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2e6f70c098
## Summary Follow-up to #4230. Two more core-sim optimizations — these are **behavior-affecting in controlled ways** (unlike #4230, which was hash-identical), so both come with dedicated test coverage written before the change. Combined results (`npm run perf:game`, same machine, before → after): | run | mean tick | ticks/sec | p99 | peak heap | |---|---|---|---|---| | default (world, 400 bots, 1800 ticks) | 7.98 → **6.96 ms** | 125 → **144** | 21.2 → **19.0 ms** | 438 → **294 MB** | | giantworldmap, 600 ticks | 17.4 → **15.2 ms** | 58 → **66** | 32.6 → 30.5 ms | | Cumulative with #4230 vs. the original baseline: default run mean 9.04 → 6.96 ms (111 → 144 ticks/sec); giantworldmap 22.5 → 15.2 ms (44 → 66 ticks/sec, max tick 52.8 → 40.1 ms). ### 1. `PseudoRandom`: seedrandom ARC4 → inline sfc32 - ARC4 was ~4% of profiled self time. The new engine is sfc32 with splitmix32 seed expansion and a warmup, using only 32-bit integer ops — sequences are identical across platforms. The class API is unchanged. - This **removes the `seedrandom` dependency entirely**, making `src/core` actually dependency-free (the import was the only violation of that rule). - ⚠️ **The random stream differs, so the deterministic game-state hash changes.** All clients run the same code, so cross-client sync is unaffected; the harness reproduces the same hash on repeated runs per seed. New reference hashes: - `--map world --ticks 200 --bots 100` → `5607618202213430` - default run → `29309648281599524` - `--map giantworldmap --ticks 600` → `39945089450032050` - New `tests/PseudoRandom.test.ts` (15 tests) pins the engine-agnostic contract: per-seed determinism, ranges, uniformity, adjacent-seed decorrelation, and every API method. The tests were verified green against the old engine first, then the swap. - The stream change exposed a test that passed **by RNG luck**: in `AiAttackBehavior.test.ts`, "nation cannot attack allied player" was actually being blocked by the difficulty dice gate in `shouldAttack`, not the alliance check — hiding that the test's `AiAttackBehavior` was constructed without its `NationEmojiBehavior`. The test now supplies one and verifies the real protection layer (`AttackExecution`'s alliance check), robust to any dice outcome. ### 2. `PlayerImpl.toFullUpdate`: allocation-free empty collections - `toFullUpdate` runs for every player every tick and allocated ~10 collections each (allies, embargoes Set, attacks, alliance views, …) even when all were empty — the common case for most of 472 players. Because `lastSentUpdate` retains each snapshot for a full tick, these objects survived minor GC, got promoted, and accumulated as old-space garbage between major GCs — that's the peak-heap drop. - Empty collections now reuse shared **frozen** module-level singletons, so `diffPlayerUpdate`'s existing `a === b` fast paths skip structural comparison entirely. Non-empty collections build in single passes. Freezing makes accidental in-worker mutation throw loudly instead of silently corrupting every player; consumers across the worker boundary get mutable structured clones as before. (`Set` cannot be frozen — `EMPTY_EMBARGOES` is documented as never-mutate.) - Value-identical: the game-state hash is unchanged by this part (verified against the post-PRNG baseline). - New `tests/PlayerUpdateDiff.test.ts` (8 tests): full-snapshot shape, null-when-unchanged, embargo/alliance/target/attack diffs through the real tick pipeline, and the freeze contract. ### Verification - Full suite passes: 124 files / 1408 tests (23 new) + server tests; lint and prettier clean. - Hash reproducibility confirmed: repeated runs with identical args produce identical hashes on all three configs. 🤖 Generated with [Claude Code](https://claude.com/claude-code) --------- Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
156 lines
4.6 KiB
TypeScript
156 lines
4.6 KiB
TypeScript
import { PseudoRandom } from "../src/core/PseudoRandom";
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describe("PseudoRandom", () => {
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test("same seed produces an identical sequence", () => {
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const a = new PseudoRandom(42);
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const b = new PseudoRandom(42);
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for (let i = 0; i < 1000; i++) {
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expect(a.next()).toBe(b.next());
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}
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});
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test("same seed produces identical derived values", () => {
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const a = new PseudoRandom(987654);
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const b = new PseudoRandom(987654);
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for (let i = 0; i < 100; i++) {
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expect(a.nextInt(0, 1000)).toBe(b.nextInt(0, 1000));
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}
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expect(a.nextID()).toBe(b.nextID());
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const arr = [1, 2, 3, 4, 5, 6, 7, 8];
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expect(a.shuffleArray(arr)).toEqual(b.shuffleArray(arr));
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});
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test("different seeds produce different sequences", () => {
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const a = new PseudoRandom(1);
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const b = new PseudoRandom(2);
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let same = 0;
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for (let i = 0; i < 100; i++) {
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if (a.next() === b.next()) same++;
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}
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expect(same).toBeLessThan(5);
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});
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test("consecutive integer seeds are not correlated", () => {
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// Weak seeding schemes make adjacent seeds (common: tick numbers,
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// sequential hashes) produce similar streams.
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const values: number[] = [];
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for (let seed = 1000; seed < 1100; seed++) {
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values.push(new PseudoRandom(seed).nextInt(0, 100));
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}
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const distinct = new Set(values).size;
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expect(distinct).toBeGreaterThan(50);
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});
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test("next() stays within [0, 1)", () => {
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const r = new PseudoRandom(7);
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for (let i = 0; i < 10000; i++) {
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const v = r.next();
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expect(v).toBeGreaterThanOrEqual(0);
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expect(v).toBeLessThan(1);
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}
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});
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test("next() is roughly uniform", () => {
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const r = new PseudoRandom(1234);
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const n = 20000;
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let sum = 0;
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const buckets = new Array(10).fill(0);
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for (let i = 0; i < n; i++) {
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const v = r.next();
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sum += v;
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buckets[Math.floor(v * 10)]++;
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}
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expect(sum / n).toBeGreaterThan(0.48);
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expect(sum / n).toBeLessThan(0.52);
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for (const count of buckets) {
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// Expected 2000 per bucket; allow generous slack.
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expect(count).toBeGreaterThan(1700);
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expect(count).toBeLessThan(2300);
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}
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});
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test("nextInt returns integers in [min, max)", () => {
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const r = new PseudoRandom(99);
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const seen = new Set<number>();
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for (let i = 0; i < 10000; i++) {
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const v = r.nextInt(3, 8);
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expect(Number.isInteger(v)).toBe(true);
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expect(v).toBeGreaterThanOrEqual(3);
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expect(v).toBeLessThan(8);
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seen.add(v);
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}
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expect([...seen].sort()).toEqual([3, 4, 5, 6, 7]);
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});
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test("nextInt with a single-value range always returns it", () => {
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const r = new PseudoRandom(5);
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for (let i = 0; i < 100; i++) {
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expect(r.nextInt(4, 5)).toBe(4);
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}
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});
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test("nextInt floors non-integer bounds", () => {
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const r = new PseudoRandom(5);
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for (let i = 0; i < 100; i++) {
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const v = r.nextInt(1.9, 4.7);
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expect(v).toBeGreaterThanOrEqual(1);
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expect(v).toBeLessThan(4);
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}
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});
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test("nextFloat stays within [min, max)", () => {
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const r = new PseudoRandom(11);
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for (let i = 0; i < 1000; i++) {
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const v = r.nextFloat(2.5, 3.5);
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expect(v).toBeGreaterThanOrEqual(2.5);
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expect(v).toBeLessThan(3.5);
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}
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});
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test("nextID returns 8 alphanumeric characters", () => {
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const r = new PseudoRandom(123);
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for (let i = 0; i < 100; i++) {
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expect(r.nextID()).toMatch(/^[0-9a-z]{8}$/);
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}
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});
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test("randElement picks members and throws on empty", () => {
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const r = new PseudoRandom(77);
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const arr = ["a", "b", "c"];
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for (let i = 0; i < 100; i++) {
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expect(arr).toContain(r.randElement(arr));
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}
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expect(() => r.randElement([])).toThrow();
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});
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test("randFromSet picks members and throws on empty", () => {
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const r = new PseudoRandom(78);
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const set = new Set(["x", "y", "z"]);
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for (let i = 0; i < 100; i++) {
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expect(set.has(r.randFromSet(set))).toBe(true);
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}
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expect(() => r.randFromSet(new Set())).toThrow();
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});
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test("chance(1) is always true, chance(large) is mostly false", () => {
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const r = new PseudoRandom(31);
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for (let i = 0; i < 100; i++) {
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expect(r.chance(1)).toBe(true);
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}
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let hits = 0;
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for (let i = 0; i < 1000; i++) {
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if (r.chance(1000)) hits++;
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}
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expect(hits).toBeLessThan(10);
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});
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test("shuffleArray returns a permutation and leaves the input unchanged", () => {
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const r = new PseudoRandom(55);
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const input = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10];
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const copy = [...input];
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const shuffled = r.shuffleArray(input);
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expect(input).toEqual(copy);
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expect([...shuffled].sort((a, b) => a - b)).toEqual(copy);
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});
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});
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