Holy on Dev
Defeating Legacy Code with Living Documentation And Business-Level Tests
The big struggle when entering a new code base is distinguishing the essential business logic from the incidental aspect of how it is implemented. Both are just code - but which parts must be there and the way they are and which can be changed? If you don’t know then you fear to change anything. And that is exactly what happened when we took over the application MB. What to do? How to save the code from becoming an incomprehensible mess of legacy code, and dying?! We found an answer: clj-concordion. (Read: Functional Core, Imperative Shell and Specification by Example.) (Click here to read the rest...)
Fixing JSON out-of-memory error with streaming and MapDB
Once upon time, an API returned 0.5 GB JSON and our server crashed. We cried, we re-implemented it with streaming parser and a disk-backed Map and it worked again - but was slow. We measured and tweaked and finally got to a pretty good time and decent memory and CPU consumption. Come to learn about our journey, streaming JSON parsing, and MapDB! (Click here to read the rest...)
Applying Spec-based generative testing to a transformation pipeline - tips and lessons learned
Having the computer generate tests for you, trying tens of devious inputs you would never have thought of, is awesome. There is however far less experience with and knowledge of generative (a.k.a property-based) testing so I would like to share what we have learned and what worked for us when testing an important data transformation pipeline. We mostly leveraged our existing Clojure Spec data specifications to generate the tests, while regularly reaching down to clojure.test.check to create custom generators and for low-level control. (Click here to read the rest...)
Clojure: Common beginner mistakes (WIP)
Common mistakes made by Clojure beginners and style recommendations. (Click here to read the rest...)
How generating test data saved us from angry customers and legal trouble
Me: Simple! First we sum all the raw charges. Then we sum the totals over all categories - the two should be equal. Computer: … Nope! Me: What?! Let me see… Are you telling me that is different from ?! Computer: Yep! That’s how I learned (again) to never use doubles for monetary amounts. I would never have thought of it myself (though I should have), hadn’t we used generative testing to produce random test data (popular troublemakers included) - data I wouldn’t have thought of, such as 0.01 + 0.05 that cannot be represented precisely with a double. Now that we switched safely over to BigDecimals and angry customers and law suits are off the table, you might wonder what is this generative testing about and how does it work. Instead of hardcoding inputs and expected outputs, as in the traditional “example-based” testing, inputs are randomly generated and outputs are checked against rules (“properties”) that you define, such as “the output of should have the same elements and length as ; also, each element is >= its predecessor”. And you can generate as many inputs as you want, for instance 100 is a popular choice. (Click here to read the rest...)
Highlights from the talk 'Exploring four hidden superpowers of Datomic'
During our regular “tech lunch,” we have got our brains blown by the talk Lucas Cavalcanti & Edward Wible - Exploring four hidden superpowers of Datomic (slides) that summarizes the key benefits a startup bank in Brazil got from using this revolutionary database as the core of their technical backbone. We would like to share our highlights from the talk and a few other interesting resources. (Click here to read the rest...)
Translating an enterprise Spring webapp to Clojure
How to translate the concepts and patterns we know from enterprise Java applications to Clojure? It is not just a different syntax, the whole philosophy of the language is different. The thing is, many concepts and patterns do not translate - you just do things differently. We will look shortly at how we can solve common enterprise concerns in Clojure, compared to Java. This post is intended for an experienced Java developer curious about how his object-oriented, enterprise know-how would translate into the world of functional programming. If you are short on time then just scan the Summary table and read Basic principles perhaps together with Clojure primer to make sense of it. (Click here to read the rest...)
Clojure vs Java: Troubleshooting an application in production
I have just gone through the painful experience of troubleshooting a remote Java webapp in a production-like environment and longed for Clojure’s explore-and-edit-running-app REPL. I want to demonstrate and contrast the tools the two languages offer for this case. (Click here to read the rest...)
Clojure vs Java: The benefit of Few Data Structures, Many Functions over Many Unique Classes
In Clojure we use again and again the same data structures and have many functions operating on them. Java programmers, on the other hand, create a unique class for every grouping of data, with its own “API” (getters, setters, return types, …) for accessing and manipulating the data. Having been forced to translate between two such “class APIs” I want to share my experience and thus demonstrate in practical terms the truth in the maxim It is better to have 100 functions operate on one data structure than to have 10 functions operate on 10 data structures. - Alan Perils in Epigrams on Programming (1982) (Click here to read the rest...)
Solution design in Java/OOP vs. Clojure/FP - I/O anywhere or at the boundaries? - experience
As a Clojure developer thrown into an “enterprise” Java/Spring/Groovy application, I have a unique opportunity to experience and think about the differences between functional (FP) and object-oriented programming (OOP) and approach to design. Today I want to compare how the solution would differ for a small subsystem responsible for checking for and progressing the process of fixing data discrepancies. The main question we will explore will be where do we deal with external effects, i.e. I/O. (We are going to explore here an application of the Functional Core, Imperative Shell architecture. You can learn more about it in the Related resources section.) (Click here to read the rest...)
It will only take one hour... (On why programmers suck at estimating and the perils of software development)
“It will only take about an hour,” I said to her. Two days later, a pull request awaits review. Where has all that time gone? What are the sources of delay in software development and how can we make it faster? (Click here to read the rest...)
Java/Spring App Troubleshooting on Steroids with Clojure REPL
(Published originally at the Telia Engineering blog.) We have a Java/Groovy Spring Boot webapp, mainly running a bunch of batch jobs… (Click here to read the rest...)
How to use Clojure 1.10 pREPL to connect to a remote server (WIP)
Clojure 1.10 includes a new, program-friendly REPL or prepl (pronounced as “preppy,” not p-repl). However there is still very little documentation about it, though it is reportedly in making (it is alpha, after all). Here I want to demonstrate how to start it and how to connect to it in a primitive way (I hope to improve the user experience of the client eventually). Update 22/3: Check out O. Caldwell’s Clojure socket prepl cookbook. (Click here to read the rest...)
AWS RDS: How to find out login credentials to the database
To log in to your AWS RDS database (Oracle in my case) you need login credentials, but what are these for a newly created DB? The password is the master user password you entered during DB creation and which you can change via the Console. To find out the master user name: (Click here to read the rest...)
Migrating from Wordpress.com to a static site generated by GatsbyJS
I am moving my blog over from Wordpress.com to a statically generated site using Gatsby. Wordpress has served me well in many years but it… (Click here to read the rest...)
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