Building your first MongoDB application - Nosh Petigara, 10gen

This talk will introduce the features of MongoDB by walking through how one can building a simple location-based checkin application using MongoDB. The talk will cover the basics of MongoDB's document model, query language, map-reduce framework and deployment architecture.

Transcript

1. http://mongodb.org http://10gen.com Building applications with MongoDB – An introduction MongoSeattle – December 1, 2011 Nosh Petigara nosh@10gen.com @noshinosh
2. Today`s Talk • MongoDB: Data modeling, queries, geospatial, updates, map reduce • Using a location-based app as an example • Example Works in MongoDB JS shell
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: [..., ..., ]
 } Unique ID Simple Values Arrays Embedded docs
5. Collections doc1, doc2, doc3 Places Users Checkins doc3, doc4, doc5 doc6, doc7, doc8
6. place1 = { name: "10gen HQz, 
 address: z134 5th Avenue 3rd Floorz,
 city: "New Yorkz,
 zip: "10011z
 } db.places.find({zip:z10011z}).limit(10) Places v1
7. place1 = { name: "10gen HQz, 
 address: "17 West 18th Street 8th Floorz,
 city: "New Yorkz,
 zip: "10011z, tags: [lbusinessz, lrecommendedz]
 } db.places.find({zip:z10011z, tags:zbusinessz}) Places v2
8. place1 = { name: "10gen HQz, 
 address: "17 West 18th Street 8th Floorz,
 city: "New Yorkz,
 zip: "10011z, tags: [lbusinessz, lcool placez], latlong: [40.0,72.0]
 } db.places.ensureIndex({latlong:z2dz}) db.places.find({latlong:{$near:[40,70]}}) Places v3
9. place1 = { name: "10gen HQz, 
 address: "17 West 18th Street 8th Floorz,
 city: "New Yorkz,
 zip: "10011z,
 latlong: [40.0,72.0], tags: [lbusinessz, lcool placez],
 
 tips: [ {user:"nosh", time:6/26/2010, tip:"stop by for office hours on Wednesdays from 4-6pm"}, {.....}, {.....} ]
 } Places v4
10. Creating your indexes db.places.ensureIndex({tags:1}) db.places.ensureIndex({name:1}) db.places.ensureIndex({latlong:z2dz}) Finding places: db.places.find({latlong:{$near:[40,70]}}) With regular expressions: db.places.find({name: /^typeaheadstring/) By tag: db.places.find({tags: lbusinessz}) Querying your Places
11. Initial data load: db.places.insert(place1) Updating tips: db.places.update({name:"10gen HQ"}, {$push :{tips: {user:"nosh", time:6/26/2010, tip:"stop by for office hours on Wednesdays from 4-6"}}}}
12. $set, $unset, $rename $push, $pop, $pull, $addToSet $inc 
 Atomic Updates
13. Application Goals Places Check ins (1) Q: Current location A: Places near location (2) Add user generated content (3) Record user checkins (4) Stats about checkins
14. user1 = { name: lnoshz email: lnosh@10gen.comz, . . . checkins: [4b97e62bf1d8c7152c9ccb74, 5a20e62bf1d8c736ab]
 } checkins [] = ObjectId reference to checkin collection Users
15. checkin1 = { place: l10gen HQz, ts: 6/7/2011 10:12:00, userName: “Nosh” } Check-in = 2 ops Insert check in object [checkin collection] Update ($push) user object [user collection] Indexes: db.checkins.ensureIndex({place:1, ts:1}) db.checkins.ensureIndex({ts:1}) Checkins
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: l10gen HQz) db.checkins.find({place: l10gen HQz}) .sort({ts:-1}).limit(10) db.checkins.find({place: l10gen HQz, ts: {$gt: midnight}}).count() db.checkins.find().sort(ts:-1)}.limit(50)
18. Stats with MapReduce mapFunc = function() { emit(this.place, 1);} reduceFunc = function(key, values) { return Array.sum(values); } db.checkins.mapReduce(mapFunc,reduceFunc, {query: {timestamp: {$gt:nowminus3hrs}}, out: lresultz}) result = [{_id:z10gen HQz, value: 17}, {…..}, {….}] db.result.find({ value: {$gt: 15}})
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
20. Single Master Deployments Primary/Master Secondary/Slave • Configure as a replica set for automated failover • Add more secondaries to scale reads
21. Auto Sharded Deployment Primary/Master Secondary/Slave MongoS • Autosharding distributes data among two or more replica sets • Mongo Config Server(s) handles distribution & balancing • Transparent to applications Mongo Config
22. Nosh Petigara nosh@10gen.com Director of Product Strategy, 10gen http://mongodb.org http://10gen.com - We are hiring! - @mongodb nosh@10gen.com @noshinosh
23. Use Cases • RDBMS replacement for high-traffic web applications • Content Management-type applications • Real-time analytics • High-speed data logging Web 2.0, Media, SaaS, Gaming, Finance, Telecom, Healthcare
24. MongoDB in Production