Terraform on Business Information Technology Services

Arden University

Sat, 01 Jan 2022 00:00:00 +0000

For Arden, there were two projects to deliver: an Amazon Elastic Kubernetes Service (EKS) based platform and development of their serverless integrations.

In order to help with the delivery of these projects, some open source contributions were made.

Elastic Kubernetes Service (EKS) Platform

A new platform was required to migrate existing on-premises and Amazon EC2 workloads to. The new platform had to allow developers to easily manage their own deployments, allow for centralised logging and monitoring, had to be secure and allow for management of things such as DNS records and TLS certificates to be fully automated.

The platform had to be delivered ‘as code’ so it could be easily managed, upgraded and replicated. This was done using mainly Terraform with some base Kubernetes services being deployed using the Terraform Helm provider.

Any base Kubernetes services that ArgoCD did not depend on explicitly were managed and deployed by ArgoCD along with the business applications hosted on the platform.

Managing Deployments with ArgoCD

The main interface into the platform for developers was through ArgoCD. This allowed developers to deploy applications, see the status of their workloads and get immediate logs and events to diagnose failures. Being able to deploy and diagnose simple issues themselves allowed developers to spend less time being blocked waiting for infrastructure engineers to help out.

ArgoCD was integrated with Microsoft Entra to allow single-sign on which improves security and convenience. Developers could be assigned to individual projects meaning they only have access to the applications that they are working with.

ArgoCD Image Updater was also used in development environments so that successful builds of an application could be deployed automatically.

Monitoring & Logging

For the monitoring and logging, open source tools were used as they provide excellent flexibility and of course value.

For monitoring, Prometheus was used with Grafana. Prometheus was also configured to make a future expansion to Thanos as easy as possible, should the need ever arise.

Loki was used for logging, it integrated perfectly with the monitoring metrics since it also uses Grafana as the main user interface.

Alerts were configured with Grafana unified alerts, allowing alerts to be raised from either metric or logging data.

Automated Management

Tools such as Certificate Manager and External DNS were used to allow automation of day to day administrative tasks.

Certificate Manager automatically provisions and renews TLS certificates for applications hosted within the platform whilst External DNS creates and cleans up DNS records.

Future Ready

Whilst not immediately implemented due to possible conflicts with the business the platform was developed and tested with IPv6 support and support for ARM based compute allowing for easy adoption later on.

Serverless Integrations Development

Arden was implementing a new student record system called SITS. This was a huge project that affected all areas of the business. A significant problem was integrating existing systems that do not understand SITS, such as: the virtual learning environment, Active Directory, time tabling products and many more.

To enable this communication between systems, a group of serverless ‘middlewares’ were used to keep things scalable and event driven.

Since the integrated systems spanned across AWS and Azure as well as including software as a service (SaaS) solutions, many technologies were used to integrate them. These technologies included: Typescript based Lambda functions deployed with the Amazon CDK, Azure Logic Apps, Azure Functions, Amazon SNS, Amazon SQS and Azure Service Bus.

These integrations handled large amounts of traffic especially as SITS went live at Arden. They also helped the business implement complex processes with systems that cannot be directly integrated.

Open Source Contributions

The following open source contributions were made whilst delivering these projects:

Scalable Ways to Manage Terraform Remote State

Sat, 31 Jul 2021 13:40:22 +0100

Introduction

There are many guides on the Internet for getting started with Terraform and even setting up remote state. There seems however to be very few (if any) that suggest ways of setting it up in a scalable way. Normally, in such guides the remote state is statically declared in the Terraform code. This forbids the code to be easily re-used for different environments without duplicating it all.

The purpose of this post is to put forward some ways that I have used myself or seen used over the past few years.

This post will focus on the AWS S3 remote backend, but the concepts will apply to others as well, such as the Azure storage account (azurerm) backend.

Starting Point

Both Terraform and the AWS CLI tool are installed.
An S3 bucket exists that can be used for Terraform remote state.
The current AWS CLI tool profile has read and write access to the Terraform remote state bucket.
There is some Terraform code that needs to be deployed to multiple environments. In this example the following code is used as a starting point. Everything is in a single file to make the example more simple and the remote backend is statically defined, as per most examples on the Internet:

s3.tf

###############################################################################

terraform {
  required_version = ">= 1.0.0"

  backend "s3" {
    key     = "object-store/terraform.state"
    bucket  = "example-terraform-state-bitservices"
    region  = "eu-west-1"
    encrypt = true
  }

  required_providers {
    aws = {
      source = "hashicorp/aws"
    }
  }
}

###############################################################################

provider "aws" {
  region = "eu-west-1"
}

###############################################################################

variable "account"     { default = "bitservices" }
variable "environment" { default = "default"     }

###############################################################################

variable "encryption_key"  { default = null     }
variable "encryption_type" { default = "AES256" }

###############################################################################

variable "acl"           { default = "private"      }
variable "service"       { default = "object-store" }
variable "force_destroy" { default = false          }

###############################################################################

locals {
  name           = format("%s-%s-%s", var.service, var.environment, var.account)
  encryption_key = var.encryption_type == "aws:kms" ? var.encryption_key : null
}

###############################################################################

resource "aws_s3_bucket" "scope" {
  acl           = var.acl
  bucket        = local.name
  force_destroy = var.force_destroy

  tags = {
    Name        = local.name
    Account     = var.account
    Service     = var.service
    Environment = var.environment
  }

  dynamic "server_side_encryption_configuration" {
    for_each = lower(var.encryption_type) == "none" ? [] : tolist([var.encryption_type])

    content {
      rule {
        apply_server_side_encryption_by_default {
          sse_algorithm     = server_side_encryption_configuration.value
          kms_master_key_id = local.encryption_key
        }
      }
    }
  }
}

###############################################################################

Please note: This example does not include state locking or the use of Terraform modules to try and keep the post as on-topic and as short as possible.

Option 1: Workspaces

One of the simplest ways to make some Terraform code re-usable across different environments is to use Terraform Workspaces.

The easiest way to use workspaces in this way is to ensure each resource identifier includes the workspace name. Settings, for example instance sizes or if encryption is to be enabled or not can be defined in maps with the workspace names as keys. This allows settings to be looked up based on the currently enabled workspace.

The example Terraform code above modified to work with multiple workspaces could look something like this:

###############################################################################

terraform {
  required_version = ">= 1.0.0"

  backend "s3" {
    key                  = "object-store/terraform.state"
    bucket               = "example-terraform-state-bitservices"
    region               = "eu-west-1"
    encrypt              = true
    workspace_key_prefix = "object-store-env"
  }

  required_providers {
    aws = {
      source = "hashicorp/aws"
    }
  }
}

###############################################################################

provider "aws" {
  region = "eu-west-1"
}

###############################################################################

variable "account" { default = "bitservices" }

###############################################################################

variable "encryption_key"  { default = null }
variable "encryption_type" { default = {
  "default" = "none",
  "prod"    = "AES256"
}}

###############################################################################

variable "acl"           { default = "private"      }
variable "service"       { default = "object-store" }
variable "force_destroy" { default = false          }

###############################################################################

locals {
  name            = format("%s-%s-%s", var.service, local.environment, var.account)
  environment     = terraform.workspace
  encryption_key  = var.encryption_type == "aws:kms" ? var.encryption_key : null
  encryption_type = lookup(var.encryption_type, local.environment, "none")
}

###############################################################################

resource "aws_s3_bucket" "scope" {
  acl           = var.acl
  bucket        = local.name
  force_destroy = var.force_destroy

  tags = {
    Name        = local.name
    Account     = var.account
    Service     = var.service
    Environment = local.environment
  }

  dynamic "server_side_encryption_configuration" {
    for_each = lower(local.encryption_type) == "none" ? [] : tolist([local.encryption_type])

    content {
      rule {
        apply_server_side_encryption_by_default {
          sse_algorithm     = server_side_encryption_configuration.value
          kms_master_key_id = local.encryption_key
        }
      }
    }
  }
}

###############################################################################

Please note: normally it is best to use encryption for all environments. This was just changed for the purpose of being an example.

Make sure Terraform has been initialised:

$ terraform init

Initializing the backend...

Successfully configured the backend "s3"! Terraform will automatically
use this backend unless the backend configuration changes.

Initializing provider plugins...
...

Then, to create a new workspace for production, run:

$ terraform workspace new prod
Created and switched to workspace "prod"!

You're now on a new, empty workspace. Workspaces isolate their state,
so if you run "terraform plan" Terraform will not see any existing state
for this configuration

Now when running a plan or apply, a new, unique S3 bucket will be managed.

Using workspaces still uses a statically defined remote backend key. Each non-default workspace state file will have an automatically appended prefix.

Option 2: Simple Wrapper Script

Another way of splitting out remote state can be to mirror the remote storage backend with the local file system. This is more useful if there are lots of different pieces of Terraform within one repository but there are not multiple environments for each individual piece.

For this to work with our example we would have to do the following:

Turn our workspace into a Git repository, if it is not already one:

$ git init

Move our s3.tf file into its own folder. This is based on the original s3.tf file and NOT the one modified to work with workspaces:

$ mkdir s3
$ mv ./s3.tf ./s3/s3.tf

Remove the following line from the backend configuration in our s3.tf file, since we will be generating it with scripts:

    key     = "object-store/terraform.state"

Create a common.sh file with the content below. This file should NOT be executable as it will only ever be sourced:

common.sh

###############################################################################

if [[ "${BASH_SOURCE[0]}" == "${0}" ]]; then
  echo "Please do not run this file directly!"
  exit 1
fi

###############################################################################

TF_BASE="$(git rev-parse --show-toplevel)"

###############################################################################

if [ -n "${TF_PREFIX}" ] && [ -d "${TF_PREFIX}" ]; then
    cd "${TF_PREFIX}"
    TF_PATH="$(pwd -P)"
else
  echo "Error: No Terraform folder specified or folder does not exist!"
  exit 1
fi

###############################################################################

if [[ "${TF_PATH}" != "${TF_BASE}"* ]]; then
  echo "Error: The folder given does not exist within the Git repository."
  exit 1
fi

###############################################################################

S3_STATE_FILENAME="terraform.tfstate"
S3_STATE_KEY="$(git rev-parse --show-prefix)${S3_STATE_FILENAME}"

###############################################################################

TF_VAR_base="${TF_BASE}"
TF_VAR_path="${TF_PATH}"

###############################################################################

terraform --version

###############################################################################

echo ""
echo ":: Base  : ${TF_BASE}"
echo ":: Path  : ${TF_PATH}"
echo ":: S3 Key: ${S3_STATE_KEY}"
echo ""

################################################################################

terraform init --input=false --backend=true --backend-config="key=${S3_STATE_KEY}"

################################################################################

Create a plan.sh file with the below content. This file SHOULD be executable as it will be directly called to do a Terraform plan:

plan.sh

#!/bin/bash -e
###############################################################################

set -o pipefail

###############################################################################

TF_PREFIX="${1}"

###############################################################################

source "./common.sh"

###############################################################################

terraform plan

###############################################################################

Based on plan.sh, create apply.sh, destroy.sh, etc.

From here the shell scripts are used to call Terraform and set the remote state key based on what local folder we are running Terraform against.

For example:

$ ./apply.sh s3

Will create our S3 bucket and put the remote state in the following S3 key: s3/terraform.tfstate.

If we created another folder called ec2 that had code to create an EC2 instance and called it with the same scripts:

$ ./apply.sh ec2

Will create the EC2 instance and put the remote state in the following S3 key: ec2/terraform.tfstate.

Whilst fairly simple this approach does have some drawbacks:

Management of a small shell script for each Terraform sub-command.
Difficult to manage environments for the same pieces of Terraform code without complex use of symlinks.

Option 3: Full Wrapper Library

Ultimately this is where I have ended up and many organisations that use Terraform extensively are also likely to end up. A Terraform wrapper can be created to not only organise the remote state storage, it could manage Terraform binary versions, manage authentication with the cloud provider, do a degree of configuration management, make calling from CI or locally the same and any other organisation specific things.

A wrapper can be created in any language, though it can be nice to have it integrate with a build system like GNU Make or Rake so calling Terraform and non-Terraform tasks feel the same.

The Terraform wrapper I use and maintain is located: https://rubygems.org/gems/terraform-wrapper. Sadly it is not yet documented at this stage.

This wrapper integrates with the Rake build system and provides Terraform related tasks to multiple folders containing Terraform infrastructure.

$ rake -T
[I] [TerraformWrapper] Terraform Wrapper for Ruby - version: 1.2.0
[I] [TerraformWrapper] Building tasks for service: account, component: bootstrap...
[I] [TerraformWrapper] Building tasks for service: account, component: account...
rake account:apply[config,plan]           # Applies infrastructure with Ter...
rake account:binary                       # Downloads and extracts the expe...
rake account:clean                        # Cleans a Terraform infrastructu...
rake account:destroy[config]              # Destroys infrastructure with Te...
rake account:import[config,address,id]    # Import a piece of existing infr...
rake account:init[config]                 # Initialises the Terraform infra...
rake account:plan[config,out]             # Creates a Terraform plan for a ...
rake account:plan-destroy[config,out]     # Creates a Terraform destroy pla...
rake account:upgrade                      # Upgrades the Terraform infrastr...
rake account:validate                     # Validates the Terraform code fo...
rake bootstrap:apply[config,plan]         # Applies infrastructure with Ter...
rake bootstrap:binary                     # Downloads and extracts the expe...
rake bootstrap:clean                      # Cleans a Terraform infrastructu...
rake bootstrap:destroy[config]            # Destroys infrastructure with Te...
rake bootstrap:import[config,address,id]  # Import a piece of existing infr...
rake bootstrap:init[config]               # Initialises the Terraform infra...
rake bootstrap:plan[config,out]           # Creates a Terraform plan for a ...
rake bootstrap:plan-destroy[config,out]   # Creates a Terraform destroy pla...
rake bootstrap:upgrade                    # Upgrades the Terraform infrastr...
rake bootstrap:validate                   # Validates the Terraform code fo...

Another example of a Terraform wrapper I have seen used and works well is located: https://rubygems.org/gems/rake_terraform.

Drivvn

Thu, 02 Jul 2020 00:00:00 +0000

A project to pick up support for and improve an existing Kubernetes based infrastructure platform hosted on Microsoft Azure.

Skip to Result

Stabilising Platform

Initially, the primary focus had been to automate continual time consuming tasks and to reduce the number of support tickets. This has been achieved by implementing technologies such as External DNS and Certificate Manager. Reliability has been improved by consolidating and improving complex build pipelines by utilising YAML libraries for Azure DevOps and introducing infrastructure as code with Terraform.

Evolving Platform

After stabilising the platform focus shifted to evolving the platform to be more performant, scalable and developer friendly. In addition to everything outlined below, lots of work went into upgrades of Kubernetes clusters and services, improvements as and when they were identified and migrations to new Azure subscriptions/tenants.

Developer Friendly

To help improve continuous delivery, ArgoCD was implemented. This allowed developers to publish Docker images in their build pipelines which would then automatically be picked up and deployed into a Kubernetes environment. ArgoCD also allowed the configuration for the application to be completely separated from other infrastructure as code meaning developers could have full control over the configuration of their deployed applications without requiring permissions to alter other parts of infrastructure.

Infrastructure as Code

There was a large focus on infrastructure as code. Terraform was used to automate the creation of all base infrastructure: such as resource groups, virtual networks, security groups and Kubernetes clusters - including all supporting infrastructure and Active Directory objects. This allowed much quicker and reliable provision of core infrastructure.

Any new services deployed were also fully automated so that required databases, Kubernetes permissions and the application themselves could also be deployed in a reliable and repeatable way.

Monitoring & Logging

As the platform grew it became important to be able to monitor workloads. Initially Prometheus was used with Grafana as well as some other custom exporters to get certain metrics that were otherwise unavailable.

However as things grew Prometheus was not scaling so eventually the monitoring stack was upgraded with Thanos. This allowed gathering of huge amounts of metrics from multiple Prometheus instances and storing the data in cost efficient blob storage. Thanos also allowed a ‘single pane of glass’ view of the entire estate of multiple clusters through Grafana.

Centralised logging was also set up utilising ElasticSearch at first and later New Relic.

Ingress

A lot of work was carried out around ingress to the platform. Initially cluster ingresses were standardised with Ingress Nginx. Security and CDN capability was then added using Cloudflare and Cloudflare Access. This allowed many apps to use single sign-on that originally did not support it. It also enabled zero trust access to internal services without having to set up and maintain a VPN.

Result

Due to multiple reasons - the good work that Drivvn do, the pandemic and just the way things are going - significantly more people are now buying cars online. Making the infrastructure more scalable and resilient has helped make it possible to meet these suddenly increasing demands.

Having the infrastructure as code ready allowed us to work with one of the Drivvn development teams to deliver a new product from inception to launch within a few weeks. The initial infrastructure was provisioned and available within hours. We then worked with the team to refine the build pipelines and infrastructure as they developed the product. Since launch, the infrastructure supporting this product has been extremely reliable. Having ArgoCD set up allowed developers to manage their own configuration and ‘self-serve’ deployments all the way to production - with easy roll backs if required.

Eclipse Group Solutions

Mon, 06 Nov 2017 00:00:00 +0000

A very exciting ‘greenfield’ project creating a new infrastructure platform using Amazon Web Services, Docker and Kubernetes for SAP Commerce based e-commerce websites.

It had been a great pleasure working with the team at Eclipse whilst delivering this project.

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Project Breifing

The project aim was to create an Amazon Web Services based, fully automated infrastructure platform to host SAP Commerce e-commerce websites. The platform must be constructed in a way that it can be used on other cloud service providers with little effort later on.

Developers and testers needed the ability to get code and features through testing pipelines much quicker. In addition, production websites need to handle peak demand seamlessly.

As well as delivering a platform, existing teams were to be assisted with picking up new tools, technologies and concepts to enable on-going support of the platform.

Solution

The final solution comprised of many components. These components are outlined below.

Infrastructure as Code

Writing infrastructure as code was key to this solution. This is what enabled infrastructure to be provisioned in a reliable and repeatable way at the click of a button. By taking advantage of Terraform module sources it was possible to define a collection of infrastructure objects (such as subnets, route tables, gateways) in a single place but allow variables (such as name, CIDR ranges) to be passed in depending on the environment being built. As a result all infrastructure met defined standards, human error is vastly reduced and development/production parity was achieved.

By taking advantage of Amazon Web Services availability zones and infrastructure as code, all production environments were highly available and could withstand the loss of an Amazon data centre without any downtime.

Immutable Software

To allow software (SAP Commerce in particular) to run in a dynamically scaled environment there were a few challenges to overcome. There needed to be a way to start SAP Commerce very quickly in ‘scale-up’ situations additionally the state of any running SAP Commerce instances had to be externalised in case of scale-down.

Docker was chosen to containerise software. Containerisation enabled the application and all dependencies, configurations, etc to be packed into an image that can be started very quickly. By using SAP Commerce ‘aspects’ a single Docker image can be capable of running in multiple environments in multiple modes. A single image can be promoted all the way through the testing pipeline just by using tags.

To make running containers stateless, services such as Amazon S3 and EFS were used.

Service Orchestration

To enable service-level auto scaling, auto healing, multi-tenant clustering and service health checks, Kubernetes was implemented. This allowed the platform to meet the scaling requirements. The healing features of Kubernetes allowed the platform to be more resilient to virtual machine failure or network outages resulting in a higher service availability.

Technologies & Tools

Multiple technologies and tools were used to deliver this project.

Amazon Web Services

The initial cloud provider chosen was Amazon. Amazon Web Services (AWS) is a mature cloud service with endless offerings. It is effortless to build highly-available and highly-performant infrastructure stacks. Most tools (such as Terraform and Kops below) offer excellent support for AWS. By using the many AWS services available such as IAM and availability zones it was possible to create secure and resilient infrastructure.

Hashicorp Terraform

Terraform was chosen to build foundation and networking infrastructure. Terraform has excellent Amazon Web Services support and code can easily be ported to work with other cloud providers too - including OpenStack for managing resources on-premises. Terraform made it very easy to meet the project requirements to stay cloud agnostic and to fully automate infrastructure.

Docker

Containerisation was chosen to help simplify development. Rather than pushing a codebase that may have a complete different set of steps to deploy depending on target environment, containerisation makes that a single image that may take the target environment as a parameter. Additionally, all dependencies and libraries required to run the application are packed into the container, meaning that the same container can be ran locally, on tin or in the cloud with minimal effort. Docker was chosen specifically due to it being well proven and mature.

Kubernetes

Kubernetes is fast becoming an industry standard for container orchestration. It can also run on any cloud provider or even on-premises on tin. Native support for service-level auto-scaling and ‘cluster autoscaler’ add-on for the scaling of underlying virtual machines allowed scaling requirements to be met. The many different types of services (deployments, statefulsets, daemonsets), specifications (disruption budgets, affinities) and probes (liveness, readiness) made it possible to build a platform that is resilient against hypervisor, network or even data centre failures.

Other Tools

Other tools used to deliver this project include: Kops, Hasicorp Packer and Ansible.

Result

The result of this project allows Eclipse to offer their customers an improved hosting service. Development environments can now be provisioned rapidly and easily decommissioned when not in use to match project demands. Automatic scaling allows customers websites to seamlessly handle high load during sales and events whilst running economically during quiet periods. The platform allows for zero-downtime code deployments and platform updates. High availability is achieved by always running across multiple availability zones (data centres) in addition to having automatic health checks and repairs.

FoodFriends

Fri, 01 Sep 2017 00:00:00 +0000

A small project carried out for a family member.

The Problem

FoodFriends have a small Wordpress website that was hosted with a traditional hosting provider. The provider, unfortunately had crippling limits such as only 50MiB of disk for website media. FoodFriends required that website stayed on the Wordpress platform.

The Solution

The website was moved to Amazon Web Services.

To remove future support costs and time, the supporting infrastructure was automated with Hashicorp Terraform. The installation and configuration of software fully automated with Ansible - even down to the renewal of the TLS certificate!

By using Amazon S3 backups are automatically taken and rotated daily. Restoration of data is managed by the Ansible playbooks meaning the entire infrastructure can be built from nothing with all data restored in just minutes.

The Result

There is now gigabytes of available diskspace for media. As Amazon Web Services is an enterprise grade platform capable of hosting even the most busiest of e-commerce websites the performance of the FoodFriends website has increased dramatically.

Having TLS configured correctly and being made mandatory the FoodFriends website has had its ranking increased on Google.

The current cost of hosting the infrastructure is approximately $1 a month for a couple of Route53 zones. Once the free-tier expires, the overall cost will still be only a third of what it was with the previous host.