Qlik Sense is very much my go-to tool for many data related questions. It’s very powerful to be able to throw together a proof of concept or test a hypothesis in half an hour. Seeing is however believing, so those prototypes does need some polishing before they can be seen as production grade.
I have always pushed my peers in the Qlikosphere to become better at using icons for their Qlik Sense apps in general, and also for the sheets within those apps. Having an icon for a sheet within an app makes it way easier for the user to find a particular sheet – humans are visual by nature.
Butler SOS has matured quite a bit during past couple of years, with latest additions being fine-grained monitoring of what users are connected to what servers. Opens up for various interesting use cases, including notification to users before server restarts etc.
A brand new doc site (butler-sos.ptarmiganlabs.com) also goes live today (built with Hugo and Docsy, vastly better than the previous one-pager at GitHub.
Butler SOS 4.0 is out, adding features that make it easier than ever to monitor large Qlik Sense environments. We’ll return to this topic of course, but let’s first take a few steps back.
There are many variants of that quote: “If you can’t measure it, you can’t improve it”, “Measure what matters”, “Measure what is measurable, and make measurable what is not so.” and others. The last one supposedly originates with Galileo Galilei. Smart guy.
The development of Butler SOS continues in that spirit. Qlik Sense provides an awesome platform on top of which all kinds of data analysis, visualisation and presentation solutions can be built. A key word there is platform. Sense does not offer solutions to all software development challenges, nor should it. Instead, use the tools and best practices that millions of developers around the world have refined over the years.
Qlik Sense does on the other hand offer a very comprehensive set of APIs that give developers access to its internals – and this is part of why it’s such a powerful platform. Butler SOS taps into some of these APIs, exposing their data in the form of real-time dashboards, charts and alerts. Suddenly sysadmins know can get both an overview of how all servers are doing, as well as look at detailed server metrics when so needed. All made possible using the Sense APIs, but in general powered by various open source tools.
We’re basically back to Galileo – let’s make sure the important parts of Qlik Sense are measurable. It is then possible to improve the parts that don’t work well.
The basics are the same, i.e. one-click creation of Qlik Sense apps, using regular Sense apps as templates. Several new features however take the tool to a new level, making it easier to set up, manage and more enterprise grade. Good news thus!
For years I have thought about ways to get data lineage info for all apps in a Qlik Sense Enterprise environment.
It would be super useful to know exactly what apps use a particular data source, as well as vice versa (what data sources are used by a specific app). I know there are commercial tools doing this and much more, but I wanted something easy to use, yet still effective and free.
Same thing for app load scripts: Extracting and storing them to disk in human readable format has more than once save days of work, when something has gone badly wrong in an app. Dumping load scripts to disk was possible in my original Butler tool, but then only one app at a time. So not quite what was needed in an enterprise context.
The latest version fo Butler SOS is out, taking the version number to 3.0. A lot of the code has been fine tuned to better meet the needs of enterprise grade Qlik Sense deployments.
Docker (or some compatible container platform) is now the preferred and recommended way of running Butler SOS. Butler SOS has been developed and tested on Linux and Mac OS, but should in theory run also other Docker enabled platforms.
Version 3 adds a few – but useful – features:
Per-server config option “serverGroup”. Use this to group or categorize servers, for example as being part of a production vs development Qlik Sense cluster. This enables the creation of Grafana dashboards that use Grafana variables to automatically show metrics for all PRODUCTION servers. This greatly simplifies using Butler SOS in large Qlik Sense environments, where servers are frequently added/removed. No need to manually update the Grafana dashboards any more.
Config option “queryPeriod” for controlling how far back querying for Sense log entries should be done. Used together with the logdb.pollingInterval setting, it is now possible to fine tune how often the Qlik Sense log database is queried for errors and warnings.
Docker is one of those tools that have the potential to fundamentally transform how you develop and run software – once you have tried Docker it is hard to imagine going back to something else.
In previous posts we have seen how Butler, Butler SOS and Butler CW can be run as Docker containers.
But we can do even better – why not control all the Butler tools from a single docker-compose file? Maybe even specifying the dependencies on influxdb and mqtt in there too?
Setting this up is incredibly easy – a single docker-compose file tells Docker what containers to use, and some config files tells the Butler tools where to find things.
Following up on previous posts (here and here) about the Butler family of tools being Dockerized, here is another one on the same topic:
Butler SOS can now be run in a Docker container.
This is good news as it makes it a lot easier to set up real-time monitoring of a Qlik Sense enterprise environment, compared to the previous (still working, btw) method of installing Node.js and then running Butler SOS on top of Node.
Following up on the previous post about pulling sensor data from sensors in a Verisure alarm system, here is a slightly more hardware focused post.
One use case that the new Verisure alarm system does not handle nicely is machine-to-machine notification when the alarm is disarmed (turned off). Such notifications are sent to the mobile app – so the data exists somewhere. The problem is that it’s all undocumented and thus hard to build something on top of.
Let’s hack a solution instead – be aware, some soldering required….
I recently upgraded our house alarm system to the latest model from Verisure.
Pretty nice actually, the new system comes with a good mobile app, remote querying of both alarm status and status of the various sensors (smoke, movement etc) that are included in the system. Oh, the new system actually cost less per month too, compared to the old one. Go figure.
Being the geek that I am, I immediately wondered if it was possible to get hold of the sensor readings (temperature and humidity) that were shown in the mobile app.
Turns out it is pretty simple. There is a REST API which can be queried, and from there you get back all kinds of status for the system. With that data nicely structured in a JSON it’s then a breeze to send the data to a MQTT pub-sub broker for later use by whatever system that might need it. Easy in theory.