The eli4d Gazette – Issue 013

Issue 013: 2016-08-31

Tech Pick

I’ve recently heard a great Mobile Couch episode about dependency injection. Ben and Daniel do an excellent job of demystifying this pattern. Fundamentally, dependency injection is giving an object (via instance variables) access to whatever it depends on rather than letting the object access its dependencies directly.

I looked around for a good example and I found more complication than clarity. The best example that I found can be seen here: I like this example because it starts from a simple example and builds up from it. It also shows the usefulness of this pattern when it comes to testing.

Media Pick

Recently, I’ve heard an excellent Ruby Rogues episode about contempt culture. The episode has a Fresh Air vibe to it, and it focuses on the reality of contempt in the software industry. Whether it’s opinions about the “best” programming language (even though there is no such thing) or specific language based hate (I can’t count the number of times I’ve heard or read a “PHP is horrible” opinion).

Religious wars in software development are not that far off from actual religious war (thankfully they’re less bloody). Whether about languages or programming trends (like “object oriented design is dead, long live functional programming”), I suppose it’s our human tendency to be attracted to a group and its ethos (or lack thereof).

Using the Boring / Old / Popular (BOP) criteria for server side software evaluation


Episode 14 of the “Under the Radar” podcast covered the specifics of how to best architect a back-end service for you mobile-app, web service, web application, and so on. It’s a follow-up to a previous episode ( about the Parse shutdown and the potentially high cost of external dependencies. The one part of this conversation that really caught my ear was around 09:15 and it contained the following interesting approach:

“What you want most of all when choosing server software – if you don’t want to be administering and tweaking your server constantly – what you want is old, boring, and popular. Those 3 things – old, boring, and popular. New and trendy does not always mean better.”

Marco and David emphasize that you should reserve the exciting technology for the customer facing side. Whether it’s your mobile app or a browser side JavaScript framework that will amaze your customers. The back-end of your application, the “infrastructure” should be technology that is boring, old, and popular (lets call it BOP since you can never have enough acronyms) because you want solid reliability in the same way that when you’re home you want a solid source of water and electricity. After all, usually the frontier of front end development is…the front 🙂 (of course this is a generalization for business-to-consumer applications).

A word of thanks

I’ve approached this by looking for numbers and meaning at and Obviously projects (like the Apache web server) cannot be looked at in this way because the direct stats aren’t there.

Special thanks goes out to:

  • Marco and David for the content of their podcast and the BOP idea/approach
  • Rachel Berry from GitHub for answering my questions about the best way to interpret GitHub statistics
  • Andrew Nesbitt from for answering my incessant questions about’s statistics

Note that I discovered through the amazing Changelog podcast (episode 188). If you’re looking for a tool that will help you figure out your open source compliance (as well as many other things) – check out’s services (I would suggest that you listen to the Changelog podcast to get a clear understanding of’s value).

Lets break this down

If you’re new to this, the first question is where to begin?

I think the place to start is to find some sort of categories that are related to back-end technologies. After all, there’s no point to compare Linux (an operating system) to Ruby on Rails (a web framework).

Two sources that seem interesting in terms of such categories are:

GitHub’s showcases page

In terms of back-end technologies (i.e. server side software) that are shown on the showcases pages the following areas seem more relevant:

  • Web application frameworks
  • Programming languages
  • Open Source Operating Systems
  • Projects that power GitHub (i.e. seeing the components that run a huge enerprise like GitHub – some of these components will likely fit the BOP model; some of course will not fit this since GitHub can afford to hire devs for very niche and young projects)

Note: The image below is an aggregation of the 3 pages of this showcase and the “Search showcases” fields is great to finding a category for a specific project.

GitHub's showcases page main page has lots of different ways to look for projects. The keyword section at the bottom seems quite interesting. main page

Boring, Old, Popular: What does ‘Old’ mean?

While I initially wanted to start with ‘Boring’ because BOP starts with it (and BOP is memorable), I realized that the better way was to start with the property that is easiest to figure out, or at least something that seemed easier.

What does ‘old’ mean in terms of software? Is 2 year old software ‘old’, or does 10 year old software count as ‘old’? (in the case of this post ‘software’ means ‘open source project’)

The definitive answer is “it depends” but that doesn’t help much. I think the better question is “is this piece of software ‘old’ within its category?” In the following examples, we’ll look at the web applications framework showcase on GitHub.

Boring, Old, Popular:  What does 'Old' mean?

Rails is 12 years old…that’s definitely old – isn’t it?

Rails is 12 years old...that's definitely old - isn't it?

Express is 6 years old

Express is 6 years old

Laravel is 5 years old…so what gives?

Laravel is 5 years what gives?

Meteor is 5 years old….but is that old?

Meteor is 5 years old....but is that old?

What about the age of the Internet?

Good lord – that depends on your definition. Is it starting from the 1950s when computers were more widely used by governments and universities?

If I’m going to pick a number – I’m going to use HTTP as my criteria so: 2016 – 1989 = 27 years.

What about the age of the Internet?

Damn it – what is ‘old’?

I was tempted to use log2 to help figure the numbers (because logarithms are COOL), but then I thought about what it means to be ‘old’ as an adult and used that to figure out ages of adolescence, young adulthood, middle age, and old age. Here’s an imperfect attempt at figuring this (I use percentage of LEB to help with range indication for age stages).

Note that I’m using Soulver for these calculations (the best-est ‘human’ usable spreadsheet program out there).

Damn it - what is 'old'?

So if I use the age of the Internet as 27

Umm…this is a bit of a chicken and egg thing in terms of current technology and the origin of technology.

So if I use the age of the Internet as 27

Lets make InternetLEB 16

I definitely feel that Rails is ‘old’. What if I take 16 as the InternetLEB. 2000 seems like the ‘right’ year for Web 1.5/2.0 – doesn’t it?

This makes more sense to me but you can picke whatever InternetLEB works for you. So here’s a criteria of judging the age of a project. Based on the Marco/David criteria – you would want a project that is in the middle-age to old-age area. That is the definition that I’m picking for the ‘Old’ part from the BOP criteria.

Lets make InternetLEB 16

Boring, Old, Popular: What does ‘Boring’ mean?

Stepping back for a second to the Under the Radar episode about this whole BOP criteria, the discussion centers around backend software. Software that resides on the server, software that is supposed to be rock steady so you don’t have to worry about your web site or web service falling down on its face on a frequent basis. So we’re talking ‘boring’ in this context, not ‘boring’ as in “uninteresting and tiresome; dull.”

Still, what’s a better definition in this context?

My definition for this is “software that has clarity in terms of usage and is used in many projects because of this clarity”. To me ‘clarity’ refers to a couple of things:

  • how it is used in the context of application/service (i.e. well defined use)
  • used by many others, which in turn leads to clarity in terms of direct documentation or indirect documentation (i.e. stack overflow answers that add up to common and clear usage practices)

Now in terms of hard numbers – I’m not sure how to define and discover ‘boring’ in terms of GitHub or The closest thing that I can think of is the “Dependent Repositories” number from’s SourceRank number (example shown for Rails). I was unclear about the difference between “Dependent Projects” and “Dependent Repositories” and I got the following clarification from Andrew Nesbitt:

*Dependent repos and dependent projects are two separate things, for dependent projects of a rubygem, it’s the number of other projects that list that as a dependencies, for rails there are ~7940 other rubygems that depend on it: *

For dependent repos, it’s every Github repository that has rails listed as a dependency in it’s Gemfile or Gemfile.lock, which there are around 60,000: * *

I asked Rachel Berry if there was anything equivalent on GitHub and there didn’t seem to be anything that was directly equivalent. She suggested the use of code search to provide a rough statistic. So something like or could provide a possible alternative. The problem with this approach is that you need to know how a dependency is included and then deal with the various variations in inclusion strings (besides other issues like different package managers for different software).

Overall, I don’t think there is any “hard” number that can easily capture the ‘boring’ criteria. I think that in this case ‘boring’ is really the result of looking at ‘old’ and ‘popular’. So instead of the BOP criteria it should perhaps be (B)OP or B/OP. Moving forward from this point – I’m going to go with (B)OP.

Boring, Old, Popular:  What does 'Boring' mean?

Boring, Old, Popular: What does ‘Popular’ mean?

I left the “best” for last – POPULARITY. What the heck is ‘popular’ when it comes to the BOP criteria?

Is popularity based on GitHub stars?

How useful are GitHub stars in evaluating popularity? They seem somewhat transient and unreliable for this criteria.

Is popularity based on GitHub stars?

What about popularity based on GitHub forks?

Forks by their very nature are other people’s experimentation with a project. Of course there could be upstream contribution but how much of forks are actual contributions back to the project?

Forks seem like a way of learning and modifying a project’s code but I don’t think that they have anything to do with popularity.

What about popularity based on GitHub forks?

What about project members?

So the “Members” graph is a visual representation of the Forks number (i.e. “members” of the fork network). It’s another view of forks, and therefore its ‘popularity’ usefulness is questionable.

What about project members?

What about a project’s contributors as a reflection of popularity?

I think that this is similar to forks – specific people being interested in a project for their own reasons.

What about a project's contributors as a reflection of popularity?

Something that ‘trends’ is popular – isn’t it?

Something that is trending may reflect momentary popularity. But it is certainly in conflict with the ‘old’ and ‘boring’ criteria, so this is definitely not a good measure.

Something that 'trends' is popular - isn't it?


Actually I don’t but I’ll take a run at it anyway.

I don’t know what’s popular or how to best evaluate popular in terms of the BOP criteria. Maybe it’s one of those I’ll know it when I see it things. Still, it doesn’t help anyone who is new to backend software infrastructure. The best thing that I can come with at this point is’s SourceRank number as a decent data point for popularity. Is it the best? Probably not. But I don’t see anything that’s better at this point.

Note: We need to keep in mind that log values are used in the creation of SourceRank so a difference of 2 between the SourceRank numbers of two projects could be quite significant


(B)OP Comparison Example

So essentially – the (B)OP criteria boils down more to the O and P, since B falls under O or P – your choice.

  • Old = age based on the previously mentioned age/stage criteria using the year 2000 as a baseline
  • Popular = SourceRank at this point or using a GitHub source search if the project is unavailable on

With the above in mind – lets compare Rails and Express.

The (B)OP criteria for Rails

So for Rails we’re looking at:

  • Old = 12 years with an age factor of %75; so its at middle-age about to hit old-age
  • Popular = SourceRank of 28

The (B)OP criteria for Rails

The (B)OP criteria for Express

So for Express we’re looking at:

  • Old = 6 years with an age factor of %44; so its at middle-age
  • Popular = SourceRank of 26

The (B)OP criteria for Express

Which to choose?

So all things being equal (discounting for things like experience in Ruby/JavaScript which could easily change the decision), the choice in this case would be Rails. This is due both the O and P factors. Granted, other comparisons might be much closer, and then it comes to preferences of programming language, educational interest in a particular project or technology, and time for experimentation and implementation.


So in summary – make your back-end server and services the best they could be by choosing the most (B)OPish (boring, old, and popular) technology when looking at the server side level of your technology stack. This advice would seem to contradict the “I want to develop on the latest and greatest technology”, but it is the best path to system administration sanity and it takes away nothing in terms of the fun part of your product and using the latest/greatest in there.

Some other resources that I came across

While researching and reflecting on this post I came across some resources that might be useful for those that are looking for ways to distinguish different projects (this is not limited to server side type of projects):

Some other resources that I came across

About this post

This post was written by @eli4d and it originally appeared on on March 10, 2016.

The Laravel Podcast Episode 42 and the Meaning of …

I really enjoyed last week’s Laravel Podcast episode 42. Now since it is episode number 42 – I expected it to contain the answer to the ultimate question of development.

Now when you listen to the episode, you might think that the ultimate question that’s being answered is “which is the best object relational mapping approach/pattern – ActiveRecord pattern or the Data Mapper pattern?”

Or perhaps the ultimate question that’s being answered is “Should the ‘Single Responsibility Principle’ be violated when it comes to ORMs?”

Of course you need to listen to episode 42 to make your own decision. Perhaps it’s all ORM drama and dogma that is just a mystery wrapped in a Twinkie.

Personally, I think that the ultimate question is “how should you approach feature creation when it comes to software development?” And the answer is stated at the 46th minute of episode 42 (if only it was the 42nd minute…it would have been perfect…it’s time to repeat ‘serenity now’^100 and come to terms with this lack of symmetry). So what is the answer is:

“Don’t do it until you need it.”

Sounds simple – doesn’t it?

Fullstack Radio Podcast Episode with DHH – shaping your technical patterns based on your organizational patterns

On the Fullstack Radio Podcast this week there is a great technical/design discussion with DHH about technical versus organizational patterns, Basecamp 3 and Ruby on Rails 5. Sadly there was not enough cowbell in the form of curse words (only around 5 🙂 ). I kid around about this but one of the great things about DHH is his opinionated and eminently pragmatic approach. He justifies his reasons really well and he stands his ground regardless of the sh*t storms that stir up around him.

Beyond all the technical choices and decisions for Basecamp 3 the discussion that caught my attention was the one about technical patterns versus organizational patterns (starting around 09:19 and ending around 18:45. Most outlets of technical information (whether high profile developers, companies, etc…) focus on architectural patterns and there’s never any talk about organizational patterns. In other words, does the architectural pattern that you choose fit your organizational pattern?

DHH discusses the intersection between organizational patterns and technical patterns. For a small team (like Basecamp’s) of 12 developer/designers a micro-services architecture would be disastrous in terms of implementation and maintenance. Whereas micro-services might be a perfect fit for an organization like AWS. In the case of Basecamp 3 the organizational pattern (i.e. very small dev team) causes the following choices in architectural patterns:

  • hybrid native apps (i.e. do as much on the server as possible with fast web views while doing native side optimizations for high fidelity features)
  • Basecamp 3 as a “majestic monolith” rather than a constellation of micro-service (11:08)

The point is that you have to fit your technical pattern to your organizational pattern, not the other way around. The question fundamentally is: “does this technical pattern fit our organizational shape?”

Best quote of the episode: “whatever Facebook is doing do the complete opposite of that and in many cases you’re closer to finding patterns to your organizational shape if you’re a company of 5, 10, or under 50” (13:50). Basically, trying to clone architectural patterns of companies with unlimited resources is a very bad approach.

Micro-services make complete sense for someone like Amazon (15:05). Amazon has lots of people and lots of business units. Amazon was an early adopter of service oriented architecture. Team sizes are what they are at Amazon but you need teams to collaborate, so micro-services fit this model.

The majestic monolith has wrongly been discarded because (second best quote) “people have been looking at giants for inspiration for ants” (17:42).

This is a very interesting approach. I never thought about it in this way always looking at the technical patterns. I’ve been at start-ups where the software architect is so focused and in love with technical patterns that s/he loses all perspective of anything else. In fact, I don’t recall any start-up where the organizational pattern shaped the decisions of the architectural pattern.

For RoR enthusiasts there are lots of Basecamp and RoR 5 information beyond the above section.

More DHH info can be found here: