This tutorial will show you how you can run 64bit Ubuntu Linux Virtual Machines on a Apple Mac M1 arm64 architecture macbook using UTM.
Installation
Head over to their documentation and download the UTM.dmg file and install it, once it is installed and you have opened UTM, you should see this screen:
Creating a Virtual Machine
In my case I would like to run a Ubuntu VM, so head over to the Ubuntu Server Download page and download the version of choice, I will be downloading Ubuntu Server 22.04, once you have your ISO image downloaded, you can head over to the next step which is to “Create a New Virtual Machine”:
I will select “Emulate” as I want to run a amd64 bit architecture, then select “Linux”:
In the next step we want to select the Ubuntu ISO image that we downloaded, which we want to use to boot our VM from:
Browse and select the image that you downloaded, once you selected it, it should show something like this:
Select continue, then select the architecture to x86_64, the system I kept on defaults and the memory I have set to 2048MB and cores to 2 but that is just my preference:
The next screen is to configure storage, as this is for testing I am setting mine to 8GB:
The next screen is shared directories, this is purely optional, I have created a directory for this:
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mkdir ~/utm
Which I’ve then defined for a shared directory, but this depends if you need to have shared directories from your local workstation.
The next screen is a summary of your choices and you can name your vm here:
Once you are happy select save, and you should see something like this:
You can then select the play button to start your VM.
The console should appear and you can select install or try this vm:
This will start the installation process of a Linux Server:
Here you can select the options that you would like, I would just recommend to ensure that you select Install OpenSSH Server so that you can connect to your VM via SSH.
Once you get to this screen:
The installation process is busy and you will have to wait a couple of minutes for it to complete. Once you see the following screen the installation is complete:
On the right hand side select the circle, then select CD/DVD and select the ubuntu iso and select eject:
Starting your VM
Then power off the guest and power on again, then you should get a console login, then you can proceed to login, and view the ip address:
SSH to your VM
Now from your terminal you should be able to ssh to the VM:
We can also verify that we are running a 64bit vm, by running uname --processor:
Thank You
Thanks for reading, feel free to check out my website, feel free to subscribe to my newsletter or follow me at @ruanbekker on Twitter.
In this post we will run a Kakfa cluster with 3 kafka brokers on docker compose and using a producer to send messages to our topics and a consumer that will receive the messages from the topics, which we will develop in python and explore the kafka-ui.
What is Kafka?
Kafka is a distributed event store and stream processing platform. Kafka is used to build real-time streaming data pipelines and real-time streaming applications.
But on a high level, the components of a typical Kafka setup:
Zookeeper: Kafka relies on Zookeeper to do leadership election of Kafka Brokers and Topic Partitions.
Broker: Kafka server that receives messages from producers, assigns them to offsets and commit the messages to disk storage. A offset is used for data consistency in a event of failure, so that consumers know from where to consume from their last message.
Topic: A topic can be thought of categories to organize messages. Producers writes messages to topics, consumers reads from those topics.
Partitions: A topic is split into multiple partitions. This improves scalability through parallelism (not just one broker). Kafka also does replication
For great in detail information about kafka and its components, I encourage you to visit the mentioned post from above.
Launch Kafka
This is the docker-compose.yaml that we will be using to run a kafka cluster with 3 broker containers, 1 zookeeper container, 1 producer, 1 consumer and a kafka-ui.
You can verify that the brokers are passing their health checks with:
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docker-compose ps
NAME IMAGE COMMAND SERVICE CREATED STATUS PORTS
broker-1 confluentinc/cp-kafka:7.4.0 "/etc/confluent/dock…" broker-1 5 minutes ago Up 4 minutes (healthy) 0.0.0.0:9091->9091/tcp, :::9091->9091/tcp, 9092/tcp
broker-2 confluentinc/cp-kafka:7.4.0 "/etc/confluent/dock…" broker-2 5 minutes ago Up 4 minutes (healthy) 0.0.0.0:9092->9092/tcp, :::9092->9092/tcp
broker-3 confluentinc/cp-kafka:7.4.0 "/etc/confluent/dock…" broker-3 5 minutes ago Up 4 minutes (healthy) 9092/tcp, 0.0.0.0:9093->9093/tcp, :::9093->9093/tcp
consumer ruanbekker/kafka-producer-consumer:2023-05-17 "sh /src/run.sh $ACT…" consumer 5 minutes ago Up 4 minutes
kafka-ui provectuslabs/kafka-ui:latest "/bin/sh -c 'java --…" kafka-ui 5 minutes ago Up 4 minutes 0.0.0.0:8080->8080/tcp, :::8080->8080/tcp
producer ruanbekker/kafka-producer-consumer:2023-05-17 "sh /src/run.sh $ACT…" producer 5 minutes ago Up 4 minutes
zookeeper confluentinc/cp-zookeeper:7.4.0 "/etc/confluent/dock…" zookeeper 5 minutes ago Up 5 minutes (healthy) 0.0.0.0:2888->2888/tcp, :::2888->2888/tcp, 0.0.0.0:3888->3888/tcp, :::3888->3888/tcp, 2181/tcp, 0.0.0.0:32181->32181/tcp, :::32181->32181/tcp
Producers and Consumers
The producer generates random data and sends it to a topic, where the consumer will listen on the same topic and read messages from that topic.
To view the output of what the producer is doing, you can tail the logs:
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docker logs -f producer
setting up producer, checking if brokers are available
brokers not available yet
brokers are available and ready to produce messages
message sent to kafka with squence id of 1
message sent to kafka with squence id of 2
message sent to kafka with squence id of 3
And to view the output of what the consumer is doing, you can tail the logs:
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docker logs -f consumer
starting consumer, checks if brokers are availabe
brokers not availbe yet
brokers are available and ready to consume messages
{'sequence_id': 10, 'user_id': '20520', 'transaction_id': '4026fd10-2aca-4d2e-8bd2-8ef0201af2dd', 'product_id': '17974', 'address': '71741 Lopez Throughway | South John | BT', 'signup_at': '2023-05-11 06:54:52', 'platform_id': 'Tablet', 'message': 'transaction made by userid 119740995334901'}{'sequence_id': 11, 'user_id': '78172', 'transaction_id': '4089cee1-0a58-4d9b-9489-97b6bc4b768f', 'product_id': '21477', 'address': '735 Jasmine Village Apt. 009 | South Deniseland | BN', 'signup_at': '2023-05-17 09:54:10', 'platform_id': 'Tablet', 'message': 'transaction made by userid 159204336307945'}
In this post we will use terraform to deploy a helm release to kubernetes.
Kubernetes
For this demonstration I will be using kind to deploy a local Kubernetes cluster to the operating system that I am running this on, which will be Ubuntu Linux. For a more in-depth tutorial on Kind, you can see my post on Kind for Local Kubernetes Clusters.
Installing the Pre-Requirements
We will be installing terraform, docker, kind and kubectl on Linux.
Now we can test if kubectl can communicate with the kubernetes api server:
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kubectl get nodes
In my case it returns:
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NAME STATUS ROLES AGE VERSION
rbkr-control-plane Ready control-plane 6m20s v1.24.0
Terraform
Now that our pre-requirements are sorted we can configure terraform to communicate with kubernetes. For that to happen, we need to consult the terraform kubernetes provider’s documentation.
As per their documentation they provide us with this snippet:
And from their main page, it gives us a couple of options to configure the provider and the easiest is probably to read the ~/.kube/config configuration file.
But in cases where you have multiple configurations in your kube config file, this might not be ideal, and I like to be precise, so I will extract the client certificate, client key and cluster ca certificate and endpoint from our ~/.kube/config file.
If we run cat ~/.kube/config we will see something like this:
First we will create a directory for our certificates:
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mkdir ~/certs
I have truncated my kube config for readability, but for our first file certs/client-cert.pem we will copy the value of client-certificate-data:, which will look something like this:
Then we will copy the contents of client-key-data: into certs/client-key.pem and then lastly the content of certificate-authority-data: into certs/cluster-ca-cert.pem.
So then we should have the following files inside our certs/ directory:
Your host might look different to mine, but you can find your host endpoint in ~/.kube/config.
For a simple test we can list all our namespaces to ensure that our configuration is working. In a file called namespaces.tf, we can populate the following:
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data "kubernetes_all_namespaces""allns"{}output "all-ns"{value= data.kubernetes_all_namespaces.allns.namespaces
}
Now we need to initialize terraform so that it can download the providers:
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terraform init
Then we can run a plan which will reveal our namespaces:
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terraform plan
data.kubernetes_all_namespaces.allns: Reading...
data.kubernetes_all_namespaces.allns: Read complete after 0s [id=a0ff7e83ffd7b2d9953abcac9f14370e842bdc8f126db1b65a18fd09faa3347b]Changes to Outputs:
+ all-ns =[ + "default",
+ "kube-node-lease",
+ "kube-public",
+ "kube-system",
+ "local-path-storage",
]
We can now remove our namespaces.tf as our test worked:
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rm namespaces.tf
Helm Releases with Terraform
We will need two things, we need to consult the terraform helm release provider documentation and we also need to consult the helm chart documentation which we are interested in.
As we are working with helm releases, we need to configure the helm provider, I will just extend my configuration from my previous provider config in providers.tf:
In our main.tf I will use two ways to override values in our values.yaml using set and templatefile. The reason for the templatefile, is when we want to fetch a value and want to replace the content with our values file, it could be used when we retrieve a value from a data source as an example. In my example im just using a variable.
variable "release_name" {type = stringdefault = "nginx"description = "The name of our release."}variable "chart_repository_url" {type = stringdefault = "https://charts.bitnami.com/bitnami"description = "The chart repository url."}variable "chart_name" {type = stringdefault = "nginx"description = "The name of of our chart that we want to install from the repository."}variable "chart_version" {type = stringdefault = "13.2.20"description = "The version of our chart."}variable "namespace" {type = stringdefault = "apps"description = "The namespace where our release should be deployed into."}variable "create_namespace" {type = booldefault = truedescription = "If it should create the namespace if it doesnt exist."}variable "atomic" {type = booldefault = falsedescription = "If it should wait until release is deployed."}
Terraform used the selected providers to generate the following execution plan. Resource actions are indicated with the following symbols:
+ create
Terraform will perform the following actions:
# helm_release.nginx will be created + resource "helm_release""nginx"{ + atomic=false + chart="nginx" + cleanup_on_fail=false + create_namespace=true + dependency_update=false + disable_crd_hooks=false + disable_openapi_validation=false + disable_webhooks=false + force_update=false + id=(known after apply) + lint=false + manifest=(known after apply) + max_history= 0
+ metadata=(known after apply) + name="nginx" + namespace="apps" + pass_credentials=false + recreate_pods=false + render_subchart_notes=true + replace=false + repository="https://charts.bitnami.com/bitnami" + reset_values=false + reuse_values=false + skip_crds=false + status="deployed" + timeout= 300
+ values=[ + <<-EOT nameOverride: "nginx" ## ref: https://hub.docker.com/r/bitnami/nginx/tags/ image: registry: docker.io repository: bitnami/nginx tag: 1.23.3-debian-11-r3 EOT,
] + verify=false + version="13.2.20" + wait=false + wait_for_jobs=false + set{ + name="image.tag" + value="1.23.3-debian-11-r3"}}Plan: 1 to add, 0 to change, 0 to destroy.
Changes to Outputs:
+ metadata=(known after apply)
Once we are happy with our plan, we can run a apply:
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terraform apply
Plan: 1 to add, 0 to change, 0 to destroy.
Changes to Outputs:
+ metadata=(known after apply)Do you want to perform these actions?
Terraform will perform the actions described above.
Only 'yes' will be accepted to approve.
Enter a value: yes
helm_release.nginx: Creating...
helm_release.nginx: Still creating... [10s elapsed]metadata= tolist([{"app_version"="1.23.3""chart"="nginx""name"="nginx""namespace"="apps""revision"= 1
"values"="{\"image\":{\"registry\":\"docker.io\",\"repository\":\"bitnami/nginx\",\"tag\":\"1.23.3-debian-11-r3\"},\"nameOverride\":\"nginx\"}""version"="13.2.20"},
])
Then we can verify if the pod is running:
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kubectl get pods -n apps
NAME READY STATUS RESTARTS AGE
nginx-59bdc6465-xdbfh 1/1 Running 0 2m35s
Importing Helm Releases into Terraform State
If you have an existing helm release that was deployed with helm and you want to transfer the ownership to terraform, you first need to write the terraform code, then import the resources into terraform state using:
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terraform import helm_release.nginx apps/nginx
Where the last argument is <namespace>/<release-name>. Once that is imported you can run terraform plan and apply.
If you want to discover all helm releases managed by helm you can use:
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kubectl get all -A -l app.kubernetes.io/managed-by=Helm
Thank You
Thanks for reading, feel free to check out my website, feel free to subscribe to my newsletter or follow me at @ruanbekker on Twitter.
We will create a terraform pipeline which will run the plan step automatically and a manual step to run the apply step.
During these steps and different pipelines we need to persist our terraform state remotely so that new pipelines can read from our state what we last stored.
Gitlab offers a remote backend for our terraform state which we can use, and we will use a basic example of using the random resource.
Prerequisites
If you don’t see the “Infrastructure” menu on your left, you need to enable it at “Settings”, “General”, “Visibility”, “Project features”, “Permissions” and under “Operations”, turn on the toggle.
For more information on this see their documentation
Authentication
For this demonstration I created a token which is only scoped for this one project, for this we need a to create a token under, “Settings”, “Access Tokens”:
Select the api under scope:
Store the token name and token value as TF_USERNAME and TF_PASSWORD as a CICD variable under “Settings”, “CI/CD”, “Variables”.
Terraform Code
We will use a basic random_uuid resource for this demonstration, our main.tf:
Where the magic happens is in the terraform init step, that is where we will initialize the terraform state in gitlab, and as you can see we are taking the TF_ADDRESS variable to define the path of our state and in this case our state file will be named default-terraform.tfstate.
If it was a case where you are deploying multiple environments, you can use something like ${ENVIRONMENT}-terraform.tfstate.
When we run our pipeline, we can look at our plan step:
Once we are happy with this we can run the manual step and do the apply step, then our pipeline should look like this:
When we inspect our terraform state in the infrastructure menu, we can see the state file was created:
Thank You
Thanks for reading, feel free to check out my website, feel free to subscribe to my newsletter or follow me at @ruanbekker on Twitter.
Helm, its one amazing piece of software that I use multiple times per day!
What is Helm?
You can think of helm as a package manager for kubernetes, but in fact its much more than that.
Think about it in the following way:
Kubernetes Package Manager
Way to templatize your applications (this is the part im super excited about)
Easy way to install applications to your kubernetes cluster
Easy way to do upgrades to your applications
Websites such as artifacthub.io provides a nice interface to lookup any application an how to install or upgrade that application.
How does Helm work?
Helm uses your kubernetes config to connect to your kubernetes cluster. In most cases it utilises the config defined by the KUBECONFIG environment variable, which in most cases points to ~/kube/config.
If you want to follow along, you can view the following blog post to provision a kubernetes cluster locally:
Once you have provisioned your kubernetes cluster locally, you can proceed to install helm, I will make the assumption that you are using Mac:
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brew install helm
Once helm has been installed, you can test the installation by listing any helm releases, by running:
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helm list
Helm Charts
Helm uses a packaging format called charts, which is a collection of files that describes a related set of kubernetes resources. A sinlge helm chart m
ight be used to deploy something simple such as a deployment or something complex that deploys a deployment, ingress, horizontal pod autoscaler, etc.
Using Helm to deploy applications
So let’s assume that we have our kubernetes cluster deployed, and now we are ready to deploy some applications to kubernetes, but we are unsure on how we would do that.
Let’s assume we want to install Nginx.
First we would navigate to artifacthub.io, which is a repository that holds a bunch of helm charts and the information on how to deploy helm charts to our cluster.
Then we would search for Nginx, which would ultimately let us land on:
But before we do that, if we think about it, we add a repository, then before we install a release, we could first find information such as the release versions, etc.
So the way I would do it, is to first add the repository:
Then since we have added the repository, we can update our repository to ensure that we have the latest release versions:
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$ helm repo update
Now that we have updated our local repositories, we want to find the release versions, and we can do that by listing the repository in question. For example, if we don’t know the application name, we can search by the repository name:
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$ helm search repo bitnami/ --versions
In this case we will get an output of all the applications that is currently being hosted by Bitnami.
If we know the repository and the release name, we can extend our search by using:
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$ helm search repo bitnami/nginx --versions
In this case we get an output of all the Nginx release versions that is currently hosted by Bitnami.
Installing a Helm Release
Now that we have received a response from helm search repo, we can see that we have different release versions, as example:
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NAME CHART VERSION APP VERSION DESCRIPTION
bitnami/nginx 13.2.22 1.23.3 NGINX Open Source is a web server that can be a...
bitnami/nginx 13.2.21 1.23.3 NGINX Open Source is a web server that can be a...
For each helm chart, the chart has default values which means, when we install the helm release it will use the default values which is defined by the helm chart.
We have the concept of overriding the default values with a yaml configuration file we usually refer to values.yaml, that we can define the values that we want to override our default values with.
To get the current default values, we can use helm show values, which will look like the following:
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$ helm show values bitnami/nginx --version 13.2.22
That will output to standard out, but we can redirect the output to a file using the following:
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$ helm show values bitnami/nginx --version 13.2.22 > nginx-values.yaml
Now that we have redirected the output to nginx-values.yaml, we can inspect the default values using cat nginx-values.yaml, and any values that we see that we want to override, we can edit the yaml file and once we are done we can save it.
Now that we have our override values, we can install a release to our kubernetes cluster.
Let’s assume we want to install nginx to our cluster under the name my-nginx and we want to deploy it to the namespace called web-servers:
upgrade --install - meaning we are installing a release, if already exists, do an upgrade
my-nginx - use the release name my-nginx
bitnami/nginx - use the repository and chart named nginx
--values nginx-values.yaml - define the values file with the overrides
--namespace web-servers --create-namespace - define the namespace where the release will be installed to, and create the namespace if not exists
--version 13.2.22 - specify the version of the chart to be installed
Information about the release
We can view information about our release by running:
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$ helm list -n web-servers
Creating your own helm charts
It’s very common to create your own helm charts when you follow a common pattern in a microservice architecture or something else, where you only want to override specific values such as the container image, etc.
In this case we can create our own helm chart using:
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$ mkdir ~/charts
$ cd ~/charts
$ helm create my-chart
This will create a scaffoliding project with the required information that we need to create our own helm chart. If we look at a tree view, it will look like the following:
In our example it will create a service account, service, deployment, etc.
As you can see the spec.template.spec.containers[].image is set to nginx:1.16.0, and to see how that was computed, we can have a look at templates/deployment.yaml:
As you can see in image: section we have .Values.image.repository and .Values.image.tag, and those values are being retrieved from the values.yaml file, and when we look at the values.yaml file:
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image:repository:nginxpullPolicy:IfNotPresent# Overrides the image tag whose default is the chart appVersion.tag:""
If we want to override the image repository and image tag, we can update the values.yaml file to lets say:
$ helm plugin list
NAME VERSION DESCRIPTION
cm-push 0.10.3 Push chart package to ChartMuseum
Now we add our chartmuseum helm chart repository, which we will call cm-local:
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$ helm repo add cm-local http://localhost:8080/
We can list our helm repository:
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$ helm repo list
NAME URL
cm-local http://localhost:8080/
Now that our helm repository has been added, we can push our helm chart to our helm chart repository. Ensure that we are in our chart repository directory, where the Chart.yaml file should be in our current directory. We need this file as it holds metadata about our chart.
We can view the Chart.yaml:
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apiVersion:v2name:my-chartdescription:A Helm chart for Kubernetestype:applicationversion:0.1.0appVersion:"1.16.0"
Now we should update our repositories so that we can get the latest changes:
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$ helm repo update
Now we can list the charts under our repository:
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$ helm search repo cm-local/
NAME CHART VERSION APP VERSION DESCRIPTION
cm-local/my-chart 0.0.1 1.16.0 A Helm chart for Kubernetes
We can now get the values for our helm chart by running:
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$ helm show values cm-local/my-chart
This returns the values yaml that we can use for our chart, so let’s say you want to output the values yaml so that we can use to to deploy a release we can do:
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$ helm show values cm-local/my-chart > my-values.yaml
If you need a kubernetes cluster and you would like to run this locally, find the following documentation in order to do that:
- using kind for local kubernetes clusters
Thank You
Thanks for reading, feel free to check out my website, feel free to subscribe to my newsletter or follow me at @ruanbekker on Twitter.
In this post we will use Grafana Promtail to collect all our logs and ship it to Grafana Loki.
About
We will be using Docker Compose and mount the docker socket to Grafana Promtail so that it is aware of all the docker events and configure it that only containers with docker labels logging=promtail needs to be enabled for logging, which will then scrape those logs and send it to Grafana Loki where we will visualize it in Grafana.
Promtail
In our promtail configuration config/promtail.yaml:
You can see we are using the docker_sd_configs provider and filter only docker containers with the docker labels logging=promtail and once we have those logs we relabel our labels to have the container name and we also use docker labels like log_stream and logging_jobname to add labels to our logs.
Grafana Config
We would like to auto configure our datasources for Grafana and in config/grafana-datasources.yml we have:
Which uses logging: "promtail" to let promtail know this log container’s log to be scraped and logging_jobname: "containerlogs" which will assign containerlogs to the job label.
In this tutorial we will demonstrate how to use KinD (Kubernetes in Docker) to provision local kubernetes clusters for local development.
About
KinD uses container images to run as “nodes”, so spinning up and tearing down clusters becomes really easy or running multiple or different versions, is as easy as pointing to a different container image.
Configuration such as node count, ports, volumes, image versions can either be controlled via the command line or via configuration, more information on that can be found on their documentation:
Creating cluster "cluster-1" ...
✓ Ensuring node image (kindest/node:v1.24.0) 🖼
✓ Preparing nodes 📦
✓ Writing configuration 📜
✓ Starting control-plane 🕹️
✓ Installing CNI 🔌
✓ Installing StorageClass 💾
Set kubectl context to "kind-cluster-1"You can now use your cluster with:
kubectl cluster-info --context kind-cluster-1
Have a question, bug, or feature request? Let us know! https://kind.sigs.k8s.io/#community 🙂
I highly recommend installing kubectx, which makes it easy to switch between kubernetes contexts.
Create a Cluster with Config
If you would like to define your cluster configuration as config, you can create a file default-config.yaml with the following as a 2 node cluster, and specifying version 1.24.0:
kubectl get nodes -o wide
NAME STATUS ROLES AGE VERSION INTERNAL-IP EXTERNAL-IP OS-IMAGE KERNEL-VERSION CONTAINER-RUNTIME
kind-cluster-control-plane Ready control-plane 2m11s v1.24.0 172.20.0.5 <none> Ubuntu 21.10 5.10.104-linuxkit containerd://1.6.4
kind-cluster-worker Ready <none> 108s v1.24.0 172.20.0.4 <none> Ubuntu 21.10 5.10.104-linuxkit containerd://1.6.4
Deploy Sample Application
We will create a deployment, a service and port-forward to our service to access our application. You can also specify port configuration to your cluster so that you don’t need to port-forward, which you can find in their port mappings documentation
I will be using the following commands to generate the manifests, but will also add them to this post:
kubectl get deployment,pod,service
NAME READY UP-TO-DATE AVAILABLE AGE
deployment.apps/hostname 2/2 22 9m27s
NAME READY STATUS RESTARTS AGE
pod/hostname-7ff58c5644-67vhq 1/1 Running 0 9m27s
pod/hostname-7ff58c5644-wjjbw 1/1 Running 0 9m27s
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
service/hostname-http ClusterIP 10.96.218.58 <none> 80/TCP 5m48s
service/kubernetes ClusterIP 10.96.0.1 <none> 443/TCP 24m
In this tutorial I will demonstrate how to use Ansible for Homebrew Configuration Management. The aim for using Ansible to manage your homebrew packages helps you to have a consistent list of packages on your macbook.
For me personally, when I get a new laptop it’s always a mission to get the same packages installed as what I had before, and ansible solves that for us to have all our packages defined in configuration management.
Our inventory.ini will define the information about our target host, which will be localhost as we are using ansible to run against our local target which is our macbook:
Our playbook homebrew.yaml will define the tasks to add the homebrew taps, cask packages and homebrew packages. You can change the packages as you desire, but these are the ones that I use:
-hosts:localhostname:Macbook Playbookgather_facts:Falsevars:TFENV_ARCH:amd64tasks:-name:Ensures taps are present via homebrewcommunity.general.homebrew_tap:name:""state:presentwith_items:-hashicorp/tap-name:Ensures packages are present via homebrew caskcommunity.general.homebrew_cask:name:""state:presentinstall_options:'appdir=/Applications'with_items:-visual-studio-code-multipass-spotify-name:Ensures packages are present via homebrewcommunity.general.homebrew:name:""path:"/Applications"state:presentwith_items:-openssl-readline-sqlite3-xz-zlib-jq-yq-wget-go-kubernetes-cli-fzf-sshuttle-hugo-helm-kind-awscli-gnupg-kubectx-helm-stern-terraform-tfenv-pyenv-jsonnetignore_errors:yestags:-packages
In this tutorial I will demonstrate how to keep your docker container images nice and slim with the use of multistage builds for a hugo documentation project.
Hugo is a static content generator so essentially that means that it will generate your markdown files into html. Therefore we don’t need to include all the content from our project repository as we only need the static content (html, css, javascript) to reside on our final container image.
What are we doing today
We will use the DOKS Modern Documentation theme for Hugo as our project example, where we will build and run our documentation website on a docker container, but more importantly make use of multistage builds to optimize the size of our container image.
Our Build Strategy
Since hugo is a static content generator, we will use a node container image as our base. We will then build and generate the content using npm run build which will generate the static content to /src/public in our build stage.
Since we then have static content, we can utilize a second stage using a nginx container image with the purpose of a web server to host our static content. We will copy the static content from our build stage into our second stage and place it under our defined path in our nginx config.
This way we only include the required content on our final container image.
Then we can review the size of our container image, which is only 27.4MB in size, pretty neat right.
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docker images --filter reference=ruanbekker/hashnode-docs-blogpost
REPOSITORY TAG IMAGE ID CREATED SIZE
ruanbekker/hashnode-docs-blogpost latest 5b60f30f40e6 21 minutes ago 27.4MB
Running our Container
Now that we’ve built our container image, we can run our documentation site, by specifying our host port on the left to map to our container port on the right in 80:80:
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docker run -it -p 80:80 ruanbekker/hashnode-docs-blogpost:latest
When you don’t have port 80 already listening prior to running the previous command, when you head to http://localhost (if you are running this locally), you should see our documentation site up and running:
