Webinar: Building Your First Application with MongoDB

Building your First App withMongoDBBlossom CoryatSolutions Architect, 10gen

Today‟s Talk• Introduction to MongoDB• Discuss data modeling, queries, indexes, updates and aggregation in MongoDB within the context of building a location-based app

What is MongoDB?

MongoDB is a ___________database• Document Oriented• Open source• High performance• Highly Available• Horizontally scalable• Full featured

Document Oriented• A document is essentially an associative array• Document == JSON object• Document == PHP Array• Document == Python Dict• Document == Ruby Hash• Etc.

Open Source• MongoDB is an open source project – Started and maintained by 10gen• Licensed under the AGPL• Commercial licenses available• On GitHub• Contributions welcome

High Performance• Written in C++• Runs nearly everywhere• Extensive use of memory-mapped files• Data serialized as BSON (fast parsing)• Full support for primary & secondary indexes• Document model = less work

Full Featured• Ad Hoc queries• Real time aggregation• Rich query capabilities• Flexible schema• Geospatial features• Support for most programming languages

MongoDB drivers• Official Support for 12 languages• Community drivers for tons more• Drivers connect to mongo servers• Drivers translate BSON into native types• Installed using typical means (npm, pecl, gem, pip)

Highly Available

Replica Sets• Redundancy and Failover• Multi-DC deployments• Zero downtime for upgrades and maintenance

Horizontally Scalable

Sharding• Partition your data• Auto-balancing• Scale write throughput• Increase capacity

Document Database

RDBMS MongoDBRow ➜ DocumentTable, View ➜ CollectionIndex ➜ IndexJoin ➜ Embedded DocumentForeign ➜ ReferenceKeyPartition ➜ ShardTerminology

Typical (relational) ERD

MongoDB ERD

Example Document{ _id : ObjectId("4c4ba5c0672c685e5e8aabf3"), author : "Blossom", date : ISODate("2012-11-02T11:52:27.442Z"), title: "Intro to MongoDB", tags : [ "tech", "databases”, ”documents" ], comments : [{ author : "Fred", date : ISODate("2012-11-03T17:22:21.124Z"), text : ”interesting article, lots of great nuggets of wisdom" }], comment_count : 1 }

Building a check-in webapp

Requirements• Users should be able to check in to a location• Should be able to view check-ins by location• Ability to assign categories to places• Find nearby places• Generate check-in statistics

First step in any application isDetermine your entities

Check-in App Entities• People checking in• Places to check in to• Check-in events

In a relational based appWe would start by doingschema design

Relational schema design• Large entity relationship diagrams• Complex create table statements• Tables just to join tables together• ORMs to map tables to objects• Lots of revisions until we get it just right

In a MongoDB based appWe start building ourand let the schema evolveapp

MongoDB collections• Users• Places• Check-ins – Separate collection to manage discrete check-in events and support high workload of writes

We‟ll use the JS shell to getstarted

Start with an object(or array, hash, dict, etc)> user = { firstname: fred, lastname: jones,}

Insert the record> db.users.insert(user) No collection creation needed

Querying for the user> db.users.findOne(){ "_id" : ObjectId("50804d0bd94ccab2da652599"), "firstname" : "fred", "lastname" : "jones"}

_id• _id is the primary key in MongoDB• Automatically indexed• Automatically created as an ObjectId if not provided• Any unique immutable value could be used

ObjectId• ObjectId is a special 12 byte value• Guaranteed to be unique across your cluster• ObjectId("50804d0bd94ccab2da652599") |---------||-----||-----||--------| ts mac pid inc

Places v1> db.places.insert({ name: “Exploratorium”, address: “3601 Lyon St” city: “San Francisco”, postcode: 94123}

Places v1> db.places.insert({ name: “Exploratorium”, address: “3601 Lyon St” city: “San Francisco”, postcode: 94123}> db.places.find({name:”Exploratorium”})

Places v2> db.places.insert({ name: “Exploratorium”, address: “3601 Lyon St” city: “San Francisco”, postcode: 94123, categories: [“Social”, “Arts”, “Museums”]}

Places v2> db.places.insert({ name: “Exploratorium”, address: “3601 Lyon St” city: “San Francisco”, postcode: 94123, categories: [“Social”, “Arts”, “Museums”]}> db.places.ensureIndex({categories: 1})> db.places.find({categories: ”Social”})

Places v3> db.places.insert({ name: “Exploratorium”, address: “3601 Lyon St” city: “San Francisco”, postcode: 94123, categories: [“Social”, “Arts”, “Museums”], location: [37.803963,-122.448531]}> db.places.ensureIndex({location: “2d”})> db.places.find({location:{$near:[37.81,-122.45]}})

Indexes• Can be created on any field – Including fields within sub-documents or embedded arrays• Support for compound indexes on any combination of keys• Indexes should exist for primary, common, and user-facing queries

Indexes on Places// creating indexes> db.places.ensureIndex({name:1})> db.places.ensureIndex({categories:1})> db.places.ensureIndex({location:”2d”})// finding by regular expression> db.places.find({name:/^explor/i})// finding nearby> db.places.find({location:{$near:[37.81,-122.45]}})// finding by category> db.places.find({categories: ”Social”})

Updating Documents• MongoDB supports atomic, in-place document updates• $inc• $set, $unset• $push, $pushAll• $addToSet, $each• $pop, $pull, $pullAll• $rename• $bit

Updating> db.checkins.insert({ place_id: pid, user_id: uid, datetime: new Date()})// update the “checkins” count on place and user// “checkins” will be added if it doesn‟t exist> db.places.update({_id: pid}, {$inc: {checkins: 1}})> db.users.update({_id: uid}, {$inc: {checkins: 1}})

Simple Statistics// find the last 10 checkins for a place:> p = db.places.find({name:”Exploratorium”})> db.checkins.find({place_id:p[„_id‟]}) .sort({datetime:-1}).limit(10)// how many people checked in today> db.checkins.find({place_id:p[„_id‟], datetime: {$gt: midnight}}).count()

Aggregation Framework• Documents pass through a pipeline of operations• Mechanism to calculate aggregate values across your data• Similar functionality to GROUP BY and related SQL operators• Along with computation, data can be reshaped – Add computed fields – Create virtual sub-objects – Extract sub-fields

Aggregation FrameworkOperators• $project• $match• $limit• $skip• $unwind• $group• $sort

Advanced Statistics// checkins per place per day of the week> db.checkins.aggregate( {$project: { place_id:1, dotw: { $dayOfWeek: ”$datetime”} }}, {$group: { _id: {place_id: “$place_id”, dotw: “$dotw”}, count: {$sum: 1} }})

Deployment

Deployment• Single server - need a strong backup plan P

Deployment• Single server - need a strong backup plan P• Replica sets P S S - High availability - Automatic failover

Deployment• Single server - need a strong backup plan P• Replica sets P S S - High availability - Automatic failover P S S• Sharded - Horizontally scale - Auto balancing P S S

Learn More:10gen.com/presentationseducation.10gen.com

http://www.10gen.com	904
http://mongodbphp.com	152
https://twitter.com	30
http://bigdatapress.com	13
http://www.linkedin.com	10
http://mongodbstudio.wordpress.com	6
http://drupal1.10gen.cc	3
http://twitter.com	1
http://drupal-ci.10gen.cc	1
http://www.twylah.com	1

Webinar: Building Your First Application with MongoDB

by mongodb/10gen on Jan 03, 2013

Accessibility

Upload Details

Usage Rights

10 Embeds 1,121

Statistics

Webinar: Building Your First Application with MongoDB Presentation Transcript