Marcin Bunsch

Working in tech is great, until you get called at 3am to fight a fire because something in the tech you built just broke. These situations are stressful, tiring and in general - not fun. Ideally, they would never happen, but that’s not how the world works.

Over the course of my career, I’ve been in more than a few of these crisis situations, and I’ve learned a few things about how to prepare and deal with them. I kept building up notes, and I thought I’d share them with you.

I’m breaking up this process into 3 parts:

• Preparation
• Response
• Recovery

We’re going to go over each of these parts, and I’ll give you some examples of tools that you can use to help you in each of these phases. I’m also going to share learnings from my own experience and tips on how to improve the process.

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In a recent article, provocatively named Death to C, Jon Evans proclaimed that we should no longer use C, rather move to Rust for our low level needs.

The author proposes replacing C with Rust, which looks like a great tool to get awesome performance in a production application and sleep well at night not worrying about a segfault, which a tremendous advantage. He also wants to put C and C++ to rest:

Those old warhorses have served us well, but today they are cavalry in an era of tanks. Let us put them out to pasture and move on.

I understand not wanting to hand-write C code in production to minimise risk, but not abandoning it completely. Studying C gives you - as a developer - enormous benefits and skipping it is a mistake. In the introduction to Learn C The Hard Way, Zed Shaw has brilliantly explained why C:

The C programming language’s only failing is giving you access to what is really there, and telling you the cold hard raw truth. C gives you the red pill. C pulls the curtain back to show you the wizard. C is truth.

Of this quote, I especially like the “truth” bit - three words, with which Zed has captured the essence of C. It is a simple and elegant language, so simple that you hit the walls of gigantic complexity very early on, because you need to do the basic things yourself. It’s extremely difficult to write good, secure C code, which makes it very dangerous, and to quote Zed again:

Why use C then if it’s so dangerous? Because C gives you power over the false reality of abstraction and liberates you from stupidity.

C is the sharp, hurtful reality of how a computer really works, how memory is manipulated and how you’re really just moving bits around. Instead of avoiding it, let’s embrace it. C is difficult, because C is truth.

One of the reasons I built Dotz was to see how it would survive change. The first release was simple exactly for this reason - I planned to return to add more features and see how simple or difficult it would be. I managed to add a new feature to Dotz recently - when you connect the dots so that the lines form a shape (like a rectangle or square), all dots of the same color will disappear, adding to your score. As I introduced this feature, I reflected on this refactoring exercise and SOLID principles.

Step 1: Make it work

My initial approach was to take the easy road by modifying the Move class directly. I realized that it was missing some core functionality, such as getUniqueDotCount or getGroupedDots methods. I’ve also fixed an issue in destroyDots which appeared only after it was forced to remove a lot of dots from the screen.

At the end, I’ve expanded it to check if a square was present and schedule more dots of the same color for removal.

I coded, tested and released it. And that’s when it hit me. I did two things: I fixed issues within the Move class and I’ve modified its behavior. Now, fixing issues is fine. Modifying existing behavior in the same class has short legs - what if I want to add more functionality? What if I wanted to give a choice between game modes?

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Service Object is a pattern of extracting business logic into a separate “service”. It has gotten considerable traction in the Ruby ecosystem and is worth exploring. Steve Lorek described Service Objects in the following way:

A ‘service’ describes system interactions. Usually, these will involve more than one business model in our application.

As an example; we have a User model and this encapsulates a password. If a user has forgotten their password, the business rules dictate that we have to send them an e-mail with a link to reset it. This functionality is a service.

The CodeClimate blog post on refactoring fat models has a good rulebook on when to use this pattern:

• The action is complex (e.g. closing the books at the end of an accounting period)
• The action reaches across multiple models (e.g. an e-commerce purchase using Order, CreditCard and Customer objects)
• The action interacts with an external service (e.g. posting to social networks)
• The action is not a core concern of the underlying model (e.g. sweeping up outdated data after a certain time period).
• There are multiple ways of performing the action (e.g. authenticating with an access token or password). This is the Gang of Four Strategy pattern.

I see Service Objects as a mutation of the Command pattern. They allow separation of business logic from models and controllers, but have further benefits in testing and composition.

Hello method_struct

At Base Lab, we’re implementing Service Objects using the method_struct gem. Let’s go through an example which illustrates the benefits of this approach.

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Ever since Twitter opened up their Analytics, I’ve probably been spending more time on it that I should admit to. It is a gold mine of information about how your tweets perform. As I was looking at the stats, I thought it would be fun to run an experiment on whether the Analytics data can tell me when to tweet.

Step 1: Gather Data

I was very interested in when was the best time to publish to be sure that most people see the tweet. What hours are best? What days of the week?

Unfortunately, Twitter Analytics gives a lot of information about particular tweets, but aggregate reports are missing. As Twitter exposes the ability to export your data into CSV, I decided to move in and hack it.

As I wanted to have everything in one place, I prepared a Chrome extension which introduces a new tab to Twitter where I could do all the hacking I need.

After loading and parsing the CSV via Papaparse and storing it in IndexedDB via PouchDB, I was ready to crunch the numbers.

Here’s how the Week Analysis report ended up looking:

The design is lacking, but it visualizes data well. By looking at it, I concluded that I’m getting best results in the morning (9:00 AM), before lunch (11:00-12:00) and then around 4 PM.

Of course, this was error-prone:

• The hours are when I posted the Tweet. What this means is that I have an average of 150.5 on tweets I post on Mondays at 9 AM. Although not exact, it is still very useful.
• I have no historical data - Twitter starts tracking impressions when you sign up for Analytics.

I did end up with some pointers though, so it was worth to test this hypothesis.

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