Skel

Skel is an example application written in Go.

This repository can be used as a reference--the code is well documented.

It's a good fit as starter code for developing JSON APIs which act as backends for Single-Page Applications (SPAs), mobile applications, or function as stand-alone services.

The project addresses the design choices of developing applications:

• Project structure and organization.
• Practical code patterns for creating robust and maintainable programs.

Development practices is based on guiding principles of well written Go code.

• Clarity
• Simplicity
• Correctness
• Productivity

Prerequisites

You'll need to install these softwares and tools on your machine:

• Go 1.16 or newer
• PostgreSQL database (version 12 or newer)
• command-line migrate tool for database migrations
• make utility
• reflex (optional)
• staticcheck tool to carry out some additional static analysis checks

Quickstart

First, clone the repository:

$ git clone https://github.com/cedrickchee/skel.git
$ cd skel

Environment Variables

Create a .envrc file in the root directory of this project by renaming .envrc.example to .envrc.

# PostgreSQL database DSN.
SKEL_DB_DSN=postgres://skel:pa55word@localhost/skel

Expand Database Migrations

Install the migrate tool

Detailed installation instructions for different OSes can be found here.

On Linux, the easiest method is to download a pre-built binary and move it to a location on your system path:

$ curl -L https://github.com/golang-migrate/migrate/releases/download/v4.14.1/migrate.linux-amd64.tar.gz | tar xvz
$ mv migrate.linux-amd64 $GOPATH/bin/migrate

Before you continue, please check that it’s available and working on your machine by trying to execute the migrate binary with the -version flag. It should output the current version number similar to this:

$ migrate --version
4.14.1

Then, you can build and run skel by using make:

$ make run/api

Development

Development Environment with Live Code Reloading

Run reflex which will be used for hot recompiling the code. It can be very useful for quickly testing your changes.

$ reflex -c reflex.conf

What the command means is: "watch for changes to go.mod and all files ending in .go and execute go run ./cmd/api when it happens. The -s flag stands for service and will make reflex kill previously run command before starting it again, which is exactly what we want.

Command-Line Flags

$ go run ./cmd/api --help
Usage of ./bin/linux_amd64/api:
  -cors-trusted-origins value
    	Trusted CORS origins (space separated)
  -db-dsn string
    	PostgreSQL DSN
  -db-max-idle-conns int
    	PostgreSQL max idle connections (default 25)
  -db-max-idle-time string
    	PostgreSQL max idle time (default "15m")
  -db-max-open-conns int
    	PostgreSQL max open connections (default 25)
  -env string
    	Environment (development|staging|production) (default "development")
  -limiter-burst int
    	Rate limiter maximum burst (default 4)
  -limiter-enabled
    	Enable rate limiter (default true)
  -limiter-rps float
    	Rate limiter maximum requests per second (default 2)
  -port int
    	API server port (default 4000)
  -smtp-host string
    	SMTP host (default "smtp.mailtrap.io")
  -smtp-password string
    	SMTP password (default "xxxxxxxxxxxxxx")
  -smtp-port int
    	SMTP port (default 2525)
  -smtp-sender string
    	SMTP sender (default "Skel <no-reply@example.com>")
  -smtp-username string
    	SMTP username (default "xxxxxxxxxxxxxx")
  -version
    	Display version and exit

Start the API, passing in a couple of command line flags for different purposes:

• Run the API with rate limiting disabled:

  $ go run ./cmd/api -limiter-enabled=false

• Before you run your load testing, you might like to play around with this and try changing some of the configuration parameters for the connection pool to see how it affects the behavior of the figures (from your benchmarking tool) under load.

  $ go run ./cmd/api -limiter-enabled=false -db-max-open-conns=50 -db-max-idle-conns=50 -db-max-idle-time=20s -port=4000

• Run your API, passing in http://localhost:9000 and http://localhost:9001 as CORS trusted origins like so:

  $ go run ./cmd/api -cors-trusted-origins='http://localhost:9000 http://localhost:9001'

Using Makefile

Use the GNU make utility and makefiles to help automate common tasks in out project, such as creating and executing database migrations.

Displaying help information

Execute the help target, you should get a response which lists all the available targets and the corresponding help text.

$ make
Usage:
  help                               print this help message
  run/api                            run the cmd/api application
  db/psql                            connect to the database using psql
  db/migrations/new name=$1          create a new database migration
  db/migrations/up                   apply all up database migrations
  audit                              tidy and vendor dependencies and format, vet and test all code
  vendor                             tidy and vendor dependencies
  build/api                          build the cmd/api application
  production/connect                 connect to the production server
  production/deploy/api              deploy the api to production
  production/configure/api.service   configure the production systemd api.service file
  production/configure/caddyfile     configure the production Caddyfile

Using make for Common Tasks

You should be able to execute the rules by typing the full target name when running make. For example:

$ make run/api
go run ./cmd/api
{"level":"INFO","time":"2021-09-04T14:44:36Z","message":"database connection pool established"}
{"level":"INFO","time":"2021-09-04T14:44:36Z","message":"starting server","properties":{"addr":":4000","env":"development"}}

If you run the db/migrations/up rule with the name=create_example_table argument you should see the following output:

$ make db/migrations/up name=create_example_table
Creating migration files for create_example_table
migrate create -seq -ext=.sql -dir=./migrations create_example_table
/home/cedric/dev/repo/gh/skel/migrations/000007_create_example_table.up.sql
/home/cedric/dev/repo/gh/skel/migrations/000007_create_example_table.down.sql

Quality Controlling Code

The audit rule will:

• prune dependencies
• verify module dependencies
• format all .go files, according to the Go standard
• vet code; runs a variety of analyzers which carry out static analysis
• staticcheck; carry out some additional static analysis checks
• test

To run these checks before you commit any code changes into your version control system or build any binaries.

$ make audit

Profiling Test Coverage

A feature of the go test tool is the metrics and visualizations that it provides for test coverage.

$ make coverage
Running test coverage ...
go test -cover ./...
ok      github.com/cedrickchee/skel/cmd/api     (cached)        coverage: 18.9% of statements
?       github.com/cedrickchee/skel/cmd/examples/cors/preflight [no test files]
?       github.com/cedrickchee/skel/cmd/examples/cors/simple    [no test files]
ok      github.com/cedrickchee/skel/internal/data       (cached)        coverage: 3.3% of statements
?       github.com/cedrickchee/skel/internal/jsonlog    [no test files]
?       github.com/cedrickchee/skel/internal/mailer     [no test files]
?       github.com/cedrickchee/skel/internal/validator  [no test files]
go test -covermode=count -coverprofile=/tmp/profile.out ./...
ok      github.com/cedrickchee/skel/cmd/api     0.008s  coverage: 18.9% of statements
?       github.com/cedrickchee/skel/cmd/examples/cors/preflight [no test files]
?       github.com/cedrickchee/skel/cmd/examples/cors/simple    [no test files]
ok      github.com/cedrickchee/skel/internal/data       0.115s  coverage: 3.3% of statements
?       github.com/cedrickchee/skel/internal/jsonlog    [no test files]
?       github.com/cedrickchee/skel/internal/mailer     [no test files]
?       github.com/cedrickchee/skel/internal/validator  [no test files]
go tool cover -html=/tmp/profile.out

From the results here we can see that 18.9% of the statements in our cmd/api package are executed during our tests, and for our internal/data the figure is 3.3%.

This will open a browser window containing a navigable and highlighted representation of your code.

Vendoring New Dependencies

Note: It's important to point out that there's no easy way to verify that the checksums of the vendored dependencies match the checksums in the go.sum file.

To mitigate that, it's a good idea to run both go mod verify and go mod vendor regularly. Using go mod verify will verify that the dependencies in your module cache match the go.sum file, and go mod vendor will copy those same dependencies from the module cache into your vendor directory.

# vendor rule execute both `go mod verify` and `go mod vendor` commands.
$ make vendor

Build

Build and run executable binaries for our applications.

Go supports cross-compilation, so you can generate a binary suitable for use on a different machine.

Let's create two binaries — one for use on your local machine, and another for deploying to the Ubuntu Linux server.

To build binaries, you need to execute:

$ make build/api

You should see that two binary files are now created — one for local machine at ./bin/api; with the cross-compiled binary located under the ./bin/linux_amd64 directory.

And you should be able to run this executable to start your API application, passing in any command-line flag values as necessary. For example:

$ ./bin/api -port=3000 -db-dsn=postgres://myuser:mysuperpass@localhost/skel
{"level":"INFO","time":"2021-09-06T13:00:00Z","message":"database connection pool established"}
{"level":"INFO","time":"2021-09-06T13:00:00Z","message":"starting server","properties":{"addr":":3000","env":"development"}}

Automated Version Numbers

We leverage Git to generate automated version numbers as part of your build process. The process "burn-in" a version number and build time to your application when building the binary.

The steps are: git commit recent changes, run make build/api, and then check the version number in your binaries. Like so:

$ git add .
$ git commit -m 'generate version number automatically'

$ make build/api
Building cmd/api...
go build -ldflags="-s -X main.buildTime=2021-09-07T12:07:47+08:00 -X main.version=018442b" -o=./bin/api ./cmd/api
GOOS=linux GOARCH=amd64 go build -ldflags="-s -X main.buildTime=2021-09-07T12:07:47+08:00 -X main.version=018442b" -o=./bin/linux_amd64/api ./cmd/api

$ ./bin/api -version
Version:    018442b
Build time: 2021-09-07T12:07:47+08:00

We can see that our binary is now reporting that it was been built from a clean version of the repository with the commit hash 018442b. Let's cross check this against the git log output for the project:

$ git log
commit 018442b79eec04e2c739d5b3ab1ac4ca7345f17f (HEAD -> main)
Author: Cedric Chee <cedric@no-reply-github.com>
Date:   Tue Sep 7 12:07:01 2021 +0800

    generate version number automatically

...

The commit hash in our Git history aligns perfectly with our application version number. And that means it's now easy for us to identify exactly what code a particular binary contains — all we need to do is run the binary with the -version flag and then cross-reference it against the Git repository history.

Deployment and Hosting

We're going to deploy our API application to a production server and expose it on the Internet.

Every project and project team will have different technical and business needs in terms of hosting and deployment, so it's impossible to lay out a one-size-fits-all approach here.

We'll focus on hosting the application on a self-managed Linux server and using standard Linux tooling to manage server configuration and deployment.

We'll also be automating the server configuration and deployment process as much as possible, so that it's easy to make continuous deployments and possible to replicate the server again in the future if you need to.

We'll be using Digital Ocean as the hosting provider.

In terms of infrastructure and architecture, we'll run everything on a single Ubuntu Linux server. Our stack will consist of a PostgreSQL database and the executable binary for our Skel API, operating in much the same way that we have seen so far. But in addition to this, we'll also run Caddy as a reverse proxy in front of the Skel API.

Using Caddy has a couple of benefits. It will automatically handle and terminate HTTPS connections for us — including automatically generating and managing TLS certificates via Let's Encrypt — and we can also use Caddy to easily restrict internet access to our metrics endpoint.

You are going to:

• Provision an Ubuntu Linux server running on Digital Ocean to host your application.
• Automate the configuration of the server — including creating user accounts, configuring the firewall and installing necessary software.
• Automate the process of updating your application and deploying changes to the server.
• Run your application as a background service using systemd, as a non-root user.
• Use Caddy as a reverse proxy in front of your application to automatically manage TLS certificates and handle HTTPS connections.

Server Configuration and Installing Software

Now that our Ubuntu Linux droplet has been successfully commissioned, we need to do some housekeeping to secure the server and get it ready-to-use. Rather than do this manually, we're going to create and use a reusable script to automate these setup tasks.

You can find that regular Bash script in the scripts/setup folder in your project directory. The script file is called 01.sh.

Prerequisite This step ensure your Digital Ocean droplet is up and running and you've been able to successfully connect to it over SSH.

In order to log in to droplets in your Digital Ocean account you'll need a SSH keypair.

Suggestion: If you're unfamiliar with SSH, SSH keys, or public-key cryptography generally, then I recommend reading through the first half of this guide to get an overview before continuing.

Open a new terminal window and try connecting to the droplet via SSH as the root user, using the droplet IP address. Like so:

$ ssh root@{insert your VM IP}
The authenticity of host 'X.X.X.X (X.X.X.X)' can't be established.
  ...
Welcome to Ubuntu 20.04.1 LTS (GNU/Linux 5.4.0-51-generic x86_64)
  ...
root@skel-production:~# exit
logout
Connection to X.X.X.X closed.

OK, let's now run this script on our new Digital Ocean droplet. This will be a two-step process:

1. First we need to copy the script to the droplet (which we will do using rsync).
2. Then we need to connect to the droplet via SSH and execute the script.

Go ahead and run the following command to rsync the contents of the /scripts/setup folder to the root user's home directory on the droplet. Remember to replace the IP address with your own!

$ rsync -rP --delete ./scripts/setup root@X.X.X.X:/root
sending incremental file list
setup/
setup/01.sh
          3,327 100%    0.00kB/s    0:00:00 (xfr#1, to-chk=0/2)

Now that a copy of our setup script is on the droplet, let's use the ssh command to execute the script on the remote machine as the root user.

Go ahead and run the script, entering a password for the skel PostgreSQL user, like so:

$ ssh -t root@X.X.X.X 'bash /root/setup/01.sh'
Enter password for skel DB user: mySuprs3cretPASS0121
'universe' distribution component enabled for all sources.
Hit:1 http://security.ubuntu.com/ubuntu focal-security InRelease
Hit:2 https://repos.insights.digitalocean.com/apt/do-agent main InRelease
     ...
Script complete! Rebooting...
Connection to X.X.X.X closed by remote host.
Connection to X.X.X.X closed.

Connecting to the Droplet

After waiting a minute for the reboot to complete, try connecting to the droplet as the skel user over SSH. This should work correctly (and the SSH key pair you created previously should be used to authenticate the connection) but you will be prompted to set a password.

To make connecting to the server a bit easier, and so we don't have to remember the IP address, we add a Makefile rule for initializing a SSH connection to the server as the skel user. Like so:

# Makefile
...

# ============================================================================ #
# PRODUCTION
# ============================================================================ #

production_host_ip = "INSERT YOUR IP ADDRESS"

## production/connect: connect to the production server
.PHONY: production/connect
production/connect:
	ssh skel@${production_host_ip}

Remember to replace the IP address with your own.

You can then connect to your droplet whenever you need to by simply typing:

$ make production/connect
ssh skel@'X.X.X.X'
Welcome to Ubuntu 20.04.2 LTS (GNU/Linux 5.4.0-65-generic x86_64)
  ...
skel@skel-production:~$

Deployment and Running Application

At this point our droplet is set up with all the software and user accounts that we need, so let's move on to the process of deploying and running our API application.

At a very high-level, our deployment process will consist of three actions:

1. Copying the application binary and SQL migration files to the droplet.
2. Executing the migrations against the PostgreSQL database on the droplet.
3. Starting the application binary as a background service.

To execute the first two steps automatically, we made a production/deploy/api rule in Makefile.

$ make production/deploy/api
rsync -rP --delete ./bin/linux_amd64/api ./migrations skel@"X.X.X.X":~
sending incremental file list
api
      7,618,560 100%  119.34kB/s    0:01:02 (xfr#1, to-chk=13/14)
migrations/
migrations/000001_create_movies_table.down.sql
             28 100%   27.34kB/s    0:00:00 (xfr#2, to-chk=11/14)
migrations/000001_create_movies_table.up.sql
            286 100%  279.30kB/s    0:00:00 (xfr#3, to-chk=10/14)
migrations/000002_add_movies_check_constraints.down.sql
            198 100%  193.36kB/s    0:00:00 (xfr#4, to-chk=9/14)
migrations/000002_add_movies_check_constraints.up.sql
            289 100%  282.23kB/s    0:00:00 (xfr#5, to-chk=8/14)
migrations/000003_add_movies_indexes.down.sql
             78 100%   76.17kB/s    0:00:00 (xfr#6, to-chk=7/14)
migrations/000003_add_movies_indexes.up.sql
            170 100%  166.02kB/s    0:00:00 (xfr#7, to-chk=6/14)
migrations/000004_create_users_table.down.sql
             27 100%   26.37kB/s    0:00:00 (xfr#8, to-chk=5/14)
migrations/000004_create_users_table.up.sql
            294 100%  287.11kB/s    0:00:00 (xfr#9, to-chk=4/14)
migrations/000005_create_tokens_table.down.sql
             28 100%   27.34kB/s    0:00:00 (xfr#10, to-chk=3/14)
migrations/000005_create_tokens_table.up.sql
            203 100%   99.12kB/s    0:00:00 (xfr#11, to-chk=2/14)
migrations/000006_add_permissions.down.sql
             73 100%   35.64kB/s    0:00:00 (xfr#12, to-chk=1/14)
migrations/000006_add_permissions.up.sql
            452 100%  220.70kB/s    0:00:00 (xfr#13, to-chk=0/14)
ssh -t skel@"X.X.X.X" "migrate -path ~/migrations -database $SKEL_DB_DSN up"
1/u create_movies_table (11.782733ms)
2/u add_movies_check_constraints (23.109006ms)
3/u add_movies_indexes (30.61223ms)
4/u create_users_table (39.890662ms)
5/u create_tokens_table (48.659641ms)
6/u add_permissions (58.23243ms)
Connection to X.X.X.X closed.

Running the API as a Background Service

The next step is to configure it to run as a background service, including starting up automatically when the droplet is rebooted.

We do this using systemd.

We've made a unit file (scripts/production/api.service), which informs systemd how and when to run the service.

The next step is to install this unit file on our droplet and start up the service.

Go ahead and run the Makefile rule. The output you see should look similar to this:

$ make production/configure/api.service
rsync -P ./scripts/production/api.service skel@"X.X.X.X":~
sending incremental file list
api.service
          1,266 100%    0.00kB/s    0:00:00 (xfr#1, to-chk=0/1)
ssh -t skel@"X.X.X.X" '\
        sudo mv ~/api.service /etc/systemd/system/ \
        && sudo systemctl enable api \
        && sudo systemctl restart api \
'
[sudo] password for skel:
Created symlink /etc/systemd/system/multi-user.target.wants/api.service → /etc/systemd/system/api.service.
Connection to X.X.X.X closed.

Next connect to the droplet and check the status of the new api service using the sudo systemctl status api command:

$ make production/connect
skel@skel-production:~$ sudo systemctl status api
● api.service - Skel API service
     Loaded: loaded (/etc/systemd/system/api.service; enabled; vendor preset: enabled)
     Active: active (running) since Mon 2021-09-09 03:15:35 SGT; 1min 31s ago
   Main PID: 6891 (api)
      Tasks: 6 (limit: 1136)
     Memory: 1.8M
     CGroup: /system.slice/api.service
             └─6997 /home/skel/api -port=4000 -db-dsn=postgres://skel:blahP@55blah@localhost/skel -env=production

Apr 09 03:15:35 skel-production systemd[1]: Starting Skel API service...
Apr 09 03:15:35 skel-production systemd[1]: Started Skel API service.
Apr 09 03:15:35 skel-production api[6891]: {"level":"INFO","time":"2021-09-09T07:15:35Z", ...}
Apr 09 03:15:35 skel-production api[6891]: {"level":"INFO","time":"2021-09-09T07:15:35Z", ...}

This confirms that the service is running successfully in the background and, in my case, that it has the PID (process ID) 6891.

Use Caddy as a Reverse Proxy

We're now in the state where we have Caddy running as a background service and listening for HTTP requests on port 80.

So the next step in setting up our production environment is to configure Caddy to act as a reverse proxy and forward any HTTP requests that it receives onward to our API.

To configure Caddy, we created a Caddyfile.

If you're following along, please go ahead and replace the IP address in the Caddyfile with the address of your own droplet (server).

Next deploy this Caddyfile into your droplet:

$ make production/configure/caddyfile
rsync -P ./scripts/production/Caddyfile skel@"X.X.X.X":~
sending incremental file list
Caddyfile
             53 100%    0.00kB/s    0:00:00 (xfr#1, to-chk=0/1)
ssh -t skel@"X.X.X.X" '\
        sudo mv ~/Caddyfile /etc/caddy/ \
        && sudo systemctl reload caddy \
'
[sudo] password for skel:
Connection to X.X.X.X closed.

You should see that the Caddyfile is copied across and the reload executes cleanly without any errors.

At this point you can visit http://<your_droplet_ip>/v1/healthcheck in a web browser, and you should find that the request is successfully forwarded on from Caddy to our API.

Application Metrics

The metrics are no longer publicly accessible, you can still access them by connecting to your droplet via SSH.

You can open a SSH tunnel to the droplet and view them using a web browser on your local machine. For example, you could open an SSH tunnel between port 4000 on the droplet and port 9999 on your local machine by running the following command (make sure to replace both IP addresses with your own droplet IP):

$ ssh -L :9999:X.X.X.X:4000 skel@X.X.X.X

While that tunnel is active, you should be able to visit http://localhost:9999/debug/vars in your web browser and see your application metrics.

Using a Domain Name (Optional)

For the next step of our deployment, if you want, you can configure Caddy so that you can access our droplet via a domain name, instead of needing to use the IP address.

I'm going to use the domain skel.cedricchee.com in the sample code here, but you should swap this out for your own domain if you're following along.

The first thing you'll need to do is configure the DNS records for your domain name so that they contain an A record pointing to the IP address for your droplet. So in my case the DNS record would look like this:

A     skel.cedricchee.com     X.X.X.X

Note: If you're not sure how to alter your DNS records, your domain name registrar should provide guidance and documentation.

Once you've got the DNS record in place, the next task is to update the Caddyfile to use your domain name instead of your droplet's IP address. Go ahead and swap this out like so (remember to replace skel.cedricchee.com with your own domain name):

http://skel.cedricchee.com {
    respond /debug/* "Not Permitted" 403
    reverse_proxy localhost:4000
}

And then redeploy the Caddyfile to your droplet again:

$ make production/configure/caddyfile

Once you've done that, you should now be able to access the API via your domain name by visiting http://<your_domain_name>/v1/healthcheck in your browser.

Enabling HTTPS (Optional)

Now that we have a domain name set up we can utilize one of Caddy's headline features: automatic HTTPS.

Caddy will automatically handle provisioning and renewing TLS certificates for your domain via Let's Encrypt, as well as redirecting all HTTP requests to HTTPS. It's simple to set up, very robust, and saves you the overhead of needing to keep track of certificate renewals manually.

To enable this, we just need to update our Caddyfile so that it looks like this:

# Set the email address that should be used to contact you if there is a
# problem with your TLS certificates.
{
  email you@example.com
}

# Remove the http:// prefix from your site address.
skel.cedricchee.com {
    respond /debug/* "Not Permitted" 403
    reverse_proxy localhost:4000
}

For the final time, deploy this Caddyfile update to your droplet.

$ make production/configure/caddyfile

And then when you refresh the page in your web browser, you should find that it is automatically redirected to a HTTPS version of the page.

Working with Branches

You can switch the authentication process for our API to use JSON Web Tokens (JWTs). Just git checkout the feature branch named "feat/jwt-auth".

Important: Using JWTs for this particular application doesn't have any benefits over our current "stateful token" approach. The JWT approach is more complex, ultimately requires the same number of database lookups, and we lose the ability to revoke tokens. For those reasons, it doesn't make much sense to use them here. But I still want to explain the pattern for two reasons:

• JWTs have a lot of mind-share, and as a developer it's likely that you will run into existing codebases that use them for authentication, or that outside influences mean you are forced to use them.

• It's worth understanding how they work in case you need to implement APIs that require delegated authentication, which is a scenario that they're useful in.

License

Expand License

This repository contains a variety of content; some developed by Cedric Chee, and some from third-parties. The third-party content is distributed under the license provided by those parties.

I am providing code and resources in this repository to you under an open source license. Because this is my personal repository, the license you receive to my code and resources is from me and not my employer.

The content developed by Cedric Chee is distributed under the following license:

Text

The text content is released under the CC-BY-NC-ND license. Read more at Creative Commons.

Code

The code in this repository is released under the MIT license.