/**
* This is modified from:
* https://docs.mongodb.com/getting-started/shell/
* https://docs.mongodb.com/getting-started/shell/introduction/
* https://www.mongodb.com/mongodb-architecture
* See also: 
* For Node: https://mongodb.github.io/node-mongodb-native/
* 
* A Document
{
   "_id" : ObjectId("54c955492b7c8eb21818bd09"),
   "address" : {
      "street" : "2 Avenue",
      "zipcode" : "10075",
      "building" : "1480",
      "coord" : [ -73.9557413, 40.7720266 ]
   },
   "borough" : "Manhattan",
   "cuisine" : "Italian",
   "grades" : [
      {
         "date" : ISODate("2014-10-01T00:00:00Z"),
         "grade" : "A",
         "score" : 11
      },
      {
         "date" : ISODate("2014-01-16T00:00:00Z"),
         "grade" : "B",
         "score" : 17
      }
   ],
   "name" : "Vella",
   "restaurant_id" : "41704620"
}
**/

// mongo # the mongo javascript shell, but we have to connect remotely...
// DO THE FOLLOWING IN A SHELL FROM CSLINUX!!!

mongo --host mcsdb.utm.utoronto.ca -u $UTORID -p --authenticationDatabase $UTORID_309 --shell

// When prompted: The password being the last 5 digits before the last 3 digits of the student barcode.
// or all in one line...
// DO THE FOLLOWING IN A SHELL FROM CSLINUX!!!

mongo --host mcsdb.utm.utoronto.ca -u "$UTORID" -p "PASSWORD_AS_ABOVE" --authenticationDatabase $UTORID_309 --shell

//  https://docs.mongodb.com/manual/reference/mongo-shell/

help

/**
* https://docs.mongodb.com/getting-started/shell/import-data/
* Download https://raw.githubusercontent.com/mongodb/docs-assets/primer-dataset/primer-dataset.json
* DO THE FOLLOWING IN A SHELL FROM CSLINUX!!!
**/

wget https://raw.githubusercontent.com/mongodb/docs-assets/primer-dataset/primer-dataset.json
mongoimport --host mcsdb.utm.utoronto.ca -u "$UTORID" -p "PASSWORD_AS_ABOVE" --db $UTORID_309 --collection restaurants --drop --file primer-dataset.json

/**
* now in the mongo shell (the one you launched above)...
**/

show dbs // You can't do this! You aren't allowed!!
use $UTORID_309 // switch to your database
show collections

// Collections = documents of similar 'type' , if you imported properly, you should see the restaurants collection

// CREATE

db.restaurants.insert(
   {
      "address" : {
         "street" : "2 Avenue",
         "zipcode" : "10075",
         "building" : "1480",
         "coord" : [ -73.9557413, 40.7720266 ]
      },
      "borough" : "Manhattan",
      "cuisine" : "Italian",
      "grades" : [
         {
            "date" : ISODate("2014-10-01T00:00:00Z"),
            "grade" : "A",
            "score" : 11
         },
         {
            "date" : ISODate("2014-01-16T00:00:00Z"),
            "grade" : "B",
            "score" : 17
         }
      ],
      "name" : "AVella",
      "restaurant_id" : "41704620"
   }
)

// RETRIEVE

db.restaurants.find()
db.restaurants.find().pretty() // so you can read it!

db.restaurants.find( { "borough": "Manhattan" } )     // exact match on top level field
db.restaurants.find( { "address.zipcode": "10075" } ) // embedded document
db.restaurants.find( { "grades.grade": "B" } )        // array match

// can chain commands
db.restaurants.find( { "borough": "Manhattan" } ).count();


// other operators $gt, $lt, $eq, $gte, ... 
// https://docs.mongodb.com/manual/reference/operator/query-comparison/

db.restaurants.find( { "borough" : { $eq : "Manhattan" } } )     // exact match on top level

db.restaurants.find( { "grades.score": { $gt: 30 } } )

db.restaurants.find( { "grades.score": { $lt: 10 } } )

// AND conditions
db.restaurants.find( { "cuisine": "Italian", "address.zipcode": "10075" } )
db.restaurants.find( { $and: [ {"cuisine": "Italian"}, {"address.zipcode": "10075" }] } )

// OR conditions
db.restaurants.find(
   { $or: [ { "cuisine": "Italian" }, { "address.zipcode": "10075" } ] }
)

// sort results

db.restaurants.find().sort( { "borough": 1, "address.zipcode": 1 } ) // 1 = increasing, -1 = decreasing


// UPDATE 
// Updating data: Can't modify _id
// Change one existing record ($set its fields, new or otherwise)

db.restaurants.update(
    { "name" : "Juni" },
    {
      $set: { "cuisine": "American (New)" },
      $currentDate: { "lastModified": true }
    }
)

// embedded field
db.restaurants.update(
  { "restaurant_id" : "41156888" },
  { $set: { "address.street": "East 31st Street" } }
)

// update multiple documents {multi : true}
db.restaurants.update(
  { "address.zipcode": "10016", cuisine: "Other" },
  {
    $set: { cuisine: "Category To Be Determined" },
    $currentDate: { "lastModified": true }
  },
  { multi: true}
)

// Replace a whole document: Notice no $set
db.restaurants.update(
   { "restaurant_id" : "41704620" },
   {
     "name" : "Vella 2",
     "address" : {
              "coord" : [ -73.9557413, 40.7720266 ],
              "building" : "1480",
              "street" : "2 Avenue",
              "zipcode" : "10075"
     }
   }
)

// DELETE
db.restaurants.remove( { "borough": "Manhattan" } ) // remove all matching criteria
db.restaurants.remove( { "borough": "Queens" }, { justOne: true } )

db.restaurants.remove( { } ) // leaves the collection empty
db.restaurants.drop() // remove the collection

// AGGREGATION
db.collection.aggregate( [ <stage1>, <stage2>, ... ] ) // stages are sequences of aggregate operations to perform

db.restaurants.aggregate(
   [
     { $group: { "_id": "$borough", "count": { $sum: 1 } } }
   ]
);

db.restaurants.aggregate(
   [
     { $match: { "borough": "Queens", "cuisine": "Brazilian" } },
     { $group: { "_id": "$address.zipcode" , "count": { $sum: 1 } } }
   ]

db.restaurants.aggregate(
   [      
      { $match: { "borough": "Queens", "cuisine": "Brazilian" } }, 
      { $group: { "_id": "$address.zipcode" , "average": { $avg: { $avg : "$grades.score"} } } }    
   ] 
);

f = db.restaurants.find();
var f = db.restaurants.find(); // suppress output

f[0];
f[1];
t = [];
for(var i = 0;i<f.length(); i++){ t.push(f[i].grades.length); }