Continuing on with the theme of previous cheat sheet articles, this article will help decode the format for Amazon Web Services’ Elastic Compute Cloud (EC2) instance types.
Ok, you've just launched an Amazon EC2 instance (ie, a virtual machine) and you're ready to login and get to work. Just once teeeensy problem though… you have no idea how to actually connect to the instance!
This post will walk through how to log into brand new Linux/BSD and Windows instances (the steps are slightly different for different OS families).
For the past few months I've been involved in a case study project with some colleagues at Cisco where we've been researching what the most relevant software skills are that Cisco's pre-sales engineers could benefit from. We're all freaking experts at Outlook of course (that's a joke ?) but we were interested in the areas of programming, automation, orchestration, databases, analytics, and so on. The end goal of the project was to identify what those relevant skills are, have a plan to identify the current skillset in the field, do that gap analysis and then put forward recommendations on how to close the gap.
This probably sounds really boring and dry, and I don't blame you for thinking that, but I actually chose this case study topic from a list of 8 or so. My motivation was largely selfish: I wanted to see first-hand the outcome of this project because I wanted to know how best to align my own training, study, and career in the software arena. I already believed that to stay relevant as my career moves along that software skills would be essential. It was just a question of what type of skills and in which specific areas.
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.
Cisco Encrypted Traffic Analytics (ETA) sounds just a little bit like magic the first time you hear about it. Cisco is basically proposing that when you turn on ETA, your network can (magically!) detect malicious traffic (ie, malware, trojans, ransomware, etc) inside encrypted flows. Further, Cisco proposes that ETA can differentiate legitimate encrypted traffic from malicious encrypted traffic.
The immediate mental model that springs to mind is that of a web proxy that intercepts HTTP traffic. In order to intercept TLS-encrypted HTTPS traffic, there's a complicated dance that has to happen around building a Certificate Authority, distributing the CA's public certificate to every device that will connect through the proxy and then actually configuring the endpoints and/or network to push the HTTPS traffic to the proxy. This is often referred to as “man-in-the-middle” (MiTM) because the proxy actually breaks into the encrypted session between the client and the server. In the end, the proxy has access to the clear-text communication.
Is ETA using a similar method and breaking into the encrypted session?
In this article, I'm going to use an analogy to describe how ETA does what it does. Afterwards, you should feel more comfortable about how ETA works and not be worried about any magic taking place in your network. ?
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.
Didn't I just write the 2016 statistics post like… last week? Another year has flown by and with it another year of attempting to prioritize my writing. I'll be honest, I'm not optimistic about what I'm going to find when I compare 2017 to 2016. It was a year filled with a lot of change and opportunity so I'll use that as my excuse as to why I didn't write as much or as often as I had planned.
I was thinking though: every year I set a goal of writing more posts than the previous year, but that's only 1 metric to go by. Most of my posts are very detailed and fleshed out. It's nothing to write a post that's 1000 words. I regularly eclipse 2000 words and have even hit 3000 words. Perhaps I should be thinking more about word count and not post count? Certainly a 2000 word post takes more effort than a 1000 word post. On the other hand, word count says nothing about quality and could easily lead to excessive wordiness and run-on posts just to tilt the metrics.
Enough musing. Let's review the data!
Continuing in a tradition I started early this year where I take a look back at the year that just passed, I've again been very fortunate to have had an amazing year, both in my professional and personal lives. Writing this post is my way of forcing myself to stop and take notice of what I was involved in (something I'm not very good at letting myself do in the moment) and also give readers a chance to see the “me” behind the scenes.
Let's go through the list!
For the benefit of readers who haven't worked with Flask or don't know what Flask is, it's a so-called microframework for writing web-based applications in Python. Basically, the framework takes care of all the obvious tasks that are needed to run a web app. Things like talking HTTP to clients, routing incoming requests to the appropriate handler in the app, and formatting output to send back to the client in response to their request. When you use a framework like this, you as the developer can concentrate a lot more on the application logic and worry a lot less about hooking the app into the web.
As you may have guessed from the title of this post, one of the other tasks that Flask manages is logging within the application. For example, if you want to emit a log message when a user logs in or when they upload a new photo, Flask provides a simple interface to generate those log messages.
Flask has a large community of active users built around it and as a result, there's tons of best practice information out there on scaling, talking to a database, and even whole tutorials on how to build full applications. But it's been my experience that there is very little information devoted to the topic of logging. Granted logging isn't very fun and does nothing to get your app to market quicker or increase user counts, but it is really important if you want to get any sort of feedback from the app about what it's doing during normal operation or what exactly happened if something goes wrong.
In this post, I'll look very briefly at how logging works in Flask and then examine three common methods used to record log files within Flask and how each of them can shoot you in the foot. I'll close by offering some recommendations that should keep you on safe ground.