.svg)
DevOps Template Library
Dockerfile for LAMP Stack (Linux, Apache, MySQL, PHP)
This Dockerfile sets up a LAMP stack in a Docker container, ideal for running PHP web applications. It uses an official PHP image with Apache and installs MySQL extensions, ensuring your application connects to MySQL databases effortlessly. Just add your PHP code to the src directory, build, and run the container to deploy your application quickly.
Dockerfile:
FROM php:7.4-apache
RUN docker-php-ext-install mysqli pdo pdo_mysql
COPY src/ /var/www/html/
EXPOSE 80
Instructions:
- Place this Dockerfile in the root directory of your PHP project.
- Ensure you have a src directory containing your PHP code.
- Build the image using docker build -t my-lamp-app.
- Run the container with docker run -p 80:80 my-lamp-app.
Dockerfile for MEAN Stack (MongoDB, Express.js, Angular, Node.js)
This Dockerfile is a great starting point for efficiently deploying MEAN stack web applications. It uses the official Node.js image, installs your dependencies, and sets everything up for your app to run. Build and run this Docker image, and your application will be ready on port 3000.
Dockerfile:
FROM node:14
WORKDIR /usr/src/app
COPY package*.json ./
RUN npm install
COPY . .
EXPOSE 3000
CMD [ "npm", "start" ]
The additional files index.js and package.json are essential for running your MEAN stack application within the Docker container, tailored to a Node.js environment. These are sample files designed to demonstrate a basic container setup for a Node.js application. Remember, these files are just starting points. You should replace or extend them with your own code to build a fully functional web application tailored to your requirements.
index.js
const http = require('http');
const server = http.createServer((req, res) => {
res.statusCode = 200;
res.setHeader('Content-Type', 'text/plain');
res.end('Hello World\n');
});
const PORT = process.env.PORT || 3000;
server.listen(PORT, () => {
console.log(`Server running on port ${PORT}`);
});
package.json
{
"name": "simple-node-app",
"version": "1.0.0",
"description": "A simple Node.js application",
"main": "index.js",
"scripts": {
"start": "node index.js"
},
"author": "KodeKloud",
"license": "ISC"
}
Instructions:
- Place this Dockerfile in the root of your Node.js (Express) project.
- Ensure your Node.js application listens on port 3000.
- Build the image with docker build -t my-mean-app.
- Run the container using docker run -p 3000:3000 my-mean-app.
Basic Deployment and Service YAML
The Deployment.yaml creates a Kubernetes deployment with three replicas of an application for high availability. The Service.yaml exposes these replicas externally on port 80 using a LoadBalancer, facilitating access to the application. This setup ensures the application is scalable, resilient, and accessible.
Deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: my-app
spec:
replicas: 3
selector:
matchLabels:
app: my-app
template:
metadata:
labels:
app: my-app
spec:
containers:
- name: my-app
image: my-app:latest
ports:
- containerPort: 80
Service.yaml:
apiVersion: v1
kind: Service
metadata:
name: my-loadbalancer-service
spec:
type: LoadBalancer
selector:
app: MyApp
ports:
- protocol: TCP
port: 80
targetPort: 9376
Instructions:
- Replace my-app:latest with your container image.
- Apply these configurations using kubectl apply -f Deployment.yaml and kubectl apply -f Service.yaml.
Notes:
- This setup creates a basic deployment with 3 replicas of your app.
- The service exposes the app on port 80 using a LoadBalancer.
StatefulSet Template for Database Applications
This template includes specifications for a StatefulSet that manages a database application (like PostgreSQL or MongoDB). It should define persistent volume claims for data storage, appropriate environment variables, liveness and readiness probes, and a headless service for stable networking.
StatefulSet.yaml
apiVersion: apps/v1
kind: StatefulSet
metadata:
name: <database-name>
spec:
serviceName: "<database-service>"
replicas: 3
selector:
matchLabels:
app: <database-label>
template:
metadata:
labels:
app: <database-label>
spec:
containers:
- name: <database-name>
image: <database-image>
ports:
- containerPort: <database-port>
volumeMounts:
- name: <volume-name>
mountPath: <data-directory>
volumeClaimTemplates:
- metadata:
name: <volume-name>
spec:
accessModes: [ "ReadWriteOnce" ]
resources:
requests:
storage: 10Gi
Instructions:
- Replace <database-name>, <database-service>, <database-label>, <database-image>, <database-port>, <volume-name>, and <data-directory> with your specific application details.
- Apply this configuration using kubectl apply -f StatefulSet.yaml.
Notes:
- This template sets up a StatefulSet for a database application with persistent storage.
- It ensures ordered, graceful deployment and scaling, and maintains stable network identifiers.
Horizontal Pod Autoscaler (HPA) Template:
Horizontal Pod Autoscalers automatically scale the number of pods in a deployment, replicaset, or statefulset based on observed CPU utilization or other select metrics. This template would help users quickly set up an HPA for their application, ensuring efficient resource usage and better handling of load variations.
HPA.yaml
apiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler
metadata:
name: <hpa-name>
namespace: <namespace>
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: <deployment-name>
minReplicas: 1
maxReplicas: 10
metrics:
- type: Resource
resource:
name: cpu
target:
type: Utilization
averageUtilization: 50
Instructions:
- Replace <hpa-name>, <namespace>, <deployment-name> with your specific application details.
- Adjust minReplicas, maxReplicas, and averageUtilization based on your scalability needs.
- Deploy using kubectl apply -f HPA.yaml.
Notes:
- This HPA automatically scales the number of pods in the specified deployment based on observed CPU utilization.
- It ensures efficient resource usage and helps in managing load variations.
Network Policy Template:
Network policies are crucial for securing Kubernetes networks. They define how pods can communicate with each other and other network endpoints. This template would provide a basic structure for creating a network policy to control ingress and egress traffic for a set of pods.
NetworkPolicy.yaml
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
name: <policy-name>
namespace: <namespace>
spec:
podSelector:
matchLabels:
app: <app-label>
policyTypes:
- Ingress
- Egress
ingress:
- from:
- podSelector:
matchLabels:
app: <source-app-label>
ports:
- protocol: TCP
port: <port>
egress:
- to:
- podSelector:
matchLabels:
app: <destination-app-label>
ports:
- protocol: TCP
port: <port>
Instructions:
- Fill in <policy-name>, <namespace>, <app-label>, <source-app-label>, <destination-app-label>, and <port> with appropriate values.
- Deploy the policy with kubectl apply -f NetworkPolicy.yaml.
Notes:
- This template creates a network policy to control ingress and egress traffic for a set of pods.
- It enhances the security of your Kubernetes cluster by restricting network access.
Ingress Controller Template:
An Ingress controller is a vital component in a Kubernetes cluster, as it manages access to services from outside the Kubernetes network. This template would help in setting up an Ingress controller, like Nginx or Traefik, along with basic routing rules.
Ingress.yaml
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
name: <ingress-name>
annotations:
nginx.ingress.kubernetes.io/rewrite-target: /
spec:
rules:
- host: <app-domain>
http:
paths:
- path: /
pathType: Prefix
backend:
service:
name: <service-name>
port:
number: <service-port>
Instructions:
- Modify <ingress-name>, <app-domain>, <service-name>, and <service-port> to suit your application.
- Deploy the Ingress resource with kubectl apply -f Ingress.yaml.
Notes:
- This template sets up an Ingress resource for managing external access to services in the cluster, typically via an HTTP or HTTPS route.
- It requires an Ingress controller to be effectively functioning in your cluster.
Basic Helm Chart Template
This Helm chart template provides a standardized and customizable way to deploy applications to Kubernetes. It includes a Chart.yaml file for chart metadata, a values.yaml file for configuration values, and templated Kubernetes manifest files for a deployment and service.
Chart.yaml:
apiVersion: v2
name: my-app
version: 0.1.0
Values.yaml:
replicaCount: 3
image:
repository: my-app
tag: "latest"
pullPolicy: IfNotPresent
service:
type: LoadBalancer
port: 80
templates/deployment.yaml and templates/service.yaml:
- Use the same content as in the Kubernetes YAML files, but replace hard-coded values with templated values from values.ya
Instructions:
- Create a directory for your Helm chart and place Chart.yaml and values.yaml inside.
- Create a templates directory within your chart directory.
- Place the Kubernetes deployment and service YAML files into the templates directory, modified for Helm templating.
- Install the Helm chart to your Kubernetes cluster using helm install my-app-chart ./my-app-chart.
Notes:
- Helm charts provide a more manageable way of deploying applications to Kubernetes by templating resource definitions and packaging them.
- Customize values.yaml according to your application's needs, such as image, replica count, and service type.
- Ensure you have Helm installed and configured to communicate with your Kubernetes cluster.
This example creates a basic AWS network infrastructure, an EC2 instance, and an S3 bucket. Ensure you have the latest version of Terraform installed and configured with your AWS credentials.
AWS Terraform Template:
provider "aws" {
region = "us-west-2"
}
resource "aws_vpc" "example_vpc" {
cidr_block = "10.0.0.0/16"
enable_dns_hostnames = true
tags = {
Name = "example-vpc"
}
}
resource "aws_subnet" "example_subnet" {
vpc_id = aws_vpc.example_vpc.id
cidr_block = "10.0.1.0/24"
availability_zone = "us-west-2a"
tags = {
Name = "example-subnet"
}
}
resource "aws_internet_gateway" "example_igw" {
vpc_id = aws_vpc.example_vpc.id
tags = {
Name = "example-igw"
}
}
resource "aws_instance" "example_instance" {
ami = "ami-0c55b159cbfafe1f0" # Update with latest AMI
instance_type = "t2.micro"
subnet_id = aws_subnet.example_subnet.id
tags = {
Name = "example-instance"
}
}
resource "aws_s3_bucket" "example_bucket" {
bucket = "my-example-bucket" # Update with a unique bucket name
acl = "private"
}
Instructions:
- Save this script as main.tf.
- Run terraform init to initialize Terraform.
- Run terraform plan to see the execution plan.
- Run terraform apply to create the resources.
Notes:
- The AMI ID ami-0c55b159cbfafe1f0 is a placeholder. Replace it with the latest relevant AMI ID for your region.
- The S3 bucket name must be globally unique
This example will focus on configuring an Apache web server on an Ubuntu-based system. Ensure you have Ansible installed and you can connect to your target server(s) via SSH.
- name: Setup Apache Web Server
hosts: your_web_servers # Replace with your server group or individual server
become: yes
tasks:
- name: Install Apache
apt:
name: apache2
state: present
update_cache: yes
- name: Start Apache and enable on boot
systemd:
name: apache2
enabled: yes
state: started
- name: Deploy a basic index.html
copy:
content: "<html><body><h1>Hello from Ansible</h1></body></html>"
dest: /var/www/html/index.html
Instructions:
- Save this script as setup-apache.yml.
- Update the hosts line with the name of the group or individual server defined in your Ansible inventory file.
- Run the playbook using the command ansible-playbook setup-apache.yml.
Notes:
- Make sure the target servers are accessible and you have the necessary permissions to execute commands as sudo.
- You can modify the copy module task to deploy your specific index.html or any other web content.
AWS CloudFormation Template: Basic Network Setup
This CloudFormation template will create the following resources:
- A Virtual Private Cloud (VPC).
- A Subnet within the VPC.
- An Internet Gateway attached to the VPC.
- A Security Group within the VPC.
CloudFormation YAML Template:
AWSTemplateFormatVersion: '2010-09-09'
Description: Basic Network Setup
Resources:
MyVPC:
Type: AWS::EC2::VPC
Properties:
CidrBlock: 10.0.0.0/16
EnableDnsSupport: true
EnableDnsHostnames: true
Tags:
- Key: Name
Value: MyVPC
MySubnet:
Type: AWS::EC2::Subnet
Properties:
VpcId: !Ref MyVPC
CidrBlock: 10.0.1.0/24
MapPublicIpOnLaunch: true
Tags:
- Key: Name
Value: MySubnet
MyInternetGateway:
Type: AWS::EC2::InternetGateway
Properties:
Tags:
- Key: Name
Value: MyInternetGateway
AttachGateway:
Type: AWS::EC2::VPCGatewayAttachment
Properties:
VpcId: !Ref MyVPC
InternetGatewayId: !Ref MyInternetGateway
MySecurityGroup:
Type: AWS::EC2::SecurityGroup
Properties:
GroupDescription: Allow HTTP and SSH access
VpcId: !Ref MyVPC
SecurityGroupIngress:
- IpProtocol: tcp
FromPort: 22
ToPort: 22
CidrIp: 0.0.0.0/0
- IpProtocol: tcp
FromPort: 80
ToPort: 80
CidrIp: 0.0.0.0/0
How to Deploy:
- Save this content as basic-network-setup.yaml.
- Open the AWS Management Console.
- Go to the CloudFormation service.
- Choose "Create stack" and select "With new resources (standard)".
- Upload the basic-network-setup.yaml file and follow the prompts to create the stack.
Notes:
- This template is a basic example and can be customized as needed.
- The Security Group in this template allows SSH (port 22) and HTTP (port 80) access from any IP address. Adjust the CidrIp values as necessary for your security requirements.
This CloudFormation template will create the following resources:
- A Virtual Private Cloud (VPC).
- A Subnet within the VPC.
- An Internet Gateway attached to the VPC.
- A Security Group within the VPC.
CloudFormation YAML Template:
AWSTemplateFormatVersion: '2010-09-09'
Description: Basic Network Setup
Resources:
MyVPC:
Type: AWS::EC2::VPC
Properties:
CidrBlock: 10.0.0.0/16
EnableDnsSupport: true
EnableDnsHostnames: true
Tags:
- Key: Name
Value: MyVPC
MySubnet:
Type: AWS::EC2::Subnet
Properties:
VpcId: !Ref MyVPC
CidrBlock: 10.0.1.0/24
MapPublicIpOnLaunch: true
Tags:
- Key: Name
Value: MySubnet
MyInternetGateway:
Type: AWS::EC2::InternetGateway
Properties:
Tags:
- Key: Name
Value: MyInternetGateway
AttachGateway:
Type: AWS::EC2::VPCGatewayAttachment
Properties:
VpcId: !Ref MyVPC
InternetGatewayId: !Ref MyInternetGateway
MySecurityGroup:
Type: AWS::EC2::SecurityGroup
Properties:
GroupDescription: Allow HTTP and SSH access
VpcId: !Ref MyVPC
SecurityGroupIngress:
- IpProtocol: tcp
FromPort: 22
ToPort: 22
CidrIp: 0.0.0.0/0
- IpProtocol: tcp
FromPort: 80
ToPort: 80
CidrIp: 0.0.0.0/0
How to Deploy:
- Save this content as basic-network-setup.yaml.
- Open the AWS Management Console.
- Go to the CloudFormation service.
- Choose "Create stack" and select "With new resources (standard)".
- Upload the basic-network-setup.yaml file and follow the prompts to create the stack.
Notes:
- This template is a basic example and can be customized as needed.
- The Security Group in this template allows SSH (port 22) and HTTP (port 80) access from any IP address. Adjust the CidrIp values as necessary for your security requirements.
This Jenkinsfile defines a CI/CD pipeline for a Java application using Maven. It consists of three main stages:
- Build: Executes mvn clean package, which cleans the target directory, compiles the source code, and packages the binary (typically a JAR or WAR file).
- Test: Runs mvn test, which executes unit tests defined in the project.
- Deploy: A placeholder stage where deployment steps can be added. This stage is meant to be customized based on how and where the application is deployed.
This pipeline provides a basic structure for Java Maven projects, automating the process of building, testing, and preparing for deployment.
pipeline {
agent any
stages {
stage('Build') {
steps {
sh 'mvn clean package'
}
}
stage('Test') {
steps {
sh 'mvn test'
}
}
stage('Deploy') {
steps {
// Add deployment steps here
echo 'Deploying application...'
}
}
}
}
Instructions:
- Save this script as Jenkinsfile in the root of your Java Maven project.
- Ensure your Jenkins server has Maven set up and configured.
- Create a new Jenkins pipeline job and point it to your repository where this Jenkinsfile is located.
- Run the pipeline job to execute the stages.
Notes:
- Customize the 'Deploy' stage according to your deployment environment.
- Ensure Jenkins has appropriate plugins installed for Maven and any other tools you require.
The .gitlab-ci.yml file defines a CI/CD pipeline specifically for a Node.js application, with the following stages:
- Build: Installs dependencies for the Node.js application using npm install.
- Test: Executes tests defined in the project using npm run test. This assumes that the package.json file has a test script configured.
- Deploy: A placeholder for deployment scripts. This stage should be customized to deploy the application to the desired environment.
This configuration ensures that every push to the repository triggers an automated process for building, testing, and preparing the Node.js application for deployment.
stages:
- build
- test
- deploy
build_job:
stage: build
script:
- echo "Building the application..."
- npm install
test_job:
stage: test
script:
- echo "Running tests..."
- npm run test
deploy_job:
stage: deploy
script:
- echo "Deploying the application..."
# Add deployment scripts here
Instructions:
- Save this script as .gitlab-ci.yml in the root of your Node.js project.
- Ensure your project includes a package.json with relevant scripts defined.
- Once pushed to your GitLab repository, the CI/CD pipeline will trigger automatically based on this file.
Notes:
- Modify the deploy script to match your deployment environment.
- Add any necessary environment variables or additional stages as required.
The GitHub Actions workflow is tailored for Python applications. It defines a job that runs on each push to the repository, with the following steps:
- Set up Python: Configures the Python environment using a specified version (3.8 in this case).
- Install Dependencies: Installs the required Python dependencies listed in requirements.txt.
- Run Tests: Executes unit tests using Python's built-in test framework (unittest). It assumes tests are located in a directory named tests.
The deploy step is a placeholder for actual deployment commands and should be customized based on where and how the Python application is deployed.
name: Python CI/CD
on: [push]
jobs:
build:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v2
with:
python-version: '3.8'
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -r requirements.txt
- name: Run tests
run: |
python -m unittest discover -s tests
deploy:
runs-on: ubuntu-latest
needs: build
steps:
- uses: actions/checkout@v2
- name: Deploy to Production
run: echo "Add deployment steps here"
Instructions:
- Save this as .github/workflows/python-ci.yml in your Python project repository.
- Ensure your Python project has a requirements.txt file and tests set up.
- When you push changes to your repository, GitHub Actions will automatically execute this workflow.
Notes:
- Customize the Python version and deployment steps as needed.
- Add or modify steps according to your project's specific CI/CD requirements.
Prometheus Configuration File (prometheus.yml)
global:
scrape_interval: 15s
scrape_configs:
- job_name: 'prometheus'
static_configs:
- targets: ['localhost:9090']
- job_name: 'my-application'
static_configs:
- targets: ['my-app-service:80']
Instructions:
- Place prometheus.yml in your Prometheus server directory.
- Replace my-app-service:80 with the address and port of your application.
- Start Prometheus server with ./prometheus --config.file=prometheus.yml.
Notes:
- This configuration sets Prometheus to scrape metrics from itself and your application.
- Adjust scrape_interval as needed for your monitoring requirements.
Grafana Dashboard JSON Template
{ "__inputs": [
{
"name": "DS_PROMETHEUS",
"label": "Prometheus",
"description": "",
"type": "datasource",
"pluginId": "prometheus",
"pluginName": "Prometheus"
}
],
"__requires": [/.../],
"annotations": {/.../},
"editable": true, "gnetId": null,
"graphTooltip": 0,
"id": null,
"links": [],
"panels": [/.../],
"refresh": "10s",
"schemaVersion": 16,
"style": "dark",
"tags": ["prometheus"],
"templating": {/.../},
"time": {/.../},
"timepicker": {/.../},
"timezone": "",
"title": "My Dashboard",
"uid": null,
"version": 1
}
Instructions:
- Import this JSON template in Grafana to create a new dashboard.
- Adjust the dashboard panels and queries according to your metrics.
Notes:
- Ensure your Grafana instance is configured with Prometheus as a data source.
- This template is a basic skeleton; customize it with specific panels and metrics relevant to your application.
Elastic Stack (ELK): Configurations for Log Ingestion, Processing, and Visualization
ELK Stack PlayGround : https://kodekloud.com/playgrounds/playground-elk-stack
Filebeat Configuration (filebeat.yml)
filebeat.inputs:
- type: log
paths:
- /var/log/*.log
output.elasticsearch:
hosts: ["localhost:9200"]
Instructions:
- Place filebeat.yml in your Filebeat installation directory.
- Replace /var/log/*.log with the path to your application logs.
- Ensure Elasticsearch is running and reachable at localhost:9200.
Notes:
- Filebeat is used to ship logs to Elasticsearch.
- Adjust the paths to match the locations of your application logs.
Logstash Configuration (logstash.conf)
input {
beats {
port => 5044
}
}
filter {
// Add your filters here
}
output {
elasticsearch {
hosts => ["localhost:9200"]
index => "logstash-%{+YYYY.MM.dd}"
}
}
Instructions:
- Place logstash.conf in your Logstash installation directory.
- Start Logstash with ./logstash -f logstash.conf.
Notes:
- This configuration ingests logs from Filebeat and outputs them to Elasticsearch.
- Customize the filter section to process your logs as needed.
Kibana: Dashboard Template for Log Monitoring
Kibana Dashboard JSON Template
{
"objects": [
{
"attributes": {
"description": "",
"kibanaSavedObjectMeta": {
"searchSourceJSON": {
"filter": [],
"query": {
"language": "kuery",
"query": ""
}
}
},
"title": "ELK Log Monitoring",
"uiStateJSON": {},
"version": 1,
"visState": {
"aggs": [
/* Define aggregations and metrics here /
],
"params": {
/ Define visualization parameters */
},
"type": "histogram"
}
},
"id": "log-monitoring",
"type": "visualization",
"version": "7.10.0" // Use your Kibana version
}
],
"type": "dashboard",
"version": "7.10.0" // Use your Kibana version
}
Instructions:
- Create Dashboard Template File:
- Save the above JSON content in a file named elk-log-monitoring-dashboard.json.
- Import Dashboard into Kibana:
- Open Kibana in your web browser.
- Navigate to the "Management" section.
- Choose “Saved Objects” under Kibana.
- Click on the "Import" button and select the elk-log-monitoring-dashboard.json file.
- Customize Your Dashboard:
- Once imported, you can further customize the dashboard.
- Modify the visualizations and aggregations according to your log data and monitoring needs.
Notes:
- This template is a basic skeleton for creating a log monitoring dashboard in Kibana. You need to customize it according to your specific log data and monitoring requirements.
- Ensure your Filebeat and Logstash configurations are correctly set up to send data to Elasticsearch, which Kibana will use.
- The visualization types, aggregations, and filters should be tailored to highlight the most relevant data in your logs.
ELK Stack Configuration Guide for Web Server Logs
This comprehensive guide provides you with the templates and detailed steps necessary to deploy the ELK Stack for collecting, processing, storing, and visualizing web server logs. This solution leverages Elasticsearch, Logstash, Kibana, and Filebeat to create a powerful system for real-time log monitoring and analysis.
Elasticsearch Index Template Setup
Template Configuration (ElasticsearchIndexTemplate.json):
PUT _index_template/template_web_logs
{
"index_patterns": ["web-logs-*"],
"template": {
"settings": {
"number_of_shards": 1,
"number_of_replicas": 1
},
"mappings": {
"properties": {
"timestamp": { "type": "date" },
"log_level": { "type": "keyword" },
"message": { "type": "text" },
"ip": { "type": "ip" },
"response_time": { "type": "float" }
}
}
}
}
How to Deploy:
- Replace
template_web_logswith your template name (e.g.,template_myapp_logs). - Use the Elasticsearch API or Kibana Dev Tools, replacing
localhost:9200with your Elasticsearch server address: curl -X PUT "localhost:9200/_index_template/template_web_logs" -H 'Content-Type: application/json' [email protected]
Logstash Configuration
Configuration File (LogstashConfig.conf):
input {
beats {
port => 5044
}
}
filter {
grok {
match => { "message" => "%{COMBINEDAPACHELOG}" }
}
date {
match => [ "timestamp", "dd/MMM/yyyy:HH:mm:ss Z" ]
}
}
output {
elasticsearch {
hosts => ["http://localhost:9200"]
index => "web-logs-%{+YYYY.MM.dd}"
user => "elastic"
password => "changeme"
}
}
How to Deploy:
- Adjust
hosts,user, andpasswordto match your Elasticsearch details. - Place the
LogstashConfig.confin Logstash's configuration directory. - Start Logstash:
bin/logstash -f LogstashConfig.conf.
Kibana Visualization and Dashboards
Creating Visualizations:
- Access Kibana (typically at
http://localhost:5601). - Create an Index Pattern: Go to Management → Index Patterns → Create new. Use
web-logs-*as the pattern. - Navigate to "Visualize" and create new visualizations using the fields from your logs.
- Assemble visualizations into dashboards for a comprehensive view.
Filebeat Configuration
Configuration File (filebeat.yml):
filebeat.inputs:
- type: log
enabled: true
paths:
- /var/log/apache2/*.log # Adjust the path to your log files
fields:
log_type: apache_log
output.logstash:
hosts: ["localhost:5044"] # Your Logstash server address
How to Deploy:
- Install Filebeat on the server where your logs are generated.
- Modify
filebeat.ymlwith your log paths and output destination. - Start Filebeat:
sudo service filebeat start.
Final Steps and Verification
After deploying each component:
- Ensure Data Flow: Confirm that logs are moving from Filebeat to Logstash, then to Elasticsearch, and finally visualizable in Kibana.
- Monitor System Health: Use Kibana's monitoring features to check the health of Elasticsearch, Logstash, and Filebeat.
This guide provides a foundational setup. Tailor each configuration to fit your specific logging needs and infrastructure for an effective log monitoring and analysis solution.
Git Repository Essentials for a Python Project
This comprehensive guide outlines the essentials for setting up and maintaining a Git repository for a Python project. It includes a .gitignore file to exclude unnecessary files, a template for creating a README.md, guidelines for branch naming, and a pull request template to standardize contributions.
Basic .gitignore for a Python Project
A properly configured .gitignore file is crucial for keeping your repository clean by excluding temporary files, environment-specific configurations, and other non-essential files from being tracked by Git.
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*.so
# Environment files
.env
# Virtual environment
venv/
# IDE settings
.idea/
# Log files
*.log
Instructions: Save this content as .gitignore in the root of your Python project to automatically ignore common unnecessary files.
README Template
A well-documented README.md helps users and contributors understand, install, and use your project effectively.
# Project Title
## Description
Short description of the project.
## Installation
Steps to install the project.
## Usage
How to use the project.
## Contributing
Guidelines for contributing to the project.
## License
Specify the project license (e.g., MIT, GPL).
Instructions: Customize each section of this README.md template with your project details and save it in the root of your repository.
Simple Branch Naming Guidelines
Consistent branch naming helps organize and manage changes in your repository.
- Feature branches:
feature/<feature-name> - Bug fixes:
bugfix/<bug-name> - Hotfixes:
hotfix/<hotfix-name> - Releases:
release/v<version>
Instructions: Adopt these naming conventions for branches in your project to maintain clarity and order.
Pull Request Template
A pull request template ensures that all contributions are consistent and provide the necessary information for review.
## Description
A brief summary of the changes.
## Type of Change
- [ ] New feature
- [ ] Bug fix
- [ ] Documentation update
## How Has This Been Tested?
Describe how you've tested the changes.
## Checklist
- [ ] I have followed the contribution guidelines.
- [ ] My changes do not generate new warnings.
Instructions: Save this template as .github/PULL_REQUEST_TEMPLATE.md in your repository. It will automatically populate the description field for new pull requests.
Enhancements and Best Practices
- Continuous Integration (CI): Consider setting up CI workflows using tools like GitHub Actions to automate testing and linting for every push or pull request.
- Code Reviews: Encourage code reviews for pull requests to improve code quality and foster collaboration.
- Documentation: Keep your documentation, including the
README.md, up to date with project changes and releases.
Bash/PowerShell: Scripts for Common System Administration Tasks
Bash Script for Basic System Updates (update_system.sh)
#!/bin/bash
# Update and upgrade system packages
echo "Updating and upgrading system packages..."
sudo apt-get update && sudo apt-get upgrade -y
# Clean up
echo "Cleaning up..."
sudo apt-get autoremove -y
echo "System update complete."
Instructions:
- Save this script as update_system.sh.
- Make the script executable with chmod +x update_system.sh.
- Run with ./update_system.sh.
Notes:
- This script is for Debian-based systems. Modify package manager commands for other distributions.
- Ensures the system is updated and removes unnecessary packages.
Python Script for AWS S3 File Upload (s3_upload.py)
import boto3
from botocore.exceptions import NoCredentialsError
def upload_to_aws(local_file, bucket, s3_file):
s3 = boto3.client('s3')
try:
s3.upload_file(local_file, bucket, s3_file)
print(f"Upload Successful: {s3_file}")
return True
except FileNotFoundError:
print("The file was not found")
return False
except NoCredentialsError:
print("Credentials not available")
return False
uploaded = upload_to_aws('local_file.txt', 'mybucket', 's3_file.txt')
Instructions:
- Install boto3 with pip install boto3.
- Replace local_file.txt, mybucket, and s3_file.txt with your file, bucket name, and S3 file name.
- Ensure AWS credentials are configured.
Notes:
- This script uploads a file to an AWS S3 bucket.
- Handles basic exceptions like file not found or missing credentials.
Python Script for Downloading Files from AWS S3
s3_download.py:
import boto3
from botocore.exceptions import NoCredentialsError, ClientError
import logging
def download_file_from_s3(bucket, s3_object, local_file):
s3 = boto3.client('s3')
try:
s3.download_file(bucket, s3_object, local_file)
print(f"Downloaded {s3_object} from {bucket} to {local_file}")
except ClientError as e:
logging.error(e)
return False
except NoCredentialsError:
logging.error("Credentials not available")
return False
return True
# Example Usage
bucket_name = 'your-bucket-name'
s3_object_name = 'your-object-name'
local_file_path = 'path/to/save/file'
download_file_from_s3(bucket_name, s3_object_name, local_file_path)
Python Script to Automate EC2 Instance Creation
create_ec2_instance.py:
import boto3
def create_ec2_instance(image_id, instance_type, keypair_name):
ec2 = boto3.resource('ec2')
instance = ec2.create_instances(
ImageId=image_id,
MinCount=1,
MaxCount=1,
InstanceType=instance_type,
KeyName=keypair_name
)
print(f"EC2 Instance {instance[0].id} created")
# Example Usage
image_id = 'ami-12345'
# Replace with actual AMI ID
instance_type = 't2.micro'
keypair_name = 'your-keypair-name'
# Replace with your keypair name
create_ec2_instance(image_id, instance_type, keypair_name)
Python Script for Basic Data Processing
Data_processing.py:
import pandas as pd
def load_and_process_data(file_path): # Load data df = pd.read_csv(file_path)
# Data processing steps
# Example: df = df.dropna() # Removing missing values
return df
# Example Usage
file_path = 'path/to/your/data.csv'processed_data = load_and_process_data(file_path)print(processed_data.head())