The document discusses using MongoDB for a location-based application, including modeling of places, users, and check-ins data. It also covers querying, indexing, and aggregating the data through simple operations and MapReduce to calculate statistics. Several deployment architectures for MongoDB are presented, along with examples of use cases where MongoDB has been successfully used in production environments.

1. Nosh Petigara [email_address] @noshinosh http://mongodb.org http://10gen.com Building applications with MongoDB – An introduction MongoAustin – Febraury 15, 2011

3. Application Goals Places Check ins (1) Q: Current location A: Places near location (2) Add user generated content (3) Record user checkins (4) Stats about checkins

4. Documents doc1 = { _id: 4b97e62bf1d8c7152c9ccb74 , key1: value1, key2: value2, key3: {..., ..., ...}, key4: [..., ..., ] }

5. Collections doc1, doc2, doc3 Places Users Checkins doc3, doc4, doc5 doc6, doc7, doc8

6. Places v1 place1 = { name: "10gen HQ”, address: ”134 5 th Avenue 3 rd Floor”, city: "New York”, zip: "10011” } db.places.find({zip:”10011”}).limit(10)

7. Places v2 place1 = { name: "10gen HQ”, address: "17 West 18th Street 8th Floor”, city: "New York”, zip: "10011”, tags: [“business”, “recommended”] } db.places.find({zip:”10011”, tags:”business”})

8. Places v3 place1 = { name: "10gen HQ”, address: "17 West 18th Street 8th Floor”, city: "New York”, zip: "10011”, tags: [“business”, “cool place”], latlong: [40.0,72.0] } db.places.ensureIndex({latlong:”2d”}) db.places.find({latlong:{$near:[40,70]}})

9. Places v4 place1 = { name: "10gen HQ”, address: "17 West 18th Street 8th Floor”, city: "New York”, zip: "10011”, latlong: [40.0,72.0], tags: [“business”, “cool place”], tips: [ {user:"nosh", time:6/26/2010, tip:"stop by for office hours on Wednesdays from 4-6pm"}, {.....}, {.....} ] }

10. Querying your Places Creating your indexes db.places.ensureIndex({tags:1}) db.places.ensureIndex({name:1}) db.places.ensureIndex({latlong:”2d”}) Finding places: db.places.find({latlong:{$near:[40,70]}}) With regular expressions: db.places.find({name: /^ typeaheadstring /) By tag: db.places.find({tags: “business”})

11. Inserting and updating places Initial data load: db.places.insert(place1) Updating tips: db.places.update({name:"10gen HQ"}, {$push :{tips: {user:"nosh", time:6/26/2010, tip:"stop by for office hours on Wednesdays from 4-6"}}}}

12. Application Goals Places Check ins (1) Q: Current location A: Places near location (2) Add user generated content (3) Record user checkins (4) Stats about checkins

13. Users user1 = { name: “nosh” email: “nosh@10gen.com”, . . . checkins: [ 4b97e62bf1d8c7152c9ccb74, 5a20e62bf1d8c736ab ] } checkins [] = ObjectId reference to checkin collection

14. Checkins checkin1 = { place: “10gen HQ”, ts: 9/20/2010 10:12:00, userId: <objectid of user> } Check-in = 2 ops Insert check in object [checkin collection] Update ($push) user object [user collection] Indexes: db.checkins.ensureIndex({place:1, ts:1}) db.checkins.ensureIndex({ts:1})

15. Atomic Updates $set, $unset, $rename $push, $pop, $pull, $addToSet $inc

16. Application Goals Places Check ins (1) Q: Current location A: Places near location (2) Add user generated content (3) Record user checkins (4) Stats about checkins

17. Simple Stats db.checkins.find({place: “10gen HQ”) db.checkins.find({place: “10gen HQ”}) .sort({ts:-1}).limit(10) db.checkins.find({place: “10gen HQ”, ts: {$gt: midnight}}).count()

18. Stats with MapReduce mapFunc = function() { emit(this.place, 1);} reduceFunc = function(key, values) { return Array.sum(values); } res = db.checkins.mapReduce(mapFunc,reduceFunc, {query: {timestamp: {$gt:nowminus3hrs}}}) res = [{_id:”10gen HQ”, value: 17}, ….., ….] res.find({ value: {$gt: 15}, _id: {$in: [….., ….., …..]} })

19. Application Goals Places Check ins (1) Q: Current location A: Places near location (2) Add user generated content (3) Record user checkins (4) Stats about checkins

23. MongoDB in Production