Apache Spark groupByKey and reduceByKey

March 7, 2020

I was answering a question on stackoverflow and came up with a pictorial representation for groupByKey and reduceByKey. Thought of sharing the same so that I myself do not lose it over the time.
Spark documentation itself is quite clear about this so not providing lot of text in here.

Both the examples below are summing up the values for a key.
1. groupByKey – Shuffles data first based on the keys and later gives us opportunity to work with the values.





2. reduceByKey – Computes the reduce function first locally and then shuffling the results and run the reduce function once again to achieve final result.
Hence reduceByKey is like a combiner in Map-reduce world. It helps in reducing amount of data shuffled during the process.




GroupBy and count using Spark DataFrame

June 18, 2019

Here we are trying to group by keys and run a count against them.

val datardd = sc.parallelize(Seq(“a”->1,”b”->1,”a”->1,”c”->1))

val mydf = datardd.toDF

mydf.groupBy($”name”).agg(“count” -> “count”).

name noofoccurrences
a 2
b 1
c 1

Kill Tomcat service running on Windows

October 25, 2018

If you terminate a running Spring boot application from within the eclipse, at times the port on which embedded tomcat listens does not free up. I found the below commands on one of the StackOverflow posts which are really handy.

C:\yourdir>netstat -aon |find /i “listening” |find “8080”


Now grab the PID (68 in this case) and run the below command to kill it.

C:\yourdir>taskkill /F /PID 68

Big + Far Math Challenge @ ICC

April 22, 2017


Recently I participated and won First prize in Big Far Math challenge hosted by ICC. The challenge description can be found here – http://big-far.webflow.io/

Participating in it was a quite exciting and learning experience for me. I could explore different technical areas while gathering data and preparing visualizations with it.

I have shared the source code and a static version of the visualization on GitHub. The dynamic version was hosted on Apache Solr running on my local desktop.

You can visit the project page @ https://amitlondhe.github.io/bigfarmathchallenge/ from which you can navigate to the visualizations that I came up with.

I am also sharing the presentation given to the judges as part of assessment if you are looking for more details.

– Amit

Running Apache Spark on Windows

July 10, 2016

Running hadoop on windows is not trivial, however running Apache Spark on Windows proved not too difficult. I came across couple of blogs and stackoverflow discussion which made this possible. Putting down my notes below which are outcome of these reference material.

  1. Download http://d3kbcqa49mib13.cloudfront.net/spark-1.6.0-bin-without-hadoop.tgz ( http://spark.apache.org/downloads.html )
  2. Download Hadoop distribution for Windows from http://www.barik.net/archive/2015/01/19/172716/
  3. Create hadoop_env.cmd  in {HADOOP_INSTALL_DIR}/conf directory.
    SET JAVA_HOME=C:\Progra~1\Java\jdk1.7.0_80
  4. In a new command window run hadoop-env.cmd followed by  {HADOOP_INSTALL_DIR}/bin/hadoop classpath
    The output of this command is used to initialize SPARK_DIST_CLASSPATH in spark-env.cmd (You may need to create this file.)
  5. Create spark-env.cmd in {SPARK_INSTALL_DIR}/conf
     #spark-env.cmd content
     SET HADOOP_HOME=C:\amit\hadoop\hadoop-2.6.0
     set SPARK_DIST_CLASSPATH=<Output of hadoop classpath>
     SET JAVA_HOME=C:\Progra~1\Java\jdk1.7.0_80
  6. Now run the examples or spark shell from {SPARK_INSTALL_DIR}/bin directory. Please note that you may have to run spark-env.cmd explicitly prior running the examples or spark-shell.

References :

Big O notation

May 14, 2016


Came across a very nice introductory article on Big O notation.


Big Data For Social Good Challenge

March 16, 2015


During this winter, I participated in

Big Data For Social Good Challenge

which I just stumbled upon while searching something.

This challenge was about using IBM Bluemix’s “Analytics For Hadoop” service to process a data set that is minimum 500MB in size.

This was a wonderful opportunity to get some hands on on IBM Bluemix ( IBM is giving extended trial access if you are a participant). Apart from this I was also keen to build some Data visualization app on my own.

I selected CitiBike data for one year (2013-2014). Initially I did not had a clue about what insights I could gather from the dataset, but as soon as I ran some Apache Pig scripts and started looking at the output, I could see more and more use cases around the dataset.  I could not address all the use cases I thought as I soon hit the deadline pressure. I had to finish the video demonstration and write some write up about the project.

Overall it was a very enriching experience as I did so many things for the very first time.

Listing some of them below

  • IBM Bigsheets and  BigSQL
  • Using Chart.js library
  • Using Google Maps JavaScript APIs –  It was remarkably simpler than I thought. Much appreciate these APIs from Google.
  • Creating the custom Map icon – Never realized it would be this difficult
  • HTML 5/CSS challenges when putting up the UI
  • Last but not the least GitHub’s easy way to publish your work online.

Now that the challenge is in Public voting and judging phase, appreciate if you could take a look at


and provide your feedback and vote if you like it.