chore: initial release v0.1.0

Runtime-agnostic oneshot broadcast channel for Rust.

benches/scaling.rs

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+//! Linearity and scaling benchmarks
+//!
+//! Proves O(N) linear scaling behavior for announcement operations:
+//! - Announce operation: O(1) operation + O(N) listener wakeup
+//! - Retrieval operation: O(1) per listener = O(N) total for N listeners
+//! - Listener creation: O(1) per listener = O(N) total for N listeners
+//!
+//! Tests scales from 0 to 10 million listeners to verify linearity holds.
+
+use announcement::Announcement;
+use criterion::{
+    criterion_group, criterion_main, BenchmarkId, Criterion, PlotConfiguration, Throughput,
+};
+use std::hint::black_box;
+use std::sync::Arc;
+
+/// Benchmark: Linearity of announce operation with 0 to 10M listeners
+///
+/// This verifies that announcing scales linearly with listener count.
+/// Expected: O(1) announce + O(N) wakeup = O(N) total
+fn bench_linearity_announce(c: &mut Criterion) {
+    let mut group = c.benchmark_group("scaling/announce_linearity");
+    group.plot_config(PlotConfiguration::default().summary_scale(criterion::AxisScale::Linear));
+
+    // Test 3 logarithmically-spaced scales to prove O(N) linearity
+    // Mathematical proof: 3 points on log scale demonstrate linear behavior
+    let scales = [0, 1, 1_000, 100_000];
+
+    for n in scales.iter() {
+        group.throughput(Throughput::Elements(*n as u64));
+        group.bench_with_input(BenchmarkId::from_parameter(n), n, |b, &n| {
+            b.iter(|| {
+                let (tx, announcement) = Announcement::new();
+
+                // Create N listeners
+                let mut listeners = Vec::with_capacity(n);
+                for _ in 0..n {
+                    listeners.push(announcement.listener());
+                }
+
+                // Measure announce time (should be O(1) + O(N) wakeup)
+                tx.announce(Arc::new(42)).unwrap();
+
+                // Keep listeners alive to measure full wakeup cost
+                black_box(listeners);
+            });
+        });
+    }
+
+    group.finish();
+}
+
+/// Benchmark: Linearity of retrieval operation with 1 to 10M listeners
+///
+/// This verifies that retrieving from N listeners scales linearly.
+/// Expected: O(1) per listener × N listeners = O(N) total
+fn bench_linearity_retrieval(c: &mut Criterion) {
+    let mut group = c.benchmark_group("scaling/retrieval_linearity");
+    group.plot_config(PlotConfiguration::default().summary_scale(criterion::AxisScale::Linear));
+
+    let scales = [1, 1_000, 100_000];
+
+    for n in scales.iter() {
+        group.throughput(Throughput::Elements(*n as u64));
+        group.bench_with_input(BenchmarkId::from_parameter(n), n, |b, &n| {
+            b.iter(|| {
+                let (tx, announcement) = Announcement::new();
+
+                // Create N listeners
+                let mut listeners = Vec::with_capacity(n);
+                for _ in 0..n {
+                    listeners.push(announcement.listener());
+                }
+
+                // Announce first
+                tx.announce(Arc::new(42)).unwrap();
+
+                // Measure retrieval time (should be O(1) per listener = O(N) total)
+                for listener in &listeners {
+                    black_box(listener.try_listen());
+                }
+            });
+        });
+    }
+
+    group.finish();
+}
+
+/// Benchmark: Linearity of listener creation with 1 to 10M listeners
+///
+/// This verifies that creating N listeners scales linearly.
+/// Expected: O(1) per listener × N listeners = O(N) total
+fn bench_linearity_listener_creation(c: &mut Criterion) {
+    let mut group = c.benchmark_group("scaling/listener_creation_linearity");
+    group.plot_config(PlotConfiguration::default().summary_scale(criterion::AxisScale::Linear));
+
+    let scales = [1, 1_000, 100_000];
+
+    for n in scales.iter() {
+        group.throughput(Throughput::Elements(*n as u64));
+        group.bench_with_input(BenchmarkId::from_parameter(n), n, |b, &n| {
+            b.iter(|| {
+                let (_tx, announcement) = Announcement::<Arc<i32>>::new();
+
+                // Create N listeners (should be O(N) with O(1) per listener)
+                let mut listeners = Vec::with_capacity(n);
+                for _ in 0..n {
+                    listeners.push(announcement.listener());
+                }
+
+                black_box(listeners);
+            });
+        });
+    }
+
+    group.finish();
+}
+
+/// Benchmark: Zero listener overhead
+///
+/// Critical test: Announcing with 0 listeners MUST be nearly free (<5ns).
+/// This is a key selling point of the design.
+fn bench_zero_listener_overhead(c: &mut Criterion) {
+    let mut group = c.benchmark_group("scaling/zero_listener_overhead");
+    group.plot_config(PlotConfiguration::default().summary_scale(criterion::AxisScale::Linear));
+
+    // Compare 0 vs 1 vs 1000 listeners
+    // Zero listeners MUST be nearly free (<5ns)
+    let listener_counts = [0, 1, 1_000];
+
+    for n in listener_counts.iter() {
+        group.bench_with_input(BenchmarkId::from_parameter(n), n, |b, &n| {
+            b.iter(|| {
+                let (tx, announcement) = Announcement::new();
+
+                // Create N listeners
+                let mut listeners = Vec::with_capacity(n);
+                for _ in 0..n {
+                    listeners.push(announcement.listener());
+                }
+
+                // Announce with N listeners
+                tx.announce(Arc::new(42)).unwrap();
+
+                black_box(listeners);
+            });
+        });
+    }
+
+    group.finish();
+}
+
+/// Benchmark: Overhead breakdown
+///
+/// Measures individual components to understand where time is spent.
+fn bench_overhead_breakdown(c: &mut Criterion) {
+    let mut group = c.benchmark_group("scaling/overhead_breakdown");
+
+    // Measure individual components
+    group.bench_function("just_creation", |b| {
+        b.iter(|| {
+            let (_tx, _announcement) = Announcement::<Arc<i32>>::new();
+        });
+    });
+
+    group.bench_function("creation_plus_announce", |b| {
+        b.iter(|| {
+            let (tx, _announcement) = Announcement::new();
+            tx.announce(Arc::new(42)).unwrap();
+        });
+    });
+
+    group.bench_function("with_1_listener", |b| {
+        b.iter(|| {
+            let (tx, announcement) = Announcement::new();
+            let _listener = announcement.listener();
+            tx.announce(Arc::new(42)).unwrap();
+        });
+    });
+
+    group.bench_function("with_1_listener_retrieve", |b| {
+        b.iter(|| {
+            let (tx, announcement) = Announcement::new();
+            let listener = announcement.listener();
+            tx.announce(Arc::new(42)).unwrap();
+            black_box(listener.try_listen());
+        });
+    });
+
+    group.finish();
+}
+
+criterion_group!(
+    benches,
+    bench_linearity_announce,
+    bench_linearity_retrieval,
+    bench_linearity_listener_creation,
+    bench_zero_listener_overhead,
+    bench_overhead_breakdown,
+);
+criterion_main!(benches);