If there is no opportunity then create one…

In 2005, I joined a company as a trainee programmer right after finishing my college. I did Mechanical engineering(2001-2005), but during the campus placement I was placed in a software company so I ended up in the IT industry. I had no programming experience when I started my first programming job. I know it sounds weird, but many Indian software programmers start with a limited programming knowledge/experience.

My first year as a programmer was very rough. I spent most of the time looking at other people’s screen(pair programming) and trying to understand how I can fit myself into this world. It was demotivating and most of the time I felt like giving up. Programming is tough and when you don’t know where to start it becomes much more difficult. Before you Google something you need to know what you want to Google.

During my first year appraisal meeting, my manager asked what rating he should give me? Rating was from 1 to 5 where 1 being the highest and 5 being the lowest. He said he can’t give me 5 because I have not done anything wrong in the company. And, he can’t give me 1 or 2 because I have not done anything good in my daily work. So, the only two valid choices were 3 and 4. Then, he asked me why didn’t I perform well? My answer was,  “You didn’t give me any opportunity to work.” He responded, “Why didn’t you create one?” I didn’t say anything after that. He said that he will give me 3 rating like he had given to others in my team.

This 5-minute meeting had a profound impact on me. Since my first year, I have applied this many times. And, each time it worked!

Most of the time we don’t succeed because we don’t get out of our comfort zone and ask others for their help or opinion. We stick to our old ways of working and we never try to change our mindset.  Steve Jobs once said:

Most people don’t get those experiences because they never ask. I never find anybody that didn’t want to help me if asked them for help.

Around the same time, I also had these discussions with my father. My father has a lot of positive influence on my life. My dad once told me:

You can either succeed by being the best in your field or by becoming a people pleaser.

The problem was I had neither of those qualities. Knowing myself  I realized, I can’t be a people pleaser. So, the only way to succeed is to start learning. In last 10 years, I have tried spending couple of hours every day learning and honing my skills.

Currently, I am working on a year long blog series 52-technologies-in-2016 where I learn something new every week and write about it.

Docker Machine Error Unable to Query Docker Version

Today, when I created a new docker machine I started getting Unable to query docker version: Get x509: certificate is valid for, not

To fix this error, run the following command.

docker-machine regenerate-certs default

Please change default with name of your docker machine.

Sentiment Analysis in Python with TextBlob

Welcome to the eleventh blog of 52 Technologies in 2016 blog series. If you are following this series then you would have probably noticed that I already wrote week 11 blog on tweet deduplication. I was not happy with the content so I decide to write another blog for week 11.

In week 11, I decided to spend time to learn about text processing using the Python programming language. We will only focus on Sentiment Analysis in this blog. I have written about sentiment analysis multiple times in last few years. We learnt how to do sentiment analysis in Scala using Stanford CoreNLP in week 3 blog. Sentiment analysis gives you the power to mine emotions in text. This can help you build awesome applications that understand human behavior. Few years back, I built an application that helped me decide if I should watch a movie or not by doing sentiment analysis on social media data for a movie. There are many possible applications of Sentiment analysis like understanding customer sentiment for a product by analysis of reviews.

You can read full blog here https://github.com/shekhargulati/52-technologies-in-2016/blob/master/11-textblob/README.md

Gatling: The Ultimate Load Testing Tools for Programmers

Welcome to the tenth blog of 52 Technologies in 2016 blog series. Gatling is a high performance open source load testing tool built on top of Scala, Netty, and Akka. It is a next generation, modern load testing tools very different from existing tools like Apache JMeter. Load testing is conducted to understand behavior of an application under load. You put load on the application by simulating users and measure its response time to understand how application will behave under both normal and anticipated peak load conditions.

Gatling can be used to load test your HTTP server. HTTP is not the only protocol that one can load test with Gatling. Gatling also has inbuilt support for Web Socket and JMS protocols. You can extend Gatling to support your protocol of choice.

Load testing is often neglected by most software teams resulting in poor understanding of their application performance characteristics. These days most software teams take unit testing and functional testing seriously but still they ignore load testing. They write unit tests, integration tests, and functional tests and integrate them in their software build. I think part of the reason developer still don’t write load tests has to do with the fact that most load testing tools are GUI based so you can’t code your load tests. They allow you to export your load test as XML.

You can read full blog at https://github.com/shekhargulati/52-technologies-in-2016/blob/master/10-gatling/README.md

Realtime People Counter with Google’s Cloud Vision API and RxJava

Welcome to the ninth blog of 52 Technologies in 2016 blog series. Recently, Google released Cloud Vision API that enables developers to incorporate image recognition in their applications. Image Recognition allow developers to build applications that can understand content of images. Google’s Cloud Vision API is very powerful and support following features:

  1. Image categorization: The API can help classify images into categories. You can build powerful applications like Google Photos that do automatic categorization.
  2. Inappropriate content detection: The API can detect inappropriate content in an image like nudity, violence, etc. It uses Google Safe search capabilities underneath.
  3. Emotion detection: This allows you to detect happy, sad or moderate emotions in an image.
  4. Retrieve text from the image: This allows you to extract text in multiple languages from the images.
  5. Logo detection: It can help you identify product logos within an image.

There are many possible applications that you can build using this powerful API. In this tutorial, we will learn how to build a realtime people counter. The application will subscribe to a twitter stream for a topic and would return number of people found in each image. We can then use this data to get advanced statistic like number of people in a time frame using RxJava buffer capabilities.

You can read full blog https://github.com/shekhargulati/52-technologies-in-2016/blob/master/09-cloudvision/README.md

CoreOS for Application Developers

Welcome to eighth week of 52 Technologies in 2016 blog series. This week we will learn about CoreOS, an Open source Linux distribution built to run and manage highly scalable and fault tolerant systems. It is designed to docker and rocket containers. When I started learning about CoreOS, I was overwhelmed by its complexity and different components that you have to know and interact with like etcd, systemd, fleet, Flannel. I am not an Ops guy so CoreOS documentation and many tutorials that I found on the web didn’t clicked with me. The goal of this tutorial is to help application developers understand why they should care about CoreOS and show them how to work with CoreOS cluster running on top of Amazon EC2.

What is CoreOS?

According to CoreOS website, CoreOS is a Linux for Massive Server Deployments. This means it is not a general purpose Linux distro that you can use as your development workspace instead, you will use it to run and your applications at scale.

Built on Chrome OS, CoreOS is a lean and mean operating system that runs minimal Linux. When you limit your OS to the bare minimal i.e. just openssl, ssh, linux kernel, gcc then you need a mechanism to run package and run applications that you want to use. CoreOS does not even has a package manager like yum or Apt. CoreOS is very different from other Linux distributions as it is centered around containers. Linux Containers is an operating-system-level virtualization environment for running multiple isolated Linux systems (containers) on a single Linux control host. CoreOS uses containers to run and manage applications services. You package application along with its dependencies within a container that can be run on a single or multiple CoreOS machines. CoreOS supports both Docker and Rocket containers.

Docker is the poster child of containers. In November 2013, I first learnt and wrote about Docker. Docker is a set of toolset geared around containers. Docker clicked with everyone and overnight became the tool that everyone wanted to learn and introduce in their organization. One reason Docker became popular very quickly is its approachability to an average developer. To use Docker, you don’t have to know Linux internals and work with complicated tools.

CoreOS developers claim that it is 40% more efficient in RAM usage than an average linux installation.

Read the full blog at https://github.com/shekhargulati/52-technologies-in-2016/blob/master/08-coreos/README.md

Hugo: A Modern WebSite Engine That Just Works

This week I decided to take a break from Scala and scratch my own itch my building an online bookshelf using Hugo. Hugo is a static site generator written in Go programming language. You can use it for building modern static websites. Static site generator takes your content files written in a markup language like Markdown, apply layouts you have defined, and generate static HTML files that can be delivered to the user. Static websites are nothing new, they date back to the first ever website in human history. We started with static websites, then moved to dynamic websites, and finally we are moving back to static websites for use-cases where it make sense. Most common use-cases for static websites are blogs, product documentation, help guides, tutorials, online portfolio or resume.

You can read full blog https://github.com/shekhargulati/52-technologies-in-2016/blob/master/07-hugo/README.md