Bluemix Demo

Today I gave a presentation and hands-on demonstration of IBM Bluemix. This had given me an opportunity to spend a number of hours over the past week or two trying to understand more about Bluemix, its relationship to Cloud Foundry, and the whole concept of Platform as a Service (PaaS).

For me, PaaS is a powerful concept that can provide significant advantages, though you also have to understand its limitations. For example, when you are composing an application from multiple third-party services all hosted in different clouds and accessed over the internet, you clearly can’t guarantee any SLA’s. It was also strange binding an instance of a database such as MongoDB, and having no sysadmin control over its configuration. Effectively, you are consuming it as a service and shouldn’t have to worry. There are always going to be corner cases where you need extreme throughput or performance that requires a lot of optimisation, but with modern databases and technologies, that seems to be the exception rather than the norm.

I ran four simple demos:
1) The first was configuring a simple Java web app from Intellij, and then pushing this to Bluemix using just the Cloud Foundry command line interface (CLI). I also used the CF CLI to scale the number of instances/memory etc.
2) For the second demo, I ran node-red on my macbook as a part of a flow that was listening for messages on an open source MQTT broker (mosquitto), and would change the colour of the Blink1 USB device that I had plugged in. I configured an HTTP input that allowed people in the room to access a URL on their smart phones and automatically control the colour of the Blink1.
3) For the third demo, I subscribed to a public Twitter feed, fed the incoming tweets through a Sentiment Analyser, and then used this to communicate with the Blink1 to show whether the tweets were positive, negative or neutral.
4) Finally, I subscribed to a public Twitter feed, and pushed these tweets into a collection on a MongoDB instance. I then exposed them from MongoDB via a URL. I was going to call it a REST API, but in reality it was just an HTTP endpoint that returned a JSON message with a collection of documents from MongoDB.

It was definitely impressive showing what you could knock up from scratch in just a matter of minutes. When I get a chance, I intend to play around with the Watson cognitive computing APIs, to see if there is something interesting I can come up with here.