tidaldb/tidal-server/benches/scatter.rs

#![allow(clippy::unwrap_used, clippy::cast_precision_loss)]

//! Criterion benchmark for scatter-gather RETRIEVE fan-out.
//!
//! Today `scatter_gather_retrieve` spawns one OS thread *per shard, per query*
//! behind a single global `Mutex<usize>` + `Condvar` permit semaphore (perf
//! sweep 2026-06-13, finding rank 2). Before this bench existed there was no
//! before/after for that threading model. It measures:
//!
//! - **`scatter_fanout/regions{4,16}`** — single-query fan-out latency. The gap
//!   between 4 and 16 regions is the per-shard thread create/teardown +
//!   2 MiB-stack-reservation cost, since the underlying per-shard reads over the
//!   tiny replicated dataset are near-instant.
//!
//! - **`scatter_fanout_concurrent/regions{4,16}_q8`** — 8 concurrent queries
//!   issued at once, so `8 * regions` workers all contend the one global
//!   semaphore. This is the lock-bounce / queue-depth signal that a reused
//!   worker pool (wave 5) must improve without regressing single-query latency.
//!
//! Run:
//! ```bash
//! cargo bench -p tidal-server --bench scatter
//! ```

use std::collections::HashMap;
use std::sync::Arc;
use std::time::{Duration, Instant};

use criterion::{Criterion, criterion_group, criterion_main};
use tidal_server::scatter_gather::scatter_gather_retrieve;
use tidaldb::query::retrieve::Retrieve;
use tidaldb::replication::shard::RegionId;
use tidaldb::schema::{DecaySpec, EntityId, EntityKind, Schema, SchemaBuilder, Window};
use tidaldb::testing::SimulatedCluster;
use tidaldb::testing::cluster::ClusterConfig;

fn bench_schema() -> Schema {
    let mut builder = SchemaBuilder::new();
    let _ = builder
        .signal(
            "view",
            EntityKind::Item,
            DecaySpec::Exponential {
                half_life: Duration::from_secs(7 * 24 * 3600),
            },
        )
        .windows(&[Window::OneHour])
        .velocity(false)
        .add();
    builder.build().unwrap()
}

/// Build an `n`-region replicated cluster pre-seeded with 64 viewed items.
/// Returns the cluster plus the shard list and region-name map that
/// `scatter_gather_retrieve` consumes.
fn build_cluster(
    n: u16,
) -> (
    Arc<SimulatedCluster>,
    Vec<RegionId>,
    HashMap<RegionId, String>,
) {
    let regions: Vec<RegionId> = (0..n).map(RegionId).collect();
    let config = ClusterConfig {
        regions: regions.clone(),
        leader_region: RegionId(0),
        schema: bench_schema(),
        profiles: Vec::new(),
        transports: None,
    };
    let cluster = Arc::new(SimulatedCluster::build(config));

    // Replicated topology: write to the leader, all regions see all data.
    for i in 1..=64u64 {
        let eid = EntityId::new(i);
        cluster
            .write_item_with_metadata(eid, &HashMap::new())
            .unwrap();
        cluster.write_signal("view", eid, i as f64).unwrap();
    }

    let names: HashMap<RegionId, String> = regions
        .iter()
        .map(|&r| (r, format!("region-{}", r.0)))
        .collect();
    (cluster, regions, names)
}

fn trending_query() -> Retrieve {
    Retrieve::builder()
        .profile("trending")
        .limit(20)
        .build()
        .unwrap()
}

fn fanout_latency(c: &mut Criterion) {
    let mut group = c.benchmark_group("scatter_fanout");
    group.sample_size(30);
    for n in [4u16, 16] {
        let (cluster, shards, names) = build_cluster(n);
        let query = trending_query();
        group.bench_function(format!("regions{n}"), |b| {
            b.iter(|| {
                let (result, _meta) =
                    scatter_gather_retrieve(&cluster, &query, &shards, &names, None).unwrap();
                assert!(!result.items.is_empty());
            });
        });
    }
    group.finish();
}

fn fanout_concurrent(c: &mut Criterion) {
    const CONCURRENCY: usize = 8;
    let mut group = c.benchmark_group("scatter_fanout_concurrent");
    group.sample_size(20);
    group.measurement_time(Duration::from_secs(12));
    for n in [4u16, 16] {
        let (cluster, shards, names) = build_cluster(n);
        let query = trending_query();
        group.bench_function(format!("regions{n}_q{CONCURRENCY}"), |b| {
            b.iter_custom(|iters| {
                let mut elapsed = Duration::ZERO;
                for _ in 0..iters {
                    let start = Instant::now();
                    let threads: Vec<_> = (0..CONCURRENCY)
                        .map(|_| {
                            let cluster = Arc::clone(&cluster);
                            let query = query.clone();
                            let shards = shards.clone();
                            let names = names.clone();
                            std::thread::spawn(move || {
                                let (result, _meta) = scatter_gather_retrieve(
                                    &cluster, &query, &shards, &names, None,
                                )
                                .unwrap();
                                assert!(!result.items.is_empty());
                            })
                        })
                        .collect();
                    for t in threads {
                        t.join().unwrap();
                    }
                    elapsed += start.elapsed();
                }
                elapsed
            });
        });
    }
    group.finish();
}

criterion_group!(benches, fanout_latency, fanout_concurrent);
criterion_main!(benches);