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

Overview

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 (https://www.relay.fm/radar/13) 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 github.com and libraries.io. Obviously non-github.com 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 Libraries.io for answering my incessant questions about Libraries.io’s statistics

Note that I discovered libraries.io 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 Libraries.io’s services (I would suggest that you listen to the Changelog podcast to get a clear understanding of Libraries.io’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

Libraries.io main page

Libraries.io has lots of different ways to look for projects. The keyword section at the bottom seems quite interesting.

Libraries.io 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 old...so 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 libraries.io. The closest thing that I can think of is the “Dependent Repositories” number from Libraries.io’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: *https://libraries.io/rubygems/rails/dependents

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: *https://libraries.io/rubygems/rails/dependent-repositories *

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 https://github.com/search?utf8=%E2%9C%93&q=gem+rails+path%3A%2F&type=Code&ref=searchresults or https://github.com/search?utf8=%E2%9C%93&q=%22gem+rails+5%22+path%3A%2F&type=Code&ref=searchresults 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?

OK – I FOUND IT – I KNOW THE DEFINITION OF POPULAR!

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 Libraries.io’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

OK - I FOUND IT - I KNOW THE DEFINITION OF POPULAR!

(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 libraries.io

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.

Conclusion

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 eli4d.com 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?

Laravel Podcast Episode 36 – Dev School – the unofficial/informal show notes

Summary

I really like the Laravel podcast. It’s a (typically) very short podcast covering both development and the Laravel framework. I like this podcast so much that I recommend it to my PHP students. It’s a nice informal conversation between seasoned PHP developers that covers the Laravel framework, PHP and other general development aspects.

The only down side of this show is that there aren’t any show notes (at least not for recent episodes). I found episode 36 to be really good in terms of pragmatic advice to those beginning to code. I think it will be really useful for my programming students which is why I decided to write a quick post providing my version of the show notes for this specific episode.

Things to note:

  • The episode is great up to time mark 41:40 (i.e. 41 minutes / 40 seconds) and then it deviates into a discussion about comfortable clothing. Feel free to skip this part. Of course if you want fashion hints from seasoned developers – then feel free to listen 🙂

  • The episode location is on the laravel podcast site. However, the linked time marks use Overcast.fm since that site provides a web player to specific time marks and it is my podcast player of choice where I listened to this episode. If you have an iPhone you should definitely give Overcast a try – it’s fully featured and free. Of course if you like it – you should donate.

  • Should you believe anything these guys say? Well – you shouldn’t believe anyone. Take it with a grain of salt and see if it makes sense. I think the perspective of this episode is useful because of the following:

    • Taylor: super backend developer and creator of the Laravel framework; very down-to-earth
    • Jeffrey: implementer of Laravel for his business (Laracasts) that involves teaching so he he has an interesting perspective on the how-to-learn-programming side
    • Matt: started as a ‘designer’ and ended as a front-end developer that now has his own company (so both a developer and business owner perspective)

Detail

I’ve arranged these show notes based on new beginner developer questions and the relevant time marks. I’ve paraphrased the questions but you’ll hear the exact question that Matt asks when you listen to the specified time marks. The usual disclaimers apply.

If I want to be a crack Laravel developer and I’m a complete beginner – where would I start? (01:30)

Time mark: 01:30

An interesting discussion of how to get from being a newbie to becoming experienced in Laravel. But it applies to any interesting framework/language.

What should I build when I’m learning? (14:35)

Time mark: 14:35

A good discussion of whether your learning project(s) should be ‘real’ and whether toy projects are the way to go (short answer: yes).

Boot camps: are they worth it? (15:38)

Time mark: 15:38

This frequently comes up in my in-person class. Up to now I didn’t have a great answer but this part of the episode covers this really well both from a what-do-you-learn perspective and from the job search will-an-employer-hire-me perspective.

What are the quintessential books every backend developer should read? (20:52)

Time mark: 20:52

This part of the podcast covers many more books than what a backend developer would use. There’s some dead air in the podcast for this question when Jeffrey speaks. Here are the books that I deciphered (I will update this if I hear back from Taylor/Matt/Jeffrey via twitter for anything that I missed).

Are there any tricks/pitfalls that you have fallen into as you were learning? (30:41)

Time mark: 30:41

This addresses the Law of the instrument question.

Knowing what you know now is there any one thing you wish you would have known or done when you began to learn programming? (32:36)

Time mark: 32:36

A long time ago I heard the “knowing what you know now” question in a Brian Tracy audio book. It applies to lots of thing including programming.

The clothing question that you can safely skip: is there any piece of clothing that you would fight about if your spouse threw it away? (41:40)

Time mark: 41:40

Thinking of my ripped gray hoodie still makes me sad.

Conclusion

Hopefully you’ve found this useful. Send me a tweet if you did.