Extreme Fan-Out

This demo teaches one simple idea:

One write. 100,000 dependents. All of them update — because they all actually depend on what changed.

The opposite of Hemisphere Scale. There, k was tiny and the graph was huge. Here, k is intentionally enormous: every single node in the graph depends on one root value. Change the root — measure how long it takes to recompute all 100,000 leaves.

.me Docs · Extreme Fan-Out Source Code

The Tiny Mental Model

me.master.factor(1)

// 100,000 nodes, each derived from the same root
me.dep[i]["="]("out", "value * master.factor")

// one write
me.master.factor(2)
// → all 100,000 `out` values recompute

O(k) is honest — if k is 100,000, the cost is 100,000. The point is: the graph never recomputes more than k.

What The Demo Builds

Step 1: Wire 100,000 Dependents

me.master.factor(1)

for (let i = 1; i <= 100_000; i++) {
  me.dep[i].value(i)
  me.dep[i]["="]("out", "value * master.factor")
}

Every node declares its derivation at setup time. The dependency graph is built once.

Initial state:

me("dep[1].out")       // 1
me("dep[100000].out")  // 100000

Step 2: Mutate the Root

me.master.factor(2)

All 100,000 leaves recompute:

me("dep[1].out")       // 2
me("dep[100000].out")  // 200000

Explainability

The cascade is fully traceable:

me.explain("dep[100000].out")
// → {
//     expr: "value * master.factor",
//     dependsOn: ["dep.100000.value", "master.factor"],
//     k: 100000,
//     sourcePath: "master.factor",
//     recomputed: ["dep.1.out", "dep.2.out", ..., "dep.100000.out"]
//   }

k: 100000 — the graph recomputed exactly as many nodes as depended on master.factor. No more, no less.

Build It Yourself

cd npm
npm install
node tests/Demos/Root_Fanout_100k.ts

Override N:

FANOUT_N=50000 node tests/Demos/Root_Fanout_100k.ts

To run every demo:

npm run test:demos:run-all

The Big Idea

Fan-out is not a pathological case. It is the natural shape of broadcast: one configuration value, one price, one policy — many things that depend on it.

.me handles it without special-casing. The same O(k) guarantee that makes Hemisphere Scale cheap also makes Extreme Fan-Out correct: the graph recomputes everything that needs to change, and nothing that does not.

1 root mutation → k=100,000 → mutation time measured in milliseconds