Data flow vs. procedural programming: How to put your algorithms into Flink

June 23, 2015Mikio L. Braun, Data Flow vs. Procedural Programming, Berlin Flink Meetup 1
Flink Meetup #8
Data flow vs. procedural
programming: How to put your
algorithms into Flink
June 23, 2015
Mikio L. Braun
@mikiobraun

Programming how we're used to
● Computing a sum
● Tools at our disposal:
– variables
– control flow (loops, if)
– function calls as basic piece of abstraction
def computeSum(a):
sum = 0
for i in range(len(a))
sum += a[i]
return sum

Data Analysis Algorithms
Let's consider centering
becomes
or even just
def centerPoints(xs):
sum = xs[0].copy()
for i in range(1, len(xs)):
sum += xs[i]
mean = sum / len(xs)
for i in range(len(xs)):
xs[i] -= mean
return xs
xs -
xs.mean(axis=0)

Don't use for-loops
● Put your data into a matrix
● Don't use for loops

Least Squares Regression
● Compute
● Becomes
What you learn is thinking in matrices, breaking
down computations in terms of matrix algebra
def lsr(X, y, lam):
d = X.shape[1]
C = X.T.dot(X) + lam * pl.eye(d)
w = np.linalg.solve(C, X.T.dot(y))
return w

Basic tools
Advantage
– very familiar
– close to math
Disadvantage
– hard to scale
● Basic procedural programming paradigm
● Variables
● Ordered arrays and efficient functions on those

Parallel Data Flow
Often you have stuff like
Which is inherently easy to scale
for i in someSet:
map x[i] to y[i]

New Paradigm
● Basic building block is an (unordered) set.
● Basic operations inherently parallel

Computing, Data Flow Style
Computing a sum
Computing a mean
sum(x) = xs.reduce((x,y) => x + y)
mean(x) = xs.map(x => (x,1))
.reduce((xc, yc) => (xc._1 + yc._1, xc._2 + yc._2))
.map(xc => xc._1 / xc._2)

Apache Flink
● Data Flow system
● Basic building block is a DataSet[X]
● For execution, sets up all computing nodes,
streams through data

Apache Flink: Getting Started
● Use Scala API
● Minimal project with Maven (build tool) or
Gradle
● Use an IDE like IntelliJ
● Always import
org.apache.flink.api.scala._

Centering (First Try)
def computeMeans(xs: DataSet[DenseVector]) =
xs.map(x => (x,1))
.map(xc => xc._1 / xc._2)
def centerPoints(xs: DataSet[DenseVector]) = {
val mean = computeMean(xs)
xs.map(x => x – mean)
}
You cannot nest DataSet operations!

Sorry, restrictions apply.
● Variables hold (lazy) computations
● You can't work with sets within the operations
● Even if result is just a single element, it's a
DataSet[Elem].
● So what to do?
– cross joins
– broadcast variables

Centering (Second Try)
Works, but seems excessive because the mean
is copied to each data element.
xs.map(x => (x,1))
.map(xc => xc._1 / xc._2)
xs.crossWithTiny(mean).map(xm => xm._1 – xm._2)
}

Broadcast Variables
● Side information sent to all worker nodes
● Can be a DataSet
● Gets accessed as a Java collection

class BroadcastSingleElementMapper[T, B, O](fun: (T, B) => O)
extends RichMapFunction[T, O] {
var broadcastVariable: B = _
@throws(classOf[Exception])
override def open(configuration: Configuration): Unit = {
broadcastVariable = getRuntimeContext
.getBroadcastVariable[B]("broadcastVariable")
.get(0)
}
override def map(value: T): O = {
fun(value, broadcastVariable)
}
}
Broadcast Variables

Centering (Third Try)
xs.map(x => (x,1))
.map(xc => xc._1 / xc._2)
xs.mapWithBcVar(mean).map((x, m) => x – m)
}

Intermediate Results pattern
val x = someDataSetComputation()
val y = someOtherDataSetComputation()
val z = dataSet.mapWithBcVar(x)((d, x) => …)
val result = anotherDataSet.mapWithBcVar((y,z)) {
(d, yz) =>
val (y,z) = yz
…
}
x = someComputation()
y = someOtherComputation()
z = someComputationOn(dataSet, x)
result = moreComputationOn(y, z)

Matrix Algebra
● No ordered sets per se in Data Flow context.

Vector operations by explicit joins
● Encode vector (a1, a2, …, an) with
{(1, a1), (2, a2), … (n, an)}
● Addition:
– a.join(b).where(0).equalTo(0)
.map((ab) => (ab._1._1, ab._1._2 + ab._2._2))
after join: {((1, a1), (1, b1)), ((2, a1), (2, b1)), … }

Back to Least Squares Regression
Two operations: computing X'X and X'Y
def lsr(xys: DataSet[(DenseVector, Double)]) = {
val XTX = xs.map(x => x.outer(x)).reduce(_ + _)
val XTY = xys.map(xy => xy._1 * xy._2).reduce(_ + _)
C = XTX.mapWithBcVar(XTY) { vars =>
val XTX = vars._1
val XTY = var.s_2
val weight = XTX XTY
}
}

Summary and Outlook
● Procedural vs. Data Flow
– basic building blocks elementwise operations on
unordered sets
– can't be nested
– combine intermediate results via broadcast vars
● Iterations
● Beware of TypeInformation implicits.

Data flow vs. procedural programming: How to put your algorithms into Flink

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