At DrDoctor we’ve been using Octopus Deploy for about a year and half now, and in that time we have developed a very robust way of releasing the core of our system. That is, two IIS websites and a bunch of windows services. Release automation has vastly increased the frequency at which we release software into production and in turn the speed at which the business can operate. The missing part in all of this are the integration projects, when DrDoctor begins working with a new hospital we do a bunch of work integrating their PAS system into our system.
If you’re a .Net developer and not currently doing automated deployments then you really should be. If you’d like some help getting started send me a message in the comments and I’ll drop you an email. The core of any deployment is the ability to easily manage and apply configuration values, across environments and across your different apps/web apps. Octopus Deploy has first class support for managing variables, including setting different scopes or environments and steps in your deployment process.
At DrDoctor we are broadly speaking following the Agile Scrum methodology; we do Sprint planning, work on user stories, hold retrospectives etc… Where are break away from the traditional ‘sprint’ methodology is how we go about getting stories “accepted” and released. Traditionally in Scrum you demo all the stories at the end of the sprint in the Sprint Review meeting, whereas we tend to demo stories (and have them accepted by the PO) as they are completed and then we aim to have accepted stories released to production within 3 days.
At DrDoctor we do a lot with SMS, we let patients reschedule appointments, book appointments when they move to the top of a waiting list and we provide patients with the ability to move their appointment forward by sending them lists of slots as capacity becomes available. In all of this we use workflows to represent what is going on between the patient’s mobile phone and our system. We call them workflows but really they are just state machines built on top of the excellent .
Over the past couple of weeks we’ve noticed that our Reporting service is terribly slow. The above chart is part of our APM (Application Performance Monitoring) solution built on top of MiniProfiler and Splunk (see this blog post for more details). Our reporting service works by processing messages which are queued up by various other services onto a queue in RabbitMQ. What this chart is showing is that the reporting service is working at a very constant and slow pace.