zeppelin/docs/quickstart/tutorial.md

---
layout: page
title: "Apache Zeppelin Tutorial"
description: "This tutorial page contains a short walk-through tutorial that uses Apache Spark backend. Please note that this tutorial is valid for Spark 1.3 and higher."
group: quickstart
---
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{% include JB/setup %}

# Zeppelin Tutorial

<div id="toc"></div>

This tutorial walks you through some of the fundamental Zeppelin concepts. We will assume you have already installed Zeppelin. If not, please see [here](./install.html) first.

Current main backend processing engine of Zeppelin is [Apache Spark](https://spark.apache.org). If you're new to this system, you might want to start by getting an idea of how it processes data to get the most out of Zeppelin.

## Tutorial with Local File

### Data Refine

Before you start Zeppelin tutorial, you will need to download [bank.zip](http://archive.ics.uci.edu/ml/machine-learning-databases/00222/bank.zip). 

First, to transform csv format data into RDD of `Bank` objects, run following script. This will also remove header using `filter` function.

```scala

val bankText = sc.textFile("yourPath/bank/bank-full.csv")

case class Bank(age:Integer, job:String, marital : String, education : String, balance : Integer)

// split each line, filter out header (starts with "age"), and map it into Bank case class
val bank = bankText.map(s=>s.split(";")).filter(s=>s(0)!="\"age\"").map(
    s=>Bank(s(0).toInt, 
            s(1).replaceAll("\"", ""),
            s(2).replaceAll("\"", ""),
            s(3).replaceAll("\"", ""),
            s(5).replaceAll("\"", "").toInt
        )
)

// convert to DataFrame and create temporal table
bank.toDF().registerTempTable("bank")
```

### Data Retrieval

Suppose we want to see age distribution from `bank`. To do this, run:

```sql
%sql select age, count(1) from bank where age < 30 group by age order by age
```

You can make input box for setting age condition by replacing `30` with `${maxAge=30}`.

```sql
%sql select age, count(1) from bank where age < ${maxAge=30} group by age order by age
```

Now we want to see age distribution with certain marital status and add combo box to select marital status. Run:

```sql
%sql select age, count(1) from bank where marital="${marital=single,single|divorced|married}" group by age order by age
```

<br />
## Tutorial with Streaming Data 

### Data Refine

Since this tutorial is based on Twitter's sample tweet stream, you must configure authentication with a Twitter account. To do this, take a look at [Twitter Credential Setup](https://databricks-training.s3.amazonaws.com/realtime-processing-with-spark-streaming.html#twitter-credential-setup). After you get API keys, you should fill out credential related values(`apiKey`, `apiSecret`, `accessToken`, `accessTokenSecret`) with your API keys on following script.

This will create a RDD of `Tweet` objects and register these stream data as a table:

```scala
import org.apache.spark.streaming._
import org.apache.spark.streaming.twitter._
import org.apache.spark.storage.StorageLevel
import scala.io.Source
import scala.collection.mutable.HashMap
import java.io.File
import org.apache.log4j.Logger
import org.apache.log4j.Level
import sys.process.stringSeqToProcess

/** Configures the Oauth Credentials for accessing Twitter */
def configureTwitterCredentials(apiKey: String, apiSecret: String, accessToken: String, accessTokenSecret: String) {
  val configs = new HashMap[String, String] ++= Seq(
    "apiKey" -> apiKey, "apiSecret" -> apiSecret, "accessToken" -> accessToken, "accessTokenSecret" -> accessTokenSecret)
  println("Configuring Twitter OAuth")
  configs.foreach{ case(key, value) =>
    if (value.trim.isEmpty) {
      throw new Exception("Error setting authentication - value for " + key + " not set")
    }
    val fullKey = "twitter4j.oauth." + key.replace("api", "consumer")
    System.setProperty(fullKey, value.trim)
    println("\tProperty " + fullKey + " set as [" + value.trim + "]")
  }
  println()
}

// Configure Twitter credentials
val apiKey = "xxxxxxxxxxxxxxxxxxxxxxxxx"
val apiSecret = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
val accessToken = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
val accessTokenSecret = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
configureTwitterCredentials(apiKey, apiSecret, accessToken, accessTokenSecret)

import org.apache.spark.streaming.twitter._
val ssc = new StreamingContext(sc, Seconds(2))
val tweets = TwitterUtils.createStream(ssc, None)
val twt = tweets.window(Seconds(60))

case class Tweet(createdAt:Long, text:String)
twt.map(status=>
  Tweet(status.getCreatedAt().getTime()/1000, status.getText())
).foreachRDD(rdd=>
  // Below line works only in spark 1.3.0.
  // For spark 1.1.x and spark 1.2.x,
  // use rdd.registerTempTable("tweets") instead.
  rdd.toDF().registerAsTable("tweets")
)

twt.print

ssc.start()
```

### Data Retrieval

For each following script, every time you click run button you will see different result since it is based on real-time data.

Let's begin by extracting maximum 10 tweets which contain the word **girl**.

```sql
%sql select * from tweets where text like '%girl%' limit 10
```

This time suppose we want to see how many tweets have been created per sec during last 60 sec. To do this, run:

```sql
%sql select createdAt, count(1) from tweets group by createdAt order by createdAt
```


You can make user-defined function and use it in Spark SQL. Let's try it by making function named `sentiment`. This function will return one of the three attitudes( positive, negative, neutral ) towards the parameter.

```scala
def sentiment(s:String) : String = {
    val positive = Array("like", "love", "good", "great", "happy", "cool", "the", "one", "that")
    val negative = Array("hate", "bad", "stupid", "is")
    
    var st = 0;

    val words = s.split(" ")    
    positive.foreach(p =>
        words.foreach(w =>
            if(p==w) st = st+1
        )
    )
    
    negative.foreach(p=>
        words.foreach(w=>
            if(p==w) st = st-1
        )
    )
    if(st>0)
        "positivie"
    else if(st<0)
        "negative"
    else
        "neutral"
}

// Below line works only in spark 1.3.0.
// For spark 1.1.x and spark 1.2.x,
// use sqlc.registerFunction("sentiment", sentiment _) instead.
sqlc.udf.register("sentiment", sentiment _)

```

To check how people think about girls using `sentiment` function we've made above, run this:

```sql
%sql select sentiment(text), count(1) from tweets where text like '%girl%' group by sentiment(text)
```