I spent a long time creating my first Spark bot, Zpark. The first commit was in August and the first release was posted in January. So, six months elapsed time. It's also over-engineered. I mean, all it does is post messages back and forth between a back-end system and some Spark spaces and I ended up with something so complex that I had to draw a damn block diagram in the user guide to give people a fighting chance at comprehending how it works.

Its internals could've been much simpler. But that was part of the point of creating the bot: examining the proper architecture for a scalable application, learning about new technologies for building my own API, learning about message brokers, pulling my hair out over git's eccentricities and ultimately, having enough material to write this blog post.

In this post I'm going to break down the different functional components of Zpark, discuss what each does, and why-or not-that component is necessary. If I can achieve one goal, it will be to retire to a tropical island ASAP. If I can achieve a second goal, it will be to give aspiring bot creaters (like yourself, presumably) a strong mental model of a Spark bot to aid their development.

For a long while now I've been brainstorming how I could leverage the API that's present in the Cisco Spark collaboration platform to create a bot. There are lots of goofy and fun examples of bots (ie, Gifbot) that I might be able to draw inspiration from, but I wanted to create something that would provide high value to myself and anyone else that choose to download and use it. The idea finally hit me after I started using Zabbix for system monitoring. Since Zabbix also has a feature-rich API, all the pieces were in place to create a bot that would act as a bit of middle-ware between Zabbix and Spark. I call the bot: Zpark.