My presentation in HBaseCon 2013 entitled "Using Apache HBase for Large Matrices". This describes an HBase backed Mahout matrix, as input/output to machine learning algorithms

1. HBase for Dealing with
Large Matrices

2. Who am I?
Leads data team at Dilisim
Researcher at Anadolu University

3. Machine Learning
Some big problems
Classifying huge text collections
Recommending to millions of users
Predicting links in a social network

4. Recommender Systems
Recommenders input large sparse
matrices
How would you input a millions X
millions matrix?

5. Recommender Systems
m users
3.00 0.00 2.00 0.00 4.00 2.00 3.00 2.00 1.00 3.00 0.00 3.00 0.00 2.00 0.00 4.00 2.00 3.00 2.00 1.00 3.00 0.00 …
2.00 3.00 2.00 1.00 1.00 4.00 2.00 3.00 0.00 2.00 3.00 2.00 3.00 2.00 1.00 1.00 4.00 2.00 3.00 0.00 2.00 3.00…
3.00 4.00 3.00 2.00 3.00 4.00 1.00 1.00 1.00 1.00 3.00 3.00 4.00 3.00 2.00 3.00 4.00 1.00 1.00 1.00 1.00 3.00 …
0.00 0.00 0.00 0.00 4.00 1.00 3.00 4.00 2.00 1.00 0.00 0.00 0.00 0.00 0.00 4.00 1.00 3.00 4.00 2.00 1.00 0.00 …
0.00 2.00 3.00 2.00 2.00 4.00 3.00 0.00 3.00 4.00 2.00 0.00 2.00 3.00 2.00 2.00 4.00 3.00 0.00 3.00 4.00 2.00 …
4.00 2.00 1.00 4.00 2.00 3.00 3.00 0.00 3.00 1.00 2.00 4.00 2.00 1.00 4.00 2.00 3.00 3.00 0.00 3.00 1.00 2.00…
4.00 0.00 4.00 2.00 2.00 3.00 3.00 3.00 2.00 0.00 0.00 4.00 0.00 4.00 2.00 2.00 3.00 3.00 3.00 2.00 0.00 0.00…
1.00 3.00 2.00 4.00 2.00 3.00 0.00 0.00 3.00 0.00 3.00 1.00 3.00 2.00 4.00 2.00 3.00 0.00 0.00 3.00 0.00 3.00 …
1.00 4.00 1.00 1.00 3.00 4.00 2.00 2.00 0.00 1.00 4.00 1.00 4.00 1.00 1.00 3.00 4.00 2.00 2.00 0.00 1.00 4.00…
3.00 3.00 2.00 3.00 4.00 1.00 4.00 0.00 1.00 0.00 1.00 3.00 3.00 2.00 3.00 4.00 1.00 4.00 0.00 1.00 0.00 1.00…
0.00 1.00 2.00 4.00 2.00 2.00 3.00 4.00 4.00 4.00 1.00 0.00 1.00 2.00 4.00 2.00 2.00 3.00 4.00 4.00 4.00 1.00…
4.00 0.00 4.00 2.00 2.00 3.00 3.00 3.00 2.00 0.00 0.00 4.00 0.00 4.00 2.00 2.00 3.00 3.00 3.00 2.00 0.00 0.00…
1.00 3.00 2.00 4.00 2.00 3.00 0.00 0.00 3.00 0.00 3.00 1.00 3.00 2.00 4.00 2.00 3.00 0.00 0.00 3.00 0.00 3.00 …
3.00 4.00 3.00 2.00 3.00 4.00 1.00 1.00 1.00 1.00 3.00 3.00 4.00 3.00 2.00 3.00 4.00 1.00 1.00 1.00 1.00 3.00 …
0.00 0.00 0.00 0.00 4.00 1.00 3.00 4.00 2.00 1.00 0.00 0.00 0.00 0.00 0.00 4.00 1.00 3.00 4.00 2.00 1.00 0.00 …
…………………………………………………………………………………………………………………… .
…………………………………………………………………………………………………………………… .
………………………………………………………………………………………………………………… … .
n items
Input

6. Recommender Systems
State-of-the-art recommender systems learn
large models
One factor vector per each user and item
One parameter vector (on side info) per
each user and item

7. Recommender Systems
m users
3.00 0.00 2.00 0.00 4.00 2.00 3.00 2.00 1.00 3.00 0.00 …
2.00 3.00 2.00 1.00 1.00 4.00 2.00 3.00 0.00 2.00 3.00 …
3.00 4.00 3.00 2.00 3.00 4.00 1.00 1.00 1.00 1.00 3.00 …
0.00 0.00 0.00 0.00 4.00 1.00 3.00 4.00 2.00 1.00 0.00 …
0.00 2.00 3.00 2.00 2.00 4.00 3.00 0.00 3.00 4.00 2.00 …
4.00 2.00 1.00 4.00 2.00 3.00 3.00 0.00 3.00 1.00 2.00 …
4.00 0.00 4.00 2.00 2.00 3.00 3.00 3.00 2.00 0.00 0.00 …
1.00 3.00 2.00 4.00 2.00 3.00 0.00 0.00 3.00 0.00 3.00 …
1.00 4.00 1.00 1.00 3.00 4.00 2.00 2.00 0.00 1.00 4.00 …
3.00 3.00 2.00 3.00 4.00 1.00 4.00 0.00 1.00 0.00 1.00 …
0.00 1.00 2.00 4.00 2.00 2.00 3.00 4.00 4.00 4.00 1.00 …
………………………………………………………… .
………………………………………………………… .
………………………………………………………… .
n items
Input
User Model
Item Model
m x k
n x k
0.54 0.48 0.83 0.75 0.28 …
0.02 0.29 0.99 0.85 0.68 …
0.05 0.53 0.60 0.98 0.19 …
0.52 0.47 0.50 0.12 0.98 …
0.26 0.39 0.29 0.91 0.50 …
0.15 0.43 0.66 0.07 0.51 …
0.52 0.36 0.01 0.87 0.53 …
…………………………. .
………………………….. .
…………………………... .
0.93 0.78 0.56 0.77 0.75 …
0.21 0.44 0.99 0.01 0.00 …
0.04 0.42 0.36 0.72 0.19 …
0.77 0.07 0.24 0.67 0.87 …
0.42 0.79 0.62 0.80 0.79 …
0.42 0.32 0.26 0.50 0.85 …
0.94 0.76 0.93 0.34 0.46 …
…………………………. .
………………………….. .
…………………………... .

8. Learning Process
What does a machine learning algorithm
require to do with that matrix?

9. Machine Learning - Techniques
Batch Learning
All parameters are updated once per
iteration

10. Machine Learning - Techniques
Batch Learning
Updates can be calculated in parallel
using MapReduce
(SequenceFile might be enough)

11. Machine Learning - Techniques
Batch Learning
Output model should provide random
access to rows

12. Machine Learning - Techniques
Online Learning
Parameters are updated per training
example

13. Machine Learning - Techniques
Online Learning
Each update results in updates in
a row
Needs random access while learning

14. Machine Learning - Techniques
Online Learning
Output model should provide random
access to rows

15. Deployment Process
How do you decide to deploy a machine
learning model in production?

16. Machine Learning - Deployment
Usual process
Works
good?
Deploy in
production
Experiment
on prototype
Y
N

17. Machine Learning - Deployment
How would you turn your prototype into
production easily?
Common matrix interface for in-
memory and persistent versions

18. HBase Backed Matrix
Implements Mahout matrix
Dense or sparse

19. HBase Backed Matrix
Random access to cells
Random access to rows
Iteration over rows
Lazy loading while iterating

20. HBase Backed Matrix
Common interface for prototype and
product
Easy to deploy (Model already persisted)

21. HBase Backed Matrix
Matrix operations with existing mahout-
math library

22. Logical Schema
Composite row keys:
12_0:
12_9:
12_22000:
data:value:0.41
data:value:0.41
data:value:0.41

23. Logical Schema
Composite row keys:
Row access by scan
Cell access by get
Atomic row update should be
handled in application

24. Logical Schema
Row indices as row keys
12:
data:0:0.41 data:22000:0.41data:9:0.41

25. Logical Schema
Row indices as row keys
Atomic updates are handled
automatically

26. Speed – Cell access/write
GET SET
row index as row key
composite row key

27. Speed – Row access/write
GET SET
row index as row key
composite row key

28. Code
github.com/gcapan/mahout/tree/hbase-matrix

29. Future Work
MatrixInputFormat
Might replace SequenceFile based
MapReduce inputs

30. Future Work – A little digression
Recommender Systems
Calculating score for a user-item
pair is easy with HBaseMatrix

31. Future Work – A little digression
Recommender Systems
top-N recommendation?
All candidate items for a user in
the user row as a nested entity
(See Ian Varley's HBase Schema
Design)

32. Thank you!