Your SlideShare is downloading. ×
0
Mongo Web Apps: OSCON 2011
Mongo Web Apps: OSCON 2011
Mongo Web Apps: OSCON 2011
Mongo Web Apps: OSCON 2011
Mongo Web Apps: OSCON 2011
Mongo Web Apps: OSCON 2011
Mongo Web Apps: OSCON 2011
Mongo Web Apps: OSCON 2011
Mongo Web Apps: OSCON 2011
Mongo Web Apps: OSCON 2011
Mongo Web Apps: OSCON 2011
Mongo Web Apps: OSCON 2011
Mongo Web Apps: OSCON 2011
Mongo Web Apps: OSCON 2011
Mongo Web Apps: OSCON 2011
Mongo Web Apps: OSCON 2011
Mongo Web Apps: OSCON 2011
Mongo Web Apps: OSCON 2011
Mongo Web Apps: OSCON 2011
Mongo Web Apps: OSCON 2011
Mongo Web Apps: OSCON 2011
Mongo Web Apps: OSCON 2011
Mongo Web Apps: OSCON 2011
Mongo Web Apps: OSCON 2011
Mongo Web Apps: OSCON 2011
Mongo Web Apps: OSCON 2011
Mongo Web Apps: OSCON 2011
Mongo Web Apps: OSCON 2011
Mongo Web Apps: OSCON 2011
Mongo Web Apps: OSCON 2011
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Mongo Web Apps: OSCON 2011

10,984

Published on

Building web applications with mongodb, an introduction

Building web applications with mongodb, an introduction

Published in: Technology
0 Comments
3 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
10,984
On Slideshare
0
From Embeds
0
Number of Embeds
16
Actions
Shares
0
Downloads
49
Comments
0
Likes
3
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide
  • 1 doc can have multiple of locations and index on it.
  • Memory mapped files, BSON, indexes, multiple data types, binary files, etc @ main datasets: places and checkins use cases: given current loc find places nearby; add notes to locations Record checkins Generate stats about checkins
  • Memory mapped files, BSON, indexes, multiple data types, binary files, etc @ main datasets: places and checkins use cases: given current loc find places nearby; add notes to locations Record checkins Generate stats about checkins
  • Documents go into collections Todays app: users , places, checkins
  • Latlong are actually real lat / long points $near gives you the closest 100
  • Latlong are actually real lat / long points $near gives you the closest 100
  • Latlong are actually real lat / long points $near gives you the closest 100
  • Memory mapped files, BSON, indexes, multiple data types, binary files, etc @ main datasets: places and checkins use cases: given current loc find places nearby; add notes to locations Record checkins Generate stats about checkins
  • Transcript

    • 1. http://mongodb.org http://10gen.com Building applications with MongoDB – An introduction Roger Bodamer [email_address] @rogerb
    • 2. Today ’s Talk <ul><li>Developing your first Web Application with MongoDB </li></ul><ul><li>What is MongoDB, Platforms and availability </li></ul><ul><li>Data Modeling, queries and geospatial queries </li></ul><ul><li>Location bases App </li></ul><ul><li>Example uses MongoDB Javascript shell </li></ul>
    • 3. Why MongoDB <ul><li>Intrinsic support for agile development </li></ul><ul><li>Super low latency access to your data </li></ul><ul><ul><li>Very little CPU overhead </li></ul></ul><ul><li>No Additional caching layer required </li></ul><ul><li>Built in Replication and Horizontal Scaling support </li></ul>
    • 4. MongoDB <ul><li>Document Oriented Database </li></ul><ul><ul><li>Data is stored in documents, not tables / relations </li></ul></ul><ul><li>MongoDB is Implemented in C++ for best performance </li></ul><ul><li>Platforms 32/64 bit Windows Linux, Mac OS-X, FreeBSD, Solaris </li></ul><ul><li>Language drivers for: </li></ul><ul><ul><li>Ruby / Ruby-on-Rails </li></ul></ul><ul><ul><li>Java </li></ul></ul><ul><ul><li>C# </li></ul></ul><ul><ul><li>JavaScript </li></ul></ul><ul><ul><li>C / C++ </li></ul></ul><ul><ul><li>Erlang Python, Perl others..... and much more ! .. </li></ul></ul>
    • 5. Design <ul><li>Want to build an app where users can check in to a location </li></ul><ul><li>Leave notes or comments about that location </li></ul><ul><li>Iterative Approach: </li></ul><ul><ul><li>Decide requirements </li></ul></ul><ul><ul><li>Design documents </li></ul></ul><ul><ul><li>Rinse, repeat :-) </li></ul></ul>
    • 6. Requirements <ul><li>Locations </li></ul><ul><ul><li>Need to store locations (Offices, Restaurants etc) </li></ul></ul><ul><ul><ul><li>Want to be able to store name, address and tags </li></ul></ul></ul><ul><ul><ul><li>Maybe User Generated Content, i.e. tips / small notes ? </li></ul></ul></ul><ul><ul><li>Want to be able to find other locations nearby </li></ul></ul>
    • 7. Requirements <ul><li>Locations </li></ul><ul><ul><li>Need to store locations (Offices, Restaurants etc) </li></ul></ul><ul><ul><ul><li>Want to be able to store name, address and tags </li></ul></ul></ul><ul><ul><ul><li>Maybe User Generated Content, i.e. tips / small notes ? </li></ul></ul></ul><ul><ul><li>Want to be able to find other locations nearby </li></ul></ul><ul><li>Checkins </li></ul><ul><ul><li>User should be able to ‘check in’ to a location </li></ul></ul><ul><ul><li>Want to be able to generate statistics </li></ul></ul>
    • 8. Terminology RDBMS Mongo Table, View Collection Row(s) JSON Document Index Index Join Embedded Document Partition Shard Partition Key Shard Key
    • 9. Collections loc1, loc2, loc3 Locations Users User1, User2
    • 10. JSON Sample Doc <ul><li>{ _id : ObjectId(&quot;4c4ba5c0672c685e5e8aabf3&quot;), </li></ul><ul><li>author : &quot;roger&quot;, </li></ul><ul><li>date : &quot;Sat Jul 24 2010 19:47:11 GMT-0700 (PDT)&quot;, </li></ul><ul><li>text : ”MongoSF&quot;, </li></ul><ul><li>tags : [ ”San Francisco&quot;, ”MongoDB&quot; ] } </li></ul><ul><li>Notes: </li></ul><ul><li>- _id is unique, but can be anything you’d like </li></ul>
    • 11. BSON <ul><li>JSON has powerful, but limited set of datatypes </li></ul><ul><ul><li>Mongo extends datypes with Date, Int types, Id, … </li></ul></ul><ul><li>MongoDB stores data in BSON </li></ul><ul><li>BSON is a binary representation of JSON </li></ul><ul><ul><li>Optimized for performance and navigational abilities </li></ul></ul><ul><ul><li>Also compression </li></ul></ul><ul><ul><li>See bsonspec.org </li></ul></ul>
    • 12. Locations v1 location1= { name: &quot;10gen East Coast ”, address: ”134 5 th Avenue 3 rd Floor”, city: &quot;New York”, zip: &quot;10011” }
    • 13. Places v1 location1= { name: &quot;10gen East Coast ”, address: ”134 5 th Avenue 3 rd Floor”, city: &quot;New York”, zip: &quot;10011” } db.locations.find({zip: ”10011”}).limit(10)
    • 14. Places v2 location1 = { name: &quot;10gen East Coast ”, address: &quot;17 West 18th Street 8th Floor”, city: &quot;New York”, zip: &quot;10011”, tags: [ “business”, “mongodb”] }
    • 15. Places v2 location1 = { name: &quot;10gen East Coast ”, address: &quot;17 West 18th Street 8th Floor”, city: &quot;New York”, zip: &quot;10011”, tags: [ “business”, “mongodb”] } db.locations.find({zip: ”10011”, tags:”business”})
    • 16. Places v3 location1 = { name: &quot;10gen East Coast ”, address: &quot;17 West 18th Street 8th Floor”, city: &quot;New York”, zip: &quot;10011”, tags: [ “business”, “mongodb”], latlong: [40.0,72.0] }
    • 17. Places v3 location1 = { name: &quot;10gen East Coast ”, address: &quot;17 West 18th Street 8th Floor”, city: &quot;New York”, zip: &quot;10011”, tags: [ “business”, “cool place”], latlong: [40.0,72.0] } db.locations.ensureIndex({latlong: ”2d”})
    • 18. Places v3 location1 = { name: &quot;10gen HQ ”, address: &quot;17 West 18th Street 8th Floor”, city: &quot;New York”, zip: &quot;10011”, tags: [ “business”, “cool place”], latlong: [40.0,72.0] } db.locations.ensureIndex({latlong: ”2d”}) db.locations.find({latlong:{$near:[40,70]}})
    • 19. Places v4 location1 = { name: &quot;10gen HQ ”, address: &quot;17 West 18th Street 8th Floor”, city: &quot;New York”, zip: &quot;10011”, latlong: [40.0,72.0], tags: [ “business”, “cool place”], tips: [ {user:&quot;nosh&quot;, time:6/26/2010, tip:&quot;stop by for office hours on Wednesdays from 4-6pm&quot;}, {.....}, ] }
    • 20. Querying your Places Creating your indexes db.locations.ensureIndex({tags:1}) db.locations.ensureIndex({name:1}) db.locations.ensureIndex({latlong: ”2d”}) Finding places: db. locations .find({latlong:{$near:[40,70]}}) With regular expressions: db. locations .find({name: /^ typeaheadstring /) By tag: db. locations .find({tags: “business”})
    • 21. Inserting and updating locations Initial data load: db.locations.insert(place1) Using update to Add tips: db.locations.update({name:&quot;10gen HQ&quot;}, {$push :{tips: {user:&quot;nosh&quot;, time:6/26/2010, tip:&quot;stop by for office hours on Wednesdays from 4-6&quot;}}}}
    • 22. Requirements <ul><li>Locations </li></ul><ul><ul><li>Need to store locations (Offices, Restaurants etc) </li></ul></ul><ul><ul><ul><li>Want to be able to store name, address and tags </li></ul></ul></ul><ul><ul><ul><li>Maybe User Generated Content, i.e. tips / small notes ? </li></ul></ul></ul><ul><ul><li>Want to be able to find other locations nearby </li></ul></ul><ul><li>Checkins </li></ul><ul><ul><li>User should be able to ‘check in’ to a location </li></ul></ul><ul><ul><li>Want to be able to generate statistics </li></ul></ul>
    • 23. Users user1 = { name: “nosh” email: “nosh@10gen.com”, . . . checkins: [{ location: “10gen HQ”, ts: 9/20/2010 10:12:00, … }, … ] }
    • 24. Simple Stats db.users.find({‘checkins.location’: “10gen HQ”) db.checkins.find({‘checkins.location’: “10gen HQ”}) .sort({ts:-1}).limit(10) db.checkins.find({‘checkins.location’: “10gen HQ”, ts: {$gt: midnight}}).count()
    • 25. Alternative user1 = { name: “nosh” email: “nosh@10gen.com”, . . . checkins: [ 4b97e62bf1d8c7152c9ccb74, 5a20e62bf1d8c736ab ] } checkins [] = ObjectId reference to locations collection
    • 26. User Check in Check-in = 2 ops read location to obtain location id Update ($push) location id to user object Queries: find all locations where a user checked in: checkin_array = db.users.find({..}, {checkins:true}).checkins db.location.find({_id:{$in: checkin_array}})
    • 27. Unsharded Deployment <ul><li>Configure as a replica set for automated failover </li></ul><ul><li>Async replication between nodes </li></ul><ul><li>Add more secondaries to scale reads </li></ul>Secondary Primary Secondary
    • 28. Sharded Deployment <ul><li>Autosharding distributes data among two or more replica sets </li></ul><ul><li>Mongo Config Server(s) handles distribution & balancing </li></ul><ul><li>Transparent to applications </li></ul>Secondary Primary MongoS config
    • 29. Use Cases <ul><li>RDBMS replacement for high-traffic web applications </li></ul><ul><li>Content Management-type applications </li></ul><ul><li>Real-time analytics </li></ul><ul><li>High-speed data logging </li></ul>Web 2.0, Media, SaaS, Gaming, Finance, Telecom, Healthcare
    • 30. http://mongodb.org http://10gen.com 10Gen is hiring! @mongodb [email_address] @rogerb

    ×