MongoDB workshop I: get started

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Posted on Feb 11, 2014

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Many thanks go to Thoughtworks NYC for providing the space!

-------------------------------------

Slides:

MongoDB Workshop from eagerdeveloper

-------------------------------------

Meetup Announcement:

This workshop could help you get familiar with MongoDB commands.

Speakers: Kannan and Roman are Data Science enthusiasts who are constantly expanding their expertise by attending technology-related meetups in New York City, and as students of online courses at Coursera and Udacity.

Kannan works as a software developer in Weitz & Luxenberg, programming in .NET, SQL Server, PHP, and MySQL. He loves machine learning, and is currently learning Python, Java, and Hadoop.

Roman has worked in marketing, data analytics and statistical modeling roles in American Express, Citicards, 1800Flowers.com, and PetCareRx.com. He currently works in Verizon Wireless.

Outline:

1. Intro: An overview of databases and MongoDB.
2. Deep dive: Walk through with examples.
3. Hack time: Instructors will go around and help people with their tasks or answer specific questions for them.

Content:

MongoDB operations: Create, Read, Update, Delete, Aggregation
MongoDB, NoSQL and Relational Databases
Administration: Replication and sharding in MongoDB

-------------------------------------

Other Useful Info Link:

You can find the data and source code here (https://github.com/eagerdeveloper/mongodb_workshop/) or see below.

Welcome to MongoDB workshop
ParksNYC.json is located in parksnyc-tennis folder
mongoimport to import the dataset
At the terminal, use: mongoimport -d tennis -c ParksNYC --type json --drop < ParksNYC.json
//import (to be run in mongo shell) mongoimport -d tennis -c ParksNYC --type json --drop < ParksNYC.json
//Mongo 
//show dbs
//use tennis
//show collections


//first command    
db.ParksNYC.insert(
{
    Prop_ID : "Q900",
    Name : "Ridge Park",
    Location : "1843 Norman St.",
    EstablishedOn: "1/1/1970"
})
// as in sql if you run this command twice it will create 2 documents with same details 
// read specific dacument
db.ParksNYC.find(
{Name : "Ridge Park"
})

// read all documents
db.ParksNYC.find()
// read first document 
db.ParksNYC.findOne()
// find specific document
// find specific fields in all documents
db.ParksNYC.find({ },{  Name: 1 })
db.ParksNYC.find({ },{ _id: 0, Name: 1 })
//Find documents meeting specific conditions 
db.ParksNYC.find(
   { Courts: { $gt: 5, $lte: 8} }
)
   
// regular expression
db.ParksNYC.find({ Name: /^F/ })

   
// update (insert) field conditional on other field criteria
db.ParksNYC.update({Prop_ID : /^X/ }, {$set: { "Boro":"Bronx"}},{ multi: true })
db.ParksNYC.findOne({Prop_ID : /^X/ })
// update conditional on field criteria $push
db.ParksNYC.update(
                    { Name : "Van Cortlandt Park"},
                    { $push: { Tennis_Type: "Clay" } }
                  )
                    
db.courts_b2.find({"Prop_ID" : "B129" })
  db.ParksNYC.update(
                    { Prop_ID: "B129" },
                    { $push: { Tennis_Type: "Grass" } })
                    
db.ParksNYC.find()
db.ParksNYC.find({"Name" : "Van Cortlandt Park" })                    
                    
db.ParksNYC.find({Tennis_Type: "Clay"}) 
db.ParksNYC.find({Tennis_Type: "Grass"}) 
// update
db.ParksNYC.update(
    { },
    { $set: {VisitDate: "1/1/2014" } },
    { multi: true }
)
db.ParksNYC.findOne()
    
// delete field
db.ParksNYC.update(
    { },
    { $unset: {VisitDate: "" } },
    { multi: true }
) 
   
//delete document
db.ParksNYC.remove(
{ Name:"Ridge Park"
})
// to check if doc has been removed
db.ParksNYC.find({ Name:"Ridge Park"})

// aggregation framework
// sort
db.ParksNYC.aggregate(
    { $sort : { Courts : -1, Accessible: 1 } }
)
 
// limit
db.ParksNYC.aggregate(
    { $limit : 5 }
) 
 
//skip   
    
db.ParksNYC.aggregate(
    { $skip : 70 }
)
// $group by
db.ParksNYC.aggregate(
    { $group : {
        _id : "$Accessible",
        Parks_Number : { $sum : 1 },
        Courts_Number : { $sum : "$Courts" }
    }}
)
db.ParksNYC.aggregate([ { 
    $group: { 
        _id: "$Accessible", 
        total: { 
            $sum: "$Courts" 
        } 
    } 
} ] )
//sum
db.ParksNYC.aggregate([ { 
    $group: { 
        _id: null, 
        total: { 
            $sum: "$Courts" 
        } 
    } 
} ] )
    

// unwind single document
  db.ParksNYC.find({ Name: "Mill Pond Park"}) 
  db.ParksNYC.aggregate ([
   {
      "$match":
      {
         "Name": "Mill Pond Park"
      }
   },
   {
      "$unwind": "$Tennis_Type"
   }
])
// unwind entire Tennis_Type for collection and group by park
db.ParksNYC.aggregate ([
   
   {
      "$unwind":"$Tennis_Type"
   },
   {
      "$group":
      {
         "_id":
         {
            "Name" : "$Name" 
         },
         "Surface_Type_Count":
         {
            "$sum": 1
         }
      }
   }
])
  
//unwind on tennis_type, group by parks and sort by Surface_Type_Count
db.ParksNYC.aggregate ([
   {
      "$unwind":"$Tennis_Type"
   },
   {
      "$group":
      {
         "_id":
         {
            "Name" : "$Name" 
         },
         "Surface_Type_Count":
         {
            "$sum": 1
         }
      }
   },
{
      "$sort":
      {
         "Surface_Type_Count":-1,
         "Name":1
      }
   }
])     
//$unwind on tennis_type, $group by parks , $limit to only top 6 parks, save results in new 'summary' collection  
db.ParksNYC.aggregate ([
   
   {
      "$unwind":"$Tennis_Type"
   },
   {
      "$group":
      {
         "_id":
         {
            "Name" : "$Name" 
         },
         "Surface_Type_Count":
         {
            "$sum": 1
         }
      }
   },
{
      "$sort":
      {
         "Surface_Type_Count":-1,
         "Name":1
      }
   }, 
   {
      "$limit":6,
   },
     { $out : "summary" }
])
//check
     db.summary.find()
//exportsummary to csv file
//mongoexport -d tennis -c summary --out summary.csv

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