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MongoDB Learning Labs

Overview

MongoDB Labs Overview

This page maps each hands-on lab to the theory topic it reinforces.
Labs are available in two formats: MongoDB shell scripts (mongosh) in labs/ and Python/Jupyter notebooks in notebooks/.

Running Labs

MongoDB Shell (`mongosh`)

# Start the cluster first
cd docker && docker compose up -d

# Run a specific lab
docker exec -it mongo1 mongosh --file /labs/01_database_basics.js

# Interactive shell
docker exec -it mongo1 mongosh

Lab Summary

Lab	Shell Script	Python Notebook	Description	Theory Module
01	`01_database_basics.js`	`01_database_basics.ipynb`	CRUD operations, projections, bulk writes	NoSQL & MongoDB, Core Concepts
02	`02_document_modeling.js`	`02_document_modeling.ipynb`	Embedding vs referencing, 1-to-many, many-to-many	Data Modeling
03	`03_indexes.js`	`03_indexes.ipynb`	Single-field, compound, multikey, text, partial, sparse; explain()	Indexes & Aggregation
04	`04_aggregation_pipeline.js`	`04_aggregation_pipeline.ipynb`	$match, $group, $project, $unwind, $sort, $count	Indexes & Aggregation
05	`05_transactions.js`	`05_transactions.ipynb`	Multi-document ACID transactions, retry logic	Transactions & Consistency
06	`06_ttl_and_capped.js`	`06_ttl_and_capped.ipynb`	TTL indexes, capped collections, change stream intro	TTL & Change Streams
07	`07_advanced_aggregation.js`	`07_advanced_aggregation.ipynb`	$lookup, $facet, $bucket, $graphLookup, $setWindowFields	Advanced Aggregation
08	`08_schema_patterns.js`	`08_schema_patterns.ipynb`	Bucket, Computed, Polymorphic, Outlier, Subset patterns	Data Modeling
09	`09_replica_set.js`	`09_replica_set.ipynb`	rs.status(), read preferences, writeConcern, replication lag	Core Concepts, Advanced Topics
10	`10_security_basics.js`	`10_security_basics.ipynb`	createUser, built-in roles, custom roles, TLS guidance	Advanced Topics
11	`11_monitoring_and_performance.js`	`11_monitoring_and_performance.ipynb`	explain(), profiler, $indexStats, serverStatus, currentOp	Advanced Topics

Python / Jupyter Notebooks

cd notebooks
jupyter notebook 01_database_basics.ipynb

Python notebooks are located in the notebooks/ folder in the repository. Open any notebook file with jupyter notebook <filename>.ipynb after starting your environment.