The document discusses MongoDB commands and operations for working with databases, collections, and documents. It shows how to import sample data, query and filter documents, count documents, create indexes, insert new documents, and remove documents. Examples demonstrate finding documents by field values, projecting specific fields, sorting results, and getting distinct field values.

1. For starters:
mongod - MongoDB Daemon
mongo - MongoDB Shell
From the shell:
db.help() - DB level help. Help is organized around the MongoDB hierarchy.
MongoDB is organized by databases which contain collections which contain
documents
show dbs - Display a list of databases
db.getCollectionNames() - Shows the collections in the current database
I ve downloaded a test dataset from Mongo for this talk at
https://raw.githubusercontent.com/mongodb/docs-assets/primer-
dataset/dataset.json Then, from a terminal command prompt:
mongoimport --db test --collection restaurants --drop --file
~/Documents/MongoDBUG/15.9.3/dataset.json
use test - Sets the test database as the current database. Note, if the
database doesn t exist, it will be created for you but no file footprint until
you put something in it.
show collections - same as the db.getCollecitonNames() above except show
collections is a list where the other returns an array.
db.restaurants.help() - Help for the restaurants collection we just created.
db.restaurants.count() - Number of documents in the restaurants collection.
db.restaurants.findOne()
db.restaurants.getIndexes() - Note changed in current version.
db.restaurants.ensureIndex({"name": 1}) - When a command expects parameters,
they are almost always a JSON document. Here I ve requested an index on the
name key, the 1 denotes ascending and -1 would have denoted descending. Note,
deprecated in the newest version, now its createIndex.
db.restaurants.find() - Finds matching documents. In this example no parameters
were passed in so it just finds things in no particular order.
db.restaurants.find({"name": "Glorious Food"})
db.restaurants.find({"name": "Glorious Food"}).pretty()
Query a subdocument
db.restaurants.find({"address.zipcode": "10021"})
db.restaurants.find({"address.zipcode": "10021"}).count()
db.restaurants.find({"address.zipcode": "10021"}).limit(3).pretty()
db.restaurants.find({"address.zipcode": "10021"}).sort({"name":
1}).limit(3).pretty()
Find restaurants that have received a score greater than 20:
db.restaurants.find({"grades.score": {$gt: 20}}).count()
Narrowing what you see with projections:
db.restaurants.find({"grades.score": {$gt: 20}}, {"name": 1, "cuisine": 1})
db.restaurants.distinct("cuisine") - Get a list of distant values for cuisine
db.restaurants.distinct("grades.grade")
db.restaurants.distinct("cuisine").sort("cuisine")
db.restaurants.count()
db.restuarants.insert({"address" : {"building" : "1361", "street" : "S Orange
Ave", "zipcode" : "32806"}, "cuisine" : "American ", "name" : "Wendy s",
"restaurant_id" : "89976993"})
db.restaurants.count()
Fix our fat finger
db.restuarants.drop()
db.restaurants.insert({"address" : {"building" : "1361", "street" : "S Orange

2. Ave", "zipcode" : "32806"}, "cuisine" : "American ", "name" : "Wendy s",
"restaurant_id" : "89976993"})
db.restaurants.remove({"address.zipcode": "32806"})