View
5.391
Download
0
Category
Preview:
Citation preview
October 13, 2015Mikio L. Braun, Data Flow vs. Procedural Programming, Berlin Flink Forward 1
Flink Forward 2015
Data flow vs. proceduralprogramming: How to put your
algorithms into Flink
October 13, 2015
Mikio L. Braun,Zalando SE
@mikiobraun
October 13, 2015Mikio L. Braun, Data Flow vs. Procedural Programming, Berlin Flink Forward 2
Python vs Flink
● Coming from Python, what are the differencesin programming style I have to know to getstarted in Flink?
October 13, 2015Mikio L. Braun, Data Flow vs. Procedural Programming, Berlin Flink Forward 3
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 = 0for i in range(len(a))
sum += a[i]return sum
October 13, 2015Mikio L. Braun, Data Flow vs. Procedural Programming, Berlin Flink Forward 4
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)
October 13, 2015Mikio L. Braun, Data Flow vs. Procedural Programming, Berlin Flink Forward 5
Don't use for-loops
● Put your data into a matrix
● Don't use for loops
October 13, 2015Mikio L. Braun, Data Flow vs. Procedural Programming, Berlin Flink Forward 6
Least Squares Regression
● Compute
● Becomes
What you learn is thinking in matrices, breakingdown 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
October 13, 2015Mikio L. Braun, Data Flow vs. Procedural Programming, Berlin Flink Forward 7
Basic tools
Advantage
– very familiar
– close to math
Disadvantage
– hard to scale
● Basic procedural programming paradigm
● Variables
● Ordered arrays and efficient functions on those
October 13, 2015Mikio L. Braun, Data Flow vs. Procedural Programming, Berlin Flink Forward 8
Parallel Data Flow
Often you have stuff like
Which is inherently easy to scale
for i in someSet:map x[i] to y[i]
October 13, 2015Mikio L. Braun, Data Flow vs. Procedural Programming, Berlin Flink Forward 9
New Paradigm
● Basic building block is an (unordered) set.
● Basic operations inherently parallel
October 13, 2015Mikio L. Braun, Data Flow vs. Procedural Programming, Berlin Flink Forward 10
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)
October 13, 2015Mikio L. Braun, Data Flow vs. Procedural Programming, Berlin Flink Forward 11
Apache Flink
● Data Flow system
● Basic building block is a DataSet[X]
● For execution, sets up all computing nodes,streams through data
October 13, 2015Mikio L. Braun, Data Flow vs. Procedural Programming, Berlin Flink Forward 12
Apache Flink: Getting Started
● Use Scala API
● Minimal project with Maven (build tool) orGradle
● Use an IDE like IntelliJ
● Always import org.apache.flink.api.scala._
October 13, 2015Mikio L. Braun, Data Flow vs. Procedural Programming, Berlin Flink Forward 13
Centering (First Try)
def computeMeans(xs: DataSet[DenseVector]) =xs.map(x => (x,1))
.reduce((xc, yc) => (xc._1 + yc._1, xc._2 + yc._2)) .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!
October 13, 2015Mikio L. Braun, Data Flow vs. Procedural Programming, Berlin Flink Forward 14
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 aDataSet[Elem].
● So what to do?
– cross joins
– broadcast variables
October 13, 2015Mikio L. Braun, Data Flow vs. Procedural Programming, Berlin Flink Forward 15
Centering (Second Try)
Works, but seems excessive because the meanis copied to each data element.
def computeMeans(xs: DataSet[DenseVector]) =xs.map(x => (x,1))
.reduce((xc, yc) => (xc._1 + yc._1, xc._2 + yc._2)) .map(xc => xc._1 / xc._2)
def centerPoints(xs: DataSet[DenseVector]) = { val mean = computeMean(xs) xs.crossWithTiny(mean).map(xm => xm._1 – xm._2)}
October 13, 2015Mikio L. Braun, Data Flow vs. Procedural Programming, Berlin Flink Forward 16
Broadcast Variables
● Side information sent to all worker nodes
● Can be a DataSet
● Gets accessed as a Java collection
October 13, 2015Mikio L. Braun, Data Flow vs. Procedural Programming, Berlin Flink Forward 17
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
October 13, 2015Mikio L. Braun, Data Flow vs. Procedural Programming, Berlin Flink Forward 18
Centering (Third Try)def computeMeans(xs: DataSet[DenseVector]) =
xs.map(x => (x,1)) .reduce((xc, yc) => (xc._1 + yc._1, xc._2 + yc._2)) .map(xc => xc._1 / xc._2)
def centerPoints(xs: DataSet[DenseVector]) = {val mean = computeMean(xs)
xs.mapWithBcVar(mean).map((x, m) => x – m)}
October 13, 2015Mikio L. Braun, Data Flow vs. Procedural Programming, Berlin Flink Forward 19
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)
October 13, 2015Mikio L. Braun, Data Flow vs. Procedural Programming, Berlin Flink Forward 20
Matrix Algebra
● No ordered sets per se in Data Flow context.
October 13, 2015Mikio L. Braun, Data Flow vs. Procedural Programming, Berlin Flink Forward 21
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)), … }
October 13, 2015Mikio L. Braun, Data Flow vs. Procedural Programming, Berlin Flink Forward 22
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 }}
October 13, 2015Mikio L. Braun, Data Flow vs. Procedural Programming, Berlin Flink Forward 23
Summary and Outlook
● Procedural vs. Data Flow
– basic building blocks elementwise operations onunordered sets
– can't be nested
– combine intermediate results via broadcast vars
● Iterations
● Beware of TypeInformation implicits.
Recommended