Tag Archive for: Configuration Management

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Best Practices: Ansible

This entry is part 2 of 4 in the series Best Practices

Synopsis

This technical guide provides a detailed overview of best practices for working with Ansible in a busy DevOps team. It covers important concepts such as idempotency and how to secure sensitive information using Ansible Vault. The guide also includes information on how to organize Ansible code in a git repository and best practices for committing changes to a repository.

Summary

This technical guide provides best practices for working with Ansible in a busy DevOps team. It covers important concepts such as idempotency and how to use Ansible Vault to secure sensitive information. The guide also includes information on how to organize Ansible code in a git repository and best practices for committing changes to a repository.

Introduction

Ansible is a popular tool for automating the configuration and management of systems. In a busy DevOps team, it is important to follow best practices when working with Ansible to ensure that the codebase is maintainable and easy to work with.

One key concept to keep in mind when working with Ansible is idempotency. An idempotent operation is one that has the same result whether it is performed once or multiple times. In other words, if an operation is idempotent, it will not change the system
state if it is run multiple times with the same parameters. This is important in Ansible because it allows you to run plays multiple times without causing unintended changes to the system.

To ensure idempotency in ansible, it is important to use the state parameter in tasks. The state the parameter allows you to specify the desired state of a resource, such as whether a package should be installed or uninstalled. Using the state parameter ensures that ansible will only make changes to the system if the specified state is not already met.

Another important aspect of working with ansible is securing sensitive information. It is important to not store sensitive information such as passwords and access keys in plaintext in the ansible codebase. Instead, you can use ansible vault to encrypt sensitive information and store it securely. To use ansible vault, you can create a vault file and use the ansible-vault command to encrypt and decrypt the file as needed.

It is also important to consider how to organize ansible code in a git repository. One way to do this is to create a separate directory for each environment, such as production, staging, and development. This can make it easier to manage and track changes to the ansible codebase.

When committing changes to a git repository, it is important to follow best practices for commit messages and branch names. Commit messages should be concise and describe the changes made in the commit. Branch names should be descriptive and follow a consistent naming convention.

In addition to following best practices for commit messages and branch names, it is also important to use tickets to track development updates. Tickets should include a clear description of the work to be done and any relevant details such as links to relevant resources or dependencies.

Conclusion

By following best practices such as ensuring idempotency and securing sensitive information using ansible vault, and organizing ansible code in a git repository in a structured way, DevOps teams can effectively work with ansible to automate the configuration and management of systems. By following these guidelines, teams can ensure that their codebase is maintainable and easy to work with, enabling them to deliver new features and updates more efficiently.

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Best Practices: Packer

This entry is part 1 of 4 in the series Best Practices

Synopsis

This technical guide provides a detailed overview of best practices for working with Packer in a busy DevOps team. It includes information on concepts such as idempotency and naming standards, as well as code examples and templates for organizing Packer code in a git repository. The guide also covers considerations for security and provides templates for a README file, HCL file, and .gitignore file for a Packer repository.

Summary

This technical guide provides best practices for working with Packer in a busy DevOps team. It covers important concepts such as idempotency and naming standards, as well as providing code examples and structured into appropriate sections. The guide also includes information on how to organize Packer code in a git repository, including considerations for security, as well as templates for a README file, HCL file, and .gitignore file for a Packer repository.

Introduction

Packer is a popular tool for automating the creation of machine images. In a busy DevOps team, it is important to follow best practices when working with Packer to ensure that the codebase is maintainable and easy to work with.

One key concept to keep in mind when working with Packer is idempotency. An idempotent operation is one that has the same result whether it is performed once or multiple times. In other words, if an operation is idempotent, it will not change the system state if it is run multiple times with the same parameters. This is important in Packer because it allows you to run builds multiple times without causing unintended changes to the system.

To ensure idempotency in Packer, it is important to use the only and except parameters in the provisioner block. The only and except parameters allow you to specify the conditions under which a provisioner should run, such as the operating system or the type of machine image being built. Using these parameters ensures that Packer will only run a provisioner if the specified conditions are met.

Naming standards are another important aspect of working with Packer in a busy DevOps team. It is a good idea to use consistent naming conventions for Packer templates and variables to make the codebase easier to read and understand.

Code Examples

Here is an example of a Packer template that follows a consistent naming convention:

{
  "variables": {
    "aws_access_key": "{{env `AWS_ACCESS_KEY_ID`}}",
    "aws_secret_key": "{{env `AWS_SECRET_ACCESS_KEY`}}",
    "aws_region": "us-east-1"
  },
  "builders": [
    {
      "type": "amazon-ebs",
      "access_key": "{{aws_access_key}}",
      "secret_key": "{{aws_secret_key}}",
      "region": "{{aws_region}}",
      "source_ami": "ami-0f2176987ee50226e",
      "instance_type": "t2.micro",
      "ssh_username": "ec2-user",
      "ami_name": "packer-example {{timestamp}}"
    }
  ],
  "provisioners": [
    {
      "type": "shell",
      "inline": [
        "sudo yum update -y",
        "sudo yum install -y nginx"
      ]
    }
  ]
}

In this example, the variables are named aws_access_key, aws_secret_key, and aws_region, and the Packer template is named packer-template.json.

Organizing Packer Code in a Git Repo

When working with Packer in a busy DevOps team, it is important to organize the codebase in a way that is maintainable and easy to work with. One way to do this is to split the Packer code into different files and directories within a git repository.

One way to organize the Packer code is to separate the provisioners, builders, and variables into different files. This can make it easier to find and modify specific parts of the codebase. For example, you could create a provisioners directory to store all of the provisioner scripts, a builders directory to store the Packer templates, and a variables directory to store the variable definitions.

It is also important to consider security when organizing the Packer code in a git repository. Sensitive information such as access keys and secrets should not be stored in the repository in plaintext. Instead, you can use tools such as Hashicorp’s Vault to securely store and manage sensitive information.

Template README.md for a Packer Repo:

# Packer Repository

This repository contains Packer templates and scripts for building machine images.

## Directory Structure

The repository is organized as follows:

- `builders`: Packer templates for building machine images
- `provisioners`: Scripts for provisioning machine images
- `variables`: Variable definitions for Packer templates

## Usage

To build a machine image using a Packer template, run the following command:

```bash
packer build -var-file=variables/example.json builders/example.json
```

Replace example.json with the appropriate file names for your build.

## Contributing

To contribute to this repository, follow these steps:

+ Fork the repository
+ Create a new branch for your changes
+ Make your changes and commit them to the new branch
+ Push the branch to your fork
+ Create a pull request from your fork to the main repository

Please make sure to follow the repository's style guidelines and to run any relevant tests before submitting a pull request.

## License

This repository is licensed under the MIT License.

## Template HCL file with Headers Summarized:

## Packer Template

This Packer template is used to build a machine image.

### Builders

The following builders are used in this template:

 + Amazon Elastic Block Store (EBS)

### Provisioners

The following provisioners are used in this template:

 + Shell

### Variables

The following variables are used in this template:

+ `aws_access_key: AWS access key`
+ `aws_secret_key: AWS secret key`
+ `aws_region: AWS region`

### Usage

To build a machine image using this Packer template, run the following command:

```bash
packer build -var-file=variables/example.json template.json
```

Replace example.json with the appropriate file name for your variables.

### Contributing

To contribute to this Packer template, follow these steps:

+ Fork the repository
+ Create a new branch for your changes
+ Make your changes and commit them to the new branch
+ Push the branch to your fork
+ Create a pull request from your

Conclusion

By following best practices such as ensuring idempotency and using consistent naming conventions, and organizing Packer code in a git repository in a structured and secure way, DevOps teams can effectively work with Packer to automate the creation of machine images. By following these guidelines, teams can ensure that their codebase is maintainable and easy to work with, enabling them to deliver new features and updates more efficiently.

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Introduction to Ansible

This entry is part 2 of 4 in the series DevOps

Ansible is an open-source automation platform that allows you to automate the configuration and management of systems and applications. It uses a simple, human-readable language called YAML to describe the tasks that need to be performed, and it can be used to automate a wide variety of tasks including provisioning and configuration of infrastructure, deploying applications, and managing software and system updates.

One of the key benefits of Ansible is that it is agentless, meaning that it does not require any software to be installed on the target systems in order to manage them. This makes it easy to get started with ansible, as there is no need to install and configure agents or other software on your servers. Instead, ansible relies on the use of SSH to connect to the target systems and execute tasks.

Ansible uses a concept called “playbooks” to describe the tasks that need to be performed. Playbooks are written in YAML and are made up of a series of “plays” that define the tasks to be executed and the systems on which they should be executed. Playbooks can be used to define the desired state of a system or application, and ansible will ensure that the system is configured accordingly.

Ansible also uses the concept of an “inventory” to define the systems that it should manage. The inventory is a list of the systems in your environment and can be defined in a variety of formats including INI and YAML. The inventory can be used to group systems together, making it easy to target specific subsets of systems when running ansible playbooks.

Here is an example ansible playbook that installs and starts the Apache web server on a group of systems:

---
- hosts: webservers
  tasks:
  - name: Install Apache
    yum:
      name: httpd
      state: present
  - name: Start Apache
    service:
      name: httpd
      state: started

This playbook consists of a single play that targets the “webservers” group in the inventory. The play consists of two tasks: the first task installs the Apache web server package using the yum package manager, and the second task starts the Apache service. When this playbook is run, ansible will connect to each of the systems in the “webservers” group and execute these tasks, ensuring that the Apache web server is installed and running on all of the systems.

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