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1. Building a Cross Channel Content Delivery
Platform with MongoDB
Webinar Building a Cross Channel Content Delivery Platform with MongoDB will begin
at 6:00 AM EDT / 4:00 AM PDT / 11:00 AM UTC
Audio should start immediately when you log into the event via Audio
Broadcast. You will need a Voip headset and reliable internet
connection for Audio Broadcast. If you are having issues connecting,
please dial 1-877-668-4493; Access code: 666 687 901.
There is a Q&A following the webinar.
A recording of the webinar will be available 24 hours after the
event is complete.

2. 10gen Overview
10gen is the
company
behind
MongoDB –
the leading
NoSQL
database
2

3. 10gen Overview
170+
employees
3

4. 10gen Overview
500+
customers
4

5. 10gen Overview
$73M
in funding from
top investors
5

6. Leading Organizations Rely on
MongoDB
6

7. Media Company Change
Application
RDBMS
7

8. Media Company Change
Application
RDBMS
8

9. Media Company Change
Application
RDBMS
9

10. Media Company Change
Application
RDBMS
10

11. Media Company Change
Application
RDBMS
11

12. So What?
1. Slow onboard of each channel
due to complex schema changes.
2. Unable to scale database to
understand our customer!
3. Reduce in user experience due
to performance
12

13. Schema
1. Slow onboard of each channel
due to complex schema changes.
13

14. Example?
Real Twitter
{
"_id" : ObjectId("4f47b268fb1c80e141e9888c"),
"user" : {
"friends_count" : 73,
"location" : "Brazil",
"screen_name" : "Bia_cunha1",
"name" : "Beatriz Helena Cunha",
"followers_count" : 102,
}
}
14

15. Media Company Change
JSON
Application
RDBMS
15

16. Media Company Change
JSON JSON JSON
JSON
Application
SQL!
RDBMS
16

17. Media Company Change
JSON JSON JSON
JSON
Application
JSON
17

18. Example Tracker
click = {user: "Chris",
date: new Date(),
url: "http://www.mongodb.org"}
> db.tracker.save(click)
18

19. Example Tracker
db.click.update(
{url: "http://www.mongodb.org" },
{$push: {tags: [“MongoDB”, “NoSQL”]}}
)
19

20. Example Tracker
click = {user: "Chris",
date: new Date(),
url: "http://www.mongodb.org",
tags : [“MongoDB”, “NoSQL”]}
20

21. Now we can answer...
What are our customers doing?
21

22. Example - twitter
Real Twitter
{
"_id" : ObjectId("4f47b268fb1c80e141e9888c"),
"user" : {
"friends_count" : 73,
"location" : "Brazil",
"screen_name" : "Bia_cunha1",
"name" : "Beatriz Helena Cunha",
"followers_count" : 102,
}
}
22

23. Example - twitter
db.tweets.aggregate(
{$match:
{"user.friends_count": { $gt: 0 },
"user.followers_count": { $gt: 0 }
}
},
{$project:
{ location: "$user.location",
friends: "$user.friends_count",
followers: "$user.followers_count"
}
},
{$group:
{_id: "$location",
friends: {$sum: "$friends"},
followers: {$sum: "$followers"}
}
}
);
23

24. Example - twitter
db.tweets.aggregate(
{$match:
{"user.friends_count": { $gt: 0 }, Predicate
"user.followers_count": { $gt: 0 }
}
},
{$project:
{ location: "$user.location",
friends: "$user.friends_count",
followers: "$user.followers_count"
}
},
{$group:
{_id: "$location",
friends: {$sum: "$friends"},
followers: {$sum: "$followers"}
}
}
);
24

25. Example - twitter
db.tweets.aggregate(
{$match:
{"user.friends_count": { $gt: 0 },
Predicate
"user.followers_count": { $gt: 0 }
}
},
{$project: Parts of the
{ location: "$user.location",
friends: "$user.friends_count", document you
followers: "$user.followers_count" want to project
}
},
{$group:
{_id: "$location",
friends: {$sum: "$friends"},
followers: {$sum: "$followers"}
}
}
);
25

26. Example - twitter
db.tweets.aggregate(
{$match: Predicate
{"user.friends_count": { $gt: 0 },
"user.followers_count": { $gt: 0 }
}
},
{$project:
{ location: "$user.location",
Parts of the
friends: "$user.friends_count", document you
followers: "$user.followers_count" want to project
}
},
{$group:
{_id: "$location",
friends: {$sum: "$friends"}, Function to apply
followers: {$sum: "$followers"}
} to the result set
}
);
26

27. Example - twitter
{
"result" :[
{
"_id" : "Far Far Away",
"friends" : 344,
"followers" : 789
},
...
],
"ok" : 1
}
27

28. Scale!
Let’s scale our database to
understand ALL our customer!
28

29. Sharding - Range distribution
sh.shardCollection("test.tweets", {_id: 1})
shard01 shard02 shard03
29

30. Sharding - Range distribution
a-i j-r s-z
shard01 shard02 shard03
30

31. Sharding - Splits
a-i ja-jz s-z
shard01 k-r
shard02 shard03
31

32. Sharding - Splits
a-i ja-ji s-z
shard01 ji-js
shard02 shard03
js-jw
jz-r
32

33. Sharding - Auto Balancing
a-i ja-ji s-z
ji-js
shard01 shard02 shard03
js-jw js-jw
jz-r jz-r
33

34. Sharding - Auto Balancing
a-i ja-ji s-z
shard01 ji-js
shard02 shard03
js-jw jz-r
34

35. Performance
Improve user experience due to
increased performance
35

36. Memory
MongoDB revolves around memory
mapped files

37. Operating System map files on the
Filesystem to Virtual Memory
• 200 gigs of MongoDB files creates 200 gigs
of virtual memory
• OS controls what data in RAM
• When a piece of data isn't found in memory
– OS goes to disk to fetch the data
• Indexes are part of the Regular Database files
• Deployment Trick: Pre-Warm your Database
(PreWarming your cache) to prevent cold

38. A Few Words on OS Choice
• For production: Use a 64 bit OS and a 64 bit
MongoDB Build
– 32 Bit has a 2 gig limit; imposed by the operating
systems for memory mapped files
– Clients can be 32 bit
• MongoDB Supports (little endian only)
– Linux, FreeBSD, OS X (on Intel, not PowerPC)
– Windows
– Solaris (Intel only, Joyent offers a cloud service
which works for Mongo)

39. Sharding for caching
96 GB Mem
3:1 Data/Mem
a-i
300 GB Data
j-r
shard01
s-z
300 GB
39

40. Aggregate Horizontal Resources
96 GB Mem 96 GB Mem 96 GB Mem
1:1 Data/Mem 1:1 Data/Mem 1:1 Data/Mem
a-i j-r s-z
300 GB Data
j-r shard02 shard03
s-z
100 GB 100 GB 100 GB
40

41. Sharding Features
• Shard data without no downtime
• Automatic balancing as data is written
• Commands routed (switched) to correct node
• Inserts - must have the Shard Key
• Updates - can have the Shard Key
• Queries
• With Shard Key - routed to nodes
• Without Shard Key - scatter gather
• Indexed / Sorted Queries
• With Shard Key - routed in order
• Without Shard Key - distributed sort merge
41

42. Summary

43. Relational Database Challenges
Data Types Agile Development
• Unstructured data • Iterative
• Semi-structured data • Short development
cycles
• Polymorphic data
• New workloads
Volume of Data New Architectures
• Petabytes of data • Horizontal scaling
• Trillions of records • Commodity servers
• Tens of millions of queries per • Cloud computing
second
43

44. mongoDB Adoption
Resource User Data Management
44

45. Global MongoDB Community
41,000+
Monthly Unique Downloads
24,000+
Online Education Registrants
12,000+
MongoDB User Group Members
10,000+
Annual MongoDB Days Attendees

46. Leading Organizations Rely on
MongoDB
46

47. Questions
• 10Gen Services
– Development Support
– Consultancy
– TAM
– Production Support
• Free online MongoDB
training
– Develop
– Deploy
– Classes start Oct. 2012
47