Presentation at Cassandra Summit 2013

1. #CASSANDRA13
Ken
Krugler
|
President,
Scale
Unlimited
Suicide Prevention Using Social Media and Cassandra

2. #CASSANDRA13
What we will discuss today...
*Using Cassandra to store social media content
*Combining Hadoop workflows with Cassandra
*Leveraging Solr search support in DataStax Enterprise
*Doing good with big data
This material is based upon work supported by the Defense Advance Research Project Agency (DARPA),
and Space Warfare Systems Center Pacific under Contract N66001-11-4006. Any opinions, findings, and
conclusions or recommendations expressed in this material are those of the authors(s) and do not
necessarily reflect the views of the Defense Advance Research Program Agency (DARPA) and Space and
Naval Warfare Systems Center Pacific.
Fine Print!

3. #CASSANDRA13
Obligatory Background
*Ken Krugler, Scale Unlimited - Nevada City, CA
*Consulting on big data workflows, machine learning & search
*Training for Hadoop, Cascading, Solr & Cassandra

4. #CASSANDRA13
Durkheim Project Overview
Including things we didn't work on...

5. #CASSANDRA13
What's the problem?
*More soldiers die from suicide than combat
*Suicide rate has gone up 80% since 2002
*Civilian suicide rates are also climbing
*More suicides than homicides
*Intervention after an "event" is often too late
Graph of suicide rates

6. #CASSANDRA13
What is The Durkheim Project?
*DARPA-funded initiative to
help military physicians
*Uses predictive analytics to
estimate suicide risk from
what people write online
*Each user is assigned a
suicidality risk rating of red,
yellow or green.
Émile Durkheim
Named after Emile Durkheim, late 1800's sociologist who first used text analytics to help define suicide risk.

7. #CASSANDRA13
Current Status of Durkheim
*Collaborative effort involving Patterns and Predictions, Dartmouth
Medical School & Facebook
*Details at http://www.durkheimproject.org/
*Finished phase I, now being rolled out to wider audience
Patterns and Predictions has its background expertise in predicting financial market events and trends from news, which led to the development of the predictive
models used in Durkheim

8. #CASSANDRA13
Predictive Analytics
*Guessing at state of mind from text
-"There are very few people in this world that know the REAL me."
-"I lay down to go to sleep, but all I can do is cry"
*Uses labeled training data from clinical notes
*Phase I results promising, for small sample set
-"ensemble" of predictors is a powerful ML technique

9. #CASSANDRA13
Clinician Dashboard
*Multiple views on patient
*Prediction & confidence
*Backing data (key phrases, etc)
So this is the goal - give medical staff indications of who they should be most concerned about.

10. #CASSANDRA13
Data Collection
Where _do_ you put a billion text snippets?
The previous section was the project overview, which was work done by others in the project.
Now we get to the part that we worked, which involves Cassandra

11. #CASSANDRA13
Saving Social Media Activity
*System to continuous save new activity
-Scalable data store
*Also needs a scalable, reliable way to access data
-Processed in bulk (workflows)
-Accessed at individual level
-Searched at activity level
For the current size of the project, MySQL would be just fine.
But we want an architecture that can scale if/when the project is rolled out to everyone

12. #CASSANDRA13
Data Collection
*Pink is what we wrote
*Green is in Cassandra
*Key data path in red
Exciting Social
Media Activity
Gigya
Daemon
Durkheim
Social API
Users
Table
Durkheim
App
Gigya
Service
Activity
Table

13. #CASSANDRA13
Designing the Column Families
*What queries do we need to handle?
-Always by user id (what we assign)
*We want all the data for a user
-Both for Users table, and Activities table
-Sometimes we want a date range of activities
*So one row per user
-And ordered by date in the Activities table

14. #CASSANDRA13
Users Table (Column Family)
*One row per user - row key is a UUID we assign
*Standard "static" columns
-First name, last name, opt_in status, etc.
*Easy to add more xxx_id columns for new services
row key first_name last_name facebook_id twitter_id opt_in

15. #CASSANDRA13
Activities Table (Column Family)
*One row per user - row key is a UUID we assign
*One composite column per social media event
-Timestamp (long value)
-Source (FB, TW, GP, etc)
-Type of column (data, activity id, user id, type of activity)
row key ts_src_data ts_src_id ts_src_providerUid ts_src_type
Remember we wanted to get slices of data by date?
So we use timestamp as the first (primary) ordering for the columns.
We can use regular millisecond timestamp since it's for one user, assume we don't get multiple entries.

16. #CASSANDRA13
Two Views of Composite Columns
*As a row/column view
*As a key-value map 213_FB_data
213_FB_id
213_FB_providerUid
213_FB_type
"I feel tired"
"FB post #32"
"FB user #66"
"Status update"
"uuid1"
"uuid1" 213_FB_data 213_FB_id 213_FB_providerUid 213_FB_type
"I feel tired" "FB post #32" "FB user #66" "Status update"

17. #CASSANDRA13
Implementation Details
*API access protected via signature
*Gigya Daemon on both t1.micro servers
-But only active on one of them
*Astyanax client talks to Cassandra
*Cluster uses 3 m1.large servers
Durkheim
Social API
Durkheim
App
AWS Load
Balancer
EC2 m1.large
servers
Durkheim
Social API
EC2 t1.micro
servers

18. #CASSANDRA13
Predictive Analytics at Scale
Running workflows against Cassandra data

19. #CASSANDRA13
How to process all this social media goodness?
*Models are defined elsewhere
*These are "black boxes" to us
213_FB_data 213_FB_id 213_FB_providerUid 213_FB_type
"I feel tired" "FB post #32" "FB user #66" "Status update"
307_TW_data 307_TW_id 307_TW_providerUid 307_TW_type
"Where am I?" "Tweet #17" "TW user #109" "Tweet"
Feature
Extraction
Model
model rating probability keywords
Models are data used by PA engine to generate scores
We do not have or want access to the data used to generate the models
Generating the model is often NOT something that needs scalability
Amount of labeled data is typically pretty small.
Training often works best on a single server.

20. #CASSANDRA13
Why do we need Hadoop?
*Running one model on one user is easy
-And n models on one user is still OK
*But when a model changes...
-all users with the model need processing
Models can change frequently
And when a user changes...
- that user with all models needs processing
- adding/removing models is also a change

21. #CASSANDRA13
Batch processing is OK
*No strict minimum latency requirements
*So we use Hadoop, for scalability and reliability

22. #CASSANDRA13
Hadoop Workflow Details
*Implemented using Cascading
*Read Activities Table using Cassandra Tap
*Read models from MySQL via JDBC

23. #CASSANDRA13
Hadoop Bulk Classification Workflow
Convert to Cassandra
Write Classification Result Table
Run Classifier models
CoGroup by user profile ID
Convert from Cassandra
Read User Profiles Table
Convert from Cassandra
Read Social Media Activity Table
Separate from this, we've loaded the models into memory and serialized them with the classification step
This is all done using Cascading to define the workflow.

24. #CASSANDRA13
Workflow Issues
*Currently manual operation
-Ultimately needs a daemon to trigger (time, users, models)
*Runs in separate cluster
-Lots of network activity to pull data from Cassandra cluster
-With DSE we could run on same cluster
*Fun with AWS security groups

25. #CASSANDRA13
Solr Search
Poking at the data

26. #CASSANDRA13
Solr Search
*Model results include key terms for classification result
-"feel angry" (0.732)
*Now you want to check actual usage of these terms
Maybe actual text was "I don't feel angry when my wifi connection drops".

27. #CASSANDRA13
Poking at the Data
*Hadoop turns petabytes into
pie-charts
*How do you verify results?
*Search works really well here
Maybe before you'd use a spreadsheet printout to argue.
But that would be Satan's Spreadsheet with billions of rows.

28. #CASSANDRA13
Solr Search
*Want "narrow" table for search
-Solr dynamic fields are usually not a great idea
-Limit to 1024 dynamic fields per document
*So we'll replicate some of our Activity CF data into a new CF
*Don't be afraid of making copies of data

29. #CASSANDRA13
The "Search" Column Family
*Row key is derived from Activity CF UUID + target column name
*One column ("data") has content from that row + column in Activity CF
row key "data"
"uuid1_213_FB "I feel tired"
"uuid1" 213_FB_data 213_FB_id
"I feel tired" "FB post #32"
Activity Column Family
Search Column Family

30. #CASSANDRA13
Solr Schema
*Very simple (which is how we like it)
*Direct one-to-one mapping with Cassandra columns
*Hits have key field, which contains UUID/Timestamp/Service
<fields>
<field name="key" type="string" indexed="true" stored="true" />
<field name="data" type="text" indexed="true" stored="true" />
</fields>
So once we have a hit, we can access information in activity table if needed.

31. #CASSANDRA13
Combined Cluster
*One Cassandra Cluster can allocate nodes for Hadoop & Search

32. #CASSANDRA13
Security
Locking things down

33. #CASSANDRA13
The Most Important Detail
*We don't have any personal medical data!!!
*We don't have any personal medical data!!!
*We don't have any personal medical data!!!
As soon as you've got personal medical data, it's a whole new ballgame.
At least an order of magnitude more work to make it really secure.
Likely that you couldn't use AWS cloud
We still care about security, because we're collecting social media activity that isn't necessarily public.

34. #CASSANDRA13
Three Aspects of Security
*Server-level
-ssh via restricted private key
*API-level
-validate requests using signature
-secure SHA1 hash
*Services-level
-Restrict open ports using security groups
So even if you knew which server was running OpsCenter, you couldn't just start poking around.
Access to Cassandra is only via t1.micro servers, which are in same security group
t1.micro servers only open up ssh and port needed for external API request
[include picture?]

35. #CASSANDRA13
Summary
Bringing it all home

36. #CASSANDRA13
*You can effectively use Cassandra as:
A repository for social media data
The data source for workflows
A search index, via Solr integration
Key Points...

37. #CASSANDRA13
*It is possible to do more with big data than optimize ad yields
And the Meta-Point

38. #CASSANDRA13
THANK YOU