Webinar: Building Your First Application with MongoDB
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This document discusses building a location-based check-in app using MongoDB. It begins with an introduction to MongoDB's key features like being document-oriented, open source, high performance, and horizontally scalable. It then demonstrates modeling the app's entities of users, places, and check-ins as MongoDB collections. It shows examples of queries, indexes, and updates used in the app. The document concludes by discussing more advanced features like aggregation and deployment options for MongoDB.
![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
}](https://image.slidesharecdn.com/01042012-buildingyourfirstmongodbapp-130103151817-phpapp01/85/Webinar-Building-Your-First-Application-with-MongoDB-20-320.jpg)
![Places v2
> db.places.insert({
name: “Exploratorium”,
address: “3601 Lyon St”
city: “San Francisco”,
postcode: 94123,
categories: [“Social”, “Arts”, “Museums”]
}](https://image.slidesharecdn.com/01042012-buildingyourfirstmongodbapp-130103151817-phpapp01/85/Webinar-Building-Your-First-Application-with-MongoDB-37-320.jpg)
![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”})](https://image.slidesharecdn.com/01042012-buildingyourfirstmongodbapp-130103151817-phpapp01/85/Webinar-Building-Your-First-Application-with-MongoDB-38-320.jpg)
![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:{$near:[37.81,-122.45]}})](https://image.slidesharecdn.com/01042012-buildingyourfirstmongodbapp-130103151817-phpapp01/85/Webinar-Building-Your-First-Application-with-MongoDB-39-320.jpg)
![Indexes on Places
// creating indexes
> db.places.ensureIndex({name:1})
> db.places.ensureIndex({categories:1})
> db.places.ensureIndex({location:”2d”})
// finding by regular expression
> db.places.find({name:/^explor/i})
// finding nearby
> db.places.find({location:{$near:[37.81,-122.45]}})
// finding by category
> db.places.find({categories: ”Social”})](https://image.slidesharecdn.com/01042012-buildingyourfirstmongodbapp-130103151817-phpapp01/85/Webinar-Building-Your-First-Application-with-MongoDB-41-320.jpg)
![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()](https://image.slidesharecdn.com/01042012-buildingyourfirstmongodbapp-130103151817-phpapp01/85/Webinar-Building-Your-First-Application-with-MongoDB-44-320.jpg)
Webinar: Building Your First Application with MongoDB
- 1. Building your First App with MongoDB Blossom Coryat Solutions Architect, 10gen
- 2. 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
- 4. MongoDB is a ___________ database • Document Oriented • Open source • High performance • Highly Available • Horizontally scalable • Full featured
- 5. Document Oriented • A document is essentially an associative array • Document == JSON object • Document == PHP Array • Document == Python Dict • Document == Ruby Hash • Etc.
- 6. Open Source • MongoDB is an open source project – Started and maintained by 10gen • Licensed under the AGPL • Commercial licenses available • On GitHub • Contributions welcome
- 7. 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
- 8. Full Featured • Ad Hoc queries • Real time aggregation • Rich query capabilities • Flexible schema • Geospatial features • Support for most programming languages
- 9. 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)
- 12. Highly Available
- 13. Replica Sets • Redundancy and Failover • Multi-DC deployments • Zero downtime for upgrades and maintenance
- 15. Sharding • Partition your data • Auto-balancing • Scale write throughput • Increase capacity
- 17. RDBMS MongoDB Row ➜ Document Table, View ➜ Collection Index ➜ Index Join ➜ Embedded Document Foreign ➜ Reference Key Partition ➜ Shard Terminology
- 19. MongoDB ERD
- 20. 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 }
- 21. Building a check-in web app
- 22. 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
- 23. First step in any application is Determine your entities
- 24. Check-in App Entities • People checking in • Places to check in to • Check-in events
- 25. In a relational based app We would start by doing schema design
- 26. 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
- 27. In a MongoDB based app We start building our and let the schema evolve app
- 28. MongoDB collections • Users • Places • Check-ins – Separate collection to manage discrete check-in events and support high workload of writes
- 29. We‟ll use the JS shell to get started
- 30. Start with an object (or array, hash, dict, etc) > user = { firstname: 'fred', lastname: 'jones', }
- 31. Insert the record > db.users.insert(user) No collection creation needed
- 32. Querying for the user > db.users.findOne() { "_id" : ObjectId("50804d0bd94ccab2da652599"), "firstname" : "fred", "lastname" : "jones" }
- 33. _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
- 34. ObjectId • ObjectId is a special 12 byte value • Guaranteed to be unique across your cluster • ObjectId("50804d0bd94ccab2da652599") |---------||-----||-----||--------| ts mac pid inc
- 35. Places v1 > db.places.insert({ name: “Exploratorium”, address: “3601 Lyon St” city: “San Francisco”, postcode: 94123 }
- 36. Places v1 > db.places.insert({ name: “Exploratorium”, address: “3601 Lyon St” city: “San Francisco”, postcode: 94123 } > db.places.find({name:”Exploratorium”})
- 37. Places v2 > db.places.insert({ name: “Exploratorium”, address: “3601 Lyon St” city: “San Francisco”, postcode: 94123, categories: [“Social”, “Arts”, “Museums”] }
- 38. 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”})
- 39. 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]}})
- 40. 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
- 41. 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”})
- 42. Updating Documents • MongoDB supports atomic, in-place document updates • $inc • $set, $unset • $push, $pushAll • $addToSet, $each • $pop, $pull, $pullAll • $rename • $bit
- 43. Updating > db.checkins.insert({ place_id: pid, user_id: uid, datetime: new Date() }) // update the “checkins” count on place and user // “checkins” will be added if it doesn‟t exist > db.places.update({_id: pid}, {$inc: {checkins: 1}}) > db.users.update({_id: uid}, {$inc: {checkins: 1}})
- 44. Simple Statistics // find the last 10 checkins for a place: > p = db.places.find({name:”Exploratorium”}) > db.checkins.find({place_id:p[„_id‟]}) .sort({datetime:-1}).limit(10) // how many people checked in today > db.checkins.find({place_id:p[„_id‟], datetime: {$gt: midnight}}).count()
- 45. 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
- 46. Aggregation Framework Operators • $project • $match • $limit • $skip • $unwind • $group • $sort
- 48. Advanced Statistics // checkins per place per day of the week > db.checkins.aggregate( {$project: { place_id:1, dotw: { $dayOfWeek: ”$datetime”} }}, {$group: { _id: {place_id: “$place_id”, dotw: “$dotw”}, count: {$sum: 1} }})
- 49. Deployment
- 50. Deployment • Single server - need a strong backup plan P
- 51. Deployment • Single server - need a strong backup plan P • Replica sets P S S - High availability - Automatic failover
- 52. 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