Streamlining Salary Benefit Tool Deployment with Bitbucket Pipelines

[Leo Southwell]

Hey everyone

I’ve been working on a small HR-focused web tool that helps automate salary and service-related calculations for employees. It’s a simple project, but I’m now trying to take it one step further by automating its deployment process using Bitbucket Pipelines and Docker.

Here’s my basic setup:

• Frontend + backend code in Bitbucket
• Dockerfile built and tested locally
• Using a cloud VPS for container deployment

What I want to achieve:

1. Every push to the main branch should build and deploy automatically.
2. Secrets and API keys should remain safe during deployment.
3. I’d like to explore zero-downtime deployment options.

Has anyone here automated deployment for small utility or HR web apps using Pipelines? Would love to learn from your experience best practices, YAML config examples, or even common mistakes to avoid.

For reference, the tool I’m automating is similar to this salary automation tool that helps with employee service calculations.

Thanks in advance!

[Ben Brooks]

Thanks @Leo Southwell, this is very helpful. Good point on keeping build and deploy separate instead of deploying every commit.

The secrets and zero-downtime notes make sense too. The app is a small HR/calculator tool, so the deployment is not too heavy, but I’ll look into Bitbucket packages and larger build size if Docker builds become slow.

For context, this is the type of calculator setup being automated:

https://thegratuitycalculator.ae/how-our-calculators-work/

[Aron Gombas _Midori_]

@Leo Southwell Our team deploys multiple complex apps using Pipelines with great success.

We build after every commit, but don't deploy all code versions. (We have a dedicated "deploy" build.)

Generally speaking, I think it is fairly trivial to convert an existing process or script to a pipeline.

Tips:

• Secrets should be stored as secret-type variables.
• Zero-downtime doesn't really depend on Pipelines, if you can do zero-downtime otherwise, then you should be able to do that with Pipelines as well -- it is not a limiting factor.
• There is a recent feature called packages that you may find useful as you utilize Docker containers.
• If your deployment needs CPU or memory (we do native compilation, e.g.), consider using a larger-than-default size.