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.

1. Building your First
MongoDB Application
Rick Copeland
@rick446
http://arborian.com

2. Who am I?
• Now a consultant, but formerly...
• Software engineer at SourceForge
• Author of Essential SQLAlchemy
• Primarily code Python

3. What will you learn
here?

4. What will you learn
here?
• Data Modeling

5. What will you learn
here?
• Data Modeling
• Queries

6. What will you learn
here?
• Data Modeling
• Queries
• Geospatial indexing

7. What will you learn
here?
• Data Modeling
• Queries
• Geospatial indexing
• Updates

8. What will you learn
here?
• Data Modeling
• Queries
• Geospatial indexing
• Updates
• Map/Reduce

9. What will you learn
here?
• Data Modeling
• Queries
• Geospatial indexing
• Updates
• Map/Reduce
• Deployment and Scaling Concerns

10. What will you learn
here?
• Data Modeling
• Queries
• Geospatial indexing
• Updates
• Map/Reduce
• Deployment and Scaling Concerns
• Why MongoDB?

11. Our Application
• Users can create different locations
• Users can “check in” to these locations
• Users can see who else is checked in
• Let’s call it ThreeTriangles (3tri)

12. 3tri Operations
Record
Find nearby
checkins
places
Places Checkins
User-generated Checkin
content Stats

13. MongoDB Terminology
Relational MongoDB
Database Database
Table Collection
Index Index
Row Document
Column Field

14. Documents?
doc1 = {!
_id: ObjectId('4b97e62bf1d8c7152c9ccb74'),
key1: value1,!
key2: value2,
key3: {..., ..., ...},
key4: [..., ..., ]
}

15. Collections
• No schema
enforcement
doc1,doc2,... doc5,doc6,...
• Per-collection...
Places Users
• Querying
• Indexing
doc8,doc9,...
• Updating
Checkins • Sharding

16. Places v1
place1 = {!
name: "Blake Hotel",!
address: "555 South McDowell Street",
city: "Charlotte",
zip: "28204"}
db.places.find({zip:"28204"}).limit(10)

17. Places v2
place2 = {
name: "Blake Hotel",!
address: "555 South McDowell Street",
city: "Charlotte",
zip: "28204",
tags: ["hotel", "recommended"]}
db.places.find({
zip: "28204", tags: "hotel"}).limit(10)

18. Places v3
place3 = {
name: "Blake Hotel",!
address: "555 South McDowell Street",
city: "Charlotte",
zip: "28204",
tags: ["hotel", "recommended"],
latlon: [ 35.21, 80.83 ] }
db.places.ensureIndex({latlong:"2d"})
db.places.find({latlong:{$near:[35,80]}})

19. Places v4
place4 = {
name: "Blake Hotel",!
address: "555 South McDowell Street",
city: "Charlotte",
zip: "28204",
tags: ["hotel", "recommended"],
latlon: [ 35.21, 80.83 ],
tips: [
{ user: "rick",
time: ISODateTime(...),
tip: "Come learn about #self2012"},
{ ... },
{ ... } ] }

20. Some Queries
/* First, some indexes */
db.places.ensureIndex({tags:1})
db.places.ensureIndex({name:1})
db.places.ensureIndex({latlong:"2d"})
/* Find places */
db.places.find({latlong:{$near:[40,70]}})
/* Regex searching */
db.places.find({name: /^typeaheadstring/)
/* Searching arrays */
db.places.find({tags: "business"})

21. Inserting and updating
/* Initial data load */
db.places.insert([place1, place2, ...])
/* Update-in-place */
db.places.update(
{ name:"Blake Hotel" },
{ $push : {
tips: {
user: "rick",
time: ISODateTime(...),
tip: "Come learn about #self2012" }
} } )

22. 3tri Operations
Record
Find nearby
checkins
places
Places Checkins
User-generated Checkin
content Stats

23. Users
user1 = {
name: "rick",
email: "rick@arborian.com",
...
checkins: [
ObjectId('4b97e62bf1d8c7152c9ccb74'),
... ] }
/* checkins [] = references checkin
collection _id field */

24. Checkins
db.checkins.ensureIndex({place:1, ts:1})
db.checkins.ensureIndex({ts:1})
checkin1 = {
place: {
id: ObjectId(...),
name: "Blake Hotel" },
ts: ISODateTime(...),
user: {
id: ObjectId(...), name: "rick" } }

25. Atomic Updates
• $set • $pull
• $unset • $pullAll
• $rename • $addToSet
• $push • $inc
• $pushAll • $bit
• $pop

26. 3tri Operations
Record
Find nearby
checkins
places
Places Checkins
User-generated Checkin
content Stats

27. Simple Statistics
/* All checkins */
db.checkins.find({"place.name": "Blake Hotel"})
/* Last 10 checkins */
db.checkins.find({"place.name": "Blake Hotel"})
.sort({ts:-1}).limit(10)
/* Number of checkins today */
db.checkins.find(
{ "place.name": "Blake Hotel",
ts: { $gt: ISODateTime(...)} })
.count()

28. MapReduce
mapFunc = function() {
emit(this.place.name, 1); }
reduceFunc = function(key, values) {
return Array.sum(values); }
res = db.checkins.mapReduce(
mapFunc, reduceFunc, !
{ query: { ts: { $gt: nowminus3hrs } }
out: {inline: 1} })
res.results = [
{_id:"Blake Hotel", value: 17}, ...]

29. Deployment and Scaling
Options

30. Single Master
Deployment
Read / Write Primary
Read Secondary
Read Secondary

31. Auto-Sharding
Shard 1 (0..10) Shard 2 (10..20) Shard 3 (20..30)
Primary Primary Primary
Secondary Secondary Secondary
Config
Secondary Secondary Secondary

32. Use Cases
• Replace RDBMS for high-traffic web
applications
• CMS-style applications
• Social and mobile applications
• Real-time analytics, high-speed logging
• Maybe not double-entry bookkeeping

33. What do you give up?
• No multi-document atomic operations (i.e.
transactions)
• No server-side JOINs
• No referential integrity constraints
between documents
• Data model is typically tied to query
patterns (less flexible than relational DBs)

34. Questions?
Please rate this talk at http://svy.mk/L3jM7f
Interested in training? http://Arborian.com/training
MongoDB Info & Downloads: http://mongodb.org
Rick Copeland
@rick446
http://arborian.com