NoSQL Databases using MongoDB Textbook - Lab Scripts

Complete Python, Shell, and infrastructure scripts for all 36 hands-on labs (2 per chapter x 18 chapters) plus the Appendix A capstone, from the textbook "NoSQL Databases Using MongoDB: A Practical Guide to NoSQL Concepts, Data Modeling, and Real-world Applications with MongoDB".

Book Website - https://www.lurnexa.in/textbooks/nosql-databases-using-mongodb/

---

Quick Start

# 1. Clone and install dependencies
pip install -r requirements.txt

# 2. Start MongoDB (Docker, one-liner)
docker run -d -p 27017:27017 --name mongo mongo:7

# 3. Copy and edit environment
cp .env.example .env
# Edit .env with your Atlas URI, API keys, etc.

# 4. Run any lab
python chapter-02/first_connection.py
python chapter-02/lab_02_bookstore_crud.py

---

Project Structure

nosql-mongodb-labs/
├── README.md                   # This file
├── requirements.txt            # Python dependencies
├── .env.example                # Environment variable template
├── config/
│   ├── __init__.py
│   └── connection.py           # Shared MongoDB connection utility
├── chapter-01/                 # Why NoSQL?
│   ├── lab_01_classify_databases.py
│   └── lab_02_map_application.py
├── chapter-02/                 # Cloud Computing Primer
│   ├── first_connection.py
│   ├── lab_01_setup_environment.py
│   └── lab_02_bookstore_crud.py
├── chapter-03/                 # Data Model Fundamentals
│   ├── bson_exploration.py
│   ├── lab_01_embedded_schema.py
│   ├── lab_02_embed_vs_reference.py
│   └── schema_validation.py
├── chapter-04/                 # CRUD and Query Language
│   ├── lab_01_product_query_api.py
│   └── lab_02_upsert_bulk.py
├── chapter-05/                 # Aggregation Framework
│   ├── lab_01_sales_analytics.py
│   └── lab_02_lookup_joins.py
├── chapter-06/                 # Indexing Strategies
│   ├── lab_01_index_design.py
│   └── lab_02_index_strategies.py
├── chapter-07/                 # Architecture Deep Dive
│   ├── lab_01_production_schema.py
│   └── lab_02_working_set_analysis.py
├── chapter-08/                 # Replication & HA
│   ├── docker-compose-replicaset.yml
│   ├── lab_01_deploy_replicaset.sh
│   └── lab_02_failover_observer.py
├── chapter-09/                 # CAP Theorem in Practice
│   ├── lab_01_write_concern_latency.py
│   └── lab_02_simulate_partition.sh
├── chapter-10/                 # Transactions
│   ├── lab_01_bank_transfer.py
│   └── lab_02_inventory_reservation.py
├── chapter-11/                 # Sharding
│   ├── lab_01_shard_key_eval.py
│   └── lab_02_shard_key_hotspot.py
├── chapter-12/                 # Security & CIA Triad
│   ├── lab_01_rbac_setup.sh
│   ├── lab_02_tls_setup.sh
│   └── rbac_field_redaction.py
├── chapter-13/                 # Container/Cloud Deployment
│   ├── docker-compose-production.yml
│   ├── lab_01_deploy_prod.sh
│   ├── lab_02_k8s_statefulset.py
│   └── main.tf                # Terraform for Atlas
├── chapter-14/                 # Production Operations
│   ├── lab_01_monitoring_dashboard.py
│   └── lab_02_backup_restore.py
├── chapter-15/                 # Multi-Tenant SaaS Architecture
│   ├── lab_01_multitenant_api.py
│   └── lab_02_tenant_migration.py
├── chapter-16/                 # Polyglot Persistence
│   ├── lab_01_polyglot_modeling.py
│   └── lab_02_hybrid_query_service.py
├── chapter-17/                 # Vector Search & RAG
│   ├── lab_01_rag_pipeline.py
│   └── lab_02_hnsw_vs_ivf.py
├── chapter-18/                 # Comparative Capstone
│   ├── lab_01_comparative_capstone.py
│   └── lab_02_comparative_report.py
└── appendix-a/                 # Full LLM Chat App
    ├── chat_api.py             # FastAPI backend
    ├── streamlit_app.py        # Streamlit frontend
    ├── Dockerfile
    ├── docker-compose.yml
    └── ingest_sample_data.py

---

Lab-to-Script Mapping

Ch	Title	Lab Script	Requires
1	Why NoSQL?	lab_01_classify_databases.py	None (in-memory)
1	App Mapping	lab_02_map_application.py	MongoDB
2	Setup Env	lab_01_setup_environment.py	Docker (optional)
2	Bookstore CRUD	lab_02_bookstore_crud.py	MongoDB
3	Embedded Schema	lab_01_embedded_schema.py	MongoDB
3	Embed vs Ref	lab_02_embed_vs_reference.py	MongoDB
3	BSON Types	bson_exploration.py	MongoDB
3	Schema Validation	schema_validation.py	MongoDB
4	Product Query API	lab_01_product_query_api.py	MongoDB
4	Upsert & Bulk	lab_02_upsert_bulk.py	MongoDB
5	Sales Analytics	lab_01_sales_analytics.py	MongoDB
5	$lookup Joins	lab_02_lookup_joins.py	MongoDB
6	Index Design	lab_01_index_design.py	MongoDB
6	Index Strategies	lab_02_index_strategies.py	MongoDB
7	Production Schema	lab_01_production_schema.py	MongoDB
7	Working Set	lab_02_working_set_analysis.py	MongoDB
8	Deploy Replica Set	lab_01_deploy_replicaset.sh	Docker Compose
8	Failover Observer	lab_02_failover_observer.py	Replica Set (Ch 8)
9	Write Concern Latency	lab_01_write_concern_latency.py	Replica Set (Ch 8)
9	Simulate Partition	lab_02_simulate_partition.sh	Replica Set (Ch 8)
10	Bank Transfer	lab_01_bank_transfer.py	MongoDB 4.0+
10	Inventory Reservation	lab_02_inventory_reservation.py	MongoDB 4.0+ (transactions)
11	Shard Key Eval	lab_01_shard_key_eval.py	None (analysis)
11	Shard Key Hotspot	lab_02_shard_key_hotspot.py	MongoDB (simulation, no real cluster needed)
12	RBAC Setup	lab_01_rbac_setup.sh	MongoDB + mongosh
12	TLS Setup	lab_02_tls_setup.sh	MongoDB + mongosh + OpenSSL
12	Field Redaction	rbac_field_redaction.py	MongoDB
13	Deploy Prod RS	lab_01_deploy_prod.sh	Docker Compose
13	K8s StatefulSet	lab_02_k8s_statefulset.py	kubectl + cluster (optional; generates manifests either way)
13	Terraform Atlas	main.tf	Terraform + Atlas account
14	Monitoring	lab_01_monitoring_dashboard.py	MongoDB
14	Backup & Restore	lab_02_backup_restore.py	MongoDB + Database Tools (mongodump/mongorestore)
15	Multi-Tenant API	lab_01_multitenant_api.py	MongoDB
15	Tenant Migration	lab_02_tenant_migration.py	MongoDB
16	Polyglot Modeling	lab_01_polyglot_modeling.py	None (comparison)
16	Hybrid Query Service	lab_02_hybrid_query_service.py	MongoDB (Neo4j optional; falls back to an in-memory graph)
17	RAG Pipeline	lab_01_rag_pipeline.py	MongoDB
17	HNSW vs IVF	lab_02_hnsw_vs_ivf.py	None (NumPy only, fully offline)
18	Comparative Capstone	lab_01_comparative_capstone.py	MongoDB
18	Comparative Report	lab_02_comparative_report.py	MongoDB (optional; falls back to labeled estimates if unreachable)
App	LLM Chat App	appendix-a/	FastAPI + Streamlit + Ollama/OpenAI

---

Prerequisites

Tool	Version	Purpose
Python	3.10+	All lab scripts
MongoDB	7.0+	Primary database
Docker	20.10+	Container-based labs (Ch 8, 13)
mongosh	2.0+	Shell scripts (Ch 8, 9, 12, 13)
pip	latest	Install Python packages

Optional (for specific chapters):

Tool	Chapter	Purpose
Ollama	17, App	Local LLM inference
OpenAI API key	17, App	GPT embeddings + chat
Terraform	13	Atlas infrastructure provisioning
kubectl + a cluster (minikube/kind/managed)	13	Kubernetes StatefulSet lab (manifests still generate without one)
MongoDB Database Tools	14	mongodump/mongorestore for the backup lab
OpenSSL	12	TLS certificate generation
Cassandra	16, 18	Wide-column comparison (labs run without it, using estimated figures)
Neo4j	16, 18	Graph database comparison (labs run without it, using estimated figures / an in-memory fallback graph)

---

Running the Labs

Single script (standalone)

Every Python script can be run directly from the project root:

python chapter-04/lab_01_product_query_api.py

Each script imports the shared config/connection.py module, which reads from .env for the MongoDB URI.

Shell scripts

chmod +x chapter-08/lab_01_deploy_replicaset.sh
./chapter-08/lab_01_deploy_replicaset.sh

Replica Set labs (Ch 8, 9)

These require a running 3-node replica set:

# Start the replica set
./chapter-08/lab_01_deploy_replicaset.sh

# In another terminal, observe failover
python chapter-08/lab_02_failover_observer.py

# Benchmark write concerns
python chapter-09/lab_01_write_concern_latency.py

# Simulate a network partition
chmod +x chapter-09/lab_02_simulate_partition.sh
./chapter-09/lab_02_simulate_partition.sh

Labs with a graceful no-server fallback

A few labs are designed to still run (and still teach something) even without every optional tool installed:

• chapter-13/lab_02_k8s_statefulset.py generates and validates the Kubernetes manifests even with no cluster reachable; it only attempts kubectl apply if a cluster is detected.
• chapter-16/lab_02_hybrid_query_service.py uses a real Neo4j connection if NEO4J_URI is set and the neo4j package is installed, otherwise falls back to an equivalent in-memory graph traversal.
• chapter-17/lab_02_hnsw_vs_ivf.py has no database dependency at all -- it benchmarks vector-index strategies on a synthetic in-memory dataset.
• chapter-18/lab_02_comparative_report.py measures real MongoDB numbers if a database is reachable, otherwise falls back to labeled example figures so the report still generates -- and the report text always states which numbers were measured versus estimated.

Appendix A - Full LLM Chat App

# Start MongoDB
docker run -d -p 27017:27017 --name chat-mongo mongo:7

# Option A: Run locally
pip install fastapi uvicorn streamlit openai requests
python appendix-a/ingest_sample_data.py  # populate RAG data
uvicorn appendix-a.chat_api:app --reload  # start backend
streamlit run appendix-a/streamlit_app.py --server.port 8501  # start frontend

# Option B: Docker Compose
# (requires Ollama running on host for LLM backend)
cd appendix-a && docker compose up --build

---

Notes

• All Python scripts use pymongo and include error handling for connection failures.
• Shell scripts require mongosh (not legacy mongo shell).
• Chapters 8-9 labs require a replica set (use the Ch 8 docker-compose to set one up).
• Chapter 11 (Sharding) and Chapter 16's first lab (Polyglot Modeling) are analysis/design labs; Chapter 11's second lab and Chapter 16's second lab do use a standalone MongoDB to demonstrate the pattern in code, but neither requires a real sharded cluster or a real Neo4j instance to run.
• The Appendix A chat app works with mock embeddings by default. Set OPENAI_API_KEY or VOYAGE_API_KEY in .env for real embeddings.
• Every chapter from 1 through 18 has exactly two lab scripts (36 labs total), plus the Appendix A capstone. If you're looking for a chapter's "second lab" and only see one file in an older checkout, pull the latest version of this repo.