MongoDB schema design basics

open-‐source, high-‐performance, document-‐oriented database

Schema Design Basics��

Alvin Richards��[email protected]

This talk Part One

‣  Intro ‣  Terms / Definitions

‣  Getting a flavor ‣  Creating a Schema

‣  Indexes

‣  Evolving the Schema

Part Two

‣  Data modeling ‣  DBRef

‣  Single Table Inheritance

‣  Many – Many

‣  Trees

‣  Lists / Queues / Stacks

So why model data?

A brief history of normalization •  1970 E.F.Codd introduces 1st Normal Form (1NF)

•  1971 E.F.Codd introduces 2nd and 3rd Normal Form (2NF, 3NF)

•  1974 Codd & Boyce define Boyce/Codd Normal Form (BCNF)

•  2002 Date, Darween, Lorentzos define 6th Normal Form (6NF)

Goals:

•  Avoid anomalies when inserting, updating or deleting

•  Minimize redesign when extending the schema

•  Make the model informative to users

•  Avoid bias towards a particular style of query

* source : wikipedia

Relational made normalized data look like this

Document databases make normalized data look like this

Some terms before we proceed

RDBMS Document DBs

Table Collection

Row(s) JSON Document

Index Index

Join Embedding & Linking across documents

Partition Shard

Partition Key Shard Key

DB Considerations How can we manipulate

this data ?

•  Dynamic Queries

•  Secondary Indexes

•  Atomic Updates

•  Map Reduce

Access Patterns ?

•  Read / Write Ratio

•  Types of updates

•  Types of queries

•  Data life-cycle

Considerations •  No Joins •  Document writes are atomic

Design Session

Design documents that simply map to your application

post = {author: “kyle”, date: new Date(), text: “my blog post...”, tags: [“mongodb”, “intro”]}

>db.post.save(post)

>db.posts.find()

{ _id : ObjectId("4c4ba5c0672c685e5e8aabf3"), author : "kyle", date : "Sat Jul 24 2010 19:47:11 GMT-0700 (PDT)", text : "My first blog", tags : [ "mongodb", "intro" ] }

Notes: •  ID must be unique, but can be anything you’d like •  MongoDB will generate a default ID if one is not supplied

Find the document

Secondary index for “author”

// 1 means ascending, -1 means descending

>db.posts.ensureIndex({author: 1})

>db.posts.find({author: 'kyle'})

{ _id : ObjectId("4c4ba5c0672c685e5e8aabf3"), author : "kyle", ... }

Add and index, find via Index

Verifying indexes exist

>db.system.indexes.find()

// Index on ID { name : "_id_", ns : "test.posts", key : { "_id" : 1 } }

// Index on author { _id : ObjectId("4c4ba6c5672c685e5e8aabf4"), ns : "test.posts", key : { "author" : 1 }, name : "author_1" }

Query operators Conditional operators: $ne, $in, $nin, $mod, $all, $size, $exists, $type, .. $lt, $lte, $gt, $gte, $ne,

// find posts with any tags >db.posts.find({tags: {$exists: true}})



Regular expressions: // posts where author starts with k >db.posts.find({author: /^k*/i })



Regular expressions: // posts where author starts with k >db.posts.find({author: /^k*/i })

Counting: // posts written by mike >db.posts.find({author: “mike”}).count()

Extending the Schema

new_comment = {author: “fred”, date: new Date(), text: “super duper”}

new_info = { ‘$push’: {comments: new_comment}, ‘$inc’: {comments_count: 1}}

>db.posts.update({_id: “...” }, new_info)

{ _id : ObjectId("4c4ba5c0672c685e5e8aabf3"), author : "kyle", date : "Sat Jul 24 2010 19:47:11 GMT-0700 (PDT)", text : "My first blog", tags : [ "mongodb", "intro" ], comments_count: 1, comments : [

{ author : "Fred", date : "Sat Jul 24 2010 20:51:03 GMT-0700 (PDT)", text : "Super Duper" }

]}


// create index on nested documents: >db.posts.ensureIndex({"comments.author": 1})

>db.posts.find({comments.author:”kyle”})




// find last 5 posts: >db.posts.find().sort({date:-1}).limit(5)




// find last 5 posts: >db.posts.find().sort({date:-1}).limit(5)

// most commented post: >db.posts.find().sort({comments_count:-1}).limit(1)

When sorting, check if you need an index


Map Reduce

Aggregation and batch manipulation

Collection in, Collection out

Parallel in sharded environments

Map reduce mapFunc = function () { this.tags.forEach(function (z) {emit(z, {count:1});}); }

reduceFunc = function (k, v) { var total = 0; for (var i = 0; i < v.length; i++) { total += v[i].count; } return {count:total}; }

res = db.posts.mapReduce(mapFunc, reduceFunc)

>db[res.result].find() { _id : "intro", value : { count : 1 } } { _id : "mongodb", value : { count : 1 } }

Review So Far: - Started out with a simple schema - Queried Data - Evolved the schema - Queried / Updated the data some more

Wordnik 9B records, 100M queries / week, 1.2TB {

entry : { header: { id: 0, headword: "m", sourceDictionary: "GCide", textProns : [ {text: "(em)", seq:0} ], syllables: [ {id: 0, text: "m"} ], sourceDictionary: "1913 Webster", headWord: "m", id: 1, definitions: : [ {text: "M, the thirteenth letter..."}, {text: "As a numeral, M stands for 1000"}] } }

}

Review So Far: - Started out with a simple schema - Queried Data - Evolved the schema - Queried / Updated the data some more

Observations: - Using Rich Documents works well - Simplify relations by embedding them - Iterative development is easy with MongoDB

Single Table Inheritance

>db.shapes.find() { _id: ObjectId("..."), type: "circle", area: 3.14, radius: 1} { _id: ObjectId("..."), type: "square", area: 4, d: 2} { _id: ObjectId("..."), type: "rect", area: 10, length: 5, width: 2}

// find shapes where radius > 0 >db.shapes.find({radius: {$gt: 0}})

// create index >db.shapes.ensureIndex({radius: 1})

One to Many

- Embedded Array / Array Keys - slice operator to return subset of array - hard to find latest comments across all documents

One to Many


- Embedded tree - Single document - Natural - Hard to query

One to Many


- Embedded tree - Single document - Natural - Hard to query

- Normalized (2 collections) - most flexible - more queries

Many - Many

Example:

- Product can be in many categories - Category can have many products

Products - product_id

Category - category_id

Prod_Categories -  id -  product_id -  category_id

products: { _id: ObjectId("4c4ca23933fb5941681b912e"), name: "Sumatra Dark Roast", category_ids: [ ObjectId("4c4ca25433fb5941681b912f"), ObjectId("4c4ca25433fb5941681b92af”]}

Many - Many


categories: { _id: ObjectId("4c4ca25433fb5941681b912f"), name: "Indonesia", product_ids: [ ObjectId("4c4ca23933fb5941681b912e"), ObjectId("4c4ca30433fb5941681b9130"), ObjectId("4c4ca30433fb5941681b913a"]}

Many - Many



//All categories for a given product >db.categories.find({product_ids: ObjectId("4c4ca23933fb5941681b912e")})

Many - Many



//All categories for a given product >db.categories.find({product_ids: ObjectId("4c4ca23933fb5941681b912e")})

//All products for a given category >db.products.find({category_ids: ObjectId("4c4ca25433fb5941681b912f")})

Many - Many


categories: { _id: ObjectId("4c4ca25433fb5941681b912f"), name: "Indonesia"}

Alternative



// All products for a given category >db.products.find({category_ids: ObjectId("4c4ca25433fb5941681b912f")})

Alternative



// All products for a given category >db.products.find({category_ids: ObjectId("4c4ca25433fb5941681b912f")})

// All categories for a given product product = db.products.find(_id : some_id) >db.categories.find({_id : {$in : product.category_ids}})

Alternative

Trees

Full Tree in Document

{ comments: [ { author: “rpb”, text: “...”, replies: [ {author: “Fred”, text: “...”, replies: []} ]} ]}

Pros: Single Document, Performance, Intuitive Cons: Hard to search, Partial Results, 4MB limit

Trees

Parent Links - Each node is stored as a document - Contains the id of the parent

Child Links - Each node contains the id’s of the children - Can support graphs (multiple parents / child)

Array of Ancestors - Store Ancestors of a node { _id: "a" } { _id: "b", ancestors: [ "a" ], parent: "a" } { _id: "c", ancestors: [ "a", "b" ], parent: "b" } { _id: "d", ancestors: [ "a", "b" ], parent: "b" } { _id: "e", ancestors: [ "a" ], parent: "a" } { _id: "f", ancestors: [ "a", "e" ], parent: "e" } { _id: "g", ancestors: [ "a", "b", "d" ], parent: "d" }


//find all descendants of b: >db.tree2.find({ancestors: ‘b’})


//find all descendants of b: >db.tree2.find({ancestors: ‘b’})

//find all ancestors of f: >ancestors = db.tree2.findOne({_id:’f’}).ancestors >db.tree2.find({_id: { $in : ancestors})

findAndModify Queue example

//Example: find highest priority job and mark

job = db.jobs.findAndModify({ query: {inprogress: false}, sort: {priority: -1), update: {$set: {inprogress: true, started: new Date()}}, new: true})

Cool Stuff - Aggregation - Capped collections - GridFS - Geo

Learn More •  Kyle’s presentation + video: http://www.slideshare.net/kbanker/mongodb-schema-design http://www.blip.tv/file/3704083

•  Dwight’s presentation http://www.slideshare.net/mongosf/schema-design-with-mongodb-dwight-merriman

•  Documentation Trees: http://www.mongodb.org/display/DOCS/Trees+in+MongoDB Queues: http://www.mongodb.org/display/DOCS/findandmodify+Command Aggregration: http://www.mongodb.org/display/DOCS/Aggregation Capped Col. : http://www.mongodb.org/display/DOCS/Capped+Collections Geo: http://www.mongodb.org/display/DOCS/Geospatial+Indexing GridFS: http://www.mongodb.org/display/DOCS/GridFS+Specification

Thank You :-) �

Download MongoDB�

http://www.mongodb.org

and let us know what you think @mongodb

DBRef DBRef {$ref: collection, $id: id_value}

- Think URL - YDSMV: your driver support may vary

Sample Schema: nr = {note_refs: [{"$ref" : "notes", "$id" : 5}, ... ]}

Dereferencing: nr.forEach(function(r) { printjson(db[r.$ref].findOne({_id: r.$id})); }

BSON Mongodb stores data in BSON internally

Lightweight, Traversable, Efficient encoding

Typed boolean, integer, float, date, string, binary, array...