The Apache Zeppelin is an exciting notebooking tool, designed for working with Big Data applications. It comes with great integration for graphing in R and Python, supports multiple langauges in a single notebook (and facilitates sharing of variables between interpreters), and makes working with Spark and Flink in an interactive environment (either locally or in cluster mode) a breeze. Of course, it does lots of other cool things too- but those are the features we’re going to take advantage of.

Step1: Download and Install Zeppelin

Zeppelin binaries by default use Spark 2.1 / Scala 2.11, until Mahout puts out Spark 2.1/Scala 2.11 binaries you have two options.

Option 1: Build Mahout for Spark 2.1/Scala 2.11

Build Mahout

Follow the standard procedures for building Mahout, except manually set the Spark and Scala versions - the easiest way being:

git clone http://github.com/apache/mahout
cd mahout
mvn clean package -Dspark.version=2.1.0 -Dscala.version=2.11.8 -Dscala.compat.version=2.11 -DskipTests

Download Zeppelin

cd /a/good/place/to/install/
wget http://apache.mirrors.tds.net/zeppelin/zeppelin-0.7.1/zeppelin-0.7.1-bin-all.tgz
tar -xzf zeppelin-0.7.1-bin-all.tgz
cd zeppelin*
bin/zeppelin-daemon.sh start

And that’s it. Open a web browser and surf to http://localhost:8080

Proceed to Step 2.

Option2: Build Zeppelin for Spark 1.6/Scala 2.10

We’ll use Mahout binaries from Maven, so all you need to do is clone, and build Zeppelin-

git clone http://github.com/apache/zeppelin
cd zeppelin
mvn clean package -Pspark1.6 -Pscala2.10 -DskipTests

After it builds successfully…

bin/zeppelin-daemon.sh start

And that’s it. Open a web browser and surf to http://localhost:8080

Step2: Create the Mahout Spark Interpreter

After opening your web browser and surfing to http://localhost:8080, click on the Anonymous button on the top right corner, which will open a drop down. Then click Interpreter.

Screen Shot1

At the top right, just below the blue nav bar- you will see two buttons, “Repository” and “+Create”. Click on “+Create”

The following screen should appear.

Screen Shot2

In the Interpreter Name enter mahoutSpark (you can name it whatever you like, but this is what we’ll assume you’ve named it later in the tutorial)

In the Interpreter group drop down, select spark. A bunch of other settings will now auto-populate.

Scroll to the bottom of the Properties list. In the last row, you’ll see two blank boxes.

Add the following properies by clicking the “+” button to the right.

name value
spark.kryo.referenceTracking false
spark.kryo.registrator org.apache.mahout.sparkbindings.io.MahoutKryoRegistrator
spark.kryoserializer.buffer 32
spark.kryoserializer.buffer.max 600m
spark.serializer org.apache.spark.serializer.KryoSerializer

Step 3: Add Dependendencies

You’ll also need to add the following Dependencies.

If you chose Option1 in Step 1:

Where /path/to/mahout is the path to the directory where you’ve built mahout.

artifact exclude
/path/to/mahout/core_2.11-0.14.jar
path/to/mahout/mahout-hdfs_2.11-0.14.jar
/path/to/mahout/mahout-spark_2.11-0.14.jar
/path/to/mahout/mahout-spark_2.11-0.14.jar-dependeny-reduced.jar

If you chose Option2 in Step 1:

artifact exclude
org.apache.mahout:mahout-core:0.14
org.apache.mahout:mahout-hdfs-scala_2.11:0.14
org.apache.mahout:mahout-spark_2.11:0.14
org.apache.mahout:mahout-native-viennacl-omp_2.11:0.14

OPTIONALLY You can add one of the following artifacts for CPU/GPU acceleration.

artifact exclude type of native solver
org.apache.mahout:mahout-native-viennacl_2.11:0.14 ViennaCL GPU Accelerated
org.apache.mahout:mahout-native-viennacl-omp_2.11:0.14 ViennaCL-OMP CPU Accelerated (use this if you don't have a good graphics card)

Make sure to click “Save” and you’re all set.

Step 4. Rock and Roll.

Mahout in Zeppelin, unlike the Mahout Shell, won’t take care of importing the Mahout libraries or creating the MahoutSparkContext, we need to do that manually. This is easy though. When ever you start Zeppelin (or restart) the Mahout interpreter, you’ll need to run the following code first:

%sparkMahout

import org.apache.mahout.math._
import org.apache.mahout.math.scalabindings._
import org.apache.mahout.math.drm._
import org.apache.mahout.math.scalabindings.RLikeOps._
import org.apache.mahout.math.drm.RLikeDrmOps._
import org.apache.mahout.sparkbindings._

implicit val sdc: org.apache.mahout.sparkbindings.SparkDistributedContext = sc2sdc(sc)

At this point, you have a Zeppelin Interpreter which will behave like the $MAHOUT_HOME/bin/mahout spark-shell

Except, much much more.

At the begining I mentioned a few important features of Zeppelin, that we could leverage to use Zeppelin for visualizatoins.

Example 1: Visualizing a Matrix (Sample) with R

In Mahout we can use Matrices.symmetricUniformView to create a Gaussian Matrix.

We can use .mapBlock and some clever code to create a 3D Gausian Matrix.

We can use .drmSampleToTsv to take a sample of the matrix and turn it in to a tab seperated string. We take a sample of the matrix because, since we are dealing with “big” data, we wouldn’t want to try to collect and plot the entire matrix, however, IF we knew we had a small matrix and we DID want to sample the entire thing, then we could sample 100.0 e.g. 100%.

Finally we use z.put(...) to put a variable into Zeppelin’s ResourcePool a block of memory shared by all interpreters.

%sparkMahout

val mxRnd3d = Matrices.symmetricUniformView(5000, 3, 1234)
val drmRand3d = drmParallelize(mxRnd3d)

val drmGauss = drmRand3d.mapBlock() {case (keys, block) =>
  val blockB = block.like()
  for (i <- 0 until block.nrow) {
    val x: Double = block(i, 0)
    val y: Double = block(i, 1)
    val z: Double = block(i, 2)

    blockB(i, 0) = x
    blockB(i, 1) = y
    blockB(i, 2) = Math.exp(-((Math.pow(x, 2)) + (Math.pow(y, 2)))/2)
  }
  keys -> blockB
}

resourcePool.put("gaussDrm", drm.drmSampleToTSV(drmGauss, 50.0))

Here we sample 50% of the matrix and put it in the ResourcePool under a variable named “gaussDrm”.

Now, for the exciting part. Scala doesn’t have a lot of great graphing utilities. But you know who does? R and Python. So instead of trying to akwardly visualize our data using Scala, let’s just use R and Python.

We start the Spark R interpreter (we do this because the regular R interpreter doesn’t have access to the resource pools).

We z.get the variable we just put in.

We use R’s read.table to read the string- this is very similar to how we would read a tsv file in R.

Then we plot the data using the R scatterplot3d package.

Note you may need to install scatterplot3d. In Ubuntu, do this with sudo apt-get install r-cran-scatterplot3d

%spark.r {"imageWidth": "400px"}

library(scatterplot3d)


gaussStr = z.get("gaussDrm")
data <- read.table(text= gaussStr, sep="\t", header=FALSE)

scatterplot3d(data, color="green")

A neat plot