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DevOps Antipatterns

(Compiled from AWS, Azure, Google and and other community sources)

The following are antipatterns to avoid:

Avoid:​

  • Inadequate Monitoring and Logging
    • Use tools like Datadog, New Relic, or Splunk to monitor and log your infrastructure, applications, and services to proactively detect issues and troubleshoot.
  • Lack of Automation:
    • Use tools like Puppet, Chef, or Ansible to automate your infrastructure provisioning and configuration management tasks to reduce human error and increase efficiency.
  • Over-reliance on automation: Over-automating processes can lead to inflexibility, and create maintenance issues when things change.
    • Use AWS CloudFormation to create templates for infrastructure provisioning and management, while retaining the flexibility to customize specific resources as needed.
    • Azure Resource Manager templates can be used to automate the provisioning and management of Azure resources, while also providing the flexibility to customize resources as needed.
    • In GCP, Google Cloud Deployment Manager can be used to define infrastructure as code, making it easier to automate the deployment and management of GCP resources.
  • Lack of communication: DevOps requires a high level of collaboration and communication between teams. Failing to establish an open communication can lead to conflicts, delays, and mistakes.
    • Use off 3rd-part provider like Slack and JIRA
    • Use AWS Chatbot to create chat channels for different teams and stakeholders, and use AWS CodePipeline to notify team members of changes in the codebase and deployment process.
    • Azure DevOps provides a suite of tools, including Azure Boards and Azure Repos, to facilitate communication and collaboration between team members.
    • In GCP, Google Cloud Build can be used to automate builds and deployments, and notifications can be sent via email, SMS, or chat using Cloud Pub/Sub and Cloud Functions.
  • Lack of visibility: Without proper visibility, it can be hard to identify and address issues in the development and deployment processes.
    • Use AWS CloudTrail to monitor activity across the AWS infrastructure, and use AWS X-Ray to track application performance and diagnose issues.
    • Azure Monitor can be used to monitor performance and availability across Azure resources, while also providing insights into system metrics, logs, and application traces.
    • In GCP, Google Cloud Logging and Cloud Monitoring can be used to monitor resource usage and performance, as well as alert on specific events or conditions.
  • Failure to align with business goals: Focusing solely on technical goals without aligning with business goals can lead to projects that are not useful to the business, or that are not financially viable.
    • Use AWS Budgets and Cost Explorer to track costs and make sure the resources being used are financially viable, and use AWS CloudWatch to monitor service usage and availability.
    • Azure Cost Management and Billing can be used to track costs and ensure that resources are being used in a financially responsible manner.
    • In GCP, Google Cloud Billing can be used to monitor spending and optimize costs, while also providing cost reports and insights.
  • Over-reliance on technology: Over-emphasizing technology can lead to the deployment of unnecessary features, and cause complexity and maintenance problems.
    • Use AWS Lambda to create serverless applications and APIs that only contain necessary functionality, reducing complexity and maintenance requirements.
    • Azure Functions can be used to create serverless applications that only contain the necessary functionality, reducing complexity and maintenance requirements.
    • In GCP, Google Cloud Functions can be used to create and deploy event-driven, serverless functions that automatically scale based on demand.
  • Lack of testing: Failing to thoroughly test the application and infrastructure can lead to defects, and downtime.
    • Use AWS CodePipeline to automate testing, including integration and unit tests, and use AWS Device Farm to test applications on different devices.
    • Azure Test Plans and Azure DevTest Labs can be used to automate testing, including load, performance, and security testing, and to create test environments.
    • In GCP, Google Cloud Build can be used to automate testing and build validation, and Google Cloud Test Lab can be used to test applications on real devices.
  • Lack of documentation: Failing to properly document the deployment process can lead to confusion, duplication of effort, and longer downtime during outages.
    • Use AWS Systems Manager to centrally manage documentation, and use AWS Quick Start to deploy preconfigured solutions with built-in documentation.
    • Azure DevOps provides a wiki feature that can be used to create and maintain documentation, and Azure Boards can be used to track and manage work items.
    • In GCP, Google Cloud Build triggers can be used to automatically generate and publish documentation when code is committed or pushed.
  • Failure to establish KPIs: Failing to establish KPIs can lead to a lack of accountability and make it difficult to measure the effectiveness of the DevOps process.
    • Use AWS Service Catalog to define services and their associated KPIs, and use AWS CloudWatch to monitor and report on these metrics.
    • Azure DevOps can be used to define services and establish associated KPIs, and to monitor and report on these metrics using Azure Monitor.
    • In GCP, Google Cloud Operations can be used to monitor and report on KPIs, as well as provide insights and recommendations for improvement.
  • Inadequate security: Failing to incorporate security practices into the DevOps process can lead to security vulnerabilities.
    • Use AWS Identity and Access Management (IAM) to control access to AWS resources, and use AWS Security Hub to monitor compliance with security standards and best practices.
    • Azure Security Center can be used to monitor security across Azure resources, provide security recommendations, and help mitigate security threats.
    • In GCP, Google Cloud Security Command Center can be used to monitor and manage security across GCP resources, and to help identify and remediate security risks.
  • Over-commitment: Over-committing to too many projects at once can lead to delays, burnout, and mistakes in the deployment process.
    • Use AWS Elastic Beanstalk to quickly deploy and manage applications, scaling resources as needed to meet demand, freeing up resources for other projects.
    • Azure App Service can be used to quickly deploy and manage applications, scaling resources as needed to meet demand, freeing up resources for other projects.
    • In GCP, Google App Engine can be used to deploy and manage applications, automatically scaling resources based on demand, while Google Cloud Functions can be used to handle specific functions or events, reducing the overall workload of the application.
  • Configuration Drift:
    • Use a configuration management tool like Ansible, SaltStack, or Puppet to ensure consistency across your infrastructure and prevent configuration drift.
  • Lack of continuous improvement: DevOps requires a culture of continuous improvement, and failing to make it a priority can lead to stagnation and failure to evolve with changing technology and business needs.
  • Lack of transparency: Failing to establish transparency across teams and stakeholders can lead to misunderstandings and mistrust.
  • Poorly defined roles and responsibilities: Without clear roles and responsibilities, teams may duplicate effort, waste time, or fail to address important tasks.
  • Insufficient infrastructure capacity planning: Failing to plan for infrastructure capacity needs can lead to downtime, slower performance, and decreased availability.
  • Lack of cultural alignment: DevOps requires a culture of collaboration, communication, and shared responsibility. Failing to establish this culture can lead to silos and conflict among teams, and undermine the effectiveness of the DevOps process.