Summary

AWX is a web-based task engine built on top of ansible. This guide will walk you through installing AWX on a fresh CentOS7 machine. In this guide Docker is used without Docker-compose and the bare-minimum options were selected to get the application up and running. Please refer to the official guide for more information or options.

1. Docker
2. Elasticsearch
3. Logstash

Why Log to Elasticsearch?

Elasticsearch is a fantastic tool for logging as it allows for logs to be viewed as just another time-series piece of data. This is important for any organizations’ journey through the evolution of data.

This evolution can be outlined as the following:

1. Collection: Central collection of logs with required indexing
2. Shallow Analysis: The real-time detection of specific data for event based actions
3. Deep Analysis: The study of trends using ML/AI for pattern driven actions

Data that is not purposely collected for this journey will simply be bits wondering through the abyss of computing purgatory without a meaningful destiny! In this article we will be discussing using Docker to scale out your Logstash deployment.

The Challenges

If you have ever used Logstash (LS) to push logs to Elasticsearch (ES) here are a number of different challenges you may encounter:

• Synchronization: of versions when upgrading ES and LS
• Binding: to port 514 on a Linux host as it is a reserved port
• High availability: with 100% always-on even during upgrades
• Configurations management: across Logstash nodes

When looking at solutions, the approach I take is:

• Maintainability
• Reliability
• Scalability

The Solution

Using Docker, a generic infrastructure can be deployed due to the abstraction of containers and underlying OS (Besides the difference between Windows and Linux hosts).

Docker solves the challenges inherent in the LS deployment:

• Synchronization: Service parallelism is managed by Docker and new LS versions can be deployed just by editing the Docker Compose file
• Port bindings: The docker service is able to bind on Linux hosts port 514 which can be forwarded to the input port exposed by your pipeline
• High availability: Using replica settings, you can ensure that a certain amount of LS deployments will always be deployed as long as there is always one host available. This makes workload requirements after load testing purely a calculation as follows:

1. Max required logs per LS instance = (Maximum estimated Logs)/(N Node)
2. LS container size = LoadTestNode(Max required logs per LS instance)
3. Node Size = LS container size * 1 + (Max N Node Loss)

I.e. Let’s say you have 1M logs required to be logged per day, and have a requirement to have 3 virtual machines for a maximum of 1 virtual machine loss.

1.  333,333 = 1M / 3
2. Assume 2 CPU, 4 GB RAM
3. 4 CPU, 8 GB = (2 CPU , 4 GB RAM) * 2

Why not deploy straight onto OS 3 Logstashes sized at 4 CPU and 8 GB RAM?

• Worker number does not scale automatically when you increase the CPU count and RAM, as such even with more CPU and RAM you cannot ensure that you will effectively be able to continue ingesting the same amount of logs in a “Node down” situation
• Upgrading the Logstashes become a OS level activity with associated headache
• Restrictive auto-scaling capability
• No central stout view of Logstashes

Architecture

Let’s take a look at how this architecture looks,

When a node goes down the resulting environment looks like:

A added bonus to this deployment is if you wanted to ship logs from Logstash to Elasticsearch for central and real-time monitoring of the logs its as simple as adding Filebeats in the docker-compose.

What does the docker-compose look like?

version: '3.3'

1. Estimate the Virtual Machines (VM) sizes and LS sizes based on estimated ingestion of logs and required redundancy
2. Deploy VM and install docker 
3. Create a docker swarm
4. Write a logstash.yml and either include the pipeline.yml or if you are using ES configure centralised pipeline management.
5. Copy the config to all VM’s to the same file location OR create a shared file system (that is also HA) and store the files there to centrally manage config
6. Start your docker stack after customizing the docker-compose file shared in this article
7. Set up a external load balancer pointing at all your virtual machines
8. Enjoy yummy logs!

The BUT!

As with most good things, there is a caveat. With Docker you add another layer of complexity however I would argue that as the docker images for Logstash are managed and maintained by Elasticsearch, it reduces the implementation headaches.

In saying this I found one big issue with routing UDP traffic within Docker.

This issue will cause you to lose a proportional number of logs after container re-deployments!!!

• What is your current logging deployment?
• Have any questions or comments?
• Are interested seeing a end-to-end video of this deployment?
• Comment below!

Disclaimer: This article only represents my personal opinion and should not be considered professional advice. Healthy dose of skeptism is recommended.

Let’s quickly do a checklist of what we have so far

1. SSH Accessible Virtual Machine (Running Centos 7.4)
2. Ports 22, 443, 80 are open on the virtual machine
3. Domain pointed at the public IP of the Virtual machine
4. SSL Certificate generated on the virtual machine
5. Docker CE installed on the virtual machine

If you have not completed the steps above, review part 1 and part 2.

Deploying the Final Stack

SSH into the virtual machine and swap to the root user.

Move to the root directory of the machine (Running cd /)

Creating our directories

Create two directories (This is done for simplicity)

• certs – This will be used to store the SSL certificates to be used in our NGINX container

Mkdir /certs

• docker – This will be used to store our docker related files (docker-compose.yml

Mkdir /docker

Swap to the docker directory

cd /docker

Create a docker compose file with the following content (It is case and space sensitive, read more about docker compose).

Moving and renaming our SSL Certificates

Unfortunately, Nginx-Proxy must read the SSL certificates as <domain name>.crt and the key as <domain name>.key. as such we need to move and rename the original certificates generated for our domain.

Run the following commands to copy the certificates to the relevant folders and rename:

cp /etc/letsencrypt/live/<your domain>/fullchain.pem /certs/<your domain>.crt

cp /etc/letsencrypt/live/<your domain>/privkey.pem /certs/<your domain>.key

Creating a docker-compose.yml file

The docker compose file will dictate our stack.

Run  the following command to create the file at /docker/docker-compose.yml

vi /docker/docker-compose.yml

Populate the file with the following content

Line by line:

version: "3.3"
services:  
  nginx-proxy:
    image: jwilder/nginx-proxy #nginx proxy image
    ports:
      - "443:443"  #binding the host port 443 to container 443 port
    volumes:
      - /var/run/docker.sock:/tmp/docker.sock:ro      
      - /certs:/etc/nginx/certs #Mounting the SSL certificates to the image 
    networks:
     -  webnet  
  visualizer:
    image: dockersamples/visualizer:stable 
    volumes:
      - "/var/run/docker.sock:/var/run/docker.sock"   
    environment:
      - VIRTUAL_HOST=<Your DOMAIN ie. domain.com.au>
networks:      
  - webnet

environment:

– VIRTUAL_HOST=<your domain ie. Domain.com.au>

networks:

webnet:

Save the file by press esc than :wq

Starting the stack

Start docker

systemctl start docker

Pull the images

docker pull jwilder/nginx-proxy:latest

docker pull dockersamples/visualizer

Start the swarm

docker swarm init

Deploy the swarm

docker stack deploy -c /docker/docker-compose.yml test-stack

Congratulations! If you have done everything right you should now have a SSL protected visualizer when you browse https://<your domain>

Troubleshooting

To troubleshoot any problems check all services have a running container by running

docker service ls

Check the replicas count. If the nginx image is not running, check that the mounted .certs path does exist.

If the nginx container is running, you can run

docker service <service Id> logs --follow

then try access the https://<your domain> and see whether the connection is coming through.

• If it is than check the environment variable in your docker-compose
• If it is not than check that the port 443 is open and troubleshoot connectivity to the server

One of the greatest motivations for me is seeing the current open-source projects. It is amazing to be apart of a community that truly transcends race, age, gender, education that culminates in the development of society changing technologies, it is not difficult to be optimistic about the future.

With that, lets deploy a containerized application behind a Nginx Reverse Proxy with a free SSL encrypted. This entire deployment will only cost you a domain.

Technologies Used

The technologies used in this series are:

• Docker
• Nginx
• Azure
• Nginx-Proxy

Architecture

Getting Started

To start, I would advise signing up to a Azure trial . This will help you get started without any hassle.

If you have your own hosted VM or are doing a locally hosted docker stack please feel free to skip this part and move onto part 2.

Deploying the Virtual Machine

Select the image

1. In the side menu press Virtual Machines
2. Press “Add”
3. Select the “CentOS-based 7.4” image
4. Press “Ok”

Note: Technically you can use any image that can run docker. 

Configure the machine

1.  Name the VM – ie. DockerHost
2. Change disk type to HDD (To save credit)
3. Set the username
4. Change authentication type to password for simplicity
5. Set the password
6. Confirm the password
7. Create a new Resource Group (Such as SSLTest)

Select Machine Size

1. Select a machine size, I chose the D4S_V3 (4 vCPUs, 16GB) however any image with 2 or more vCPUs and more than 8GB of RAM is sufficient.

Virtual Machine Settings

You can leave default settings for the settings. (I switched off auto-shutdown).

Note: Make sure public IP address has been enabled

Wait for the Virtual Machine to finishing deploying…

Configuring DNS

Find the public IP address

After the machine has been successfully configured, browse to the virtual machine in Azure and get the public IP.

Create the TXT file

Log onto your domain provide (i.e. godaddy.com) and create a TXT file to point your domain address to the newly created VM.

Do a simple “nslookup ​​<domain>” till you can confirm that the domain has been updated.

Opening up ports 443, 80, 22

Browse to the virtual machine and browse to “Networking” in Azure. The following ports need to be allowed for inbound traffic

443 – This will be used to receive the SSL protected HTTPS requests

80 – This will be used temporarily to recieve your SSL certificate

22 – This should be open already however if it isn’t, allow 22 traffic for SSH connections.

SSH into the VM and allow root access (Dev only)

Using putty if you are on windows or just terminal on a Mac or Linux workstation, attempt to SSH into the machine.

After successfully logging in (Using the specified credentials when creating the VM), enable the root user for ease of use for the purpose of this tutorial (Do not do this for production environments).

This can be done by running

sudo passwd root

Specify the new root password

Confirm the root password

Congratulations you have completed part 1 of this tutorial, now that you have a virtual machine ready, let move on to part 2.