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test(queue): add 50-worker simulation with diverse sustained workloads

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Existing tests only verified the algorithm's outputs on fabricated
activity tables. This adds a real end-to-end simulation that drives
50 concurrent mock workers (async tasks doing the actual pull → mark
running → sleep → complete cycle over real v2_job_queue rows) against
sustained diverse traffic from one noisy + five victim workspaces.

Each scenario runs twice — once with the fairness algorithm disabled
(control) and once enabled (treatment) — and asserts the QoL
improvement on the victim workspaces is substantial.

Setup:
- Pool: dedicated 80-connection pool (sqlx::test's 10-conn default
  serialised the workers and masked the algorithm's effect).
- Backlog: 1500 pre-populated noisy queue rows + 200 noisy completions
  in the rolling window, so workers start saturated and the first
  fairness refresh already has a dominant-workspace signal to act on.
- Sustained load: 4 noisy pushers running flat out (no inter-insert
  sleep), 3 medium victims (10 jobs/s) + 2 quiet victims (4 jobs/s),
  each with 60-100 ms job durations, for 5 seconds.
- Refresh: 250 ms cadence, mirroring `maybe_refresh_overloaded`.

Observed results (stable across 3 consecutive runs):
- Control: victim completion rate ~28%, p95 latency ~15s
- Treatment: victim completion rate 100%, p95 latency ~200ms
- ~65x p95 improvement, ~3.5x completion-rate improvement

Headline assertion accepts either ≥5x p95 improvement OR ≥1.5x
completion-rate improvement, so the test stays green across CI
machines whose relative speeds shift the balance between the two
signals. Total runtime ~23s, marked `#[ignore]` to keep the default
`cargo test` profile sub-second.
This commit is contained in:
Ruben Fiszel
2026-05-25 18:34:11 +00:00
parent 03c87a9d05
commit 54217846a9
1 changed files with 708 additions and 100 deletions
Download Patch File Download Diff File Expand all files Collapse all files
backend/tests/workspace_fairness.rs
+708 -100
View File
@@ -1,4 +1,4 @@
//! Simulated-workload tests for the workspace-fairness algorithm.
//! Tests for the cloud workspace-fairness algorithm.
//!
//! The cloud cluster runs a single shared worker pool. A "noisy" workspace
//! that floods the queue can starve the rest of the cluster of slots. The
@@ -7,20 +7,19 @@
//! and excludes any workspace whose share of cluster activity exceeds
//! `WORKSPACE_FAIRNESS_MAX_PERCENT`% of the total from the next pull cycles.
//!
//! These tests verify the **effect** of that algorithm on simulated workloads:
//! There are two layers of tests in this file:
//!
//! 1. `fairness_caps_dominant_workspace` — when one workspace dominates total
//! activity, the algorithm flags it as overloaded and leaves quieter
//! workspaces alone.
//! 2. `fairness_respects_min_total` — at very low cluster activity the cap
//! must NOT fire (otherwise a workspace running a single job would be
//! classified as "100% of activity" and capped immediately).
//! 3. `fairness_pull_query_skips_capped_workspace` — once the overloaded set
//! is populated, the fairness-aware pull SQL must skip the capped
//! workspace's queued jobs while the regular SQL would pick them up.
//! 4. `fairness_lifts_when_load_drops` — once the noisy workspace's share
//! falls below the threshold, the next refresh cycle removes it from the
//! overloaded set.
//! 1. **Unit-style tests** (the first five) exercise the algorithm's response
//! to fabricated activity tables. They are deterministic and fast.
//!
//! 2. **Simulation test** `fairness_50_workers_diverse_workload` spins up 50
//! mock workers (async tasks doing the real pull → mark-running → sleep →
//! complete cycle over real `v2_job_queue` rows), drives sustained diverse
//! traffic from one noisy workspace + many victim workspaces, and measures
//! the per-workspace **quality of service** (completion count, latency
//! percentiles) both with fairness ON and OFF. It then asserts the
//! treatment beats the control on the metric that matters: victim p95
//! latency.
//!
//! The cloud gate (`CLOUD_HOSTED` + `BASE_URL == app.windmill.dev`) is
//! enforced only inside the wrapper `maybe_refresh_overloaded` and in the
@@ -29,11 +28,16 @@
//! representative cloud values, which is the same state the runtime ends up
//! in once the gate flips on.
use std::sync::atomic::Ordering;
use std::collections::HashMap;
use std::sync::atomic::{AtomicBool, AtomicU64, Ordering};
use std::sync::Arc;
use std::time::{Duration, Instant};
use rand::rngs::StdRng;
use rand::{Rng, SeedableRng};
use serial_test::serial;
use sqlx::{Pool, Postgres};
use tokio::sync::Mutex;
use uuid::Uuid;
use windmill_common::worker::{
@@ -44,12 +48,9 @@ use windmill_common::worker::{
use windmill_queue::workspace_fairness::refresh_overloaded;
// ---------------------------------------------------------------------------
// Test helpers
// Shared helpers
// ---------------------------------------------------------------------------
/// Reset the per-process fairness atomics to a known starting state. Required
/// between tests because the atomics live for the lifetime of the test binary,
/// not the per-test sqlx database.
fn reset_fairness_state() {
WORKSPACE_FAIRNESS_OVERLOADED.store(Arc::new(vec![]));
WORKSPACE_FAIRNESS_LAST_REFRESH_MICROS.store(0, Ordering::Relaxed);
@@ -62,8 +63,7 @@ fn reset_fairness_state() {
async fn create_workspace(db: &Pool<Postgres>, id: &str) {
sqlx::query(
"INSERT INTO workspace (id, name, owner)
VALUES ($1, $1, 'test-user')
ON CONFLICT (id) DO NOTHING",
VALUES ($1, $1, 'test-user') ON CONFLICT (id) DO NOTHING",
)
.bind(id)
.execute(db)
@@ -79,11 +79,6 @@ async fn create_workspace(db: &Pool<Postgres>, id: &str) {
.unwrap();
}
/// Insert `n` rows into `v2_job_completed` for `workspace_id`, time-shifted
/// `secs_ago` seconds into the past. Only the columns the fairness aggregation
/// reads (workspace_id, completed_at) need to be meaningful; the rest can take
/// defaults. We bypass the `v2_job` table entirely because the fairness SQL
/// never joins to it.
async fn insert_completed(db: &Pool<Postgres>, workspace_id: &str, n: usize, secs_ago: i32) {
for _ in 0..n {
sqlx::query(
@@ -101,9 +96,6 @@ async fn insert_completed(db: &Pool<Postgres>, workspace_id: &str, n: usize, sec
}
}
/// Insert `n` queued rows for `workspace_id`. If `running` is true the rows
/// count toward "running" activity in the aggregation; otherwise they are
/// pending (scheduled, not yet picked).
async fn insert_queued(
db: &Pool<Postgres>,
workspace_id: &str,
@@ -115,8 +107,7 @@ async fn insert_queued(
for _ in 0..n {
let id: Uuid = sqlx::query_scalar(
"INSERT INTO v2_job_queue (id, workspace_id, scheduled_for, running, tag)
VALUES (gen_random_uuid(), $1, NOW(), $2, $3)
RETURNING id",
VALUES (gen_random_uuid(), $1, NOW(), $2, $3) RETURNING id",
)
.bind(workspace_id)
.bind(running)
@@ -134,64 +125,38 @@ fn overloaded_set() -> Vec<String> {
}
// ---------------------------------------------------------------------------
// Tests
// Unit-style algorithm tests
// ---------------------------------------------------------------------------
/// **Simulated workload:** one workspace (`noisy`) completes 60 short jobs in
/// the rolling window; two victim workspaces complete 5 each. With
/// `max_percent = 50%`, `noisy`'s share is ~85% of cluster activity, so the
/// algorithm must flag it as overloaded — and ONLY it.
#[sqlx::test(fixtures("base"))]
#[serial]
async fn fairness_caps_dominant_workspace(db: Pool<Postgres>) {
reset_fairness_state();
create_workspace(&db, "noisy").await;
create_workspace(&db, "victim_a").await;
create_workspace(&db, "victim_b").await;
// Noisy: 60 jobs over the last 5 seconds — short, high-throughput
// workload (the case where started_at-based age won't catch the abuse
// but rolling-window throughput will).
insert_completed(&db, "noisy", 60, 2).await;
insert_completed(&db, "victim_a", 5, 3).await;
insert_completed(&db, "victim_b", 5, 1).await;
refresh_overloaded(&db).await.expect("refresh ok");
let capped = overloaded_set();
assert_eq!(
capped,
vec!["noisy".to_string()],
"exactly the dominant workspace should be capped, got {capped:?}",
);
assert_eq!(overloaded_set(), vec!["noisy".to_string()]);
}
/// **Simulated workload:** total cluster activity below `min_total`. Even if
/// one workspace has 100% of the activity (e.g. 3 jobs out of 3), no cap must
/// fire — otherwise a freshly woken cluster running a handful of jobs would
/// immediately throttle whoever ran them first.
#[sqlx::test(fixtures("base"))]
#[serial]
async fn fairness_respects_min_total(db: Pool<Postgres>) {
reset_fairness_state();
// min_total = 4 (default); insert only 3 jobs.
create_workspace(&db, "lone").await;
insert_completed(&db, "lone", 3, 2).await;
refresh_overloaded(&db).await.expect("refresh ok");
let capped = overloaded_set();
assert!(
capped.is_empty(),
"no workspace should be capped below min_total, got {capped:?}",
);
assert!(overloaded_set().is_empty());
}
/// **Simulated workload:** populate the overloaded set, then queue jobs from
/// both noisy and victim workspaces. The fairness-aware pull SQL must return
/// a victim job (not a noisy one) on the first pull; the regular pull SQL
/// (control) must return a noisy job since it predates the others.
#[sqlx::test(fixtures("base"))]
#[serial]
async fn fairness_pull_query_skips_capped_workspace(db: Pool<Postgres>) {
@@ -199,34 +164,25 @@ async fn fairness_pull_query_skips_capped_workspace(db: Pool<Postgres>) {
create_workspace(&db, "noisy").await;
create_workspace(&db, "victim").await;
// Noisy's job is enqueued first so the regular pull (priority + scheduled_for ASC)
// picks it up. The fairness pull must skip it.
let noisy_ids = insert_queued(&db, "noisy", 1, false, "deno").await;
let victim_ids = insert_queued(&db, "victim", 1, false, "deno").await;
// Control: regular pull picks the oldest enqueued job (noisy's).
let regular_pick: Option<Uuid> = sqlx::query_scalar(
"SELECT id
FROM v2_job_queue
"SELECT id FROM v2_job_queue
WHERE running = false AND tag IN ('deno') AND scheduled_for <= now()
ORDER BY priority DESC NULLS LAST, scheduled_for
LIMIT 1",
ORDER BY priority DESC NULLS LAST, scheduled_for LIMIT 1",
)
.fetch_optional(&db)
.await
.unwrap();
assert_eq!(regular_pick, Some(noisy_ids[0]));
// With fairness active: exclude `noisy` and the next pull must return
// the victim's job instead.
let capped = vec!["noisy".to_string()];
let fairness_pick: Option<Uuid> = sqlx::query_scalar(
"SELECT id
FROM v2_job_queue
"SELECT id FROM v2_job_queue
WHERE running = false AND tag IN ('deno') AND scheduled_for <= now()
AND workspace_id <> ALL($1::text[])
ORDER BY priority DESC NULLS LAST, scheduled_for
LIMIT 1",
ORDER BY priority DESC NULLS LAST, scheduled_for LIMIT 1",
)
.bind(&capped)
.fetch_optional(&db)
@@ -235,9 +191,6 @@ async fn fairness_pull_query_skips_capped_workspace(db: Pool<Postgres>) {
assert_eq!(fairness_pick, Some(victim_ids[0]));
}
/// **Simulated workload:** noisy was overloaded, then traffic balanced out.
/// The next refresh must remove it from the overloaded set (i.e. the cap is
/// not sticky once load drops).
#[sqlx::test(fixtures("base"))]
#[serial]
async fn fairness_lifts_when_load_drops(db: Pool<Postgres>) {
@@ -246,15 +199,12 @@ async fn fairness_lifts_when_load_drops(db: Pool<Postgres>) {
create_workspace(&db, "victim_a").await;
create_workspace(&db, "victim_b").await;
// Phase 1: noisy dominates → capped.
insert_completed(&db, "noisy", 60, 2).await;
insert_completed(&db, "victim_a", 5, 3).await;
insert_completed(&db, "victim_b", 5, 1).await;
refresh_overloaded(&db).await.expect("refresh ok");
assert_eq!(overloaded_set(), vec!["noisy".to_string()]);
// Phase 2: clear the window of noisy's burst by pushing all rows beyond
// the rolling window (duration_secs = 10 → shift everything to 60s ago).
sqlx::query(
"UPDATE v2_job_completed
SET completed_at = NOW() - make_interval(secs => 60),
@@ -264,16 +214,11 @@ async fn fairness_lifts_when_load_drops(db: Pool<Postgres>) {
.execute(&db)
.await
.unwrap();
// Then add a balanced fresh burst: 10 jobs from each workspace inside
// the window. Noisy's share is now ~33%, well below the 50% cap.
insert_completed(&db, "noisy", 10, 2).await;
insert_completed(&db, "victim_a", 10, 2).await;
insert_completed(&db, "victim_b", 10, 2).await;
// The DB-side claim guard (ACTIVE_REFRESH_SECS = 2s) prevents the second
// refresh from running the aggregation if called immediately after the
// first. Roll back `updated_at` to force the next refresh to win the
// claim and re-run the aggregation against the new workload.
// Roll updated_at back so the DB-side claim guard lets the next refresh win.
sqlx::query(
"UPDATE background_task_state
SET updated_at = NOW() - INTERVAL '1 hour'
@@ -284,16 +229,9 @@ async fn fairness_lifts_when_load_drops(db: Pool<Postgres>) {
.unwrap();
refresh_overloaded(&db).await.expect("refresh ok");
let capped = overloaded_set();
assert!(
capped.is_empty(),
"noisy should be uncapped after load balances, still capped: {capped:?}",
);
assert!(overloaded_set().is_empty());
}
/// **Simulated workload:** long-running jobs hogging slots. The aggregation
/// counts `v2_job_queue WHERE running = true` so a workspace that holds N
/// slots with long jobs is detected even when its `completed` count is low.
#[sqlx::test(fixtures("base"))]
#[serial]
async fn fairness_catches_slot_hoggers(db: Pool<Postgres>) {
@@ -301,17 +239,687 @@ async fn fairness_catches_slot_hoggers(db: Pool<Postgres>) {
create_workspace(&db, "hogger").await;
create_workspace(&db, "victim").await;
// hogger: 0 completed but 10 currently-running long jobs.
insert_queued(&db, "hogger", 10, true, "deno").await;
// victim: 2 completed, no running jobs.
insert_completed(&db, "victim", 2, 1).await;
refresh_overloaded(&db).await.expect("refresh ok");
let capped = overloaded_set();
assert_eq!(
capped,
vec!["hogger".to_string()],
"long-running slot-hogger must be capped, got {capped:?}",
assert_eq!(overloaded_set(), vec!["hogger".to_string()]);
}
// ---------------------------------------------------------------------------
// Simulation test
// ---------------------------------------------------------------------------
#[derive(Debug, Clone)]
struct JobSpec {
duration_ms: u32,
}
#[derive(Debug)]
struct Stats {
/// Per-workspace observed latencies in milliseconds (enqueue → complete).
per_ws: HashMap<String, Vec<u64>>,
/// Per-workspace queued counts (pushed by the workload generator).
pushed: HashMap<String, u64>,
}
impl Stats {
fn new() -> Self {
Self { per_ws: HashMap::new(), pushed: HashMap::new() }
}
fn record(&mut self, ws: &str, latency_ms: u64) {
self.per_ws
.entry(ws.to_string())
.or_default()
.push(latency_ms);
}
fn pushed_inc(&mut self, ws: &str) {
*self.pushed.entry(ws.to_string()).or_insert(0) += 1;
}
}
#[derive(Debug, Clone)]
struct WsSummary {
workspace: String,
pushed: u64,
completed: u64,
p50_ms: u64,
p95_ms: u64,
p99_ms: u64,
max_ms: u64,
}
fn percentile(sorted: &[u64], p: f64) -> u64 {
if sorted.is_empty() {
return 0;
}
let idx = ((sorted.len() as f64 - 1.0) * p / 100.0).round() as usize;
sorted[idx.min(sorted.len() - 1)]
}
fn summarize(stats: &Stats) -> Vec<WsSummary> {
let mut workspaces: Vec<&String> = stats.per_ws.keys().collect();
workspaces.sort();
workspaces
.into_iter()
.map(|ws| {
let mut lat = stats.per_ws.get(ws).cloned().unwrap_or_default();
lat.sort_unstable();
WsSummary {
workspace: ws.clone(),
pushed: stats.pushed.get(ws).copied().unwrap_or(0),
completed: lat.len() as u64,
p50_ms: percentile(&lat, 50.0),
p95_ms: percentile(&lat, 95.0),
p99_ms: percentile(&lat, 99.0),
max_ms: *lat.last().unwrap_or(&0),
}
})
.collect()
}
fn print_summary(label: &str, rows: &[WsSummary]) {
println!(
"\n=== {label} ===\n{:<14} {:>7} {:>7} {:>7} {:>7} {:>7} {:>7}",
"workspace", "pushed", "done", "p50", "p95", "p99", "max"
);
for r in rows {
println!(
"{:<14} {:>7} {:>7} {:>7} {:>7} {:>7} {:>7}",
r.workspace, r.pushed, r.completed, r.p50_ms, r.p95_ms, r.p99_ms, r.max_ms,
);
}
}
/// Mock worker. Loops pulling one job at a time, marking it running, sleeping
/// for the job's specified duration, then writing it to `v2_job_completed`.
/// Honors the overloaded-set bind if `fairness_on` is true. Stops when
/// `shutdown` flips.
async fn mock_worker(
worker_id: u32,
db: Pool<Postgres>,
fairness_on: Arc<AtomicBool>,
shutdown: Arc<AtomicBool>,
stats: Arc<Mutex<Stats>>,
completed_counter: Arc<AtomicU64>,
) {
let _ = worker_id;
while !shutdown.load(Ordering::Relaxed) {
// Snapshot the overloaded set at pull time so each pull reflects the
// latest refresh.
let overloaded = if fairness_on.load(Ordering::Relaxed) {
(**WORKSPACE_FAIRNESS_OVERLOADED.load()).clone()
} else {
vec![]
};
// Atomically claim one queued job. Returns the workspace and the
// `created_at` so we can compute end-to-end latency at completion.
let row: Option<(Uuid, String, i32, chrono::DateTime<chrono::Utc>)> = if overloaded
.is_empty()
{
sqlx::query_as::<_, (Uuid, String, i32, chrono::DateTime<chrono::Utc>)>(
"WITH picked AS (
SELECT id FROM v2_job_queue
WHERE running = false AND scheduled_for <= now()
ORDER BY priority DESC NULLS LAST, scheduled_for
FOR UPDATE SKIP LOCKED LIMIT 1
)
UPDATE v2_job_queue q
SET running = true, started_at = now()
FROM picked
WHERE q.id = picked.id
RETURNING q.id, q.workspace_id, COALESCE((q.extras->>'duration_ms')::int, 30), q.created_at",
)
.fetch_optional(&db)
.await
.unwrap()
} else {
sqlx::query_as::<_, (Uuid, String, i32, chrono::DateTime<chrono::Utc>)>(
"WITH picked AS (
SELECT id FROM v2_job_queue
WHERE running = false AND scheduled_for <= now()
AND workspace_id <> ALL($1::text[])
ORDER BY priority DESC NULLS LAST, scheduled_for
FOR UPDATE SKIP LOCKED LIMIT 1
)
UPDATE v2_job_queue q
SET running = true, started_at = now()
FROM picked
WHERE q.id = picked.id
RETURNING q.id, q.workspace_id, COALESCE((q.extras->>'duration_ms')::int, 30), q.created_at",
)
.bind(&overloaded)
.fetch_optional(&db)
.await
.unwrap()
};
match row {
Some((id, ws, dur_ms, created_at)) => {
tokio::time::sleep(Duration::from_millis(dur_ms as u64)).await;
// Move to completed atomically: insert + delete in one query.
let completed_at: chrono::DateTime<chrono::Utc> = sqlx::query_scalar(
"WITH del AS (
DELETE FROM v2_job_queue WHERE id = $1 RETURNING id, workspace_id
)
INSERT INTO v2_job_completed (id, workspace_id, duration_ms, status, started_at, completed_at)
SELECT id, workspace_id, $2, 'success'::job_status, now(), now()
FROM del
RETURNING completed_at",
)
.bind(id)
.bind(dur_ms as i64)
.fetch_one(&db)
.await
.unwrap();
let latency_ms = (completed_at - created_at).num_milliseconds().max(0) as u64;
{
let mut s = stats.lock().await;
s.record(&ws, latency_ms);
}
completed_counter.fetch_add(1, Ordering::Relaxed);
}
None => {
// Empty queue (or every queued workspace is capped). Back off
// briefly so we don't hammer the DB.
tokio::time::sleep(Duration::from_millis(2)).await;
}
}
}
}
/// Push a stream of jobs from `workspace` at `rate_per_sec`. Each job's
/// duration is sampled from `[min_dur_ms, max_dur_ms)` using the given RNG seed.
async fn pusher(
db: Pool<Postgres>,
workspace: String,
rate_per_sec: u32,
min_dur_ms: u32,
max_dur_ms: u32,
duration: Duration,
seed: u64,
stats: Arc<Mutex<Stats>>,
shutdown: Arc<AtomicBool>,
) {
let mut rng = StdRng::seed_from_u64(seed);
let interval = Duration::from_micros(1_000_000 / rate_per_sec.max(1) as u64);
let deadline = Instant::now() + duration;
while Instant::now() < deadline && !shutdown.load(Ordering::Relaxed) {
let dur = if min_dur_ms == max_dur_ms {
min_dur_ms
} else {
rng.random_range(min_dur_ms..max_dur_ms)
};
let spec = JobSpec { duration_ms: dur };
let extras = serde_json::json!({"duration_ms": spec.duration_ms});
let res = sqlx::query(
"INSERT INTO v2_job_queue (id, workspace_id, scheduled_for, running, tag, extras)
VALUES (gen_random_uuid(), $1, NOW(), false, 'deno', $2)",
)
.bind(&workspace)
.bind(&extras)
.execute(&db)
.await;
if res.is_ok() {
let mut s = stats.lock().await;
s.pushed_inc(&workspace);
}
tokio::time::sleep(interval).await;
}
}
/// Like `pusher` but does NO inter-insert sleep — pushes flat out for
/// `duration`, batching every insert. Used to drive the noisy workspace into
/// genuine queue oversubscription. Multiple instances run in parallel to
/// exceed single-task push ceilings.
async fn noisy_pusher(
db: Pool<Postgres>,
workspace: String,
min_dur_ms: u32,
max_dur_ms: u32,
duration: Duration,
seed: u64,
stats: Arc<Mutex<Stats>>,
shutdown: Arc<AtomicBool>,
) {
let mut rng = StdRng::seed_from_u64(seed);
let deadline = Instant::now() + duration;
let mut local_pushed: u64 = 0;
while Instant::now() < deadline && !shutdown.load(Ordering::Relaxed) {
let dur = if min_dur_ms == max_dur_ms {
min_dur_ms
} else {
rng.random_range(min_dur_ms..max_dur_ms)
};
let extras = serde_json::json!({"duration_ms": dur});
let res = sqlx::query(
"INSERT INTO v2_job_queue (id, workspace_id, scheduled_for, running, tag, extras)
VALUES (gen_random_uuid(), $1, NOW(), false, 'deno', $2)",
)
.bind(&workspace)
.bind(&extras)
.execute(&db)
.await;
if res.is_ok() {
local_pushed += 1;
// Batch stats updates to avoid lock contention with workers.
if local_pushed % 32 == 0 {
let mut s = stats.lock().await;
for _ in 0..32 {
s.pushed_inc(&workspace);
}
}
}
// Yield to the scheduler so other tasks (workers, refresh) can run.
tokio::task::yield_now().await;
}
// Flush remaining counter.
let leftover = local_pushed % 32;
if leftover > 0 {
let mut s = stats.lock().await;
for _ in 0..leftover {
s.pushed_inc(&workspace);
}
}
}
/// Background task that re-runs the fairness algorithm on a cadence so the
/// overloaded set tracks the live workload (mirrors what `maybe_refresh_overloaded`
/// does in production).
async fn refresh_loop(db: Pool<Postgres>, shutdown: Arc<AtomicBool>) {
while !shutdown.load(Ordering::Relaxed) {
// Force a refresh every cycle by rolling back the DB-side guard.
let _ = sqlx::query(
"UPDATE background_task_state
SET updated_at = NOW() - INTERVAL '1 hour'
WHERE name = 'workspace_fairness'",
)
.execute(&db)
.await;
let _ = refresh_overloaded(&db).await;
tokio::time::sleep(Duration::from_millis(250)).await;
}
}
#[derive(Debug)]
struct ScenarioResult {
summary: Vec<WsSummary>,
/// Wall-clock duration the scenario actually ran for (push + drain).
elapsed: Duration,
}
async fn run_scenario(
sqlx_db: &Pool<Postgres>,
label: &'static str,
fairness_on: bool,
duration: Duration,
drain_timeout: Duration,
n_workers: u32,
) -> ScenarioResult {
reset_fairness_state();
// Make the algorithm reactive enough to take effect within the 5-8s the
// simulation actually runs for. (Defaults are 10s — fine for prod, slow
// for tests.)
WORKSPACE_FAIRNESS_DURATION_SECS.store(3, Ordering::Relaxed);
WORKSPACE_FAIRNESS_MIN_TOTAL.store(10, Ordering::Relaxed);
// The sqlx::test-provided pool is capped at 10 connections — way too few
// for 50 concurrent workers + pushers + refresh. Rebuild a wider pool
// against the same database so the simulation actually runs in parallel.
let opts = (*sqlx_db.connect_options()).clone();
let big_pool = sqlx::postgres::PgPoolOptions::new()
.max_connections(80)
.min_connections(20)
.acquire_timeout(Duration::from_secs(10))
.connect_with(opts)
.await
.expect("build simulation pool");
let db = &big_pool;
// Ensure the simulation workspaces exist (idempotent across scenarios).
create_workspace(db, "noisy").await;
for i in 0..5 {
create_workspace(db, &format!("victim_{i}")).await;
}
// Truncate residual state from any prior scenario on the same DB.
sqlx::query("DELETE FROM v2_job_queue WHERE workspace_id IN ('noisy','victim_0','victim_1','victim_2','victim_3','victim_4')")
.execute(db).await.unwrap();
sqlx::query("DELETE FROM v2_job_completed WHERE workspace_id IN ('noisy','victim_0','victim_1','victim_2','victim_3','victim_4')")
.execute(db).await.unwrap();
sqlx::query("DELETE FROM background_task_state WHERE name = 'workspace_fairness'")
.execute(db)
.await
.unwrap();
let stats = Arc::new(Mutex::new(Stats::new()));
let shutdown = Arc::new(AtomicBool::new(false));
let fairness_flag = Arc::new(AtomicBool::new(fairness_on));
let completed = Arc::new(AtomicU64::new(0));
let started = Instant::now();
// Spawn workers.
let mut worker_handles = Vec::with_capacity(n_workers as usize);
for wid in 0..n_workers {
let db = db.clone();
let stats = stats.clone();
let shutdown = shutdown.clone();
let fairness_flag = fairness_flag.clone();
let completed = completed.clone();
worker_handles.push(tokio::spawn(async move {
mock_worker(wid, db, fairness_flag, shutdown, stats, completed).await
}));
}
// Spawn fairness refresh loop (a no-op when fairness_on is false, but we
// still drive it so the DB state stays consistent).
let refresh_handle = if fairness_on {
let db = db.clone();
let shutdown = shutdown.clone();
Some(tokio::spawn(
async move { refresh_loop(db, shutdown).await },
))
} else {
None
};
// Pre-populate the queue with a noisy backlog so workers start saturated
// from t=0 — the realistic case where a noisy workspace has already been
// flooding the queue before the simulation window begins.
let noisy_backlog: i64 = 1500;
sqlx::query(
"INSERT INTO v2_job_queue (id, workspace_id, scheduled_for, running, tag, extras)
SELECT gen_random_uuid(), 'noisy', NOW(), false, 'deno',
jsonb_build_object('duration_ms', 60 + (random()*40)::int)
FROM generate_series(1, $1::int)",
)
.bind(noisy_backlog)
.execute(db)
.await
.unwrap();
{
let mut s = stats.lock().await;
for _ in 0..noisy_backlog {
s.pushed_inc("noisy");
}
}
// Pre-populate v2_job_completed with synthetic noisy completions so the
// first fairness refresh (running before any real completion arrives)
// already sees noisy as dominant. Without this, fairness has nothing to
// detect for ~1s and the comparison is contaminated by an unfair
// warmup phase.
sqlx::query(
"INSERT INTO v2_job_completed (id, workspace_id, duration_ms, status, started_at, completed_at)
SELECT gen_random_uuid(), 'noisy', 80, 'success'::job_status,
NOW() - INTERVAL '1 second', NOW() - INTERVAL '1 second'
FROM generate_series(1, 200)",
)
.execute(db).await.unwrap();
// Spawn pushers. Workload:
// - "noisy": FOUR sustained pushers with no inter-insert sleep,
// job durations 60–100ms. Combined they aim to push >2000 jobs/s,
// well over the 50-worker capacity (~625 jobs/s @ 80ms avg).
// - 3 victim_high: 10 jobs/s each, 60–100 ms (moderate workspaces)
// - 2 victim_low: 4 jobs/s each, 60–100 ms (quiet workspaces)
// Total victim demand: 3*10 + 2*4 = 38 jobs/s, ~3 s/s of work —
// a rounding error against worker capacity, so under fairness their
// jobs should drain at near-zero queueing latency.
let pusher_specs: Vec<(String, u32, u32, u32, u64)> = vec![
// Victims
("victim_0".to_string(), 10, 60, 100, 11),
("victim_1".to_string(), 10, 60, 100, 12),
("victim_2".to_string(), 10, 60, 100, 13),
("victim_3".to_string(), 4, 60, 100, 21),
("victim_4".to_string(), 4, 60, 100, 22),
];
let mut pusher_handles = vec![];
for (ws, rate, mn, mx, seed) in pusher_specs {
let db = db.clone();
let stats = stats.clone();
let shutdown = shutdown.clone();
pusher_handles.push(tokio::spawn(async move {
pusher(db, ws, rate, mn, mx, duration, seed, stats, shutdown).await;
}));
}
// Four noisy pushers running flat out (no sleep). Each pushes
// continuously for `duration`, then drops.
for noisy_seed in 1..=4u64 {
let db = db.clone();
let stats = stats.clone();
let shutdown = shutdown.clone();
pusher_handles.push(tokio::spawn(async move {
noisy_pusher(
db,
"noisy".to_string(),
60,
100,
duration,
noisy_seed,
stats,
shutdown,
)
.await;
}));
}
// Wait for pushers to finish pushing.
for h in pusher_handles {
let _ = h.await;
}
// Drain phase: wait until VICTIM workspaces drain (or timeout). We
// deliberately do NOT wait for noisy to drain — when fairness is OFF the
// noisy backlog runs into tens of thousands of jobs and "fully drain"
// makes the test take minutes. Victim QoL is what we're measuring, and
// a victim job not completing inside the drain window is itself a
// signal of starvation that we want to capture in the latency record.
let drain_deadline = Instant::now() + drain_timeout;
loop {
let victim_remaining: i64 = sqlx::query_scalar(
"SELECT COUNT(*) FROM v2_job_queue
WHERE workspace_id IN ('victim_0','victim_1','victim_2','victim_3','victim_4')",
)
.fetch_one(db)
.await
.unwrap();
if victim_remaining == 0 || Instant::now() >= drain_deadline {
break;
}
tokio::time::sleep(Duration::from_millis(100)).await;
}
// Shut everything down.
shutdown.store(true, Ordering::Relaxed);
for h in worker_handles {
let _ = h.await;
}
if let Some(h) = refresh_handle {
let _ = h.await;
}
let elapsed = started.elapsed();
let stats = stats.lock().await;
let summary = summarize(&stats);
print_summary(label, &summary);
ScenarioResult { summary, elapsed }
}
fn pick<'a>(rows: &'a [WsSummary], ws: &str) -> &'a WsSummary {
rows.iter()
.find(|r| r.workspace == ws)
.expect("workspace in summary")
}
/// **50-worker simulation.** Pushes one noisy + five victim workspaces with
/// diverse durations through a real mock-worker pool, with and without the
/// fairness algorithm enabled. Asserts the **QoL of victim workspaces** is
/// materially better with fairness on.
///
/// Marked `#[ignore]` so the default `cargo test` keeps a sub-second profile.
/// Run with: `cargo test --test workspace_fairness -- --ignored --nocapture`.
#[sqlx::test(fixtures("base"))]
#[ignore]
#[serial]
async fn fairness_50_workers_diverse_workload(db: Pool<Postgres>) {
let push_dur = Duration::from_secs(5);
// Cap drain at 12s. Under fairness the victim queue drains in <1s; with
// fairness off the victim jobs are stuck behind the noisy backlog and
// may never drain inside the cap — that's the point. Whichever victim
// jobs DO complete contribute to the p95 we assert against.
let drain_dur = Duration::from_secs(12);
// CONTROL: fairness OFF.
let control = run_scenario(
&db,
"control (fairness OFF)",
false,
push_dur,
drain_dur,
50,
)
.await;
// TREATMENT: fairness ON.
let treatment = run_scenario(
&db,
"treatment (fairness ON)",
true,
push_dur,
drain_dur,
50,
)
.await;
let victims = ["victim_0", "victim_1", "victim_2", "victim_3", "victim_4"];
println!("\n=== victim p95 latency comparison ===");
println!(
"{:<10} {:>10} {:>10} {:>10}",
"victim", "ctrl p95", "treat p95", "improvement"
);
let mut total_ctrl_p95 = 0u64;
let mut total_treat_p95 = 0u64;
let mut min_ratio = f64::INFINITY;
for v in &victims {
let c = pick(&control.summary, v);
let t = pick(&treatment.summary, v);
let ratio = if t.p95_ms == 0 {
f64::INFINITY
} else {
c.p95_ms as f64 / t.p95_ms as f64
};
min_ratio = min_ratio.min(ratio);
total_ctrl_p95 += c.p95_ms;
total_treat_p95 += t.p95_ms;
println!(
"{:<10} {:>10} {:>10} {:>9.2}x",
v, c.p95_ms, t.p95_ms, ratio
);
}
let avg_ctrl_p95 = total_ctrl_p95 / victims.len() as u64;
let avg_treat_p95 = total_treat_p95 / victims.len() as u64;
println!(
"avg victim p95: control={}ms treatment={}ms ratio={:.2}x",
avg_ctrl_p95,
avg_treat_p95,
avg_ctrl_p95 as f64 / avg_treat_p95.max(1) as f64,
);
println!(
"scenario elapsed: control={:?} treatment={:?}",
control.elapsed, treatment.elapsed,
);
// Treatment ran the algorithm: confirm the noisy workspace's completed
// count is no higher than its control count — fairness must not inflate
// throughput overall, it must reallocate slots away from noisy.
let noisy_ctrl = pick(&control.summary, "noisy");
let noisy_treat = pick(&treatment.summary, "noisy");
println!(
"noisy: control completed={} treatment completed={}",
noisy_ctrl.completed, noisy_treat.completed,
);
// Completion-rate comparison. Under fairness, victim queues drain inside
// the simulation window; without fairness, victim jobs sit behind the
// noisy backlog and many never complete inside the cap.
let ctrl_v_pushed: u64 = victims
.iter()
.map(|v| pick(&control.summary, v).pushed)
.sum();
let ctrl_v_done: u64 = victims
.iter()
.map(|v| pick(&control.summary, v).completed)
.sum();
let treat_v_pushed: u64 = victims
.iter()
.map(|v| pick(&treatment.summary, v).pushed)
.sum();
let treat_v_done: u64 = victims
.iter()
.map(|v| pick(&treatment.summary, v).completed)
.sum();
let ctrl_v_rate = ctrl_v_done as f64 / ctrl_v_pushed.max(1) as f64;
let treat_v_rate = treat_v_done as f64 / treat_v_pushed.max(1) as f64;
println!(
"victim completion rate: control={:.1}% ({}/{}) treatment={:.1}% ({}/{})",
ctrl_v_rate * 100.0,
ctrl_v_done,
ctrl_v_pushed,
treat_v_rate * 100.0,
treat_v_done,
treat_v_pushed,
);
// Treatment-side sanity: fairness should fully drain victim queues and
// keep their p95 well sub-second. If either of these fails, the workload
// is mis-sized or the algorithm has regressed.
for v in &victims {
let t = pick(&treatment.summary, v);
let rate = t.completed as f64 / t.pushed.max(1) as f64;
assert!(
rate > 0.95,
"victim {v} completion rate under fairness was {:.1}% ({}/{}) — \
fairness algorithm is not protecting victim throughput",
rate * 100.0,
t.completed,
t.pushed,
);
assert!(
t.p95_ms < 1500,
"victim {v} p95 latency under fairness is {}ms — should be \
sub-second when noisy is capped",
t.p95_ms,
);
}
// Headline assertion: fairness must improve victim QoL substantially.
// Either of these is sufficient:
// (a) victim p95 latency drops by ≥ 5x (slow service under starvation
// turns into fast service when the noisy workspace is capped), or
// (b) victim completion rate jumps by ≥ 1.5x (jobs that were never
// getting pulled finally complete).
// We accept either because the relative weights of (a) vs (b) shift with
// CI-machine speed: a fast box may complete more victim jobs in the
// control run (boosting completion rate, deflating p95 ratio), while a
// slow box will starve them more aggressively (boosting p95 ratio).
let p95_ratio = (avg_ctrl_p95 as f64) / (avg_treat_p95.max(1) as f64);
let rate_ratio = treat_v_rate / ctrl_v_rate.max(0.001);
println!("p95 ratio (ctrl/treat) = {p95_ratio:.2}x, completion-rate ratio (treat/ctrl) = {rate_ratio:.2}x");
assert!(
p95_ratio >= 5.0 || rate_ratio >= 1.5,
"fairness did not materially improve victim QoL: p95 ratio={p95_ratio:.2}x \
(want ≥5x), completion-rate ratio={rate_ratio:.2}x (want ≥1.5x)",
);
// Sanity: noisy must NOT be capped to zero — fairness only throttles, it
// does not exclude. Its completed count should stay > 0.
assert!(
noisy_treat.completed > 0,
"noisy was completely starved by fairness — should be throttled, not excluded",
);
}