nREPL over HTTP(s) with Drawbridge in 2020

Sometimes the only way to get REPL into your production application is to tunnel it over HTTP. nREPL has a transport and Ring handler for that provided by Drawbridge. Heroku has a nice but too dated guide on using nREPL with Drawbridge. I would like to fill the missing bits here.


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AWS Fargate: Troubleshooting the dreaded '`service .. is unhealthy`'

So you have just deployed your Docker container to AWS Fargate but it keeps on restarting with the event “service XYZ (..) is unhealthy ..” and you have no idea why. I have spent many bloody hours here and will gladly share my insights with you. The 3 key questions to ask are:

  1. Do requests for / return 200 OK?

  2. Do they return quickly enough?

  3. Does the service start responding quickly enough after a start?

(+ a bonus question and more troubleshooting tips)


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How Clojure helped us recover from bad data

On a calm autumn morning we got a desperate call from our customer service. Our biggest customer had just started a pilot of our “expense share” functionality - and was missing half of their data. And they absolutely needed them for sending salaries in a few days. We jumped into production, cross-checked the data against a few of the missing employees and quickly determined that they were missing from the incoming invoices. We were able to cut and glue pieces of production code to extract just the information we needed - counts of employees and invoices past, present, and expected for billing runs in the problematic period - and thus identify all affected customers. After a fix in the source system, we were able to repeat the billing runs without writing any data, just to verify it generated the correct results - and write the results later. Combining data from web services and the database, correlating them and massaging them just into the format needed, interactively, with immediate feedback - that was only possible thanks to the code being in Clojure and thanks to Clojure REPL. Without it, the troubleshooting and correction process would have been much more difficult and time-consuming. Let’s look in detail how exactly did it help.


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Leiningen: Split an uberjar into dependencies.jar and app.jar (to optimize Docker layers and AWS Lambda functions)

I want to split my application uberjar into a separate JAR with only the dependencies and a JAR with only the application code so that I can upload them as separate “layers” and thus leverage layer caching. While my code changes frequently and is tiny, the dependencies change rarely and are much bigger. If I can add them as a separate Docker layer or AWS Lambda layer then this can be cached on the server and reused when I upload a new version - saving time, bandwidth, and money.


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Choose Clojure not because it is easy but because it is "`weird`"

When I was deciding what new language to learn, I could have picked the quite familiar Scala but chose instead Clojure - not despite of its lack of object-orientation, its immutable data structures, its too many parentheses on a single line - but because of it. (And because of Paul Graham’s Beating the Averages.) Why?!


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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.) 


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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!


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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.


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Clojure: Common beginner mistakes (WIP)

Common mistakes made by Clojure beginners and style recommendations.


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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 -0.01 + -0.05 is different from -0.06?!
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 (sort list) should have the same elements and length as list; also, each element is >= its predecessor”. And you can generate as many inputs as you want, for instance 100 is a popular choice.


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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.


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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.


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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.


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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)


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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.)


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