Create custom jre in Docker

I recently wrote a Java application and wanted to run it in a Docker container. Unfortunately, my program needs a module from the Java jdk, which is why I can’t run it in the standard jre. When I run the program with the jdk, everything works but I have much more than I actually need and a 390MB docker container.

That’s why I set out to build my own jre, cut-down exactly to my application needs.

For this I found two Gradle plugins that help me. Jdeps checks the dependencies of an application and can summarise them in a file. This file can be used as input for the Jlink plugin. Jlink builds its own jre from it.

In the following I will present a way to build a custom jre with gradle and the two plugins in a multistage dockerfile and run an application in it.

Configuration in Gradle

First we need to do some configuration in gradle.build file. To use jdeps, the application must be packaged as an executable jar file and all dependencies must be copied in a seperate folder . First we create a task that copies all current dependencies to folder named lib. For the jar, we need to define the main class and set the class path to search for all dependencies in the lib folder.

// copies all the jar dependencies of your app to the lib folder
task copyDependencies(type: Copy) {
    from configurations.runtimeClasspath
    into "$buildDir/lib"
}

jar {
    manifest {
        attributes["Main-Class"] = "Main"
        attributes["Class-Path"] = configurations.runtimeClasspath
            .collect{'lib/'+it.getName()}.join(' ')
    }
}

Docker Image

Now we can get to work on the Docker image.

As a first step, we build our jre in a Java jdk. To do this, we run the CopyDependencies task we just created and build the Java application with gradle. Then we let jdeps collect all dependencies from the lib folder.
The output is written to the file jre-deps.info. It is therefore important that no errors or warnings are output, which is set with the -q parameter. The print-module-deps is crucial so that the dependencies are output and saved in the file.

The file is now passed to jlink and a custom-fit jre for the application is built from it. The parameters set in the call also reduce the size. The settings can be read in detail in the plugin documentation linked above.

FROM eclipse-temurin:17-jdk-alpine AS jre-build

COPY . /app
WORKDIR /app

RUN chmod u+x gradlew; ./gradlew copyDependencies; ./gradlew build

# find JDK dependencies dynamically from jar
RUN jdeps \
--ignore-missing-deps \
-q \
--multi-release 17 \
--print-module-deps \
--class-path build/lib/* \
build/libs/app-*.jar > jre-deps.info

RUN jlink \
--compress 2 \
--strip-java-debug-attributes \
--no-header-files \
--no-man-pages \
--output jre \
--add-modules $(cat jre-deps.info)

FROM alpine:3.17.0
WORKDIR /deployment

# copy the custom JRE produced from jlink
COPY --from=jre-build /app/jre jre
# copy the app dependencies
COPY --from=jre-build /app/build/lib/* lib/
# copy the app
COPY --from=jre-build /app/build/libs/app-*.jar app.jar

# run in user context
RUN useradd schneide
RUN chown schneide:schneide .
USER schneide

# run the app on startup
CMD jre/bin/java -jar app.jar

In the second Docker stage, a smaller image is used and only the created jre, the dependencies and the app.jar are copied into it. The app is then executed with the jre.

In my case, I was able to reduce the Docker container size to 110 MB with the alpine image, which is less than a third. Or, using a ubuntu:jammy image, to 182 MB, which is also more than half.

Using custom Docker containers for development with WebStorm & Co.

Docker has become one of the go-to tools of many developers these days. Not because any project should implement as many technological buzz words per se, but due to their great deal of flexibility compared with their small hassle of setup.

For stuff like node-based applications, using a Dev Container is useful because in principle, you do not need to have any of the npm stuff on your actual machine – not only you avoid having these monstrous node_modules folders, but also avoid having accidental dependencies on some specific configuration that might hold true on your device, but not generally.

For some of these reasons probably, JetBrains included Docker Dev Containers as a kind of “remote” development. In a sense, a docker container can be thought of as a remote machine, regardless of the fact that it shares your local hardware and is just a software abstraction.

In my opinion, JetBrains usually does great software, but there is some weird behaviour in their usage of Docker Dev Containers and it took us a while to find a quite general and IDE-independent solution; I’ll just use WebStorm as an example of something that appeared unusually hard to tame. I guess it will become better eventually.

For now, one might think of using the built-in config like:

  1. New Run Configuration -> npm
  2. Node Interpreter: “…”
  3. “+” -> Add Remote… -> “Docker”
  4. Use an image of your choice, either one of the node base images or a custom one (see below) with its corresponding tag

Now for reasons that seem to be completely undocumented and unavoidable (tell me if you know more!), the IDE forces you to then mount your project to /opt/project inside this container, where it gets mirrored during runtime to somewhere /tmp/<temporary uuid>/ – and in several of our projects (due to our folder structure which is not even particularly abnormal) this made this option to be completely unusable.

The way one can work without these strange idiosyncrasies is as follows:

First, create a Dockerfile in which you do all the required setup. It might be an optional idea to set the user, away from “root” to something more restricted like “node” (even though in development, you probably have your eyes on everything nevertheless). You can do more custom setup here. This can look like

FROM node:16.18.0-bullseye-slim

WORKDIR /your-home-inside-container
RUN chown node .

COPY package.json package-lock.json /your-home-inside-container

USER node

RUN npm ci --ignore-scripts

# COPY <whatever you might want> <where you want it inside>

EXPOSE 3000

CMD npm start

From that Dockerfile, build a local image in the same folder like:

# you might need -f if the Dockerfile is not named "Dockerfile"
docker build -t your-dev-image .

Then, create a new Run Configuration but choose “Shell script” (not npm)

docker run -it --rm --entrypoint= -v ${PWD}/src:/your-home-inside-container/src -p 0.0.0.0:3000:3000 your-dev-image

You might use a different “-p” port forwarding if you do not want to have your development server broadcasting on port 3000 (another advantage of Dev Containers, you can easily run multiple instances on different ports).

This is about the whole magic. But there are two further things that could be important here:

Hot Reloading (live updating whenever source files change)

This is done rather easily, however seems to change once in a while. We figured out that at least if you are using react-scripts@5.0.1 (which is what “npm start” addresses, unless you do that differently), you just need to set the environment variable “WATCHPACK_POLLING=true”. I.e put that in your Dockerfile a

ENV WATCHPACK_POLLING true

or pass it into your docker run ... -e WATCHPACK_POLLING=true ... your-dev-image line

Routing a development proxy to some “local host”

If your software e.g. adresses a backend that is running on your development machine or another Docker Dev Container, it can not just access that host from inside the Docker container. Neither is the port forwarding via “-p …:…” of any use, because that addresses the other direction – i.e. what port from the container is exposed to outside access – here, we go the other direction.

When the software inside the container would actually want to address “localhost”, it needs to be directed at the host under which your local machine appears. Docker has a special hostname for that and it is host.docker.internal

I.e. if your local backend is running on “localhost:8080” on your machine, you need to tell your Dev Container to direct its requests to “host.docker.internal:8080”.

In one of our projects, we needed some specific control over the proxy that the React development server gives you and here is way to gain that control – add a “setupProxy.js” inside your src/ folder and put in it something like

const { createProxyMiddleware } = require('http-proxy-middleware');

module.exports = function(app) {
    if (process.env.LOCAL_DEVELOPMENT) {
        return;
    }

    let httpProxyMiddleware = createProxyMiddleware({
        target: process.env.REACT_APP_PROXY || 'http://localhost:8080',
        changeOrigin: true,
    });
    app.use('/api', httpProxyMiddleware); // change to your needs accordingly
};

This way, one can always change the address via setting a REACT_APP_PROXY environment variable as in the step above; and one can also disable the whole proxying by setting the LOCAL_DEVELOPMENT env variable to true. Name these as you like, and you can even extend this setupProxy to include web sockets or different proxies for different routes, if you have any questions on that, just comment below 🙂

Improving my C++ time queue

Another code snippet that can be found in a few of my projects is the “time queue”, which is a simple ‘priority queue’ style data structure that I use to defer actions to a later time.

With this specific data structure, I have multiple implementations that clearly came from the same source. One indicator for that is a snarky comment in both about how std::list is clearly not the best choice for the underlying data structure. They have diverged a bit since then though.

Requirements

In my use case not use time points, but only durations in standard-library nomenclature. This is a pretty restrictive requirement, because otherwise any priority queue (e.g. from boost or even from the standard library) can be used quite well. On the other hand, it allows me to use floating-point durations with predictable accuracy. The queue has two important functions:

  1. insert to insert a timeout duration and a payload.
  2. tick is called with a specific duration and then reports the payloads that have timed out since their insertions.

Typically tick is called a lot more frequently than insert, and it should be fast. The payload is typically something like a std::function or an id for a state-machine that needs to be pulsed.

The basic idea is to only keep the duration difference to the previous item in the list. Only the first item keeps its total timeout. This way, when tick is called, usually only the first item needs to be updated. tick only has to touch more items when they time out.

Simple Implementation

One of the implementations for void insert(TimeType timeout, PayloadType payload) looks like this:

if (tick_active_)
{
  deferred_.push_back({ .remaining = after, .payload = std::move(payload) });
  return;
}

auto i = queue_.begin();
for (; i != queue_.end() && timeout > i->remaining; ++i)
  timeout -= i->remaining;

if (i != queue_.end())
  i->remaining -= timeout;

queue_.insert(i, { .remaining = after, .payload = std::move(payload) });

There is a special case there that guards against inserting into queue_ (which is still a very bad std::list) by instead inserting into deferred_ (which is a std::vector, phew). We will see why this is useful in the implementation for template void tick(TimeType delta, Executor execute):

tick_active_ = true;
auto i = queue_.begin();
for (; i != queue_.end() && delta >= i->remaining; ++i)
{
  delta -= i->remaining;
  execute(i->payload);
}

if (i != queue_.end())
  i->remaining -= delta;

queue_.erase(queue_.begin(), i);
tick_active_ = false;

while (!deferred_.empty())
{
  auto& entry = deferred_.back();
  insert(entry.remaining, std::move(entry.payload));
  deferred_.pop_back();
}

The timed out items are reported via a callback that is supplied as Executor execute. Of course, these can do anything, including inserting new items, which can invalidate the iterator. This is a common use case, in fact, as many deferred actions will naturally want follow ups (let’s ignore for the moment that the implementation is nowhere near exception safe…). The items that were deferred to deferred_ in insert get added to queue_ after the iteration is complete.

This worked well enough to ship, but the other implementation had another good idea. Instead of reporting the timed-out items to a callback, it just returned them in a vector. The whole tick_active_ guard becomes unnecessary, as any processing on the returned items is naturally deferred until after the iteration:

std::vector<PayloadType> tick(TimeType delta)
{
  std::vector<PayloadType> result;
  auto i = queue_.begin();
  for (; i != queue_.end() && delta >= i->remaining; ++i)
  {
    delta -= i->remaining;
    result.push_back(i->payload);
  }

  if (i != queue_.end())
    i->remaining -= delta;

  queue_.erase(queue_.begin(), i);
  return result;
}

This solves the insert-while-tick problem, and lets us use the result neatly in a range-based for-loop like this: for (auto const& payload : queue.tick(delta)) {}. Which I personally always find a little bit nicer than inversion-of-control. However, the cost is at least one extra allocation for timed-out items. This might be acceptable, but maybe we can do better for very little extra complexity.

Return of the second list

Edit: The previous version of this article tried to keep the timed-out items at the beginning of the vector before returning them as a std::span. As commenter Steffen pointed out, this again prevents us from inserting while iterating on the result, as any insert might invalidate the backing-vector.

We can get rid of the allocation for most of the tick calls, even if they return a non-empty list. Remember that a std::vector does not deallocate its capacity even when it’s cleared unless that is explicitly requested, e.g. via shrink_to_fit. So instead of returning a new vector each time, we’re keeping one around for the timed out items and return a const-ref to it from tick:

std::vector<PayloadType> const& tick(TimeType delta)
{
  timed_out_.clear();
  auto i = queue_.begin();
  for (; i != queue_.end() && delta >= i->remaining; ++i)
  {
    delta -= i->remaining;
    timed_out_.push_back(std::move(i->payload));
  }

  if (i != queue_.end())
    i->remaining -= delta;

  queue_.erase(queue_.begin(), i);
  return timed_out_;
}

This solution is pretty similar to the deferred list from the first version, but instead of ‘locking’ the main list while iterating, we’re now separating the items we’re iterating on.

Web Security for Frontend and Backend

The web is everywhere and we use it for tons of important tasks like online banking, shopping and communication. So it becomes increasingly important to implement proper security. As attacks like cross-site scripting (XSS) or cross-site request forgery (CSRF) are wide-spread browsers, web standards designers and web application developers implement more and more mechanisms to make such attacks harder or even impossible. This puts a certain burden on both frontend and backend developers.

Since security is hard and should not be an afterthought I would like to give you some advice when implementing a web app using a Javascript-frontend and a backend service written in some of the common languages/frameworks like .NET, Micronaut, Javalin, Flask or the like.

Frontend advice

I prefer traditional cookie-based sessions to JWT-based approaches for interactive web frontends because of simplicity, browser support and the possibility to use it without Javascript. For service-to-service communication bearer tokens of some kind may be more appropriate. Your Javascript client has to include the credentials in the fetch() calls to cause the browser to send the cookie.

Unfortunately, incorrect use of cookies may be insecure, so be sure to check up-to-date advice on cookies; see some hints below in the backend part because cookies are configured and issued there.

Backend advice

Modern web security requires additional measures on the server side to ensure secure authentication and communication with web clients. You should use https whereever possible to gain at least transport security and avoid many cases of sniffing credentials or changing content between client and backend.

Improving security of cookies

First of all, cookies should be HttpOnly so that scripts cannot access the contents of a cookie. Furthermore you should ideally set the SameSite and Secure attributes appropriately and use https whenever possible. That way you have mitigated the most common attacks on your session handling and authentication.

Another bonus for cookies is that browsers can inform you about problems with your cookie setup:

Configuring Cross-Origin Resource Sharing (CORS)

Nowadays it is common for web app to be served from a different host than the backend API. This is a potential problem because attackers may sneak scripts into the browser of a user and use the existing session to access the resources in an illegal way. Therefore another means of improving security of web apps running in browsers was introduced with the access control using CORS.

For browsers to be able to prevent or allow requests to certain resources the backend has to provide appropriate Access-Control-headers, most notably Access-Control-Allow-Origin and Access-Control-Allow-Credentials. Make sure to set these values correctly or your frontend will have trouble to access your backend or you introduce a potential security whole.

Fortunately many web frameworks make it easy to configure CORS, see Micronaut documentation for example.

Conclusion

Security is always important and browser vendors keep implementing additional measures to mitigate problems in the current web environment. Make sure you keep up with the latest advice and measures and implement them in your applications.

Applying the Golden Circle to software development

When I was a young and impressionable software developer (1998), the slogan of the year was “the code is the documentation”. This essentially meant that comments, and (inline) code comments in particular, were a sign of bad code. The reasoning was (and still is) that if you can’t articulate your ideas clear enough in source code, adding additional text won’t rescue your communication. “Communication” is the conveying of what you want to accomplish with your code towards two listeners: the computer and the next human that works with your code. The computer is often the easier part, because it just does as it is told without interpretation.

Some years later (2004), Jeff Atwood, the author of the influential codinghorror blog, still condemns most of the comments we could find in contemporary source code. But there is more nuance than just “comments are bad” and it is cleared up later (2006) that there is a difference in what should be expressed in source code and what should be expressed in comments. Yes, you should write comments, but the “right kind”.

According to Jeff Atwood, the source code contains the “how”. It tells the story in all its details for the next human and the computer. The comments, on the other hand, are not intended for the computer and shouldn’t contain details. They should contain the “why”, the high-level picture and the motivation behind the specific “how” that we find.

The code tells you how, the comments tell you why” (2006) is a great way to describe the expected content of both “layers”.

But my feeling was (and still is) that something is missing in that description. My programs aren’t just code and comments, there are more things that I try to tell my story with. And just a few weeks ago, I had a sudden idea that I might be able to describe what that missing piece could be. It is just an idea and the puzzle probably is still missing pieces, but it feels “right” enough that I want to write this blog post to discuss the idea. But I need to talk about something else first.

In 2009, Simon Sinek presents the Golden Circle to the world. The TED talk is probably the most energetic piece of explanation in human history. The Golden Circle is “the world’s simplest idea”, defining three layers of “clarity” to actions:

  • What: The “lowest” level, meaning that every person knows “what they are doing”.
  • How: The “intermediate” level. Some people know “how they do it”. They explicitly choose their method of doing and can reason about it.
  • Why: The “highest” level. According to Simon Sinek, only a few people know “why they do it”. He talks about “purpose, cause and belief”.

If you don’t know about the Golden Circle yet, please watch the 20 minutes TED talk while I wait here for you. If you want to think about it first – I’m patient. The Golden Circle has inspired and guided me ever since. Not that I’m very good at applying it in my business or personal life, but it stayed with me and gave me a coordinate system to categorize things.

And with that categorization practice, I feel as if the layers in Jeff Atwood’s blog post from 2006 are misnamed and one crucial layer is missing:

  • What: The code tells you what (not how!). It is the detailed step-by-step recipe to replicate a behaviour. It is so simple that even a computer can do it, without being aware of it.
  • How: This is the missing layer. I want to talk about it in a minute.
  • Why: That’s the part that is correctly placed and named: The why of a story requires the spoken word. Only comments are viable for this kind of information. The computer (as of today) has no understanding what any of it means.

The missing layer is the “How”. In my idea, I envisioned that everything that is deliberately put in the code, but not readily understood by a computer, like structures, patterns, idioms or even names (I call them “activated comments”) are there for the “How”. We structure our code not because the computer requires it, the compiling stages of our programming languages even interfold and compact it until it is a binary blob. We don’t name our variables and types because the computer would learn something from them. The first thing a compiler does is to shorten our names to unreadable “symbols”. Most of our patterns get replaced by other, more minute patterns that the compiler puts into place. We put these things in our code because it helps us understand “how the story goes”. It provides us with guidance how to make sense of the mess. The structure tells you how to approach the program.

The computer only knows about “What”. There are maybe some indications about a simple “How”-awareness in some technologies, but most of the time, the computer is deaf to human communication.

Which brings me to my description of code and comment, as an updated version of Jeff Atwood’s motto:

“The code tells you what, the structure tell you how and the comments tell you why”

By “structure”, I mean everything that gets lost during translation for the computer, but is visible for the human reader. It entails high-level things like “architecture” or “code design” and lower-end decisions like names and formatting. If you have a better word for it, let me know!

I hope that this blog post inspired you to have a thought yourself. Don’t hesitate and tell us your thought in the comment below.

SQLite in ASP.NET 6.0: Access your database file via HTTP Endpoint

It is one of our fundamental principles to always choose the most-easy-while-capable tool for a job. For this, we try not to shower our customers with the newest, most hip technology available, but to use a technology stack we are

  • comfortable with
  • quick to provide the required minimum of customer value
  • keeping enough options open in order anything changes

One of the heavily affected aspects in that regard is the choice of data storage. There are a lot of different design paradigms one can choose from, but with the “most easy” aspect at hand, the question mostly resolves around the needs of the customer, not the wants (or “might be useful one day”) of the developer.

If your customer already has their PostgreSQL databases distributed in their Kubernetes as an example, it might be advisable to aim for that. If the customer does not have any integrated structure yet, I start with the question:

Is anything more necessary than a single-file database?

For one of our ASP.NET 6.0 applications, this was answered with the choice of Sqlite, due to it being native to the Microsoft universe including Entity Framework, which has many common use cases already answered, i.e. gives you way of caring about your application logic more than their database abstractions.

(It might be said that for .NET, an interesting project seems to have been LiteDB, which also operates on a single database file, but at the time of this writing, seems to have gone stale in development / support, and therefore fell out of my favour soon. Sad.).

Now we have a project in which we are closely in touch with the customer and their live system, very often had it been useful to access their platform and take a snapshot of the database for backup or assurance of our logic, and with the technical overhead in that specific case (which required several steps of sequentially granting remote access), I thought myself:

Why can’t I have a (sufficiently secured) HTTP endpoint that gives me this SQLite file as a File download?

The solution was a bit tricky because either the file was not read-accessible during that HTTP request (having been open already), the filestream was not possible because it was being closed too early, or the encoding of the resulting file would not fit. What finally worked was:

        private readonly static System.Text.Encoding enc1252 =
            CodePagesEncodingProvider.Instance.GetEncoding(1252);

        [HttpGet("database")]
        public ActionResult GetDatabase()
        {
            var dataSource = "sqlite.db";
            if (!System.IO.File.Exists(dataSource))
            {
                return NotFound(dataSource);
            }
            db.SaveChanges();
            // Note: CloseConnection() was not required!

            using var fs = new FileStream(dataSource, FileMode.Open, FileAccess.Read, FileShare.ReadWrite);
            var reader = new StreamReader(fs, enc1252);
            var data = reader.ReadToEnd();
            var ms = new MemoryStream(enc1252.GetBytes(data));
            return new FileStreamResult(ms, "application/octet-stream");
        }

Feel free to comment on that way because I found it more than none-trivial to arrive there, but maybe I missed something obvious. Some definite stumbling stones definitively were in

  • The Mime Type “application/octet-stream”, which for some reason would not work with the more adequately sounding choice “application/x-sqlite3” – I have no idea why.
  • The Encoding, which on our system was the Windows CodePages-1252 default, which needed to be specified not only in the interpretation of our bytes stream (second location), but also in the definition of the StreamReader itself (first location).
  • Please note that if your database is encoded via CP-1252, you also need the System.Text.Encoding.CodePages package (available via NuGet)
  • What looks like a missing “using”, is really intentional: If the StreamReader was opened with “using var reader = …”, it had the effect of being disposed before the request was handled correctly – I ran into an error of FileStreamResult: “Cannot access a closed Stream.” – keeping the StreamReader open solved that and the internet told me that this is still not a memory leak; the StreamReader reader gets disposed when the FileStream fs is disposed (see the using in front of that), but it still feels weird.

If you have any comments on that, I’d be very glad to learn from them, but if you don’t and you just have another use case for that problem – I’m happy to help!

Simple abstractions are good abstractions

I think that a lot of accidental complexity in software is produced by not picking the simplest abstraction for the job. Let me lead with an example: Consider this code from a code generator that generates C++ code:

std::ostringstream extra_properties;
if (!attribute.unit.empty())
{
  extra_properties << fmt::format("\n      properties.set_unit(\"{0}\");", attribute.unit);
}
if (!attribute.min_value.empty())
{
  extra_properties << fmt::format("\n      properties.set_min_value(\"{0}\");", attribute.min_value);
}
if (!attribute.max_value.empty())
{
  extra_properties << fmt::format("\n      properties.set_max_value(\"{0}\");", attribute.max_value);
}

It has a lot of ugly duplication: basically everything but the method names and values. So, how do we get rid of the duplication? Just a couple of years ago, I would probably have used a function for that:

void property_snippet(std::ostringstream& str, std::string const& method_name, std::string const& value)
{
  if (value.empty())
    return;
  str << fmt::format("\n      properties.{0}(\"{1}\");", method_name, value);
}

And then turn the call site code into:

property_snippet(extra_properties, "set_unit", attribute.unit);
property_snippet(extra_properties, "set_min_value", attribute.min_value);
property_snippet(extra_properties, "set_max_value", attribute.max_value);

Back then, I would have said that this is a definite improvement, but nowadays I am not so sure anymore. The call-site is a lot more concise, but we still have about half its code duplicated: the first half of each line. The additional function adds lots of complexity that is not necesarily offset by the gain at the call-site: the declaration with all the parameters. And the code gets separated, which is only really good if the function does a little bit more than this one.

This variant can, however, be made simpler with lambdas that capture extra_properties instead of passing it each time. While that is a better solution, I would argue that function objects and capturing are not necessarily simple either, so this only makes second place.

Nowdays, my first go-to abstraction is an in-place list and a loop:

std::tuple<char const*, std::string> methods_and_values[] = {
  {"set_unit", attribute.unit},
  {"set_min_value", attribute.min_value},
  {"set_max_value", attribute.max_value},
};

for (auto [method_name, value] : methods_and_values)
{
  if (value.empty())
    continue;
  extra_properties << fmt::format("\n      properties.{0}(\"{1}\");", method_name, value);
}

For me, this has the added benefit that is clearly separates the ‘inert’ data part of the code and the ‘active’ transformation. While this example is C++, this works in almost languages that I know of, even such arcane beasts as Xbase++.

Help me with the Spiderman Operator

From time to time, I encounter a silly syntax in Java that I silently dubbed the “spiderman operator” because of all the syntactically pointing that’s going on. My problem is that it’s not very readable, I don’t know an alternative syntax for it and my programming style leads me more often to it than I am willing to ignore.

The spiderman operator looks like this:

x -> () -> x

In its raw form, it means that you have a function that takes x and returns a Supplier of x:

Function<X, Supplier<X>> rawForm = x -> () -> x;

That in itself is not very useful or mysterious, but if you take into account that the Supplier<X> is just one possible type you can return, because in Java, as long as the signature fits, the thing sits, it gets funnier.

A possible use case

Let’s define a type that is an interface with just one method:

public interface DomainValue {
    BigDecimal value();
}

In Java, the @FunctionalInterface annotation is not required to let the interface be, in fact, a functional interface. It only needs to have one method without implementation. How can we provide methods with implementation in Java interfaces. Default methods are the way:

@FunctionalInterface
public interface DomainValue {
    BigDecimal value();

    default String denotation() {
        return getClass().getSimpleName();
    }
}

Let’s say that we want to load domain values from a key-value-store with the following access method:

Optional<Double> loadEntry(String key)

If there is no entry with the given key or the syntax is not suitable to be interpreted as a double, the method returns Optional.emtpy(). Else it returns the double value wrapped in an Optional shell. We can convert it to our domain value like this:

Optional<DomainValue> myValue = 
    loadEntry("current")
        .map(BigDecimal::new)
        .map(x -> () -> x);

And there it is, the spiderman operator. We convert from Double to BigDecimal and then to DomainValue by saying that we want to convert our BigDecimal to “something that can supply a BigDecimal”, which is exactly what our DomainValue can do.

A bigger use case

Right now, the DomainValue type is nothing more than a mantle around a numerical value. But we can expand our domain to have more specific types:

public interface Voltage extends DomainValue {
}
public interface Power extends DomainValue {
    @Override
    default String denotation() {
        return "Electric power";
    }
}

Boring!

public interface Current extends DomainValue {
    default Power with(Voltage voltage) {
	return () -> value().multiply(voltage.value());
    }
}

Ok, this is maybe no longer boring. We can implement a lot of domain functionality just in interfaces and then instantiate ad-hoc types:

Voltage europeanVoltage = () -> BigDecimal.valueOf(220);
Current powerSupply = () -> BigDecimal.valueOf(2);
Power usage = powerSupply.with(europeanVoltage);

Or we load the values from our key-value-store:

Optional<Voltage> maybeVoltage = 
    loadEntry("voltage")
        .map(BigDecimal::new)
        .map(x -> () -> x);

Optional<Current> maybeCurrent = 
    loadEntry("current")
        .map(BigDecimal::new)
        .map(x -> () -> x);

You probably see it already: We have some duplicated code! The strange thing is, it won’t go away so easily.

The first call for help

But first I want to sanitize the code syntactically. The duplication is bad, but the spiderman operator is just unreadable.

If you have an idea how the syntax of the second map() call can be improved, please comment below! Just one request: Make sure your idea compiles beforehands.

Failing to eliminate the duplication

There is nothing easier than eliminating the duplication above: The code is syntactically identical and only the string parameter is different – well, and the return type. We will see how this affects us.

What we cannot do:

<DV extends DomainValue> Optional<DV> loadFor(String entry) {
    Optional<BigDecimal> maybeValue = load(entry);
    return maybeValue.map(x -> () -> x);
}

Suddenly, the spiderman operator does not compile with the error message:

The target type of this expression must be a functional interface

I can see the problem: Subtypes of DomainValue are not required to stay compatible to the functional interface requirement (just one method without implementation).

Interestingly, if we work with a wildcard for the generic, it compiles:

Optional<? extends DomainValue> loadFor(String entry) {
    Optional<BigDecimal> maybeValue = load(entry);
    return maybeValue.map(x -> () -> x);
}

The problem is that we still need to downcast to our specific subtype afterwards. But we can use this insight and move the downcast into the method:

<DV extends DomainValue> Optional<DV> loadFor(
	String entry,
	Class<DV> type
) {
	Optional<BigDecimal> maybeValue = load(entry);
	return maybeValue.map(x -> type.cast(x));
}

Which makes our code readable enough, but at the price of using reflection:

Optional<Voltage> european = loadFor("voltage", Voltage.class);
Optional<Current> powerSupply = loadFor("current", Current.class);

I’m not a fan of this solution, because downcasts are dangerous and reflection is dangerous, too. Mixing two dangerous things doesn’t neutralize the danger most of the time. This code will fail during runtime sooner or later, without any compiler warning us about it. If you don’t believe me, add a second method without implementation to the Current interface and see if the compiler warns you. Hint: This is what you will see at runtime:

java.lang.ClassCastException: Cannot cast java.math.BigDecimal to Current

But, to our surprise, it doesn’t even need a second method. The code above doesn’t work. Even if we reintroduce our spiderman operator (with an additional assignment to help the type inference), the cast won’t work:

<DV extends DomainValue> Optional<DV> loadFor(
    String entry,
    Class<DV> type
) {
    Optional<BigDecimal> maybeValue = load(entry);
    Optional<DomainValue> maybeDomainValue = maybeValue.map(x -> () -> x);
    return maybeDomainValue.map(x -> type.cast(x));
}

The ClassCastException just got a lot more mysterious:

java.lang.ClassCastException: Cannot cast Loader$$Lambda$8/0x00000008000028c0 to Current

My problem is that I am stuck. There is working code that uses the spiderman operator and produces code duplication, but there is no way around the duplication that I can think of. I can get objects for the supertype (DomainValue), but not for a specific subtype of it. If I want that, I have to accept duplication. Or am I missing something?

The second call for help

If you can think about a way to eliminate the duplication, please tell me (or us) in the comments. This problem doesn’t need to be solved for my peace of mind or the sanity of my code – the duplication is confined to a particular place.

Being used to roam nearly without boundaries in the Java syntax (25 years of thinking in Java will do that to you), this particular limitation hit hard. If you can give me some ideas, I would be grateful.

Docker Interpreter with Environment Variables in RubyMine

As you know from previous blog entries, we now rely on Docker dev containers as interpreters for our IDEs. This has the advantage that we don’t need local installations of for example Ruby or single packages, but all requirements are in a Docker container and our machine stays clean.
RubyMine has some pitfalls for this way. So in this blog post, I’ll present you some hard-won insights and show you what solution we came to.

Those were our first problems:

  1. When using a single Docker image as an interpreter, it does not clean up everything when exiting the application and container. For example, the Server.pids file remains on the local machine, resulting in the following error: “A server is already running. Check path/Pids/Server.pids.” This behavior can be worked around with a Before Launch script that deletes the file when the application starts, but it’s not very nice. RubyMine unfortunately does not have Docker Container Settings, different to other Jetbrain IDEs, so a simple –rm does not work.
  2. Furthermore, we need to talk to our local machine from our Docker container, for example to access a DB or to use a VPN tunnel originating from the local machine. For this, the Docker container needs to know my IP address, or the respective IP address of each developer. In our version control system, however, we don’t want to constantly overwrite the IP addresses or check before each push that you don’t accidentally write up your own IP address. Our wish was to have a local environment variable MY_MACHINE_IP where each developer writes their IP address and the Docker container fetches it when the program starts. The normal integration of the system environment variables by simply checking this option unfortunately does not work here when we start the program in Docker. This is because the IDE then integrates the environment variables of the Docker container and not those of my local machine. Also, using a local environment variable to pass it to Docker doesn’t work in Run Configuration, nor in Docker Image creation, as the images below show. Same if you want to use PATH variables of the IDE instead of environment variables.
Environment Settings in Run Configuration
Environments in Dev Container

Our solution – Docker Compose:

Our first problem is solved by using docker compose directly. The problem with the Server.pid does not occur, because RubyMine manages the Docker Compose better and removes the Server.pid automatically at startup.

Below, I explain our setup of a Docker Compose as an interpreter and how it solved all the problems.

We created a simple Docker Compose yaml with the image we want to use as an interpreter. At this point, you can also define environment variables that use local environment variables of your local machine. This solves our second problem.

Docker Compose yaml

The definition of the volume is important at this point. Docker tries to store things there during the installation and throws an internal server error if the volume is missing: 
Error response from daemon: the working directory ‘C:\repositories\my-project’ is invalid, it needs to be an absolute path.

Now an interpreter can be set up in RubyMine. To do this, a new remote interpreter must be created in the Settings under Language & Frameworks: Ruby SDK and Gems. An example is shown in the figure below:

Ruby Docker Compose Interpreter

It is important here to select the interpreter after this, otherwise you will not be able to save and will get an error that the project has no interpreter.

Now the interpreter can be stored in the run configuration.

Recap:

We are now able to run our Ruby environment in a Docker container. Thus, the environment is independent of the local circumstances such as the installed Ruby version or packages, as these can all be found in the container. Also, each programmer can run the project locally without any further adjustments via a defined environment variable and the Docker container can still talk to other local Docker containers on the machine. Thus, the status in Gitlab is generally valid and not bound to the respective programmer by a customizable IP or the like.

Don’t just useCallback() with higher-order-functions

This is a small thing that once took me longer to debug than necessary, which is why it might be useful to some of you out there.

From time to time, we have that situation in a React application where it’s just not really avoidable that a small component has to accomplish a rather expensive computation. That’s what memoization is for, i.e. reusing the results of old computations when we know that these are still applicable.

React, in its functional approach, has three ways of memoiziating things, and for whole components there is React.memo(), while for usage inside a component we have the hooks React.useMemo() most commonly used for values or value-like objects, and React.useCallback() for functions. Because JavaScript is quite a functional languare, there is a rough equivalence between the latter two – but now I’m here to look into that.

// rather trivial function – these are equal React.useMemo(() => () => x, [x]); React.useCallback(() => x, [x]); // higher-order function – they are not! React.useMemo(() => higherOrderFunction(x), [x]); React.useCallback(higherOrderFunction(x), [x]);

There are various such higher-order components that are avilable for developers to use re-existing logic. One such case is debouncing, i.e. when you expect state changes to sometimes come in very large batches, the most common case probably a <input/> field whose value is triggering a server request or something like that. Other common cases would be drag’n’drop interactions or window resizing.

With a useRef(), one can rather easily write such debouncing oneself (google it or ask in the comments), but there is lodash.debounce which take care of that with such a higher-component function.

const MILLISEC = 500;

const Component = () => {
  const [value, setValue] = React.useState("");

  const handle = React.useMemo(() => debounce(event => { ... }, MILLISEC), []);

  return <input onChange={handle} value={value}/>;
};

Now I don’t want to talk about the specific case of debounce() (but one can look at the source code to guess its doing), this is just an example. Third-party logic is helpful when not-reinventing-the-wheel, but you can’t be that sure about computational costs, especially when some of your dependencies might update in the future – so that might be a good point to use memoization without actually seeing the benefit in the time of developing. (*)

As Dmitir Pavlutin here states nicely for that specific case, you can not juse write useCallback(debounce(...), []) here in place of useMemo. It is rather trivial but you need to take care: The JavaScript engine will have no other option than to execute the debounce() on creation of the callback, it can not know that this is something to be evaluated later.

Anything that is not an arrow function () => { ... } or an old-school function() { ... } will be evaluated when the corresponding line is reached. The syntax does not allow anything to be wrapped around it in order to delay that execution to the first call.

So. Debounce might not be the most expensive thing, and in general one might not even need memoization, but if you do – always remember that something has to be a function in order for any of that to work.

(*) This is not a call for premature optimization.

It cannot be stressed enough that one shouldn’t wrap every single computation into a memoization in either case. Sure, one should care about useless computations as stated above, but always know that the memo thing itself is not free. So when in doubt, think about how to quantify your specific gain, e.g. via the React DevTools Profiler, the performance API or at least logging of Date.now() timestamps.

Also, only think about performance when doing so. If there is any case of “my application actually behaves differently” when using useMemo / useCallback, this is a red flag – drop the thought of optimization instantly and care about your overall architecture first.