MongoDB is an open source, high-performance, document-oriented database that provides scalability, high availability, and automatic sharding. It stores data in flexible, JSON-like documents, enabling fast querying and in-place updates. MongoDB can be used to build a location-based application that allows users to check-in to locations and leave tips or comments. It supports geospatial indexing and queries to find nearby locations. Data is modeled using embedded documents and references to support queries, updates, and analytics on check-in data. MongoDB can be deployed in replica sets for high availability and automatic failover.

1. Open source, high performance database
July 2012
1

2. • Quick introduction to mongoDB
• Data modeling in
mongoDB, queries, geospatial, updates
and map reduce.
• Using a location-based app as an example
• Example works in mongoDB JS shell
2

3. 3

4. MongoDB is a scalable, high-performance, open
source, document-oriented database.
• Fast Querying
• In-place updates
• Full Index Support
• Replication /High Availability
• Auto-Sharding
• Aggregation; Map/Reduce
• GridFS
4

5. MongoDB is Implemented in C++
• Windows, Linux, Mac OS-X, Solaris
Drivers are available in many languages
10gen supported
• C, C#
(.Net), C++, Erlang, Haskell, Java, JavaScript, Perl, PHP, Python, Ru
by, Scala, nodejs
• Multiple community supported drivers
5

6. RDBMS MongoDB
Table Collection
Row(s) JSON Document
Index Index
Partition Shard
Join Embedding/Linking
Schema (implied Schema)
6

7. { _id : ObjectId("4c4ba5c0672c685e5e8aabf3"),
author : "Asya",
date : ISODate("2012-02-02T11:52:27.442Z"),
text : "About MongoDB...",
tags : [ "tech", "databases" ],
comments : [{
author : "Fred",
date : ISODate("2012-02-03T17:22:21.124Z"), text : "Best
Post Ever!"
}],
comment_count : 1
}
7

8. • JSON has powerful, limited set of datatypes
– Mongo extends datatypes with Date, Int types, Id, …
• MongoDB stores data in BSON
• BSON is a binary representation of JSON
– Optimized for performance and navigational abilities
– Also compression
See: bsonspec.org
8

9. • Intrinsic support for fast, iterative development
• Super low latency access to your data
• Very little CPU overhead
• No additional caching layer required
• Built in replication and horizontal scaling support
9

10. • Want to build an app where users can check in to
a location
• Leave notes or comments about that location
10

11. "As a user I want to be able to find other
locations nearby"
• Need to store locations
(Offices, Restaurants, etc)
– name, address, tags
– coordinates
– User generated content e.g. tips / notes
11

12. "As a user I want to be able to 'checkin' to a
location"
Checkins
– User should be able to 'check in' to a location
– Want to be able to generate statistics:
• Recent checkins
• Popular locations
12

13. loc1, loc2, loc3 user1, user2 checkin1, checkin2
locations users checkins
13

14. > location_1 = {
name: "Din Tai Fung",
address: "123 Xingye Lu",
city: "Shanghai",
post_code: 200021
}
14

15. > location_1 = {
name: "Din Tai Fung",
address: "123 Xingye Lu",
city: "Shanghai",
post_code: 200021
}
> db.locations.find({name: "Din Tai Fung"})
15

16. > location_1 = {
name: "Din Tai Fung",
address: "123 Xingye Lu",
city: "Shanghai",
post_code: 200021
}
> db.locations.ensureIndex({name: 1})
> db.locations.find({name: "Din Tai Fung"})
16

17. > location_2 = {
name: "Din Tai Fung",
address: "123 Xingye Lu",
city: "Shanghai",
post_code: 200021,
tags: ["restaurant", "dumplings"]
}
17

18. > location_2 = {
name: "Din Tai Fung",
address: "123 Xingye Lu",
city: "Shanghai",
post_code: 200021,
tags: ["restaurant", "dumplings"]
}
> db.locations.ensureIndex({tags: 1})
18

19. > location_2 = {
name: "Din Tai Fung",
address: "123 Xingye Lu",
city: "Shanghai",
post_code: 200021,
tags: ["restaurant", "dumplings"]
}
> db.locations.ensureIndex({tags: 1})
> db.locations.find({tags: "dumplings"})
19

20. > location_3 = {
name: "Din Tai Fung",
address: "123 Xingye Lu",
city: "Shanghai",
post_code: 200021,
tags: ["restaurant", "dumplings"],
lat_long: [52.5184, 13.387]
}
20

21. > location_3 = {
name: "Din Tai Fung",
address: "123 Xingye Lu",
city: "Shanghai",
post_code: 200021,
tags: ["restaurant", "dumplings"],
lat_long: [52.5184, 13.387]
}
> db.locations.ensureIndex({lat_long: "2d"})
21

22. > location_3 = {
name: "Din Tai Fung",
address: "123 Xingye Lu",
city: "Shanghai",
post_code: 200021,
tags: ["restaurant", "dumplings"],
lat_long: [52.5184, 13.387]
}
> db.locations.ensureIndex({lat_long: "2d"})
> db.locations.find({lat_long: {$near:[52.53, 13.4]}})
22

23. // creating your indexes:
> db.locations.ensureIndex({tags: 1})
> db.locations.ensureIndex({name: 1})
> db.locations.ensureIndex({lat_long: "2d"})
// finding places:
> db.locations.find({lat_long: {$near:[52.53, 13.4]}})
// with regular expressions:
> db.locations.find({name: /^Din/})
// by tag:
> db.locations.find({tag: "dumplings"})
23

24. Atomic operators:
$set, $unset, $inc, $push, $pushAll, $pull, $pullAll, $bit
24

25. // initial data load:
> db.locations.insert(location_3)
// adding a tip with update:
> db.locations.update(
{name: "Din Tai Fung"},
{$push: {
tips: {
user: "Asya",
date: "28/03/2012",
tip: "The hairy crab dumplings are awesome!"}
}})
25

26. > db.locations.findOne()
{ name: "Din Tai Fung",
address: "123 Xingye Lu",
city: "Shanghai",
post_code: 200021,
tags: ["restaurant", "dumplings"],
lat_long: [52.5184, 13.387],
tips:[{
user: "Asya",
date: "28/03/2012",
tip: "The hairy crab dumplings are awesome!"
}]
}
26

27. "As a user I want to be able to 'checkin' to a
location"
Checkins
– User should be able to 'check in' to a location
– Want to be able to generate statistics:
• Recent checkins
• Popular locations
27

28. > user_1 = {
_id: "asya@10gen.com",
name: "Asya",
twitter: "asya999",
checkins: [
{location: "Din Tai Fung", ts: "28/03/2012"},
{location: "Meridian Hotel", ts: "27/03/2012"}
]
}
> db.users.ensureIndex({checkins.location: 1})
> db.users.find({checkins.location: "Din Tai Fung"})
28

29. // find all users who've checked in here:
> db.users.find({"checkins.location":"Din Tai Fung"})
29

30. // find all users who've checked in here:
> db.users.find({"checkins.location":"Din Tai Fung"})
// find the last 10 checkins here?
> db.users.find({"checkins.location":"Din Tai Fung"})
.sort({"checkins.ts": -1}).limit(10)
30

31. // find all users who've checked in here:
> db.users.find({"checkins.location":"Din Tai Fung"})
// find the last 10 checkins here: - Warning!
> db.users.find({"checkins.location":"Din Tai Fung"})
.sort({"checkins.ts": -1}).limit(10)
Hard to query for last 10
31

32. > user_2 = {
_id: "asya@10gen.com",
name: "Asya",
twitter: "asya999",
}
> checkin_1 = {
location: location_id,
user: user_id,
ts: "20/03/2010"
}
> db.checkins.ensureIndex({user: 1})
> db.checkins.find({user: user_id})
32

33. // find all users who've checked in here:
> location_id = db.checkins.find({"name":"Din Tai Fung"})
> u_ids = db.checkins.find({location: location_id},
{_id: -1, user: 1})
> users = db.users.find({_id: {$in: u_ids}})
// find the last 10 checkins here:
> db.checkins.find({location: location_id})
.sort({ts: -1}).limit(10)
// count how many checked in today:
> db.checkins.find({location: location_id,
ts: {$gt: midnight}}
).count()
33

34. // Find most popular locations
> agg = db.checkins.aggregate(
{$match: {ts: {$gt: now_minus_3_hrs}}},
{$group: {_id: "$location", numEntries: {$sum: 1}}}
)
> agg.result
[{"_id": "Din Tai Fung", "numEntries" : 17}]
34

35. // Find most popular locations
> map_func = function() {
emit(this.location, 1);
}
> reduce_func = function(key, values) {
return Array.sum(values);
}
> db.checkins.mapReduce(map_func, reduce_func,
{query: {ts: {$gt: now_minus_3_hrs}},
out: "result"})
> db.result.findOne()
{"_id": "Din Tai Fung", "value" : 17}
35

36. Deployment
36

37. • Single server
- need a strong backup plan
P
37

38. • Single server
- need a strong backup plan
P
• Replica sets P S S
- High availability
- Automatic failover
38

39. • Single server
- need a strong backup plan
P
• Replica sets P S S
- High availability
- Automatic failover
• Sharded P S S
- Horizontally scale
- Auto balancing
P S S
39

40. Content Management Operational Intelligence E-Commerce
User Data Management High Volume Data Feeds
40

41. 41

42. download at mongodb.org
conferences, appearances, and meetups
http://www.10gen.com/events
Facebook | Twitter | LinkedIn
http://bit.ly/mongofb @mongodb http://linkd.in/joinmongo
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