These days, most of my work is very related to the online world. Building web sites, reviewing web applications, integrating with web services, coordinating people who are far away from each other, etc. Whenever I find a new tool or service or an innovative, interesting idea about working online, I share it in this category.
Personally, I’m not a frequent user of debuggers. Most of the projects and code that I am involved with is easily debugged with good old “die(‘here’)”. But if you are looking for some help on how to use Vim with a debugger, have a look at the “Debugging in Vim” blog post.
Komiser is a really nice tool that provides an overview of your Amazon AWS setup. After a super simple install, you’ll have a web console which visualizes your AWS regions and the resources you run in them. It’s great for getting a quick overview, as well as for some analyses of billing, security and utilization issues.
aws-securitygroup-grapher is a handy tool that can generate a variety of graphs visualizing Amazon Security Groups. It is implemented as an Ansible role and uses GraphViz to produce the results.
This is particularly useful when you need to get familiar with a complex VPC setup by someone else, or when you want to review the results of an automated setup.
“Packets-per-second limits in EC2” is an interesting dive into network limits on the Amazon EC2. Even if you aren’t hitting any limits yet, this article provides plenty of useful information, including benchmarking tools and quick reference links for Enhanced Networking.
The conclusion of the article is:
By running these experiments, we determined that each EC2 instance type has a packet-per-second budget. Surprisingly, this budget goes toward the total of incoming and outgoing packets. Even more surprisingly, the same budget gets split between multiple network interfaces, with some additional performance penalty. This last result informs against using multiple network interfaces when tuning the system for higher networking performance.
The maximum budget for m5.metal and m5.24xlarge is 2.2M packets per second. Given that each HTTP transaction takes at least four packets, we can translate this to a maximum of 550k requests per second on the largest m5 instance with Enhanced Networking enabled.
“Why software projects take longer than you think – a statistical model” is an interesting take on the problem of bad estimations in software projects. I’m not that great with math, but even then the article is very interesting. And there is a lot that I agree with.
Here’s a quote for those of you who couldn’t make it through:
Why software tasks always take longer than you think
Assuming this dataset is representative of software development (questionable!), we can infer some more numbers. We have the parameters for the t-distribution, so we can compute the mean time it takes to complete a task, without knowing the σ for that task is.
While the median blowup factor imputed from this fit is 1x (as before), the 99% percentile blowup factor is 32x, but if you go to 99.99% percentile, it’s a whopping 55 million! One (hand wavy) interpretation is that some tasks end up being essentially impossible to do. In fact, these extreme edge cases have such an outsize impact on the mean, that the mean blowup factor of any task ends up being infinite. This is pretty bad news for people trying to hit deadlines!