replica Set:

mongod --configsvr --replSet configReplSet --port 26017 --dbpath /data/config --

bind_ip localhost

2. Start Shard Replica Set (rs0) (same as replication step above)

3. Start mongos instance:

mongos --configdb configReplSet/localhost:26017 --port 27020 --bind_ip localhost

4. Connect to mongos using Compass:

o Connection String:
mongodb://localhost:27020
5. Add Shard (from mongosh):

sh.addShard("rs0/localhost:27017,localhost:27018,localhost:27019")

6. Enable Sharding for Database:

sh.enableSharding("ecommerceDB")

7. Shard Specific Collections:

8. sh.shardCollection("ecommerceDB.customers", { "customer_id": "hashed" })
sh.shardCollection("ecommerceDB.orders", { "order_id": "hashed" })

9. Verify Shard Status:

sh.status()

Benefits Observed:

 Collections evenly distributed across shards

 High scalability with ability to add new shards
 Improved performance on large datasets
5. Read/Write Distribution Testing

Using Compass, operations tested on both primary and secondary nodes.

 Read Preference: Secondary

 Write Preference: Primary
 Observations: Compass allows controlling read/write behaviors via connection
options.

6. Conclusion

This project successfully implements a highly available, scalable e-commerce system using
MongoDB Compass. From core CRUD operations to advanced aggregations, replication,
and sharding, all requirements were fulfilled through UI-based interactions—
demonstrating complete mastery of MongoDB in a distributed environment.

Appendix

Tools Used:

 MongoDB (v8.0.5)
 MongoDB Compass (GUI)
 mongosh (for cluster setup only)

Environment:

 Windows 10
 Ports used: 27017 (primary), 27018, 27019 (secondaries), 27020 (mongos)

Sample Data Volume:

 ~38,279 documents per collection