This document provides an overview of a real-time directed content system that uses MongoDB, Hadoop, and MapReduce. It describes: - The key participants in the system and their roles in generating, analyzing, and operating on data - An architecture that uses MongoDB for real-time user profiling and content recommendations, Hadoop for periodic analytics on user profiles and content tags, and MapReduce jobs to update the profiles - How the system works over time to continuously update user profiles based on their interactions with content, rerun analytics daily to update tags and baselines, and make recommendations based on the updated profiles - How the system supports both real-time and long-term analytics needs through this integrated approach.

1. Big Data: Examples and
Guidelines for the Enterprise
Decision Maker
Solutions Architect, MongoDB
Buzz Moschetti
buzz.moschetti@mongodb.com
#MongoDB

2. Who is your Presenter?
• Yes, I use “Buzz” on my business cards
• Former Investment Bank Chief Architect at
JPMorganChase and Bear Stearns before that
• Over 25 years of designing and building systems
• Big and small
• Super-specialized to broadly useful in any vertical
• “Traditional” to completely disruptive
• Advocate of language leverage and strong factoring
• Still programming – using emacs, of course

3. Agenda
• (Occasionally) Brutal Truths about Big Data
• Review of Directed Content Business Architecture
• A Simple Technical Implementation

4. Truths
• Clear definition of Big Data still maturing
• Efficiently operationalizing Big Data is non-trivial
• Developing, debugging, understanding MapReduce
• Cluster monitoring & management, job scheduling/recovery
• If you thought regular ETL Hell was bad….
• Big Data is not about math/set accuracy
• The last 25000 items in a 25,497,612 set “don’t matter”
• Big Data questions are best asked periodically
• “Are we there yet?”
• Realtime means … realtime

5. It’s About The Functions, not the
Terms
DON’T ASK:
• Is this an operations or an analytics problem?
• Is this online or offline?
• What query language should we use?
• What is my integration strategy across tools?
ASK INSTEAD:
• Am I incrementally addressing data (esp.
writes)?
• Am I computing a precise answer or a trend?
• Do I need to operate on this data in realtime?
• What is my holistic architecture?

6. What We’re Going to “Build” today
Realtime Directed Content System
• Based on what users click, “recommended”
content is returned in addition to the target
• The example is sector (manufacturing, financial
services, retail) neutral
• System dynamically updates behavior in
response to user activity

7. The Participants and Their Roles
Directed
Content
System
Customer
s
Content
Creators
Management/
Strategy
Analysts/
Data Scientists
Generate and tag
content from a known
domain of tags
Make decisions based
on trends and other
summarized data
Operate on data to
identify trends and
develop tag domains
Developers/
ProdOps
Bring it all together:
apps, SDLC,
integration, etc.

8. Priority #1: Maximizing User value
Considerations/Requirements
Maximize realtime user value and experience
Provide management reporting and trend analysis
Engineer for Day 2 agility on recommendation engine
Provide scrubbed click history for customer
Permit low-cost horizontal scaling
Minimize technical integration
Minimize technical footprint
Use conventional and/or approved tools
Provide a RESTful service layer
…..

9. The Architecture
mongoDB HadoopApp(s) MapReduce

10. Complementary Strengths
mongoDB HadoopApp(s) MapReduce
• Standard design paradigm
(objects, tools, 3rd party products,
IDEs, test drivers, skill pool, etc.
etc.)
• Language flexibility (Java, C#, C++
python, Scala, …)
• Webscale deployment model
• appservers, DMZ, monitoring
• High performance rich shape
CRUD
• MapReduce design paradigm
• Node deployment model
• Very large set operations
• Computationally intensive, longer
duration
• Read-dominated workload

11. “Legacy” Approach: Somewhat
unidirectional
mongoDB HadoopApp(s) MapReduce
• Extract data from mongoDB and other
sources nightly (or weekly)
• Run analytics
• Generate reports for people to read
• Where’s the feedback?

12. Somewhat better approach
mongoDB HadoopApp(s) MapReduce
• Extract data from mongoDB and other
sources nightly (or weekly)
• Run analytics
• Generate reports for people to read
• Move important summary data back to
mongoDB for consumption by apps.

13. …but the overall problem remains:
• How to realtime integrate and operate upon both
periodically generated data and realtime current
data?
• Lackluster integration between OLTP and
Hadoop
• It’s not just about the database: you need a
realtime profile and profile update function

14. The legacy problem in pseudocode
onContentClick() {
String[] tags = content.getTags();
Resource[] r = f1(database, tags);
}
• Realtime intraday state not well-handled
• Baselining is a different problem than click
handling

15. The Right Approach
• Users have a specific Profile entity
• The Profile captures trend analytics as baselining
information
• The Profile has per-tag “counters” that are updated
with each interaction / click
• Counters plus baselining are passed to fetch function
• The fetch function itself could be dynamic!

16. 24 hours in the life of The System
• Assume some content has been created and tagged
• Two systemetized tags: Pets & PowerTools

17. Monday, 1:30AM EST
• Fetch all user Profiles from mongoDB; load into Hadoop
• Or skip if using the mongoDB-Hadoop
connector!
mongoDB HadoopApp(s) MapReduce

18. mongoDB-Hadoop MapReduce Example
public class ProfileMapper
extends Mapper<Object, BSONObject, IntWritable, IntWritable>
{
@Override
public void map(final Object pKey,
final BSONObject pValue,
final Context pContext )
throws IOException, InterruptedException{
String user = (String)pValue.get(”user");
Date d1 = (Date)pValue.get(“lastUpdate”);
int count = 0;
List<String> keys = pValue.get(“tags”).keys();
for ( String tag : keys) {
count += pValue.get(tag).get(“hist”).size();
)
int avg = count / keys.size();
pContext.write( new IntWritable( count), new
IntWritable( avg ) );
}
}

19. Monday, 1:45AM EST
• Grind through all content data and user Profile data to
produce:
• Tags based on feature extraction (vs. creator-applied
tags)
• Trend baseline per user for tags Pets and PowerTools
• Load Profiles with new baseline back into mongoDB
• Or skip if using the mongoDB-Hadoop connector!
mongoDB HadoopApp(s) MapReduce

20. Monday, 8AM EST
• User Bob logs in and Profile retrieved from mongoDB
• Bob clicks on Content X which is already tagged as “Pets”
• Bob has clicked on Pets tagged content many times
• Adjust Profile for tag “Pets” and save back to mongoDB
• Analysis = f(Profile)
• Analysis can be “anything”; it is simply a result. It could trigger
an ad, a compliance alert, etc.
mongoDB HadoopApp(s) MapReduce

21. Monday, 8:02AM EST
• Bob clicks on Content Y which is already tagged as “Spices”
• Spice is a new tag type for Bob
• Adjust Profile for tag “Spices” and save back to mongoDB
• Analysis = f(profile)
mongoDB HadoopApp(s) MapReduce

22. Profile in Detail
{
user: “Bob”,
personalData: {
zip: “10024”,
gender: “M”
},
tags: {
PETS: { algo: “A4”,
baseline: [0,0,10,4,1322,44,23, … ],
hist: [
{ ts: datetime1, url: url1 },
{ ts: datetime2, url: url2 } // 100 more
]},
SPICE: { hist: [
{ ts: datetime3, url: url3 }
]}
}
}

23. Tag-based algorithm detail
getRecommendedContent(profile, [“PETS”, other]) {
if algo for a tag available {
filter = algo(profile, tag);
}
fetch N recommendations (filter);
}
A4(profile, tag) {
weight = get tag (“PETS”) global weighting;
adjustForPersonalBaseline(weight, “PETS” baseline);
if “PETS” clicked more than 2 times in past 10 mins
then weight += 10;
if “PETS” clicked more than 10 times in past 2 days
then weight += 3;
return new filter({“PETS”, weight}, globals)
}

24. Tuesday, 1AM EST
mongoDB HadoopApp(s) MapReduce
• Fetch all user Profiles from mongoDB; load into Hadoop
• Or skip if using the mongoDB-Hadoop
connector!

25. Tuesday, 1:30AM EST
• Grind through all content data and user profile data to
produce:
• Tags based on feature extraction (vs. creator-applied
tags)
• Trend baseline for Pets and PowerTools and Spice
• Data can be specific to individual or by group
• Load baseline back into mongoDB
• Or skip if using the mongoDB-Hadoop connector!
mongoDB HadoopApp(s) MapReduce

26. New Profile in Detail
{
user: “Bob”,
personalData: {
zip: “10024”,
gender: “M”
},
tags: {
PETS: { algo: “A4”,
baseline: [0,0,10,4,1322,44,23, … ],
hist: [
{ ts: datetime1, url: url1 },
{ ts: datetime2, url: url2 } // 100 more
]},
SPICE: { hist: [
baseline: [0],
{ ts: datetime3, url: url3 }
]}
}
}

27. Tuesday, 1:35AM EST
• Perform maintenance on user Profiles
• Click history trimming (variety of algorithms)
• “Dead tag” removal
• Update of auxiliary reference data
mongoDB HadoopApp(s) MapReduce

28. New Profile in Detail
{
user: “Bob”,
personalData: {
zip: “10022”,
gender: “M”
},
tags: {
PETS: { algo: “A4”,
baseline: [ 1322,44,23, … ],
hist: [
{ ts: datetime1, url: url1 } // 50 more
]},
SPICE: { algo: “Z1”, hist: [
baseline: [0],
{ ts: datetime3, url: url3 }
]}
}
}

29. Feel free to run the baselining more
frequently
… but avoid “Are We There
Yet?”
mongoDB HadoopApp(s) MapReduce

30. Nearterm / Realtime Questions & Actions
With respect to the Customer:
• What has Bob done over the past 24 hours?
• Given an input, make a logic decision in 100ms or less
With respect to the Provider:
• What are all current users doing or looking at?
• Can we nearterm correlate single events to shifts in
behavior?

31. Longterm/ Not Realtime Questions &
Actions
With respect to the Customer:
• Any way to explain historic performance / actions?
• What are recommendations for the future?
With respect to the Provider:
• Can we correlate multiple events from multiple sources
over a long period of time to identify trends?
• What is my entire customer base doing over 2 years?
• Show me a time vs. aggregate tag hit chart
• Slice and dice and aggregate tags vs. XYZ
• What tags are trending up or down?

32. The Key To Success: It is One System
mongoDB
Hadoop
App(s)
MapReduce

33. Webex Q&A

34. Thank You
Buzz Moschetti
buzz.moschetti@mongodb.com
#MongoDB