I didn’t expect to find myself needing to learn PowerShell for automation purposes, but I must admit I really like it. It seems sort of like an amalgam of Bash, Perl and Python. It’s an unexpectedly impressive creation from Microsoft. I’ve been using PowerShell on macOS but it can also be used easily on Linux, and Windows of course.
I created three simple PowerShell scripts for automated provisioning of Linux EC2 instances within AWS. Running these will provision an Amazon Linux 2 EC2 instance with SSH key pair and Security Group, with a webapp deployed thereon, plus an associated DNS record in Route 53.
You can find these scripts and related config here on my GitHub.
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I created this suite of Ansible playbooks to provision a basic AWS (Amazon Web Services) infrastructure on EC2 with a Staging instance, and to deploy a webapp on the Staging instance which runs in a Docker container, pulled from Docker Hub.
Firstly a Docker image is built locally and pushed to a private Docker Hub repository, then the EC2 SSH key and Security Groups are created, then a Staging instance is provisioned. Next, the Docker image is pulled on the Staging instance, then a Docker container is started from the image, with nginx set up on the Staging instance to proxy web requests to the container. Finally, a DNS entry is added for the Staging instance in Route 53.
This is a simple Ansible framework to serve as a basis for building Docker images for your webapp and deploying them as containers on Amazon EC2. It can be expanded in multiple ways, the most obvious being to add an auto-scaled Production environment with Docker containers and a load balancer. (For Ansible playbooks suitable for provisioning an auto-scaled Production environment, check out my previous article and associated files “How to use Ansible for automated AWS provisioning”.) More complex apps could be split across multiple Docker containers for handling front-end and back-end components, so this could also be added as needed.
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I’ve recently produced a series of articles aimed at startups, entrepreneurial solo developers, etc. wanting to take their first steps into Amazon Web Services (AWS) setups for app deployment:
I then wanted to move on from discussing manual setup via the GUI interface of the AWS web console, to DevOps-style command-line programmatic setup for automated provisioning of an AWS infrastructure for app deployment, i.e. infrastructure as code (IaC). I have therefore created a suite of Ansible playbooks to provision an entire AWS infrastructure with a Staging instance and an auto-scaled load-balanced Production environment, and to deploy a webapp thereon. The resulting set of Ansible AWS provisioning playbooks and associated files can be found in my GitHub repository, so go ahead and grab it from there if you want to try them out. Keep reading for information on how to set up and use the playbooks (and you can also refer to the README in the repo folder, which contains much of the same information).
With these playbooks, firstly the EC2 SSH key and Security Groups are created, then a Staging instance is provisioned, then the webapp is deployed on Staging from GitHub, then an image is taken from which to provision the Production environment. The Production environment is set up with auto-scaled EC2 instances running behind a load balancer. Finally, DNS entries are added for the Production and Staging environments.
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For entrepreneurs, startups, and established companies trying out new projects, there hopefully comes a time when interest in the app increases such that incoming traffic levels start to rise significantly. This will likely necessitate various improvements to the infrastructure running the application so that it’s more robust, reliable and scalable.
The application may previously have been running on a cheap hosted server on a service like DigitalOcean, Linode or OVHcloud, or possibly even a single EC2 (Elastic Compute Cloud) instance on AWS (Amazon Web Services), and the desired solution would now be to move the application to a dynamically auto-scaled EC2 environment so that it can handle the increasing traffic without resource problems and site downtime.
In order to achieve this, it will also be necessary to set up the database on AWS, and the most realistic solution for this is to use Amazon’s RDS (Relational Database Service). I’ve recently covered this process in my article Migrating a MySQL database to AWS (with specific focus on RDS). You’ll also need to set up a load balancer, most likely an ELB (Elastic Load Balancer), to balance the incoming traffic across the auto-scaled EC2 application instances. I’ve recently covered this topic also, in my article Choosing and setting up a load balancer in AWS. So have a read through both of those articles to begin with, and below I’ll cover the rest of the process, i.e. auto-scaling the application instances on EC2.
Continue reading “How to migrate an application to AWS with auto-scaled EC2 instances”
If you’ve been running a web application on just one or two servers which contain your web server, application framework and database, there will likely come a time when you need to scale to cope with higher load from more incoming traffic. Whilst the web/application side of things can usually be scaled horizontally across multiple servers without too much trouble, a MySQL database is not so straightforward.
You may be using one or two EC2 instances, or your infrastructure may be hosted somewhere other than AWS. We’ll assume here that you’re migrating the whole app to AWS (if it’s not there already) and that you need to put the database somewhere within AWS as part of the scaling-up process. There are basically two options: install MySQL on one or more EC2 instances and administer it yourself; or use Amazon’s RDS (Relational Database Service) as a simpler way of hosting and managing your database.
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When you want to run your web application on more than one EC2 instance for scaling and redundancy purposes, you will probably require some form of load balancer to distribute incoming requests evenly across the instances. There are various possible solutions for this.
One option is to launch another EC2 instance and install a load balancer on it yourself. There are quite a few open source load balancing options, though I would tend to recommend HAProxy as it’s fast, efficient, secure, and very flexible. This option involves setting up your Linux instance and installing the software you need yourself, then configuring your chosen load balancer and installing your SSL certificates, etc. Additionally you would need to estimate the necessary instance size to run the load balancing software without getting overloaded and slowing the site down (bearing in mind that SSL termination can be particularly CPU-intensive), then monitoring it accordingly.
Unless there’s a particular reason to take the approach of installing a load balancer on an EC2 instance, a simpler and more effective option – especially for companies taking their first steps into scaling multiple instances for their application – is likely to be Amazon’s own ELB (Elastic Load Balancer). This doesn’t require an EC2 instance with Linux setup, software installation and configuration, etc. It provides a simple interface with easy SSL termination and it will scale itself automatically as needed, so there is little required in the way of planning and monitoring.
Continue reading “How to choose and set up a load balancer in AWS”
This article describes the process of creating a Slack notification from a CloudWatch alarm generated from an undesirable state in an SQS (Simple Queue Service) queue, via SNS (Simple Notification Service) and Lambda.
You can of course modify any of these to suit your differing requirements. For example, the source could be a different SQS state, or the source could be some other AWS service rather than SQS, or you may want to send the notification to somewhere other than Slack (in which case a different Lambda function may be required), etc.
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It was necessary to build an updated mail system for a client which would handle all incoming and outgoing email, and which could handle successfully sending out an average of one million emails per day. This was based on Postfix, since Postfix is known for reliability, robustness, security, and relative ease of administration. Building a Postfix mail system capable of handling so many emails is quite a significant aim at a time when establishing a positive reputation for independent mail servers delivering high volumes of email is quite a challenging goal.
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