Scala, React, and SSR (part 2)

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In part 1 we used scala-graal to implement SSR in a Scala / Scala.JS application but ended by acknowledging that although we have the functionality, we don’t have the speed. This article will explain how to reclaim the speed, once again, by using scala-graal.

Example scenario

Let’s say we want to provide SSR for a page with the following:

final case class Username(value: String)

final case class Props(username: Option[Username])

def performSSR(p: Props): Expr.Result[String] =
  // This is from part 1
  init.flatMap(_ => ctx.eval(renderMySpa(p)))

And let’s say that calling performSSR returns the following HTML:

Props(None)

<main>
  <h1>Hello stranger!</h1>
  <a href="/login">Login here.</a>
</main>

Props(Some(Username("scalagirl80")))

<main>
  <h1>Hello @scalagirl80!</h1>
  <p>Welcome back!</p>
</main>

Now that we’ve got our basic example scenario, the first thing you’d notice is that it takes over 1000ms the first time you run it, maybe after running it a few times it comes down to 600ms, maybe after spamming it you observe it come down to 400ms? That’s not good. Running this in Node without any warmup takes only 2-10ms. How do we achieve similar speeds?

scala-graal comes with a utility designed specifically for this case and it brings the speed down to much less than 1ms. It’s called StrFnCache and it’s a special type of optimisation for functions that return a String, which in the case of SSR, is HTML.

Let’s adapt our example.

val quicklyPerformSSR: Props => Expr.Result[String] =
  StrFnCache(performSSR)

That’s pretty concise but let’s break it down.

  • We pass our real SSR function to StrFnCache
  • We save the result as a val, not a def, because we want a single cache. If this were a def it would create a new cache every time it’s called, use it once and then discard it. That would defeat the purpose of a cache so take care not to do that.
  • The type annotation isn’t necessary - it’s just for clarity.

Now pretend you just hit compile, you’re actually going to get an error telling you that there’s no implicit instance of StrFnCacheParam[Props] available. The caching logic needs to behave differently for different types of inputs so we’ll need to tell it how our custom types affect caching. We’ll add the following:

implicit def strFnCacheParamUsername: StrFnCacheParam[Username] =
  StrFnCacheParam.apply1(Username.apply)(_.value)

implicit def strFnCacheParamProps: StrFnCacheParam[Props] =
  StrFnCacheParam.apply1(Props.apply)(_.username)

In both cases, we’re using StrFnCacheParam.apply1 because we have case classes with 1 field. If our case class had 3 fields we would use StrFnCacheParam.apply3.

That’s it! Our example is now super fast, possibly even less than a microsecond.

What’s going on?

How does it work? What just happened? Let’s go through what happens under the hood.

Firstly, it will call your real SSR function twice with the following inputs:

Props(None)

<main>
  <h1>Hello stranger!</h1>
  <a href="/login">Login here.</a>
</main>

Props(Some(Username(UNIQUE_TOKEN)))

<main>
  <h1>Hello @UNIQUE_TOKEN!</h1>
  <p>Welcome back!</p>
</main>

UNIQUE_TOKEN is not literally the string "UNIQUE_TOKEN", but it’s a random UUID with the hex chars pushed into the unicode free-use region, then wrapped in [scalagraal: / ]. Pretty safe to assume you’ll never have a real component render this:

[scalagraal:\UE697\UE6CC\UE69E\UE69A\UE69A\UE6CA\UE69D\UE69F\UE69D\UE6C7\UE6CA\UE69B\UE69A\UE6CA\UE698\UE69E\UE6C8\UE6C8\UE69E\UE69B\UE699\UE69A\UE69F\UE69C\UE69B\UE69A\UE6CA\UE69A\UE6C7\UE69E\UE69E\UE69C]

What this means is that we can now safely simulate SSR by replacing UNIQUE_TOKEN with the real username provided in subsequent calls. The real implementation is smarter and more efficient than what I’m about to show you, but basically we’ve turned our real SSR into this:

val simplifiedVersionOfTheCache: Props => Expr.Result[String] = {
  val anonymous    = "..."
  val withUsername = "..."
  val UNIQUE_TOKEN = "..."

  p => p.username match {
    case None    => Right(anonymous)
    case Some(u) => Right(withUsername.replace(UNIQUE_TOKEN, u.value))
  }
}

As you can imagine, this is very, very fast.

Automatic branching

What’s really cool about this is that when you have multiple “logic paths”, no matter how nested the conditions are, StrFnCache is smart enough to create separate caches (templates really) for each possible path.

For example if we had,

sealed trait Status
case object Ready    extends Status
case object Pending  extends Status
case object Complete extends Status

final case class Item(id      : Long,
                      status  : Status,
                      readOnly: Boolean)

final case class Props(username: Username,
                       item    : Option[Item])

Due to the info provided by the implicit StrFnCacheParams, StrFnCache will automatically create separate caches for the following patterns:

Props(_, None)
Props(_, Some(Item(_, Complete, false)))
Props(_, Some(Item(_, Complete, true)))
Props(_, Some(Item(_, Pending, false)))
Props(_, Some(Item(_, Pending, true)))
Props(_, Some(Item(_, Ready, false)))
Props(_, Some(Item(_, Ready, true)))

Caveats

Now that you understand how the caching works, you can probably see the caveats. Luckily the caveats are pretty easy to workaround if you need to, so that you can still enjoy lightning-fast SSR.

  1. Components must be pure (i.e. not contain side-effects).

    Example: Your component displays the current time.
    Workaround: Pass in the current time using your props.
  2. All inputs must be opaque or conditional.

    What I mean by that is choosing different logic paths based on inputs (like our two-path example above) is fine; passing an input as is to the output is fine; inspecting an input and deriving a result or logic-change is not fine.

    Example: Calling username.length.
    Workaround: Pass in the length using your props.

    Example: Some logic like if (username == "admin")
    Workaround: Add an admin: Boolean flag to your props.
  3. Numbers are trickier than Strings

    Two reasons that numbers are tricky:

  • Rather than UUIDs and scalagraal identifiers, we have to just pick a number to use as a token

  • Numbers are usually formatted somehow

    For this reason there isn’t an implicit StrFnCacheParam[Int] available out-of-the-box. Instead there are configurable, explicit instances that you create yourself.

    Example: Your component renders numbers with commas.
    Solution: create your own implicit that does the same:
    implicit def commaSeparatedInts =
      StrFnCacheParam.int(toString = "%,d".format(_))

What about my own ADTs?

ADTs are comprised of three elements: values, products, and sums.

1. Values

Out-of-the-box, scala-graal provides a bunch of implicits and some configurable explicit instances for numeric types. Of course you can create your own, custom implicit StrFnCacheParam if you need, but realistically in 99.99999999% of cases your custom types are going to be products or sums.

2. Products

Create an instance for your custom product by calling StrFnCacheParam.applyN where N is the number of fields in your product. Example:

final case class Eg(name: String, age: Int)

implicit val param: StrFnCacheParam[Eg] =
  StrFnCacheParam.apply2(Eg.apply)(e => (e.name, e.age))

3. Sums

Create an instance for your custom product by calling StrFnCacheParam.divideN where N is the number of subtypes of your sum. Example:

sealed trait Sum
case object Good extends Sum
final case class Bad(name: String) extends Sum

implicit val paramGood = StrFnCacheParam.const(Good)
implicit val paramBad  = StrFnCacheParam.apply1(Bad.apply)(_.name)
implicit val paramSum  = StrFnCacheParam.divide2(paramGood, paramBad)

In fact, the out-of-the-box instances for Option and Either are defined this way too:

implicit def option[A](implicit underlying: StrFnCacheParam[A]): StrFnCacheParam[Option[A]] =
  divide2(
    const(None),
    underlying.xmap(Some(_))(_.value))

implicit def either[A, B](implicit l: StrFnCacheParam[A],
                                   r: StrFnCacheParam[B]): StrFnCacheParam[Either[A, B]] =
  divide2(
    l.xmap(Left(_))(_.value),
    r.xmap(Right(_))(_.value))

When these instances are used, scala-graal creates a separate cache/template for each branch.

SPA routing

Finally, let’s also consider how to have fast SSR with an SPA.

Let’s adapt our initial example:

val baseUrl = "https://awesome.com"

def performSSR(path: String, p: Props): Expr.Result[String] =
  init >>
  ctx.eval(ReactSsr.setUrl(baseUrl + url)) >>
  ctx.eval(renderMySpa(p))

All we’ve done is add a path parameter and call ReactSsr.setUrl before rendering our component. Now that we’ve adapted our render function to be route-aware, how do we adapt the caching to be route-aware too? Two ways:

1. Total

val quicklyPerformSSR: (String, Props) => Expr.Result[String] =
  StrFnCache.withRoutes(performSSR)

We’ve changed StrFnCache( to StrFnCache.withRoutes(. That’s it. Done.

The only problem with this is that each route is cached on-demand. The caching logic has no way of knowing what all the possible urls are, so each time it encounters a new url it hasn’t seen before, it calls the real (slow) SSR.

2. Partial

val quicklyPerformSSR: (String, Props) => Expr.Result[Option[String]] =
  StrFnCache.withRouteWhitelist(performSSR)(
    "/",
    "/login",
    "/about",
  )
  • We’ve changed StrFnCache( to StrFnCache.withRouteWhitelist(.
  • We’re providing a finite set of paths - this allows scala-graal to pre-cache them all on startup.
  • The return type has changed to Expr.Result[Option[String]] and will return None when called with a path that’s not in the whitelist.

This also gives you the option of only adding SSR to a subset of your SPA.

Success!

Now we’ve seen how to have our cake and eat it too!

We’re performing real SSR in our JVM server, and by populating our template-style caching on startup, we can guarantee that we respond to requests without any expensive overhead.

I hope you’ve enjoyed this tutorial. Now go forth and create awesome apps!

Avatar of David Barri
Written by David Barri
Hi! I'm the founder and creator of ShipReq. I've been coding since I was a kid, and have been euphorically doing full-stack functional programming in Scala for 8 years and counting. I love to create that which sparks joy in others!