mongodb cli commands

Text Indexing 

db.kitchen.insertMany([
... {_id: 1, dept: "tech", description: "lime green computer" },
... {_id: 2, dept: "tech", description: "wireless red mouse" },
... {_id: 3, dept: "kitchen", description: "green placemat" },
... {_id: 4, dept: "kitchen", description: "red peeler" },
... {_id: 5, dept: "food", description: "green apple" },
... {_id: 6, dept: "food", description: "red potato" }])

Creating Index 
db.kitchen.createIndex({dept:1, description: "text"})
dept_1_description_text

Querying through the kitchen collection
db.kitchen.find({dept: "kitchen", $text: {$search: "green"}})
[ { _id: 3, dept: 'kitchen', description: 'green placemat' } ]

Excercise 10 on mongosh

Create a collection called "articles" and insert documents representing articles with fields like "title" and "content".
Create a text index on the "content" field.
Write a query to search for articles that contain a specific keyword in their content.
 
db.articles.insertMany([
... {
...         'title': 'Introduction to MongoDB',
...         'content': 'MongoDB is a NoSQL database...',
...     },
...     {
...         'title': 'Python Programming Guide',
...         'content': 'Python is a powerful programming language...',
...     },
...     {
...         'title': 'Web Development Basics',
...         'content': 'Web development involves creating...',
...     }])

db.articles.createIndex({content: "text"})
content_text

db.articles.find({$text:{$search:"python"}})
[
  {
    _id: ObjectId("64903d3389c9e74320c47ca1"),
    title: 'Python Programming Guide',
    content: 'Python is a powerful programming language...'
  }
]


2DSphere indexing

Create a collection called "restaurants" and insert documents representing restaurants with fields like "name", "location", and "cuisine".
Create a 2dsphere index on the "location" field.
Write a query to find all restaurants within a certain distance from a given latitude and longitude.

db.places.insertMany( [
...    {
...       loc : { type: "Point", coordinates: [ -73.97, 40.77 ] },
...       name: "Central Park",
...       category : "Parks"
...    },
...    {
...       loc : { type: "Point", coordinates: [ -73.88, 40.78 ] },
...       name: "La Guardia Airport",
...       category : "Airport"
...    }
... ] )

2Dsphere indexing
db.places.createIndex({loc:'2dsphere'})

Querying a 2dsphere

db.places.find({loc:{$near:{$geometry:{type:'Point',coordinates: [ -73.97, 40.77 ]},$maxDistance: 5000}}})

result
[
  {
    _id: ObjectId("649040f589c9e74320c47ca3"),
    loc: { type: 'Point', coordinates: [ -73.97, 40.77 ] },
    name: 'Central Park',
    category: 'Parks'
  }
]

Grouping and aggregate question

Given a collection of documents representing students, each containing fields like "name", "age", and "grade",
write a MongoDB query to calculate the average age and average grade for each grade level.

The inserted collection can be found in the python file of this question

db.students.aggregate([{ $group:{"_id":"$grade","averageAge":{$avg: "$age"}, "count":{$sum:1}}},
... {$sort:{"_id":1}}])

[
  { _id: 'A', averageAge: 21, count: 8 },
  { _id: 'B', averageAge: 22, count: 6 },
  { _id: 'C', averageAge: 20, count: 6 }
]


Array Fields

Create a collection called "employees" and insert documents representing employees with fields like "name" and "skills" (an array of skills).
Write a query to find all employees who possess a specific skill.


The inserted collection can be found in the python file of this question

db.employees.find({skills:{$in:['JavaScript']}})
[
  {
    _id: ObjectId("6490628fb52980403fc6e2dc"),
    name: 'Alice',
    skills: [ 'Python', 'Django', 'JavaScript' ]
  },
  {
    _id: ObjectId("6490628fb52980403fc6e2dd"),
    name: 'Bob',
    skills: [ 'JavaScript', 'React', 'Node.js' ]
  },
  {
    _id: ObjectId("6490628fb52980403fc6e2e1"),
    name: 'Frank',
    skills: [ 'Python', 'Flask', 'JavaScript' ]
  },
  {
    _id: ObjectId("6490628fb52980403fc6e2e2"),
    name: 'Grace',
    skills: [ 'JavaScript', 'Angular', 'Node.js' ]
  }
]