Amazon ECS: The Modern Cluster Manager Part 1

In the last few posts, we looked at various Docker utilities and how XL Deploy can make it easy for enterprises to adopt and use Docker. Docker streamlines software development and testing for teams that have started embracing it. The package once deploy anywhere (PODA) capability of Docker minimises the issue of environmental (like staging, quality assurance, and production) differences. Continue reading “Amazon ECS: The Modern Cluster Manager Part 1”


Upgrading Docker Compose to latest version

If you use Docker for Mac or something similar, Docker Compose will be installed along with it. Docker Compose has a different release timeline for Docker for Mac so you will not be able to try latest version of Docker compose until you upgrade Docker for Mac. This is limiting. You should be able to install Docker compose independently. To achieve that, you can run following command

$ composeVersion=1.13.0
$ curl -L$composeVersion/docker-compose-`uname -s`-`uname -m` > /usr/local/bin/docker-compose$ chmod +x /usr/local/bin/docker-compose

In the above commands, $ signify bash prompt. You don’t have to type it. Now, you can check Compose version using the command shown below.

$ docker-compose version

Talk: Container Performance Analysis

Today, I watched DockerCon 2017 talk on Container Performance Analysis. Talk is given by Brendan Gregg, Senior Performance Architect at Netflix. In his talk, he shares various linux tools that can help you understand performance of your container platform. It is a great talk for anyone trying to do performance analysis of containers. In one of his slides, he shared 10 tools that he will use to start the investigation.

  1. uptime to check load averages
  2. dmesg | tail  to check kernel errors
  3. vmstat 1 to see overall stats by time
  4. mpstat -P ALL 1 to check CPU balance
  5. pidstat 1 to check process usage
  6. iostat -xz 1 to disk I/O
  7. free -m to check memory usage
  8. sar -n DEV 1 to check network I/O
  9. sar -n TCP, ETCP 1 to view TCP stats
  10. top for overview

Continue reading “Talk: Container Performance Analysis”

Multi-stage Docker Image Build for Java Applications

A few days back, I discovered a new Docker feature — multi-stage builds. The multi-stage build feature helps you create thin Docker images by giving possibility to divide image building process into multiple stages. Artifacts produced in one stage can be resused by another stage. This is very beneficial for languages like Java as multiple steps are required to build the Docker image. The main advantage of multi-stage build feature is that it can help you create smaller size images. This feature is not yet available in stable versions of Docker. It will become available in Docker 17.05. To use this feature, you have to use edge version of Docker CE.

To build a Docker image for a Java application, you first need to build the Java project. Java build process needs JDK and a build tool like Maven, Gradle, or Ant. Once Java binary artifact is produded, you can package the binary in a Docker image. For running a Java binary, you only need JRE so you don’t have to pay the cost of bundling the whole JDK.

You can read full blog at

5 Docker Utilities You Should Know

There are a lot of cool Docker utilities that you can find on the web. Most of these are open source and available on Github. I have become an active user of Docker for last two years, using it for most of my development projects. As you start using Docker, you will find Docker is suitable for more use cases than you initially envisioned it for. You will want Docker to do a little more for you, and it will not disappoint you.

Docker community is very active, a lot of useful utilities keep popping daily. It is difficult to keep check of all the innovation happening in the community. In the following post, I have collected some interesting and useful Docker utilities which I use in my daily work. These utilities makes me more productive, otherwise would have been a manual work.

In this post, I will cover watchtower, docker-gc, docker-slim, rocker, and ctop utilities. You can read full blog at

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