Content Lifecycle Management DevOps

Overview

A leading learning content company which provides a range of technology and content creative offerings for global enterprises. With end-to-end solutions spanning across all stages of the learning content lifecycle and aims to deliver exceptional business performance within enterprise.

 

As such a cloud based solution for role based management of the content lifecycle across various content delivery methods ranging from Elearning, Video, Material to Instructor Led Training has been designed and developed on the microservices based architecture by leveraging some of the latest technology stack and design principles.

Project Info

Category

CD (DevOps)

Client

Content Central

Tags

  • AWS
  • Selenium
  • Cucumber
  • EKS
  • TestNG
  • Jenkins
  • Bitbucket

Problem Statement

The goal was to set up a completely automated continuous software delivery pipeline for the product from day zero inclusive of automated software delivery lifecycle stages of continuous inspection, continuous testing, continuous verification and continuous deployments.

Product Features

The product was running more than 20+ containerised microservices in the background and was also leveraging some of the leading cloud managed services for delivering the functionality to the end user. We have designed each stage by leveraging and setting up some of the leading open source tools to seamlessly achieve commit based continuous delivery straight from version control system to the cloud platform deployments.
01. Version Control System
  • Bitbucket cloud was used as the version control system for distributed and continuous development across all involved engineering teams.
  • GitFlow was enforced as a standard branching model for development and managing deliveries across environments.
02. Continuous Inspection
SonarQube was selected and configured as the static code analysis tool providing continuous inspection around security and coding standard adherence by continuously scanning thousands of lines of code being written daily across different languages.
03. Continuous Integration
A global Jenkins scripted pipeline library was developed in groovy to manage the commit based pipelines across 20+ repositories and execute various automated stages intelligently based on branch aware workflows. The library has support for various build tools like Maven, Gradle, NPM etc. across different languages like Java, Node, Python etc.
04. Continuous Deployments
  • Ansible playbooks and roles were developed to manage all the automated deployments across environments and for managing all the application and environment specific configurations required for successful deployments of each application.
  • Automated deployments were achieved as part of commit based pipelines to the sandbox & dev-integration environments.
  • Intelligent and automated tear down of sandbox environments were done as part of the commit based pipelines
  • Automated build promotions based on verification stage results to upstream environments.
  • Role based OnDemand automated deployment pipelines for staging & production environments.
05. Continuous Testing
  • REST-assured and TestNG were the primary libraries used to develop the rest based automation for all the java spring boot web services. All the automated tests were integrated and executed as part of the commit based pipelines. More than 200+ test cases executed as part of various commits across the day, covering functionalities at service level isolation and feature level integration.
  • Selenium and Cucumber were the primary libraries used to develop the UI automation suite covering all the end user functionalities and scenarios. More than 100+ scenarios automated and executed nightly against stable integration environments for certification.
06. Artifact Management
JFrog artifactory server was setup as the maven dependency manager for managing the maven libraries and dependencies across all the microservices.

Amazon ECR was used as the docker Image registry to store and manage all the application docker images. Different lifecycle policies were defined in each image repository for automated management & cleanup of images based on tagging.

07. Container Management
  • Amazon Elastic Kubernetes Service ( Amazon EKS ) was used as the managed Kubernetes service for hosting the Kubernetes Cluster for running & managing all the application containers.
  • Multiple namespaces inside a single kubernetes cluster were leveraged to host and manage different versions of application running under different environments.
08. Release Management
  • Helm - The Kubernetes package manager was used to manage application releases to the kubernetes cluster. Helm helps in zero downtime release rollouts for new application versions and also rollbacks of the application to previous stable versions.
  • Helm also helps in tracking the release history at different levels of abstraction.
09. Centralised Log Management
  • CloudWatch Container Insights for Kubernetes was setup to collect, aggregate and summarize metrics and logs from the containerised applications and microservices.
  • CloudWatch log query console was primarily used for querying and filtering log events for debugging application issues across all microservices.
10. Continuous Monitoring

CloudWatch metrics were used to setup dashboards which were used for monitoring across all the application containers, cluster namespaces and cluster nodes.

11. Continuous Alerting

CloudWatch alarms were defined on top of aggregated metrics to generate alerts and send timely notifications via Emails to stakeholders.

12. OnDemand Environments
  • Terraform scripts were created to provision completely new environments with all required objects in the same AWS account or for even provisioning a completely new AWS account within minutes.
  • Similarly the same scripts could be used to tear down the same environments once their usage is done to ensure optimum cost and resource utilization.
13. Business Availability
  • To ensure high business availability during peak hours of load and optimum resource utilization during off hours. Automatic scale up and scale down were implemented at both application container level and cluster node level.
  • Horizontal pod autoscaler for kubernetes was configured at each pod level inorder to ensure that it incrementally scales up the max number replicas during peak load time & also scales down to the minimum number of replicas during off hours.
  • Similarly, AutoScaling groups were defined for managing kubernetes worker nodes and were configured with CloudWatch alarms to AutoScale up and AutoScale down the worker nodes based on the resource availability and requirements.

The complete Automation & DevOps infrastructure described was set up within 3 months.

Technology Stack

Backend

Frontend

Database

DevOps

Platform Service

Automation

Conclusion

The client now has a seamless commit based continuous delivery ecosystem with zero manual intervention.

This has significantly increased productivity across all the involved engineering teams.

This has increased the velocity of feature development and deliveries across engineering sprints.

The number of defects found in sprint acceptance testing has significantly reduced ensuring high quality of releases.

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