Lab 9: Practice with MongoDB

Learn MongoDB with hands-on practice.

• Lab 9: Practice with MongoDB
  • Run MongoDB & Connect
  • Exercise 1
  • Exercise 2
  • Reference: CRUD Operations
  • Integrating with python3
  • Learn More
  • Submit

Run MongoDB & Connect

Setup

This course will be using MongoDB Atlas, a cloud-based Mongo service, for hands-on exercises. If you would like your own Atlas cluster, follow the instructions in the How To video for setup. I recommend using a Google account when creating your Atlas MongoDB Cluster.

For class exercises you can use an Atlas cluster that has already been created:

1. Open this repository in GitHub Codespaces

2. Set up (if necessary)

curl https://gist.githubusercontent.com/nmagee/8af7b3f71bbd14730f83bf365c20d878/raw/673ba528f4b9352eded70ddd131a319de02f2545/install.sh | bash

3. You will now have a command MONGO-ATLAS that will open up a mongosh connection to my MongoDB Atlas cluster. If the command is unavailable, run source ~/.bashrc to refresh.

Connect

Examine the MONGO-ATLAS command by using this command and reading the output:

cat ~/.bashrc | grep "MONGO"

You will see it is made up of a mongosh command with parameters like:

mongosh "mongodb+srv://USERNAME:[email protected]/"

Issuing this command will give you a propmt like this:

Current Mongosh Log ID:	66158541454b7620e4a690a6
Connecting to:		mongodb+srv://<credentials>@cluster0.pnxzwgz.mongodb.net/?appName=mongosh+2.2.3
Using MongoDB:		7.0.8 (API Version 1)
Using Mongosh:		2.2.3

For mongosh info see: https://docs.mongodb.com/mongodb-shell/

Atlas atlas-2o6kes-shard-0 [primary] test>

After watching the Mongo in 30 minutes video, try completing both of these exercises:

Exercise 1

This task uses sample data available on the Atlas Cloud console.

1. From within the Atlas Cloud console, load sample data using the ... button in your cluster settings. Look for the "Load Sample Dataset" option in the drop-down list.

2. Using your mongosh shell, list your databases, select the sample_mflix set, then show collections within that:

show dbs;
use sample_mflix;
show collections;

3. This should show you there is a movies collection within that database. Find all documents in the collection, then count them:

db.movies.find();
db.movies.countDocuments();

4. Search for all documents containing a year of 1921 and count the results. Then display the results:

db.movies.find({"year":1921})
db.movies.countDocuments({"year":1921})

5. Retrieve a single document based on ObjectId:

db.movies.find(ObjectId('573a1391f29313caabcd72f0'));

6. Finally, using the code below insert a new document. After insertion, can you retrieve this document?

db.movies.insertOne({
    genres: [ 'Drama' ],
    runtime: 14,
    cast: [
        'Acty Actor',
    ],
    title: "Example Movie",
    countries: [ 'USA' ],
    released: ISODate('1930-01-01T00:00:00.000Z'),
    directors: [ 'Director Name' ],
    lastupdated: '2024-04-09 01:12:08.943000000',
    year: 1930,
    imdb: { rating: 8.1, votes: 1455, id: 12999 },
    type: 'movie',
    num_mflix_comments: 0
});

Exercise 2

This exercise walks you through creating your own database and populating a collection with your own data.

1. List DBs

show dbs

2. Show the DB you are currently in

db

3. Use a specific database based on your UVA computing ID. If it does not exist, it will be created.

use mst3k
show dbs

Notice that your new database does not yet show up. This is because it needs to contain some documents first.

4. Insert a simple document. You will specify a collection within the things DB, and if it does not yet exist it will be created.

db.hobbies.insertOne({name:"horseback riding"})

5. List all documents in this collection.

db.hobbies.find()

You should get back a simple document:

{
    "_id" : ObjectId("606b5e9d37c1606354c39e3d"),
    "name" : "horseback riding",
}

6. Insert several more documents, varied in their data complexity.

db.hobbies.insertOne({"name":"cycling","equipment":["bicycle","helmet","air pump"]})
db.hobbies.insertOne({"name":"basketball","equipment":["ball","shoes","court","rim","game"]})
db.hobbies.insertOne({"name":"archery","equipment":["bow","arrows"]})

7. View all documents again:

db.hobbies.find()

8. Notice your first document lacks any equipment values. To update it

db.hobbies.updateOne({name:"horseback riding"},{$set : {equipment:["horse","saddle","helmet"]}})

9. Search for that record to see if it has been updated properly:

db.hobbies.find({name:"horseback riding"})

{
    "_id" : ObjectId("606b5e9d37c1606354c39e3d"),
    "name" : "horseback riding",
    "equipment" : [ "horse", "saddle", "helmet" ]
}

10. Search for all hobbies that require a helmet:

db.hobbies.find({equipment:"helmet"})

1. Upsert - adds a document when it does not exist from an UPDATE command

db.hobbies.update({name:"ultimate frisbee"},{name:"ultimate frisbee",equipment:["friends","frisbee"]},{upsert: true})

1. Remove a document You can remove document based on any find parameters, such as a particular value. However, the most unique key for single-row deletions is the _id of a particular row. Try deleting some documents (replace the ObjectId value with one from your collection:

# Remove by the _id:ObjectId
db.hobbies.remove({_id:ObjectId('606b5e9d37c1606354c39e3d')})

# Or use deleteOne if you know another unique value
db.hobbies.deleteOne({name:"archery"})

# Or deleteMany based on a common attribute
db.hobbies.deleteMany({equipment:"helmet"})

Reference: CRUD Operations

Let's create another collection to work with called inventory:

db.inventory.insertMany( [
   { item: "canvas", qty: 100, size: { h: 28, w: 35.5, uom: "cm" }, status: "A" },
   { item: "journal", qty: 25, size: { h: 14, w: 21, uom: "cm" }, status: "A" },
   { item: "mat", qty: 85, size: { h: 27.9, w: 35.5, uom: "cm" }, status: "A" },
   { item: "mousepad", qty: 25, size: { h: 19, w: 22.85, uom: "cm" }, status: "P" },
   { item: "notebook", qty: 50, size: { h: 8.5, w: 11, uom: "in" }, status: "P" },
   { item: "paper", qty: 100, size: { h: 8.5, w: 11, uom: "in" }, status: "D" },
   { item: "planner", qty: 75, size: { h: 22.85, w: 30, uom: "cm" }, status: "D" },
   { item: "postcard", qty: 45, size: { h: 10, w: 15.25, uom: "cm" }, status: "A" },
   { item: "sketchbook", qty: 80, size: { h: 14, w: 21, uom: "cm" }, status: "A" },
   { item: "sketch pad", qty: 95, size: { h: 22.85, w: 30.5, uom: "cm" }, status: "A" }
] );

Create

db.<db-name>.insertOne({...})
db.inventory.insertOne({ item: "calendar", qty: 80, size: { h: 20, w: 40, uom: "cm" }, status: "D" })

db.<db-name>.insertMany({...})   # See above

Read

db.<db-name>.find({...})
db.inventory.find({"status": "D"})

db.<db-name>.findOne({...})
db.inventory.findOne(ObjectId('66157d66f27a5c78964fdd80'))

Update / Upsert

An upsert is applied to all documents matching the criteria, or inserts a new document if there are no matches.

db.<db-name>.update({{"<search-key>" : "<search-value>"},{$set : {"<key>": "<updated-value>"}}})
db.<db-name>.updateOne({SingleKeyToUpdate},{Set Command})

db.inventory.updateOne(
   { item: "paper" },
   {
     $set: { "size.uom": "cm", status: "P" },
     $currentDate: { lastModified: true }
   }
)

Delete

db.<db-name>.deleteOne(<search-condition>)
db.<db-name>.delete(<search-condition>)

db.inventory.deleteMany({ status : "A" })

Integrating with python3

Try installing the pymongo library! The same operations will work in Python.

Change into the python/ directory to explore simple CRUD operations in MongoDB.

Learn More

• MongoDB Tutorials

Submit

Using the MongoDB Atlas connection we worked with in class, switch to the database you created under your UVA computing ID.

Write a single Python script that

1. Creates a new collection,
2. inserts five (5) documents, and then
3. write a query that displays exactly three (3) of those documents.

Be creative with data values or other attributes to make your find or find_many statements easy.

Save this file as a new GitHub gist here: https://gist.github.com/ and submit the URL for grading.