This presentation presents Apache Flink's approach to scalable machine learning: Composable machine learning pipelines, consisting of transformers and learners, and distributed linear algebra. The presentation was held at the Machine Learning Stockholm group on the 23rd of March 2015.

1. Till Rohrmann
Flink committer
trohrmann@apache.org
@stsffap
Machine Learning
with
Apache Flink

2. What is Flink
§ Large-scale data processing engine
§ Easy and powerful APIs for batch and real-time
streaming analysis (Java / Scala)
§ Backed by a very robust execution backend
• with true streaming capabilities,
• custom memory manager,
• native iteration execution,
• and a cost-based optimizer.
2

3. Technology inside Flink
§ Technology inspired by compilers +
MPP databases + distributed systems
§ For ease of use, reliable performance,
and scalability
case
class
Path
(from:
Long,
to:
Long)
val
tc
=
edges.iterate(10)
{
paths:
DataSet[Path]
=>
val
next
=
paths
.join(edges)
.where("to")
.equalTo("from")
{
(path,
edge)
=>
Path(path.from,
edge.to)
}
.union(paths)
.distinct()
next
}
Cost-based
optimizer
Type extraction
stack
Memory
manager
Out-of-core
algos
real-time
streaming
Task
scheduling
Recovery
metadata
Data
serialization
stack
Streaming
network
stack
...
Pre-flight
(client) Master
Workers

4. How do you use Flink?
4

5. Example: WordCount
5
case
class
Word
(word:
String,
frequency:
Int)
val
env
=
ExecutionEnvironment.getExecutionEnvironment()
val
lines
=
env.readTextFile(...)
lines
.flatMap
{line
=>
line.split("
").map(word
=>
Word(word,1))}
.groupBy("word").sum("frequency”)
.print()
env.execute()
Flink has mirrored Java and Scala APIs that offer the same
functionality, including by-name addressing.

6. Flink API in a Nutshell
§ map, flatMap, filter,
groupBy, reduce,
reduceGroup,
aggregate, join,
coGroup, cross,
project, distinct, union,
iterate, iterateDelta, ...
§ All Hadoop input
formats are supported
§ API similar for data sets
and data streams with
slightly different
operator semantics
§ Window functions for
data streams
§ Counters,
accumulators, and
broadcast variables
6

7. Machine learning with Flink
7

8. Does ML work like that?
8

9. More realistic scenario!
9

10. Machine learning pipelines
§ Pipelining inspired by scikit-learn
§ Transformer: Modify data
§ Learner: Train a model
§ Reusable components
§ Let’s you quickly build ML pipelines
§ Model inherits pipeline of learner
10

11. Linear regression in polynomial space
val
polynomialBase
=
PolynomialBase()
val
learner
=
MultipleLinearRegression()
val
pipeline
=
polynomialBase.chain(learner)
val
trainingDS
=
env.fromCollection(trainingData)
val
parameters
=
ParameterMap()
.add(PolynomialBase.Degree,
3)
.add(MultipleLinearRegression.Stepsize,
0.002)
.add(MultipleLinearRegression.Iterations,
100)
val
model
=
pipeline.fit(trainingDS,
parameters)
11
Input
Data
Polynomial
Base
Mapper
Mul4ple
Linear
Regression
Linear
Model

12. Current state of Flink-ML
§ Existing learners
• Multiple linear regression
• Alternating least squares
• Communication efficient distributed dual
coordinate ascent (PR pending)
§ Feature transformer
• Polynomial base feature mapper
§ Tooling
12

13. Distributed linear algebra
§ Linear algebra universal
language for data
analysis
§ High-level abstraction
§ Fast prototyping
§ Pre- and post-processing
step
13

14. Example: Gaussian non-negative matrix
factorization
§ Given input matrix V, find W and H such
that
§ Iterative approximation
14
Ht+1 = Ht ∗ Wt
T
V /Wt
T
Wt Ht( )
Wt+1 = Wt ∗ VHt+1
T
/Wt Ht+1Ht+1
T
( )
V ≈ WH
var
i
=
0
var
H:
CheckpointedDrm[Int]
=
randomMatrix(k,
V.numCols)
var
W:
CheckpointedDrm[Int]
=
randomMatrix(V.numRows,
k)
while(i
<
maxIterations)
{
H
=
H
*
(W.t
%*%
V
/
W.t
%*%
W
%*%
H)
W
=
W
*
(V
%*%
H.t
/
W
%*%
H
%*%
H.t)
i
+=
1
}

15. Why is Flink a good fit for ML?
15

16. Flink’s features
§ Stateful iterations
• Keep state across iterations
§ Delta iterations
• Limit computation to elements which matter
§ Pipelining
• Avoiding materialization of large
intermediate state
16

17. CoCoA
17
minw∈Rd P(w):=
λ
2
w
2
+
1
n
ℓi wT
xi( )
i=1
n
∑
#
$
%
&
'
(

18. Bulk Iterations
18
partial
solution
partial
solutionX
other
datasets
Y
initial
solution
iteration
result
Replace
Step function

19. Delta iterations
19
partial
solution
delta
setX
other
datasets
Y
initial
solution
iteration
result
workset A B workset
Merge deltas
Replace
initial
workset

20. Effect of delta iterations
0
5000000
10000000
15000000
20000000
25000000
30000000
35000000
40000000
45000000
1 6 11 16 21 26 31 36 41 46 51 56 61
#ofelementsupdated
iteration

21. Iteration performance
21
0
10
20
30
40
50
60
Hadoop Flink bulk Flink delta
Time(minutes)
61 iterations and 30 iterations of
PageRank on a Twitter follower
graph with Hadoop MapReduce
and Flink using bulk and delta
iterations
30 iterations
61 iterations
MapReduce

22. How to factorize really large
matrices?
22

23. Collaborative Filtering
§ Recommend items based on users with
similar preferences
§ Latent factor models capture underlying
characteristics of items and preferences
of user
§ Predicted preference:
23
ˆru,i = xu
T
yi

24. Matrix factorization
24
minX,Y ru,i − xu
T
yi( )
2
+ λ nu xu
2
+ ni yi
2
i
∑
u
∑
#
$
%
&
'
(
ru,i≠0
∑
R ≈ XT
Y
R
X
Y

25. Alternating least squares
§ Fixing one matrix gives a quadratic form
§ Solution guarantees to decrease overall
cost function
§ To calculate , all rated item vectors and
ratings are needed
25
xu = YSu
YT
+ λnuΙ( )
−1
Yru
T
Sii
u
=
1 if ru,i ≠ 0
0 else
"
#
$
%$
xu

26. Data partitioning
26

27. Naïve ALS
case
class
Rating(userID:
Int,
itemID:
Int,
rating:
Double)
case
class
ColumnVector(columnIndex:
Int,
vector:
Array[Double])
val
items:
DataSet[ColumnVector]
=
_
val
ratings:
DataSet[Rating]
=
_
//
Generate
tuples
of
items
with
their
ratings
val
uVA
=
items.join(ratings).where(0).equalTo(1)
{
(item,
ratingEntry)
=>
{
val
Rating(uID,
_,
rating)
=
ratingEntry
(uID,
rating,
item.vector)
}
}
27

28. Naïve ALS contd.
uVA.groupBy(0).reduceGroup
{
vectors
=>
{
var
uID
=
-­‐1
val
matrix
=
FloatMatrix.zeros(factors,
factors)
val
vector
=
FloatMatrix.zeros(factors)
var
n
=
0
for((id,
rating,
v)
<-­‐
vectors)
{
uID
=
id
vector
+=
rating
*
v
matrix
+=
outerProduct(v
,
v)
n
+=
1
}
for(idx
<-­‐
0
until
factors)
{
matrix(idx,
idx)
+=
lambda
*
n
}
new
ColumnVector(uID,
Solve(matrix,
vector))
}
}
28

29. Problems of naïve ALS
§ Problem:
• Item vectors are sent redundantly à High
network load
§ Solution:
• Blocking of user and item vectors to share
common data
• Avoids blown up intermediate state
29

30. Data partitioning
30

31. Performance comparison
31
• 40
node
GCE
cluster,
highmem-­‐8
• 10
ALS
itera4on
with
50
latent
factors
Runtimeinminutes
0
225
450
675
900
Number of non-zero entries (billion)
0 7.5 15 22.5 30
Blocked ALS Blocked ALS highmem-16 Naive ALS
5.5h
14h
2.5h
1h
Table 2
Entries in billion Naive Join Naive Join Broadcast Broadcast
80 0.08 201.326 3.35543333333333 190.723 3.17871666666667

32. Streaming machine learning
32

33. Why is streaming ML important?
§ Spam detection in mails
§ Patterns might change over time
§ Retraining of model necessary
§ Best solution: Online models
33

34. Applications
§ Spam detection
§ Recommendation
§ News feed
personalization
§ Credit card fraud
detection
34

35. Apache SAMOA
§ Scalable Advanced Massive Online
Analysis
§ Distributed streaming machine learning
framework
§ Incubation at the Apache Software
Foundation
§ Runs on multiple streaming processing
engines (S4, Storm, Samza)
§ Support for Flink is pending pull request
35

36. Supported algorithms
§ Classification: Vertical
Hoeffding Tree
§ Clustering: CluStream
§ Regression: Adaptive
Model Rules
§ Frequent pattern mining:
PARMA
36

37. Closing
37

38. Flink-ML Outlook
§ Support more algorithms
§ Support for distributed linear algebra
§ Integration with streaming machine learning
§ Interactive programs and Zeppelin
38

39. flink.apache.org
@ApacheFlink