Is your program a function or a service?

Maybe everyone knows this already, but for my own clarity, I think there are really two types of computer program:

  • A function: something that you run, and get back a result. Example: a command-line tool like ls
  • A service: something that sits around waiting for things to happen, and responds to them. Example: a web server

How functions work

Programs that are essentially functions should:

  • Validate their input and stop if it is wrong
  • Stop when they have finished their job*
  • Let you know whether they succeeded or failed

*The Halting Problem shows that you can’t prove they stop, so I won’t ask you to do that.

Writing functions is relatively easy.

How services work

Programs that are services should:

  • Start when you tell them to start, even when things are not right
  • Keep running until you tell them to stop, even when bad things happen
  • Tell the user about problems via some communication mechanism

Writing services seems a little harder than writing functions.

What about UIs?

I suggest that programs with UIs are just a special case of services. Do you agree?

What about let-it-crash?

I think that let-it-crash is a good way to build services, but when you build a service that way, I consider the whole system to be the real service: this means the code we are writing, plus the runtime. In this case, the runtime is responsible for keeping the service running (by restarting it), and telling the user about problems.

In effect, let-it-crash allows us to write programs that look like functions (which I claim is easier), and still have them behave like services, because the runtime does the extra work for us. Erlang seems like a good example of this.

What are the implications?

If you are writing a service, your program should start when asked, and keep running until it is asked to stop, even if things are bad.

For example:

  • a service that relies on a data source should keep running when that data source is unavailable, and emit errors saying that it is unable to work. It should start working when the data source becomes available. (Again, if you implement this behaviour by using a runtime that allows you to write in a let-it-crash style, good for you.)
  • a service that relies on the existence of a directory should probably create that directory if it doesn’t exist.
  • a service that needs config might want to start up with sane defaults if the config is not supplied. Or maybe it should complain loudly and poll for the file to be created?

Why not stop when things are wrong?

  • Using this approach, it doesn’t matter the order of starting services. The more services we have, the more painful it is to have an order we must follow.
  • It’s nice when things are predictable. We expect services to keep running under normal circumstances. Using this approach, our expectations are not wrong when things go wrong.

What are the down sides?

  • You must pay attention to the error reporting coming from running services – they may not be working.
  • Services will still stop, due to bugs, or at least due to hardware failures, so you still have to pay attention to whether services are running.

More: 12 Fractured Apps

Shutdown order consistency: how Rust helps

Some Java code with bugs

Here’s my main method (in Java). Can you guess the bug?

Db db = new Db();
Monitoring monitoring = new Monitoring();
Monitoring mon2 = new Monitoring();
Billing billing = new Billing(db, monitoring);
monitoring.setDb(db);

runMainLoop(billing, mon2);

db.stop();
billing.stop();
monitoring.stop();

If you would like to hunt down the 2 bugs manually, try reading the full code here: ShutdownOrder.java

But maybe you have an idea already? Maybe you’ve seen code like this before? If you have, you probably have an instinct that there’s some kind of bug, even if you can’t say for sure what it is. Code like this almost always has bugs!

This code compiles fine, but it contains two bugs.

First, we forgot to setDb() on mon2. This causes a NullPointerException, because Monitoring expects always to have a working Db.

Second, and in general harder to spot, we shut down our services in the wrong order. It turns out that Monitoring uses its Db during shutdown, so we get an exception. Even worse, if some other code needed to run after monitoring.stop(), it won’t, because the exception prevents us getting any further.

Of course, this is toy code, but this kind of problem is common (and much harder to spot) in real-life code. In fact, my team dealt with a similar bug this week.

It’s fundamentally hard to figure out your shutdown order. It’s complicated further if classes have start() methods too, which I have seen in lots of Java code.

Given that this is just a hard problem, maybe there’s no point looking for tools to make it easier?

Some Rust code without those bugs

Let’s try writing this code in Rust. Here’s the main method:

let db = Db::new();
let monitoring = Monitoring::new(&db);
let mon2 = Monitoring::new(&db);
let billing = Billing::new(&db, &monitoring);

run_main_loop(&billing, &mon2);

// drop() is called automatically on all objects here

Here’s the full code: shutdown_order.rs

This code shuts down all the services automatically at the end, and any mistakes we make in the order are compile errors, not things we find later when our code is running.

The code to shut down each service looks like this:

impl Drop for Monitoring<'_> {
    fn drop(&mut self) {
        // [Disconnect from monitoring API]
        self.db.add_record("MonitorShutDown");
    }
}

This is us implementing the Drop trait for the struct Monitoring (traits are a bit like Java Interfaces). The Drop trait is special: it indicates what to do when an instance of this struct is dropped. In Rust, this is guaranteed to happen when the instance goes out of scope, which is why our comment at the end of the main method sounds so confident.

Furthermore, Rust’s compiler shuts down everything in the reverse order in which it was created, and guarantees that nothing gets used after it has been dropped.

Rust’s lovely world gives us two relevant treats: no unexpected nulls, and lifetimes.

Treat number 1: no unexpected nulls

First, in Rust, like in other modern languages like Kotlin, we have to be explicit about items that could be missing. In our example, we were able to re-arrange the code so that db can never be missing (or null), and the compiler encouraged us to do so. If we really needed it to be missing some of the time, we could have used the Option type, and the compiler would have forced us to handle the case when it was missing, instead of unexpectedly getting a NullPointerException like we did in Java. (In fact, if we’d structured our code to use final in as many places as possible, we could have been encouraged towards basically the same solution in Java too.)

Treat number 2: lifetimes

Second, if you look a bit more closely at the full code of shutdown_order.rs you’ll see lots of confusing-looking annotations like <'a> and &'a:

struct Monitoring<'a> {
    db: &'a Db,
}

The approximate meaning of those annotations is: a Monitoring holds a reference to a Db, and that Db must last longer than the Monitoring.

This “lasts longer than” wording is what Rust Lifetimes are for. Lifetimes are a way of saying how long something lasts.

Lifetimes are really confusing when you start with Rust, and have caused me a lot of pain. Code like this is where they are both most painful and most helpful. As I mentioned earlier, the problem of shutdown order is fundamentally hard. Rust gives you that pain at the beginning, and until you understand what’s going on, the pain is very confusing and acute. But, once your code compiles, it is correct, at least as far as problems like this are concerned.

I love the sense of security it gives me to write Rust code and know the compiler has checked my code for this kind of problem, meaning it can’t crop up at 3am on Christmas Day…

Final note/caveat

This Rust code is probably over-simplified, because all the references are immutable (you can’t change the objects they point to). In practice, we may well have mutable references, and if we do we’re going have to deal with the further difficulty that Rust won’t allow two different objects to hold references to an object if any of those references are mutable. So it would object to Billing and Monitoring using the Db object at the same time. We’d need to make it immutable (as we have here), or find a different way of structuring the code: for example, we could hold the Db instance only within the run_main_loop code, and pass it in temporarily to the Billing and Monitoring objects when we called their methods. A large part of the art, fun and pain of learning Rust is finding new patterns for your code that do what you need to do and also keep the compiler happy. When you manage it, you get amazing benefits!

Edge computing providers

I’m looking into Edge computing at work. By Edge computing I mean running WASM programs in lots and lots of smallish computers in places near to actual people (rather than in huge cloud data centres). I think it’s cool because I love Rust, and Rust is the leading language to compile to WASM.

Here are some companies providing Edge computing services:

  • Fastly – good links with WASM community (hired Mozilla devs), and early adopters – custom WASM engine wasmtime.
  • Cloudflare – huge, and early adopters – WASM engine is Google V8.
  • AWS Lambda@Edge – docs are light on detail, but it looks like a real offering, probably.

Also-rans:

Who did I miss?

Schema upgrades should be reversible (also other transformations, actually)

Are you writing schema upgrade code? Then I humbly suggest you take the time to write schema downgrade code too.

“Why would I do that?” you might well ask, “I won’t ever need to downgrade.”

Now, I imagine you’re expecting me to say you actually will need to downgrade, but that isn’t what I’m saying.

Can you please get on with what you are actually saying?

Whevener you write code to transform something, be it a schema upgrade, some serialisation, or something else, I would highly recommend that you write code to transform it in both directions.

Reasons:

  • It makes testing easier. The best kinds of tests for things like this are round-trips, where you transform something in both directions and check it hasn’t changed. It’s really hard to mess up tests like that.
  • It often uncovers bugs, because it enforces clear thinking about what the transformation actually means.
  • It may improve your code, because it gets annoying writing similar-but-different code to transform in both directions, so you are pushed towards some kind of abstraction.

Also:

  • You almost certainly are going to need it. Sometimes things go wrong and you need to back up.
  • It will be incredibly useful for testing other parts of your code.

Bidirectional scheme up/downgrades are not easy in SQL, but probably worth it. If you’re writing transformation code in a normal programming language, it’s really not that difficult, and I predict it will be worth it.