In the previous part I described the basic architecture of a data processing pipeline using 5 different components rabbitmq, logstash, kafka, elasticsearch and kibana.

The last part of that article included future work and in this article I will describe two of the goals achieved here, with extra ideas for future improvements.

Data Loss when kafka servers are down

I described a scenario when data is lost if it arrives at logstash server and kafka server is down at the same time, when kafka is back again logstash does not send stuck data in the pipeline to it which causes data loss, this is not acceptable at all in any production ready system so I worked on this issue and was easily resolved by upgrading logstash server to last version v6.2.1, I will describe the upgrade process here for logstash, elasticsearch and kibana.

Upgrading Logstash

To upgrade logstash you simply need to grab the deb file of the new version and install it using the following commands:

  sudo dpkg -i logstash-6.2.1.deb

Now you are running logstash version 6.2.1 which is compatible with used kafka version and does not lose data in case kafka server goes down.

No changes in configuration are required.

Upgrading elasticsearch

We can use the same steps as above to upgrade elasticsearch server

  sudo dpkg -i elasticsearch-6.2.1.deb

Now elasticsearch version has been updated to the latest, no configuration changes are required.

Upgrading Kibana

The same steps are used to upgrade kibana

  sudo dpkg -i kibana-6.2.1-amd64.deb

Hint: I will not describe the process of upgrading elasticsearch cluster to a new versions because my work here is still experimental and once it goes to production all tested versions of software will be used from the start of the deployment.

Deploying with ansible

In the previous article we learned how to deploy the data processing pipeline using simple commands executed on the server’s shell but of course we do not want to repeat those commands every time we want to deploy our pipeline we need a way to automate the entire process which makes it easy to repeat the deployment multiple times and to move the pipeline to other servers if needed, we will use ansible for this purpose.

Following this tutorial does not require any experience with ansible, all what you need is a server to install all the components on it and you need ansible installed on your machine.

Installing ansible

You can install ansible on your machine with the following commands

  sudo apt-get update
  udo apt-get install software-properties-common -y
  sudo apt-add-repository ppa:ansible/ansible -y
  sudo apt-get update
  sudo apt-get install ansible -y

To follow along you can use this repo.

This repository contains a Vagrantfile to create a virtual machine with the IP address of with ubuntu user name, but to use this file you need to install vagrant.

Installing vagrant and virtualbox

Use these commands to install vagrant and virtualbox

sudo dpkg -i vagrant_2.0.2_x86_64.deb
sudo dpkg -i virtualbox-5.2_5.2.6-120293~Ubuntu~xenial_amd64.deb

vagrant uses virtualbox provider to run virtual machines on your own machine and you can connect to them with ansible and deploy the pipeline to it.

Hint: If you have a server and want to deploy the Data Processing Pipeline to it no need to use vagrant you just need to modify the hosts file with access information for your server including IP address, username and private key for SSH authentication.

Running the deployment

Here there are two types of deployment, the first deploys the Pipeline with a web application for testing to vagrant vm and the second only deploys the required components to run the pipeline.

Use the following commands to start the deployment process

  git clone
  cd LoggingInfrastructure
  vagrant up
  ansible-playbook deploy.yml -i hosts # deploy a web app with the pipeline, only with vagrant
  # ansible-playbook dpp.yml -i hosts  # only deploy the pipeline without a web app

The ansible playbook automates the entire deployment process except for kafka configuration, you need to manually configure kafka after deployment by following these instructions:

  • Start confluent platform with sudo confluent start
  • Edit this file /etc/kafka-connect-elasticsearch/ to specify options for kafka elasticsearch connector
  • Start the connector with sudo confluent load elasticsearch-sink
  • Make sure kafka elasticsearch connector is running with sudo confluent status connectors

After that navigate to to send a log message and then use to run kibana and check the message you sent.

You need to follow the Kibana Index Pattern in the previous article to setup kibana.

Hint: If you used dpp.yml for deployment you need to manually create a web app for testing the Data Processing Pipeline by following Web Application

Wrap up

In this article I described a solution for data loss problem and a way to automate pipeline deployment with a configuration management tool called ansible, this is very necessary in any DevOps environment where automation is a key tool to manage the infrastructure.

Future Work

I am currently working on Kafka Streams to build a scalable and highly available Java application to process data before it is sent to elasticsearch.

  • Work on deployment with other configuration management tools such as puppet and chef.
  • Describe the use of other programming languages for sending data to the pipeline such as Python, NodeJS, Go etc…

Any issues or suggestion are welcome on my github repository issue tracker.