Even better automated instance construction in C++

In the previous articles on automated instance construction (first and second) I showed how you can use constructor-argument deduction to automatically do dependency injection. While that approach worked nicely in general, one little detail was still nagging me: Since construction of the actual objects happens at the end of a recursion, the stack depth in some of those construction could get quite deep. In fact there are an additional Maxactual number of c’tor parameters functions on the stack before the c’tor is called. This effect is even worse when resolving long dependency chains, were those functions are there for each of the dependencies currently being resolved.

The previous code uses an std::index_sequence of the exactly the right length to inject the same number of mimic parameters that are then used to locate dependencies. If we knew the right length, there wouldn’t have to be any recursion around the construction. And that’s actually easy to refactor out, we can just figure out the std::index_sequence first and return, and then use it outside of the recursion:

template <class T, std::size_t Head, std::size_t... Rest>
constexpr auto
injection_parameter_sequence(std::index_sequence<Head, Rest...>,
  decltype(T{ mimic<T>{ Head }, mimic<T>{ Rest }... })* = nullptr)
{
  return std::index_sequence<Head, Rest...>{};
}

template <class T>
constexpr auto injection_parameter_sequence(std::index_sequence<>)
{
  return std::index_sequence<>{};
}

template <class T, std::size_t... Rest>
constexpr auto
injection_parameter_sequence(std::index_sequence<Rest...>)
{
  return injection_parameter_sequence<T>(std::make_index_sequence<sizeof...(Rest) - 1>{});
}

Starting with a “long” index sequence, this overload set returns the smaller index sequence for the construction. We can use a small tool function to actually create the instance:

template <class T, std::size_t... Params>
constexpr auto make_unique_injected_with_sequence(service_provider const& p, std::index_sequence<Params...>)
{
  return std::make_unique<T>(mimic<T>(p, Params)...);
}

Which can be called like this:

template <class T, std::size_t Max = 16> auto make_unique_injected(service_provider const& p)
{
  return make_unique_injected_with_sequence<T>(p,
    injection_parameter_sequence<T>(std::make_index_sequence<Max>{}));
}

Only these last two function will be added to the call stack for each constructor call, which is not a whole lot. This construction has the additional advantage that only these two need to be changed to support different kinds construction, e.g. using std::make_shared instead of std::make_unique.

Oracle DB’s Gradual Password Rollover Feature

It is good security practice to change passwords regularly. When changing a database password, however, the problem arises that applications that access this database have to be reconfigured if the password changes. If multiple applications or services use the same database user, then they all need to be reconfigured at once, typically during a scheduled downtime.

Oracle 21c introduced a new feature called Gradual Password Rollover that can help make such a password change less disruptive. The feature was also backported to Oracle 19c. If this feature is switched on for a user profile, a transition time is granted when the password is changed, during which both the old and the new password are valid. The applications can then change their configuration to the new password within this period according to their own schedule.

How to enable it

You must first be logged in as a privileged user who is allowed to manage users. The grace period for which both passwords should be valid after a password change is set via a user profile. A user profile is a set of limits on the database resources and the user password. The profile setting for this feature is called PASSWORD_ROLLOVER_TIME. You either create a new profile and specify this setting as a limit, or you adjust an existing profile. Here are both variants:

-- Create a new profile ...
CREATE PROFILE example_profile LIMIT PASSWORD_ROLLOVER_TIME 1;

-- ... or alter an existing profile
ALTER PROFILE example_profile LIMIT PASSWORD_ROLLOVER_TIME 1;

The unit of this setting is days. The minimum value is one hour (1/24) and the maximum value is 60 (days). You can assign this profile to a user with the following statement:

ALTER USER example_user PROFILE example_profile;

Now change the user’s password:

ALTER USER example_user IDENTIFIED BY thenewpassword;

Now you should be able to log in as this user with both the old and the new password. You can query the current status from the dba_users table:

SELECT username, account_status, profile
  FROM  dba_users
  WHERE username='example_user';

The value of the account_status column should have changed from OPEN to OPEN & IN ROLLOVER. This indicates that the user account is in the password rollover phase, and two passwords are active at the same time. You can end this period early with the following command:

ALTER USER example_user EXPIRE PASSWORD ROLLOVER PERIOD;

A final note: If you change the password again during the rollover period only the original password (the one before the rollover period was started) and the latest password are valid, which means a user account can’t have more than two valid passwords at the same time.

Avoid special values of the result type for error indication

As many of you may know we work with a variety of programming languages and ecosystems with very different code bases. Sometimes it may be a modern green field project using state of the art frameworks. At other times it may be a dreaded legacy project initially written many years ago (either by us or someone we do not even know) using ancient languages and frameworks like really old java stuff (pre jdk 7) or C++ (pre C++11), for example.

These old projects could not use features of modern incarnations of these languages/compilers/environments – and that is fine with me. We usually gradually modernize such systems and try to update the places where we come along to fix some issues or implement new features.

Over the years I have come across a pattern that I think is dangerous and easily leads to bugs and harder to maintain code:

Special values of the resulting type of a function to indicate errors

The examples are so numerous and not confined to a certain programming environment that they urged me to write this article. Maybe some developers using this practice will change their mind and add a few tools to their box to write safer and more expressive code.

A simple example

Let us image a function that returns a simple integer number like this:

/**
 * Here we talk to a hardware sensor. If everything works, we should
 * get a value between -50 °C and +50 °C.
 * If something goes wrong, we return -9999.
int readAmbientTemperature();

Given the documentation, clients can surely use this kind of function and if every use site interprets the result correctly, nothing will ever go wrong. The problem here is, that we need a lot of domain knowledge and that we have to check for the special value.

If we use this pattern for other values where the value range is not that clearly bounded we may either run into problems or invent other “impossible values” for each use case.

If we forget to check for the special value the users may see it an be confused or even worse it could be used in calculations.

The problem even gets worse with more flexible types like floating point numbers or strings where it is harder to compare and divide valid results from failure indicators.

Classic error message that mixes technical code and error message in a confusing, albeit funny sentence (Source: Interface Hall Of Shame)

Of course, there are slightly better alternatives like negative numbers in a positive-only domain function or MAX_INT, NaN or the like provided by most languages.

I do not find any of the above satisfying and good enough for production use.

Better alternatives

Many may argue, that their environment lacks features to implement distinct error indicators and values but I tend to disagree and would like to name a few widely used alternatives for very different languages and environments:

  • Return codes and out-parameters for C-like languages like in the unix and win32 APIs (despite all their other flaws… 😀 )
  • Exceptions for Java, Python, .NET and maybe in some cases even C++ with sufficiently specific type and details to differentiate different failures
  • Optional return types when the failures do not need special handling and absence of a value is enough
  • HTTP status code (e.g. 400 or 404) and a JSON object containing reason and details instead of a 2xx status with the value
  • A result struct or object containing execution status and either a value on success or error details on failure

Conclusion

I am aware that I probably spent way too much words on such a basic topic but I think the number of times I have encountered such a style – especially in code of autodidacts, but also professionals – justifies such an article in my opinion. I hope I provided some inspiration for those who do not know better or those who want to help others improve.

What else can we do?

A common code structure to implement a decision is the if-statement, or in its complete form, the if-else-statement:

By using the explicit if-else-statement, you essentially partition a part of your code into two “execution lanes” that are used mutually exclusive. Instead of writing them one upon the other, we could, if our code editors supported it, write them side by side:

There are some graphical code editors that tried this tabular approach. It certainly looks unfamiliar to the eye trained on the first notation, but it makes one thing clear: The code flow will go through only one of the columns, not both.

Dependence on explicit conditionals

Using the if-else-statement became so second-nature to most developers that they acted confused and helpless when presented with a simple restriction:

“Don’t use the else keyword”

Jeff Bay, Object Calisthenics, 2008

The restriction is imposed as the second of nine rules from the object calisthenics by Jeff Bay. In the explanation of the rule, he stated that the rule should act as a first step towards implicit conditional statements. Paraphrased: There are 99 ways to express an else statement without using the keyword, but the average developer knows none of them.

In my opinion, the rule is merely the warm-up phase to a bigger challenge, as stated by the “anti-if campaign”: To get rid of if-statements (and else-statements by that matter) in all contexts where alternatives prove more effective.

In order to decide when not to use if-statements, we should learn about the alternatives. There are plenty to choose from! (refer to slide #4)

But we should also learn about the if-statement itself. The goal isn’t to abandon it, but to use it when appropriate and then use it to its full potential.

An interesting thought about the “else”

We already know everything about the if and else? I had the opportunity to learn something new not long ago. The hint came from Kevlin Henney in one of his talks (Non-Functional Coding):

The talk is fairly recent and has some traditional “Kevlin parts” in it. The part I highlighted is unusually aggressive for him. The reasoning is sound, but the nearly personal attack towards the audience (to “piss them off”) is uncalled for.

But, the “volume up to 200 %”-style works more often than not and the bit got me thinking. The culprit in question is this code:

According to Kevlin, this style “is just wrong”. Let’s try to find out why.

There is one principle that is mentioned by Kevlin in passing: The “Single Level of Abstraction” principle that states that you should not mix different levels of abstraction in one block of code (the principle talks about methods). It is a foundation for the first rule in the object calisthenics: “Only one level of indentation per method”.

If you look at the if-code and else-code, they operate on the same level of abstraction. Maybe not on the same level of probability, but they deal with the same topic. Elevating one part by eliminating the else-block in favor of an early return means that this part is more important. It also designates the if-code and in fact the whole if-statement to be a guard clause. Guard clauses typically deal with invalid state and don’t complement the desired functionality. They act as gatekeepers and interdict the invalid state to enter the method’s main body. As a metaphor: The bouncers in front of a club are like guard clauses. To say that being denied entry by a bouncer is comparable fun to being in the club is probably not a widespread opinion.

Unfinished reflection

I still reflect on other clues that are name-dropped by Kevlin, like the stated reduction of refactoring opportunities, but that’s probably because I don’t have enough comparison material.

There is one thing that I haven’t got a proper hold on yet and that’s the term “control state“. My google kung-fu is not mighty enough to reach past some obscure ASP.NET concepts from ten years ago. I haven’t heard the term in books – at least I don’t remember it.

So here is my call for help: Can you provide some source or explanation about what Kevlin Henney means by “control state“?

And what else do you think about the whole discussion?

Arrow Anti-Pattern

When you write code, it can happen that you nest some ifs or loops inside each other. Here is an example:

Because of the shape of the indentation, this code smell is called an anti-arrow pattern. The deepest indentation depth is the tip of the arrow. In my opinion, such a style is detrimental to readability and comprehension.

In the following, I would like to present a simple way of resolving such arrow anti-patterns.

Extract Method

First we extract the arrow pattern as a new method. This allows us to use return values instead of variable assignments and makes the code clearer.

public string PrintElephantMessage(Animal animal)
{
    Console.WriteLine(IsAElephant(animal));
}
public string IsAElephant(Animal animal)
{
    if (animal.IsMammal())
    {
        if (animal.IsGrey())
        {
            if (animal.IsBig())
            {
                if (animal.LivesOnLand())
                {
                    return "It is an elephant";
                }
                else
                {
                    return "It is not an elephant. Elephants live on land";
                }
            }
            else
            {
                return "It is not an elephant. Elephants are big";
            }
        }
        else
        {
            return "It is not an elephant. Elephants are grey";
        }
    }
    else
    {
        return "It is not an elephant. Elephants are mammals";
    }
}

Turn over ifs

A quick way to eliminate the arrow anti-pattern is to invert the if conditions. This will make the code look like this:

public string IsAElephant(Animal animal)
{
    if (!animal.IsMammal())
    {
        return "It is not an elephant. Elephants are mammals";
    }
    if (!animal.IsGrey())
    {
        return "It is not an elephant. Elephants are grey";
    }
    if (!animal.IsBig())
    {
        return "It is not an elephant. Elephants are big";
    }
    if (!animal.LivesOnLand())
    {
        return "It is not an elephant. Elephants live on land";
    }
    return "It is an elephant";
}

Some IDEs like Visual Studio can help flip the Ifs.

Conclusion

Arrow anti-pattern are code smells and make your code less legible. Fortunately, you can refactor the code with a few simple steps.

How to migrate a create-react-app project to vite

It seems that the React community is finally accepting that their old way of scaffolding a new projects, create-react-app (CRA in short), has outlived its usefulness. While there is no official statement about that, there was no update on npm in about a year, which in the JS universe screams “TOXIC WASTE” in very clear words, and meanwhile also has vanished from the official “Start a new React Project” docs.

In search for possibilities, one can do some quick google searches (e.g. this or that or maybe this) and at the moment, I’m giving vite a chance and it has not disappointed me yet, as the opposite:

  • the build definitely feels faster (as the French would say: plus vite), but I never quantified it
  • that over 9000 deprecation warnings one was accustomed to using CRA – gone TO ZERO
  • and the biggest point, no dependency on webpack. Webpack has this weird custom to introduce brutally breaking changes between their versions and then you have to polyfill Node JS core modules or whatever floats their boat, giving users not a choice – i.e. making it highly TOXIC in itself

But still, the react-scripts which CRA employs have played quite a role in development, as it also helped with the “npm start” development server and also as a test runner – so generally, if you have developed your project over some years, you might have relied on it quite a bit, and now you don’t want to recreate everything from scratch.

I recently migrated one of our projects and this is what worked for me. There were three main concerns

  • switch the general infrastructure to vite, so we can develop and build again
  • introduce vitest as a test runner
  • migrate Redux store tests specifically

Let’s focus today on the thing without tests and I will come back to that next time.

Migrate to vite INFRASTRUCTURE

This was actually surprisingly concise, I just had to

npm install -D vite @vitejs/plugin-react
npm uninstall react-scripts

(when in doubt, remove the node_modules folder and run npm install again, but I didn’t have to), then I adjusted package.json to:

  "scripts": {
    "start": "vite",
    "build": "vite build", 
  },

You might prefer to call your dev server via “npm run dev” instead of “npm start”, in that case just replace the "start": "vite" with "dev": "vite" above.

The Vite templates prefer to include a script "preview": "vite preview" but I do not use it, so I didn’t copy that.

It also was required to set this package.json entry:

  // somewhere top-level, i.e. next to "version" or somewhere like that
  "type": "module",

(I’m not entirely sure whether we can now safely remove the “browserslist” or “babel” entries from the package.json because they might be useless now, but I will have to think about in another minute.)

Now, some real code changes. One of the larger todos here might be to make sure that every JSX-containing source file ends with .jsx – there have been discussions about this and beforehand, it was still possible to just place your <App/> etc. inside an App.js, but vite does not like that anymore, so this is a thing you have to do.

So the code changes amount to:

  • Rename every .js file which has some JSX in it to .jsx – pro tip: do it via the IDE so you do not have to care for every import / require-Statement manually!
  • move the template in ./public/index.html directly to ./index.html and in there, replace every mentioning of %PUBLIC_URL% just by the single slash /
  • In the index.html <body>, include your index.jsx e.g. like:
  <body>
    <noscript>You need to enable JavaScript to run this app.</noscript>
    <div id="root"></div>
    <script type="module" src="/src/index.jsx"></script>
  </body>

It might be said that the vite templates like to call their index file “main.jsx”, but it’s not important – just match whatever you put inside the <script src="..."/>.

Now in order not to change your habits too much, i.e. keep your CI build as it is, plus maybe some Docker Dev Containers or even browser bookmarks, you can use this vite.config.js – see docs:

import { defineConfig } from 'vite';
import react from '@vitejs/plugin-react';

export default defineConfig({
  plugins: [react()],
  server: {
    port: 3000,
    host: true
  },
  build: {
    outDir: './build'
  },
});

otherwise, vite prefers to run its dev server on port 5173 (guess it’s Leetspeak) and build in ./dist – just so you know.

Addon: Using ReactComponents from SVGs with Vite. Also with refs.

Since today morning, when I wrote this article, I already learned something new. In another project we were importing SVG files via the approach

import {ReactComponent as Bla} from "./bla.svg";

const ExampleUsage = () => {
  return <Bla />;
};

Doing so now results in

Uncaught SyntaxError: ambiguous indirect export: ReactComponent

This can be solved by npm install vite-plugin-svgr and then updating vite.config.js:

import {defineConfig} from "vite";
import react from "@vitejs/plugin-react";
import svgr from "vite-plugin-svgr";

export default defineConfig({
    plugins: [
        svgr({
            svgrOptions: {
                ref: true,
            },
        }),
        react(),
    ],
    server: {
        port: 3000,
        host: true,
    },
    build: {
        outDir: "./build",
    },
});

The { svgrOptions: {ref: true} } was a specific requirement for our use case, it is necessary if you ever want to access the imported ReactComponents ref; i.e. in our ExampleUsage we needed a specification <Bla ref={...}/> . Leaving the svgrOption ref then at false (its default) gives us the error:

Warning: Function components cannot be given refs. Attempts to access this ref will fail. Did you mean to use React.forwardRef()?

Then, Make the tests work again

As mentioned above, these were a bit trickier, and while I found a way to leave most tests untouched, there was some specific tweaking to be done with Redux store tests, and also with mocking a foreign class (GraphQLClient from “graphql-request” in my case).

But as also mentioned above, I guess this might be a topic for my next blog post. In case you urgently need that knowledge, drop us a mail or something.. 🙂

Have we made things too easy?

One of the old mantras for API design is “Make doing the right thing easy and the wrong thing hard”. This, of course, applies to much broader topics as well, such as software development or UX.

For software development specifically, are we maybe making “doing the wrong thing” too easy as well? Here are a two examples:

Web Requests

In the old times, requesting data from a web server required first setting up the request, sending it, and then getting the result back to your application either via polling or callbacks. Dave Mark once adequately called this solving the “waiting problem”. It was cumbersome, to say the least. It was clear that making such a request was something to be avoided. You did it when you had to, but you avoided setting up too many different kinds of requests implictly.

Nowadays, with the advent anonymous functions/lambdas in most mainstream programming languages, continuations became the new way handle these things: do_request(...).then(result -> ...) This already made this a lot easier. And even better, now we have some form of coroutines in many languages were you can just do result = await do_request(...). It even looks almost like a normal function call.

With this, programmers can just do requests one after the other. Need one thing from a server? Do one request. Need ten things from a server? Do ten requests. Of course, this is horribly wasteful: each request will incur the full overhead of http/https and a server roundtrip. In the old times, doing the request was painful, so you automatically looked for ways to avoid doing more, and bundle your asks into one request, argueable leading to a better program.

Dependencies

Before nice package-managers where a thing, handling dependencies was a huge pain. You would have to manually get, unpack, configure and install the dependency for each developer and/or consumer system. As a consequence, libraries were big and often duplicated foundational things. But it also caused developers carefully grooming their library selections.

Now with package managers, libraries have started to become small. Duplication within libraries certainly seems to have decreased, and the average library size has decreased. But this also caused developers to be much less cautious when adopting a dependency, with package managers handling thousands of dependencies that no one developer can possibly have a full understanding of. And this then leads to things like the leftpad disaster.

Better or worse?

I am pretty sure that both having nice abstractions to deal with asynchronicity and package managers are good things. But if they make certain things too easy, how can we deal with that? The only thing I can currently think of is figuratively sticking warning-labels on these things during review time, but because those things are now so easy and subtle, it is also easy to miss them.

Are there other examples were we maybe made the wrong thing too easy? Do you have any ideas how to deal with this problem?

Materialized views in Oracle

Most relational database management systems (RDBMs) support not only views, but also materialized views. Materialized views and normal views are both database objects used to present data to users, but they work in different ways.

A database view is a virtual table or a named query that presents data from one or more tables in a specific way. They are not physical tables and do not store data directly, but instead retrieve data from the underlying tables based on a specified query.

Materialized views are similar to normal views, but they store pre-computed results. When a materialized view is created, the results of the underlying query are computed and stored in the database. One advantage of materialized views is faster query performance by avoiding the need to compute the same results repeatedly. This is especially useful for complex and time-consuming queries, as the results can be stored and accessed quickly.

The syntax for creating a normal view in an Oracle database is as follows:

CREATE VIEW view_name AS SELECT … FROM … WHERE …;

To create a materialized view instead of a normal view you add the MATERIALIZED keyword:

CREATE MATERIALIZED VIEW view_name AS SELECT … FROM … WHERE …;

When creating a materialized view you should think about and decide on three aspects of materialized views:

  1. the refresh method,
  2. the refresh interval,
  3. and the storage properties

The refresh method

The refresh method determines how the data in the materialized view is updated or refreshed to reflect changes in the base tables.

  • COMPLETE: This one completely rebuilds the materialized view from scratch. It drops the existing contents of the materialized view and then re-executes the query to populate it with fresh data. This method can be resource-intensive and slow, especially for large materialized views.
  • FAST: Updates only the rows in the materialized view that have changed since the last refresh. It uses the materialized view logs on the base tables to identify the changed rows and then applies the changes to the materialized view. It can be much faster than a complete refresh, especially if there are only a few changes to the data.
  • FORCE: Tries to perform a fast refresh if possible, but falls back to a complete refresh if necessary. This method is useful if you want to try to perform a fast refresh, but you’re not sure if it will be possible due to the nature of the data or the query.

You can specify the refresh method when creating the materialized view using the REFRESH keyword:

CREATE MATERIALIZED VIEW view_name
  REFRESH FAST
  AS SELECT ...;

If you do not specify a refresh mode FORCE is the default.

The refresh interval

The refresh interval controls how often the materialized view is automatically refreshed. It determines how frequently the materialized view is updated to reflect changes in the underlying data.

Some refresh interval options in Oracle are:

  • ON COMMIT: The materialized view is refreshed automatically every time a transaction that modifies the underlying data is committed. This interval is useful when you need to keep the materialized view up-to-date in near real-time.
  • ON DEMAND: The materialized view is refreshed only when you explicitly request a refresh using the DBMS_MVIEW.REFRESH.
  • START WITH … NEXT: With this interval, the materialized view is refreshed automatically at regular intervals. It is useful when you want to balance the need for up-to-date data with the resources required to refresh the materialized view.

You can specify the refresh interval when creating the materialized view by adding it to the REFRESH clause when creating the view:

CREATE MATERIALIZED VIEW view_name
  REFRESH FAST ON COMMIT
  AS SELECT ...;

The following materialized view gets refreshed every hour:

CREATE MATERIALIZED VIEW view_name
  REFRESH FAST START WITH SYSDATE NEXT SYSDATE + 1/24
  AS SELECT ...;

Storage properties

Storage properties affect how the data in the materialized view is stored and accessed. In Oracle, some of these are:

  • CACHE: The data is stored in the database buffer cache, which is a portion of memory used to cache frequently accessed data. It improves query performance by reducing disk I/O, but it can consume a significant amount of memory.
  • LOGGING: Changes to the materialized view data are logged in the database redo logs. This property ensures that changes to the materialized view can be recovered in case of a system failure but can result in additional overhead.
  • TABLESPACE: Allows you to specify the tablespace where the materialized view data is stored.

Again, you can specify these properties when creating the materialized view:

CREATE MATERIALIZED VIEW view_name
  CACHE
  LOGGING
  TABLESPACE tablespace_name
AS SELECT ... FROM ... WHERE ...;

Now you know the basics for creating materialized views in an Oracle database when needed. There is still more to learn about them. You can find the full reference here.

Grails Domain update optimisation

As many readers may know we are developing and maintaining some Grails applications for more than 10 years now. One of the main selling points of Grails is its domain model and object-relational-mapper (ORM) called GORM.

In general ORMs are useful for easy and convenient development at the cost of a bit of performance and flexibility. One of the best features of GORM is the availability of several flexible APIs for use-cases where dynamic finders are not enough. Let us look at a real-world example.

The performance problem

In one part of our application we have personal messages that are marked as read after viewing. For some users there can be quite a lot messages so we implemented a “mark all as read”-feature. The naive implementation looks like this:

def markAllAsRead() {
    def user = securityService.loggedInUser
    def messages = Messages.findAllByUserAndTimelineEntry.findAllByAuthorAndRead(user, false)
    messages.each { message ->
        message.read = true
        message.save()
    }
    Messages.withSession { session -> session.flush()}
 }

While this is both correct and simple it only works well for a limited amount of messages per user. Performance will degrade because all the domain objects are loaded into domain objects, then modified and save one-by-one to the session. Finally the session is persisted to the database. In our use case this could take several seconds which is much too long for a good user experience.

DetachedCriteria to the rescue

GORM offers a much better solution for such use-cases that does not sacrifice expressiveness. Instead it offers a succinct API called “Where Queries” that creates DetachedCriteria and offers batch-updates.

def markAllAsRead() {
    def user = securityService.loggedInUser
    def messages = Messages.where {
        read == false
        addressee == user
    }
    messages.updateAll(read: true)
}

This implementation takes only a few milliseconds to execute with the same dataset as above which is de facto native SQL performance.

Conclusion

Before cursing GORM for bad performance one should have a deeper look at the alternative querying APIs like Where Queries, Criteria, DetachedCriteria, SQL Projections and Restrictions to enhance your ORM toolbox. Compared to dynamic finders and GORM-methods on domain objects they offer better composability and performance without resorting to HQL or plain SQL.

The Optional Wildcast

This blog post presents a particular programming technique that I happen to use more often in recent months. It doesn’t state that this technique is superior or more feasible than others. It’s just a story about a different solution to an old programming problem.

Let’s program a class hierarchy for animals, in particular for mammals and birds. You probably know where this leads up to, but let’s start with a common solution.

Both mammals and birds behave like animals, so they are subclasses of it. Birds have the additional behaviour of laying eggs for reproduction. We indicate this feature by implementing the Egglaying interface.

Mammals feed their offsprings by giving them milk. There are two mammals in our system, a cow and the platypus. The cow behaves like the typical mammal and gives a lot of milk. The platypus also feeds their young with milk, but only after they hatched from their egg. Yes, the platypus is a rare exception in that it is both a mammal and egglaying. We indicate this odd fact by implementing the Egglaying interface, too.

If our code wants to access the additional methods of the Egglaying interface, it has to check if the given object implements it and then upcasts it. I call this type of cast “wildcast” because they seem to appear out of nowhere when reading the code and seemingly don’t lead up or down the typical type hierarchy. Why would a mammal lay eggs?

One of my approaches that I happen to use more often recently is to indicate the existence of real wildcast with a Optional return type. In theory, you can wildcast from anywhere to anyplace you want. But only some of these jumps have a purpose in the domain. And an explicit casting method is a good way to highlight this purpose:

public abstract class Mammal {
	public Optional<Egglaying> asEgglaying() {
		return Optional.empty();
	}
}

The “asEgglaying()” method might return an Egglaying object, or it might not. As you can see, on default, it returns only an empty Optional. This means that no cow, horse, cat or dog has to think about laying eggs, they just aren’t into it by default.

public class Platypus extends Mammal implements Egglaying {
	@Override
	public Optional<Egglaying> asEgglaying() {
		return Optional.of(this);
	}
}

The platypus is another story. It is the exception to the rule and knows it. The code “Optional.of(this)” is typical for this coding technique.

A client that iterates over a collection of mammals can now incorporate the special case with more grace:

for (Mammal each : List.of(mammals())) {
	each.lactate();
	each.asEgglaying().ifPresent(Egglaying::breed);
}

Compare this code with a more classic approach using a wildcast:

for (Mammal each : List.of(mammals())) {
	each.lactate();
	if (each instanceof Egglaying) {
		((Egglaying) each).breed();
	}
}

My biggest grief with the classic approach is that the instanceof is necessary for the functionality, but not guided by the domain model. It comes as a surprise and has no connection to the Mammal type. In the Optional wildcast version, you can look up the callers of “asEgglaying()” and see all the special code that is written for the small number of mammals that lay eggs. In the classic approach, you need to search for conditional upcasts or separate between code for birds and special mammal code when looking up the callers.

In my real-world projects, this “optional wildcast” style facilitates domain discovery by code completion and seems to lead me to more segregated type systems. These impressions are personal and probably biased, so I would like to hear from your experiences or at least opinions in the comments.