2014 Big Data Hackathon – Charlotte

A local tech company – Tresata recently sponsored a 24 hour hackathon in Charlotte around big data in the retail space. It was a ton of fun. I think I was the only person who brought along their own monitor, but I was quite prepared 🙂

My experience with statistical analysis, python and Jupyter was quite limited, so I decided to focus my attention on building a mobile focused use case with some big data integration and I ended up winning the innovation award. A pretty cool pair of Google Glasses 🙂

Here is the app I developed and presented:

And here are all my scribbled the notes/commands/datasets from the event:

emacs test.scala

eval-tool test.scala –hdfs –input bsv%/sample/data_with_headers/hackathon_data_headers –output bsv%upc_counts

hadoop fs -ls upc_counts

hadoop fs -cat upc_counts/part-* > upc_counts

wc -l upc_counts 

less upc_counts 

hadoop fs -cat upc_counts/part-* > upc_counts

python2.6 -m SimpleHTTPServer 8889

import com.twitter.scalding._

import com.tresata.scalding.Dsl._

import com.tresata.scalding.util.ScaldingUtil

import org.joda.time.format.DateTimeFormat

(args: Args) => {

  new Job(args) {

val format = new java.text.SimpleDateFormat(“yyyy-MM-dd”)

val likelyProducts =  ScaldingUtil.sourceFromArg(args(“input”))

      .filter(‘HHID){ HHID : String => HHID == args(“homeid”)}

      .groupBy(‘HHID,’UPC_NUMBER,’ITEM_DESCRIPTION) { group =>


        .average(‘ITEM_QUANTITY -> ‘AvgQtyOrdered)

        .max(‘TRANSACTION_DATETIME -> ‘LastPurchased)







User Creation


  • Note takes ~5min for user creation across the cluster
  • If you have any DNS issues direct ips are:
  • slave01
  • slave02
  • slave03
  • slave04
  • And user creation at:


Machines avaliable to work on:

> slave01.hackathonclt.org

> slave02.hackathonclt.org

> slave03.hackathonclt.org

> slave04.hackathonclt.org

Please spread yourselvs out across the machines

NOTE: if you have any dns issues, speak to staff

Getting Started

Ssh into the server where you can access the retail data stored on the hackathon HDFS cluster.

> ssh username@slave01.hackathonclt.org

and enter the password you specified in user creation.

We made Hive, Spark, and pySpark command-line interfaces available, and included a tool to compile and run simple Scalding scripts on-the-fly.


Give Hive a whirl and run a sample query:

> hive

Try pasting the following query into the hive command-line interface:


This will a launch a (map-only) MapReduce job and return the specified fields for the first ten items in the ‘hackathon’ table.


Now give the Spark-shell a test:

> spark-shell

Read in the data and run a simple query that calculates the number of purchases for each upc in the sample data:

>>> val dataRDD = sc.textFile(“hdfs://master.hackathonclt.org:8020/sample/data_with_headers/hackathon_data_headers”)

>>> val upcs = dataRDD.flatMap(line => line.split(“\\|”).take(1))

>>> val wordCounts = upcs.map(word => (word, 1)).reduceByKey((a, b) => a + b)

>>> wordCounts.collect()


You can also do the same query using a python version of the Spark shell.

> pyspark

>>> dataRDD = sc.textFile(“hdfs://master.hackathonclt.org:8020/sample/data_with_headers/hackathon_data_headers”)

>>> upcs = dataRDD.map(lambda line: line.split(‘|’)[0])

>>> wordCounts = upcs.map(lambda word: (word, 1)).reduceByKey(lambda a, b: a + b)

>>> wordCounts.take(10)


In addition to the Hive and Spark shells, we’re also packaging Eval-tool, a tool to compile and run Scalding scripts without having to create a project. If you create a file called test.scala with the following contents:

import com.twitter.scalding._

import com.tresata.scalding.Dsl._

import com.tresata.scalding.util.ScaldingUtil

(args: Args) => {

new Job(args) {


.groupBy(‘UPC_NUMBER) { _.size }




you can run a query on the data set sample from the command-line:

> eval-tool test.scala –hdfs –input bsv%/sample/data_with_headers/hackathon_data_headers –output bsv%upc_counts

This will generate a bar-separated file called ‘upc_counts’ in your HDFS home directory, containing the upc numbers along with their total counts.

Job Tracker


Spark Job Tracker


Namenode information


Data Dictionary

UPC_NUMBER int unique product code of item

MASTER_UPC_NUMBER int master UPC number, UPC numbers go under this 

ITEM_DESCRIPTION string describes item

DEPARTMENT_NUMBER int department number

DEPARTMENT_DESCRIPTION string describes department

CATEGORY_NUMBER int category number of item

CATEGORY_DESCRIPTION string describes category of item

SUBCATEGORY_NUMBER int subcategory of item

SUBCATEGORY_DESCRIPTION string describes subcategory of item

RECEIPT_NUMBER string recipe number of the purchase

ITEM_QUANTITY int how many items was bought

EXTENDED_PRICE_AMOUNT float actual sale per swipe

DISCOUNT_QUANTITY float number of coupons applied

EXTENDED_DISCOUNT_AMOUNT float amount discounted

TENDER_AMOUNT float amount tendered by the customer for the transaction

TRANSACTION_DATETIME string date of transaction

EXPRESS_LANE int flag of whether the purchase was through Express Lane, tagged to recipe number. 1 mean yes, 0 means no

HHID string house hold id