MongoDB Basics with IMDb movies extensive dataset - Bakari Mustafa

Connecting to MongoDB Using Compass

Please download Compass from the MongoDB Download Center. If you downloaded Compass before today, please make sure you are using version 1.8 or later and upgrade if necessary.
Install Compass on your computer from the download.
Launch Compass.

When Compass opens you will see a page titled “Connect to Host”.

Content

The movies dataset includes 85,855 movies with attributes such as movie description, average rating, number of votes, genre, etc.

The rating dataset includes 85,855 rating details from a demographic perspective.

The names dataset includes 297,705 cast members with personal attributes such as birth details, death details, height, spouses, children, etc.

The title principals dataset includes 835,513 cast members roles in movies with attributes such as IMDb title id, IMDb name id, order of importance in the movie, role, and characters played.

Download the IMDB movies data (CSV format) from the following website: https://www.kaggle.com/stefanoleone992/imdb-extensive-dataset

Commands

show dbs -> show databases residing in a cluster.

use video -> switching to another database (with name video).

show collections -> command for displaying collections in a database.

db -> refers to the current referred database.

db.movies.find().pretty() -> seeing documents that are there in the movies collection of video database.

db.movies.find().count() -> count the number of documents in the movies collection of video database.

The Atlas clusters we’ve looked at are replica sets. Replica sets are designed so that if the primary node goes down, one of the other nodes will step up to take its place so that clients can continue reading and writing data as if nothing had happened. The mongo shell is one such client.

To begin creating your Atlas Sandbox cluster, visit register for atlas and complete the account creation form you see on that page.

Connecting to our Atlas sandbox cluster from the Mongo shell

mongo "mongodb://sandbox-shard-00-00-2xiao.mongodb.net:27017,sandbox-shard-00-01-2xiao.mongodb.net:27017,sandbox-shard-00-02-2xiao.mongodb.net:27017/test?replicaSet=Sandbox-shard-0" --ssl --authenticationDatabase admin --username your-username --password your-password

Loading data (.js file) into your sandbox cluster

cd M001 (in your home directory which contains the .js file)
Run the MongoDB shell:

mongo "mongodb://sandbox-shard-00-00-2xiao.mongodb.net:27017,sandbox-shard-00-01-2xiao.mongodb.net:27017,sandbox-shard-00-02-2xiao.mongodb.net:27017/video?replicaSet=Sandbox-shard-0" --ssl --authenticationDatabase admin --username your-username --password your-password

video is the db that you are loading your collections into. We can change to some other db as well.
Once connected to sandbox cluster run load("loadMovieDataSet.js")

Connecting to our Atlas sandbox cluster from Compass

Go to Compass -> Connect to host
In the cloud.mongodb.com

a.) Select the sandbox cluster Sandbox -> Select Primary node and copy the Url sandbox-shard-00-00-2xiao.mongodb.net and paste in the hostname field of a compass.

b.) In the authentication phase -> select username/ password system and provide the username -> your-username and password -> your-password

c.) Add Favorite name M001 Sandbox. Click Connect

Inserting Documents from MongoDB Shell

use video

show collections

One document at a time

Without _id (default id is supplied)

db.moviesScratch.insertOne({title:"Star Trek II: The Wrath of Khan", year:1982, imdb:"tt0084726"})

With _id

db.moviesScratch.insertOne({_id:"tt0084726", title:"Star Trek II: The Wrath of Khan", year:1982, imdb:"tt0084726"})

Many documents at a time

//Ordered

db.moviesScratch.insertMany(
[
{
"_id" : "tt0084726",
"title" : "Star Trek II: The Wrath of Khan",
"year" : 1982,
"type" : "movie"
},
{
"_id" : "tt0796366",
"title" : "Star Trek",
"year" : 2009,
"type" : "movie"
},
{
"_id" : "tt0084726",
"title" : "Star Trek II: The Wrath of Khan",
"year" : 1982,
"type" : "movie"
},
{
"_id" : "tt1408101",
"title" : "Star Trek Into Darkness",
"year" : 2013,
"type" : "movie"
},
{
"_id" : "tt0117731",
"title" : "Star Trek: First Contact",
"year" : 1996,
"type" : "movie"
}
]
);


//Unordered

db.moviesScratch.insertMany(
[
{
"_id" : "tt0084726",
"title" : "Star Trek II: The Wrath of Khan",
"year" : 1982,
"type" : "movie"
},
{
"_id" : "tt0796366",
"title" : "Star Trek",
"year" : 2009,
"type" : "movie"
},
{
"_id" : "tt0084726",
"title" : "Star Trek II: The Wrath of Khan",
"year" : 1982,
"type" : "movie"
},
{
"_id" : "tt1408101",
"title" : "Star Trek Into Darkness",
"year" : 2013,
"type" : "movie"
},
{
"_id" : "tt0117731",
"title" : "Star Trek: First Contact",
"year" : 1996,
"type" : "movie"
}
],
{
"ordered": false 
}
);

Filtering with queries

In compass

{$and:[{"awards.wins":2},{"awards.nominations":2}]}

In MongoDB command shell

db.movieDetails.find({$and:[{"awards.wins":2},{"awards.nominations":2}]}).count()

db.movies.find({cast:["Jeff Bridges", "Tim Robbins"]}).pretty()

db.movieDetails.find({genres: 'Family'}).count()

db.movieDetails.find({"genres.1": 'Western'}).count()

Cursors

The find() the method returns a cursor. A cursor is essentially a pointer to the current location in a result set. For queries that return more than just a few documents, MongoDB will return the results in batches to our client (Mongo Shell). We use the cursor in our client to iterate through the results.

Projections

Projections reduce the network overhead and processing requirements by limiting the fields that are returned in the resulting documents. In the query, projection is added as the second argument to find() method.

Example:

db.movies.find({genre:"Action, Adventure"}, {title: 1})

_id the field is returned as well by default and you can exclude it by supplying _id:0 as follows db.movies.find({genre:"Action, Adventure"}, {title: 1, _id: 0})

If we want to exclude a couple of fields then we need to type as: db.movies.find({genre:"Action, Adventure"}, {viewerRating: 0, viewerVotes: 0, runtime: 0, _id: 0})

Updating documents

db.movieDetails.updateOne({
title: "The Martian"
},{
$set:{
poster: ""    
}
})


db.movieDetails.updateOne({
title: "The Martian"
},{
$set:{
"awards": {
"wins": 8,
"nominations": 14,
"text": "Nominated for 3 Golden Globes. Another 8 wins and 14 nominations."
}    
}
});

$set replaces the value of the field with the specified value.

The difference between updateOne and updateMany is that updateMany will make the same modification to all documents that match the filter.

db.movieDetails.updateMany({
rated: null
},{
$unset:{
rated: ""    
}
})

We can insert while doing the update step as shown in the below example.

db.movieDetails.updateOne({
"imdb.id": detail.imdb.id
},{
$set: detail
},{
upsert: true
});

Upsert updates the documents matching the filter. If there are none, insert the update document as the new document in the collection.

db.movieDetails.replaceOne({
"imdb.id": detail.imdb.id
},
detailDoc
);

QUERY OPERATORS

db.movieDetails.find({runtime: {$gt: 90}})

db.movieDetails.find({runtime: {$gt: 90}}, {_id: 0, title: 1, runtime: 1})

db.movieDetails.find({runtime: {$gt: 90, $lt: 120}}, {_id: 0, title: 1, runtime: 1})

db.movieDetails.find({runtime: {$gte: 90, $lte: 120}}, {_id: 0, title: 1, runtime: 1})

db.movieDetails.find({runtime: {$gte: 180}, "tomato.meter": 100}, {_id: 0, title: 1, runtime: 1})

db.movieDetails.find({rated: {$ne: "UNRATED"}}, {_id: 0, title: 1, rated: 1})

db.movieDetails.find({rated: {$in: ["G", "PG"]}}, {_id: 0, title: 1, rated: 1})

db.movieDetails.find({rated: {$in: ["G", "PG", "PG-13"]}}, {_id: 0, title: 1, rated: 1}).pretty()

db.movieDetails.find({rated: {$in: ["R", "PG-13"]}}, {_id: 0, title: 1, rated: 1}).pretty()

COMPARISON OPERATORS

db.movies.find({cast: {$in: ["Jack Nicholson", "John Huston"]}, viewerRating: {$gt: 7}, mpaaRating: "R"}).count()

Note: The above post is in reference to material related to M001-Basics course offered by MongoDB University.

Reference

Learn MongoDB from MongoDB - M001: MongoDB Basics