MongoDB Conference - Strategies

gistfile1.md

scalar-vector-operations

multi-key indexing allows for indexing properties within an array. It makes things fast [ red, blue, green ]

On the way out Is red in the colors array? Is red not in the colors array? Scalar: $ne, $mod, $exists, $type, $lt, $gt, $gte, $ne Vector: $in, $nin, $all, $size

On the way in: Atomicness scalar: $inc, $set, $unset vector: $push, $pop, $pull, $pullAll, $addToSet

Cast Scenarios

//Case #1: As a librarian, when I swipe a patrons cards, I want to verify their address //• One-to-one relationships = "Belongs to". They are ofect embedded

patron = {
  _id: "joe",
	name: "joe trojan",
	address: {
		street: "123 face st. ",
		city: "Los Angeles", 
		state: "MA", 
		Zip: 10000
	}
}

//Case #2: As a librarian, I want to store multiple addresses so I have a better chance of hunting you down

patron = {
	_id: "joe",
	name: "joe trojan",
	join_date: ISODate("2011-10-14")
	address: [
		{ street: "123 face st. ", city: "Los Angeles", state: "MA",  Zip: 10000 }
		{ street: "456 face st. ", city: "Los Angeles", state: "MA",  Zip: 10000 }
	]
}

3. As a librariean, I want to see the publisher of a book //• publisher puts out a lot of book

book = {
	_id: "123", 
	title: "Mongo DB Book",
	authors: [],
	published_date: ISODate("2010-09-24"),
	pages: 215,
	language: "English",
	publisher: {
		name: "O'Reilery media",
		founded: 1980,
		location: "CA"
	}
}

//This causes trouble because it'll be hard to find all the publishers in a query. Plus, this data is immutable (meaning that it will never change) but

4. A librarian wants to do a query for all the publshers in the system //If you dont' care about history, you don't need to worry about changes to the publisher but data is history so you must respect it.

publisher = {
	_id: "oreilly",
	name: "O'Reilley media ",
	founded: 1980,
	location: "CA"
}

books = {
	_id: "123",
	publisher_id: "oreilly"
}

//Attempt #3: bad Idea because this grows

publisher = {
	_id: "oreilly",
	name: "O'Reilley media ",
	founded: 1980,	
	books: [ "123", ...]
	
}

//Case #6: Find authors of book 'foo'

book = {
	_id: "123", 
	title: "Mongo DB Book",
	authors: [
		{ id: "kchodoworow", name: "kristina cho" },
		{ id: "mdirol", name: "Mike dirioli" }		
	],
	published_date: ISODate("2010-09-24"),
	pages: 215,
	language: "English",
}

author = {
	_id: "kchodoworow",
	name: "kristina cho",
	hometown: "New York"
}

author = {
	_id: "mdirol",
	name: "Mike dirioli",
	hometown: "CA"
}

//Attempe #2: Let's put teh book in the author

book = {
	_id: "123", 
	title: "Mongo DB Book",
	published_date: ISODate("2010-09-24"),
	pages: 215,
	authors: [
		{ id: "kchodoworow", name: "kristina cho" },
		{ id: "mdirol", name: "Mike dirioli" }		
	],
	language: "English",
}


author = {
	_id: "kchodoworow",
	name: "kristina cho",
	hometown: "New York"
	books: [
		{ id: "123", title: "Mongo DB Definitive guide" }
	]
}

Linking vs. embedding

Embedding

• Great for read performances
  • One seek to laod entire object
  • one roundtrip to database
• writese can be slow
• maintaining data integrigy
• MongoDB has a 16mb data cap. Guttenberg bible is 4mb

Linking

• MOre flexibility
• Data integrity is maintained
• Work is done during reads

COmmon patterns

If a book has categories, then we can put books //Problem

book = {
	category: "MongoDB"
}

category = { _id: "mongo db", parent: "databases" }
category = { _id: "databases", parent: "programming" }

//Option #1: Store the category heirarchy

book = {
	//If you index the category array, you'll be efficient
	categories: [ "MongoDB", "Databases", "Programming" ]
}

book = {
	categories: [ "MySQL", "Databases", "Programming" ]
}

book = {
	categories: [ "MySQL", "Databases", "Programming" ]
}

//Optione #2: Putting things into a string

book = {
	category: "Programming/Databases/MySQL"
}
book = {
	category: "MongoDB/Databases/Programming"
}

//If you have a problem you can solve with Regular expressions have two problems

//Option #3" Interval trees

gistfile2.js

db.author.insert({ first_name: "chris", last_name: "aiv", "bio": "Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum." });

db.books.insert({ title: "my first book",  author: ObjectId("5125126cbcbbf83e9b712aa2"),  year: 1948, genre: [ "fantasy", "advenure" ] })

//Query on the date $ <= Query operator
db.books.findOne( { "publication.date": { $lt: new Date('21 June 1960')}}

//1 means ASC index
db.books.ensureIndex( {title: 1});

//Keys wiht multiple values
db.books.ensureIndex( { genre: 1 });

//DESC for sorting or range queries
db.books.ensureIndex( { "publication.date": -1 })

//Reg Exp=> "Document that has a title that starts with fell"
db.books.findOne( { title: /^fell/ });

----------------------------------------------------
//Dealing with many records
----------------------------------------------------

db.books.insert({
title: "title2",
author: ObjectId("5125126cbcbbf83e9b712aa2"),
publication: { name: "George Allen and Unwin", location: "London", date: new Date("20 Oct 1955") } })

//Cursors, iterate through a result set
db.books.find({ author: ObjectId("5125126cbcbbf83e9b712aa2") }).sort({"publication.date": -1}).limit(1)

//Page through a Cursor result set through iteration
page_num = 3;
results_per_page = 10;
//Simplified pager
cursor = db.books.find().sort({ "publication.date": -1 }).skip((page_num - 1) * results_per_page).limit(results_per_page)

//Find an author from a particular book record
book = db.books.findOne({ "title": "return of the king, the"})
db.author.findOne({ _id: book.author })