🚀Day 75 - Sending Docker Log to Grafana😃

🔶 Task-01: Install Docker and start docker service on a Linux EC2 throughUSERDATA.

     #!/bin/bash

       sudo apt update
       sudo apt install -y apt-transport-https ca-certificates curl software-properties-common
       curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg
       echo "deb [arch=amd64 signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
       sudo apt update
       sudo apt install -y docker-ce docker-ce-cli containerd.io
       sudo systemctl start docker
       sudo systemctl enable docker
       sudo usermod -aG docker $USER
       sudo reboot

🔶 Task-02: Create 2 Docker containers and run any basic application on those containers (A simple todo app will work).

• 1. Connect to EC2 instance. Now create a docker-compose.yml file.

          version: "3"
     
          volumes:
            prometheus-data:
              driver: local
            grafana-data:
              driver: local
     
          services:
            # Grafana service
            grafana:
              image: grafana/grafana-oss:latest
              container_name: grafana
              ports:
                - "3000:3000"
              volumes:
                - grafana-data:/var/lib/grafana
              restart: unless-stopped
     
            # Prometheus service
            prometheus:
              image: prom/prometheus:latest
              container_name: prometheus
              ports:
                - "9090:9090"
              volumes:
                - /etc/prometheus:/etc/prometheus
                - prometheus-data:/prometheus
              command: "--config.file=/etc/prometheus/prometheus.yml"
              restart: unless-stopped
     
            # Code-Server service
            code-server:
              image: lscr.io/linuxserver/code-server:latest
              container_name: code-server
              environment:
                - PUID=1000
                - PGID=1000
                - TZ=Etc/UTC
              volumes:
                - /etc/opt/docker/code-server/config:/config
              ports:
                - 8443:8443
              restart: unless-stopped
     
            # Jenkins service
            jenkins:
              image: jenkins/jenkins:lts
              container_name: jenkins
              privileged: true
              user: root
              volumes:
                - /etc/opt/docker/jenkins/config:/var/jenkins_home
                - /var/run/docker.sock:/var/run/docker.sock
                - /usr/local/bin/docker:/usr/local/bin/docker
              ports:
                - 8081:8080
                - 50000:50000
              restart: unless-stopped
     
            # cAdvisor service
            cadvisor:
              image: gcr.io/cadvisor/cadvisor:v0.47.0
              container_name: cadvisor
              ports:
                - 8080:8080
              network_mode: host
              volumes:
                - /:/rootfs:ro
                - /var/run:/var/run:ro
                - /sys:/sys:ro
                - /var/lib/docker/:/var/lib/docker:ro
                - /dev/disk/:/dev/disk:ro
              devices:
                - /dev/kmsg
              privileged: true
              restart: unless-stopped
     

     1. Now run the docker container by using docker-compose up -d

        

     2. Configure Prometheus to scrap metrics.
        Modify prometheus.yml file for your instance IP address

        COPY

        COPY

          # my global config
          global:
            scrape_interval: 15s # Set the scrape interval to every 15 seconds. Default is every 1 minute.
            evaluation_interval: 15s # Evaluate rules every 15 seconds. The default is every 1 minute.
            # scrape_timeout is set to the global default (10s). 
          #Alertmanager configuration
          alerting:
            alertmanagers:
              - static_configs:
                  - targets:
                    # - alertmanager:9093
        
          # Load rules once and periodically evaluate them according to the global 'evaluation_interval'.
          rule_files:
            # - "first_rules.yml"
            # - "second_rules.yml"
        
          # A scrape configuration containing exactly one endpoint to scrape:
          # Here it's Prometheus itself.
          scrape_configs:
            # The job name is added as a label `job=<job_name>` to any timeseries scraped from this config.
            - job_name: "prometheus"
        
              # metrics_path defaults to '/metrics'
              # scheme defaults to 'http'.
        
              static_configs:
                - targets: ['3.89.110.158:9090'] #use public IP of Instance
        
            - job_name: "cadvisor"
              static_configs:
                - targets: ['3.89.110.158:8080'] #use public IP of Instance
        

        

     3. Restart Prometheus and access the Prometheus dashboard to check target nodes. To access Prometheus use a Public IP address with port 9090.

        

        ---

🔶 Task-03: Now integrate the docker containers and share the real-time logs with Grafana (Your Instance should be connected to Grafana and the Docker plugin should be enabled on Grafana).

1. Access the Grafana Dashboard by using the instance IP address with port 3000

2. Select Dashboard Click on the + sign at the top right and choose Import

3. Dashboard.

   

1. Go to the Grafana Dashboards Library and search for cAdvisor. Choose your preferred dashboard and copy the ID Number. In our case, we'll use the Cadvisor exporter dashboard.

   

2. Paste the ID number on the Grafana dashboard and select to load. then Select our Prometheus server and import it.

   

   ---

🔶 Task-04: Check the logs or docker container names on Grafana UI.

In conclusion,

Day 75's task has equipped us with a valuable skillset in container monitoring, leveraging the power of Grafana to make informed decisions, troubleshoot effectively, and ensure the optimal performance of our containerized applications. This knowledge is not only beneficial for our current projects but is also a foundational skill in the DevOps landscape, where observability and agility are paramount. Keep exploring and expanding your expertise in the dynamic world of DevOps!

Happy Learning :)