Review of past issues:
Chapter 1: [Cloud native concepts and technologies]
Chapter 2: [Containerized application design and development]
Chapter 3: [Container-based deployment, management and expansion]
Chapter 4: [Microservice architecture design and implementation]
Chapter 5: [5.1 Automated building and packaging of container images]
Chapter 5: [5.2 Automated testing and integration testing]
Basic knowledge of continuous delivery and DevOps practices
5.3.1 What is continuous delivery
Continuous Delivery under cloud native is a software development method that aims to achieve high-quality, reliable and sustainable software delivery. It emphasizes that through automated processes and tool chains, the software building, testing and deployment processes can be carried out frequently and maintain consistency and reliability. Below is a detailed sample code that demonstrates building and deploying during continuous delivery.
# 示例代码：使用Jenkins进行持续交付
# Jenkinsfile
pipeline {
agent any
stages {
stage('Build') {
steps {
sh 'mvn clean package'
}
}
stage('Test') {
steps {
sh 'mvn test'
}
}
stage('Deploy to Staging') {
steps {
sh 'ansible-playbook deploy-staging.yaml'
}
}
stage('Test Staging') {
steps {
sh 'mvn integration-test'
}
}
stage('Deploy to Production') {
steps {
sh 'ansible-playbook deploy-production.yaml'
}
}
}
}
The above example shows a Jenkinsfile written in Jenkins, which defines a continuous delivery pipeline with multiple stages.
Build phase : In this phase, Maven is used to build the project. The mvn clean package command cleans the project, and then performs operations such as compilation and packaging.
Test phase : In this phase, Maven is used for unit testing. The mvn test command runs the unit test cases of the project to ensure code quality.
Deploy to Staging (deploy to pre-production environment) phase : In this phase, Ansible is used for automated deployment. The ansible-playbook deploy-staging.yaml command deploys the application to the pre-production environment based on the predefined playbook configuration.
Test Staging (pre-production environment testing) phase : In this phase, Maven is used for integration testing. The mvn integration-test command runs the integration test cases of the project to verify the functionality and performance of the application in the pre-production environment.
Deploy to Production phase : In this phase, Ansible is used again for automated deployment. The ansible-playbook deploy-production.yaml command deploys the application to the production environment based on another playbook configuration.
Through the above pipeline, development teams can automatically build, test and deploy applications to achieve continuous delivery. After each code submission, Jenkins will trigger the pipeline and execute the operations of each stage in sequence.
5.3.2 Basic knowledge of DevOps practice
DevOps is a practical method that integrates development and operations. It aims to promote collaboration and communication between development teams and operations teams to achieve fast and high-quality software delivery. Below are some detailed basics and sample code illustrating key elements of DevOps practices.
5.3.2.1 Continuous Integration
Continuous Integration: Continuous Integration is a key concept in DevOps practice, which refers to frequently integrating code into the trunk branch and ensuring the quality of the code through automated build and testing processes. The following is a sample code that demonstrates the process of continuous integration.
# 示例代码：使用Jenkins进行持续集成
# Jenkinsfile
pipeline {
agent any
stages {
stage('Build') {
steps {
sh 'mvn clean package'
}
}
stage('Test') {
steps {
sh 'mvn test'
}
}
}
}
The above example shows a Jenkinsfile written in Jenkins, defining a continuous integration pipeline that includes build and test phases. After each code submission, Jenkins will trigger the pipeline and perform build and test operations in sequence.
5.3.2.2 Continuous Deployment
Continuous Deployment: Continuous Deployment is another important concept in DevOps practice, which refers to the automated deployment of code to the production environment. The following is a sample code that demonstrates the process of continuous deployment.
# 示例代码：使用Ansible进行持续部署
# Ansible playbook示例
---
- name: 部署应用
hosts: target_servers
become: yes
tasks:
- name: 安装依赖
apt:
name: "{
{ item }}"
state: present
with_items:
- openjdk-8-jdk
- nginx
- name: 拷贝应用程序文件
copy:
src: /path/to/app.jar
dest: /opt/myapp/app.jar
- name: 启动应用
command: java -jar /opt/myapp/app.jar
The above example shows that using a playbook file written in Ansible can automatically complete the operations of installing dependencies, copying application files, and starting the application. Through such automated configuration, continuous deployment can be achieved and applications can be quickly deployed to the target server.
5.3.2.3 Automatic resource management
Automated resource management: DevOps emphasizes the use of automated tools to manage infrastructure and resources to achieve scalability and flexibility. Below is a sample code that demonstrates automated management of infrastructure using Terraform.
# 示例代码：使用Terraform进行资源自动化管理
# main.tf
provider "aws" {
region = "us-west-2"
}
resource "aws_instance" "example" {
ami = "ami-0c55b159cbfafe1f0"
instance_type = "t2.micro"
key_name = "my-key"
tags = {
Name = "example-instance"
}
}
The above example shows the main.tf file written using Terraform, which defines an AWS EC2 instance resource. This instance can be automatically created and managed by running Terraform commands.
5.3.2.4 Log monitoring and analysis
Log monitoring and analysis: DevOps emphasizes monitoring and analyzing logs of applications and infrastructure in order to detect and solve problems in a timely manner. The following is a sample code that demonstrates log monitoring and analysis using the Elasticsearch, Logstash, and Kibana (ELK) stack.
# 示例代码：使用ELK进行日志监控与分析
# Filebeat配置文件
filebeat.inputs:
- type: log
enabled: true
paths:
- /var/log/myapp.log
output.elasticsearch:
hosts: ["http://localhost:9200"]
# Logstash配置文件
input {
beats {
port => 5044
}
}
filter {
grok {
match => {
"message" => "%{
COMBINEDAPACHELOG}" }
}
}
output {
elasticsearch {
hosts => ["localhost:9200"]
index => "myapp-%{
+YYYY.MM.dd}"
}
}
# Kibana：通过Web界面可视化和查询日志数据
The above example shows configuration files using Filebeat, Logstash, and Elasticsearch to collect, transform, and store log data. Kibana provides a web interface that can visualize and query log data.