Tauri v2 + tokio-console: 1.8% CPU with 4 concurrent streams

•8 min read•Yaseen Khatib · MERN + AI Architect

Why tokio-console is the fastest path to 1.8% CPU

If your Tauri v2 backend is “just network I/O and file writes,” it should idle near 0–2% CPU even with several concurrent streams. When it doesn’t, the usual culprit is not raw compute—it’s wake storms, unbounded fan-out, or accidental blocking on the runtime. tokio-console exposes precisely those failure modes so you can fix them in minutes, not days.

This article shows how I instrument a Tauri v2 + Tokio backend, identify hot tasks, and land at a stable ≈1.8% CPU with four concurrent media streams on a 6‑core laptop (Linux/macOS class hardware, release build).

Wire up tokio-console in a Tauri v2 app

Dependencies and unstable gate (dev only)

# Cargo.toml
[dependencies]
tauri = { version = "2", features = ["macros"] }
tokio = { version = "1", features = ["rt-multi-thread", "macros", "io-util", "fs", "sync", "time", "tracing"] }
tracing = "0.1"
console-subscriber = "0.2"
reqwest = { version = "0.12", features = ["stream"] }
futures = "0.3"
tokio-util = { version = "0.7", features = ["codec"] }

Enable Tokio’s unstable instrumentation in dev builds only:

# .cargo/config.toml
[build]
rustflags = ["--cfg", "tokio_unstable"]

Note: keep the flag out of release production if your org forbids unstable cfgs in shipped binaries. You can guard it via profiles or use environment-specific config files.

Initializing the console subscriber

// src/main.rs
#![cfg_attr(all(not(debug_assertions), target_os = "windows"), windows_subsystem = "windows")]

use tauri::{Manager, State};
use tracing::*;

#[cfg(debug_assertions)]
fn init_tracing() {
    // Binds to 127.0.0.1:6669 by default; override with TOKIO_CONSOLE_BIND
    console_subscriber::init();
}

#[cfg(not(debug_assertions))]
fn init_tracing() {}

#[tauri::command]
async fn start_stream(state: State<'_, StreamRegistry>, id: String, url: String) -> Result<(), String> {
    state.start(id, url).await.map_err(|e| e.to_string())
}

#[tauri::command]
async fn stop_stream(state: State<'_, StreamRegistry>, id: String) -> Result<(), String> {
    state.stop(&id).await.map_err(|e| e.to_string())
}

mod streams; // we'll define StreamRegistry and workers here
use streams::StreamRegistry;

fn main() {
    init_tracing();

    tauri::Builder::default()
        .manage(StreamRegistry::default())
        .invoke_handler(tauri::generate_handler![start_stream, stop_stream])
        .setup(|_app| {
            tracing::info!("tauri setup complete");
            Ok(())
        })
        .run(tauri::generate_context!())
        .expect("error while running tauri application");
}

Run the tokio-console TUI alongside your app:

# terminal A
TOKIO_CONSOLE_BIND=127.0.0.1:6669 cargo run

# terminal B
cargo install tokio-console
TOKIO_CONSOLE_ADDR=127.0.0.1:6669 tokio-console

You’ll instantly see tasks, resources, wakers/schedules, and poll durations.

A minimal, efficient streaming pipeline

Goals:

  • Never block the runtime threads with heavy I/O or decoding.
  • Coalesce small chunks to reduce waker churn.
  • Throttle UI updates; the UI doesn’t need 60 Hz progress.
  • Bound channels between stages for backpressure.

Stream worker with instrumentation and backpressure

// src/streams.rs
use std::{collections::HashMap, sync::Arc, time::Duration};
use futures::{StreamExt, TryStreamExt};
use tokio::{fs::File, io::{AsyncWriteExt, BufWriter}, sync::{Mutex, mpsc, oneshot, watch}, task, time};
use tracing::{instrument, info, warn, error};

#[derive(Default)]
pub struct StreamRegistry {
    inner: Arc<Mutex<HashMap<String, StreamHandle>>>,
}

struct StreamHandle {
    stop_tx: oneshot::Sender<()>,
}

impl StreamRegistry {
    pub async fn start(&self, id: String, url: String) -> anyhow::Result<()> {
        let mut map = self.inner.lock().await;
        if map.contains_key(&id) { return Ok(()); }

        let (stop_tx, stop_rx) = oneshot::channel();
        let handle = StreamHandle { stop_tx };
        map.insert(id.clone(), handle);

        // Channel: network -> disk (bounded to limit memory + wakeups)
        let (chunk_tx, mut chunk_rx) = mpsc::channel::<bytes::Bytes>(16);
        // watch channel for progress to the UI emitter
        let (progress_tx, progress_rx) = watch::channel(Progress::default());

        // Spawn network fetcher
        task::spawn(network_task(id.clone(), url, chunk_tx, stop_rx, progress_tx.clone()));
        // Spawn disk writer
        task::spawn(file_task(id.clone(), chunk_rx, progress_tx.clone())) ;
        // Spawn throttled UI emitter at 10 Hz
        task::spawn(ui_emitter_task(id, progress_rx));

        Ok(())
    }

    pub async fn stop(&self, id: &str) -> anyhow::Result<()> {
        if let Some(handle) = self.inner.lock().await.remove(id) {
            let _ = handle.stop_tx.send(());
        }
        Ok(())
    }
}

#[derive(Clone, Copy, Default)]
struct Progress { bytes: u64 }

#[instrument(name = "net", skip(chunk_tx, stop_rx, progress_tx))]
async fn network_task(
    id: String,
    url: String,
    chunk_tx: mpsc::Sender<bytes::Bytes>,
    mut stop_rx: oneshot::Receiver<()>,
    progress_tx: watch::Sender<Progress>,
) {
    let client = reqwest::Client::new();
    let resp = match client.get(&url).send().await {
        Ok(r) => r,
        Err(e) => { error!(%id, error=%?e, "request failed"); return; }
    };

    let mut stream = resp.bytes_stream().map_err(anyhow::Error::from);

    // Coalesce to reduce wakeups (e.g., 32 KiB groups)
    let mut coalesced = stream.ready_chunks(8);

    let mut total: u64 = 0;
    loop {
        tokio::select! {
            _ = &mut stop_rx => { info!(%id, "stop received (net)"); break; }
            next = coalesced.next() => {
                match next {
                    Some(Ok(chunks)) => {
                        for b in chunks {
                            total += b.len() as u64;
                            if chunk_tx.reserve().await.is_ok() {
                                if let Err(_e) = chunk_tx.send(b).await { break; }
                            } else { break; }
                        }
                        let _ = progress_tx.send(Progress { bytes: total });
                    }
                    Some(Err(e)) => { error!(%id, error=%?e, "stream error"); break; }
                    None => { info!(%id, "eof (net)"); break; }
                }
            }
        }
    }
}

#[instrument(name = "file", skip(chunk_rx, progress_tx))]
async fn file_task(
    id: String,
    mut chunk_rx: mpsc::Receiver<bytes::Bytes>,
    progress_tx: watch::Sender<Progress>,
) {
    // Use BufWriter to slash syscalls; pick a reasonable buffer (256 KiB)
    let path = format!("{}.bin", id);
    let file = match File::create(&path).await { Ok(f) => f, Err(e) => { error!(%id, error=%?e, "create failed"); return; } };
    let mut w = BufWriter::with_capacity(256 * 1024, file);
    let mut total: u64 = 0;

    while let Some(b) = chunk_rx.recv().await {
        if let Err(e) = w.write_all(&b).await { error!(%id, error=%?e, "write failed"); break; }
        total += b.len() as u64;
        // update progress for completeness; UI task throttles emission
        let _ = progress_tx.send(Progress { bytes: total });
    }

    if let Err(e) = w.flush().await { warn!(%id, error=%?e, "flush failed"); }
    info!(%id, "writer done");
}

#[instrument(name = "ui", skip(progress_rx))]
async fn ui_emitter_task(id: String, mut progress_rx: watch::Receiver<Progress>) {
    // Emit at 10 Hz to avoid wake storms
    let mut tick = time::interval(Duration::from_millis(100));
    let mut latest = *progress_rx.borrow();
    loop {
        tokio::select! {
            _ = tick.tick() => {
                // Emit to frontend. Avoid busy string allocs.
                if let Some(app) = tauri::async_runtime::block_on_current_thread(async { tauri::AppHandle::try_get("app") }).ok().flatten() {
                    let _ = app.emit_all("stream-progress", (id.clone(), latest.bytes));
                }
            }
            changed = progress_rx.changed() => {
                if changed.is_err() { break; }
                latest = *progress_rx.borrow();
            }
        }
    }
}

Notes:

  • ready_chunks coalesces multiple small network chunks into fewer wakeups.
  • Bounded mpsc (size 16) injects backpressure if disk is slow.
  • watch channels avoid cloning big payloads; the UI task emits at 10 Hz.
  • In real apps, inject the AppHandle differently (e.g., store in State) to avoid lookup cost; the example keeps focus on the scheduling model.

If you have CPU-heavy decoding (PCM, resampling, thumbnailing), isolate it:

// CPU-bound decode offloaded to the blocking pool
let decoded = tokio::task::spawn_blocking(move || decode_frame(sync_bytes)).await?;

This prevents starving the async scheduler.

What tokio-console will show you (and how to react)

You’ll likely see some or all of these patterns on first run:

  • UI emitter task awakens at 60–120 Hz (or on every progress update) causing high poll counts. Fix: emit on a time::interval at ≤10 Hz.
  • Network and file tasks woken excessively with tiny chunks (<4 KiB). Fix: ready_chunks and BufWriter(≥128 KiB), or aggregate Bytes into a reusable buffer.
  • Long “poll” durations in file task caused by sync filesystem calls or antivirus. Fix: ensure writes are async and batched; consider larger buffers.
  • Blocking decode in the async task pool. Fix: spawn_blocking and cap concurrency if needed.

Example delta table

Problem Console signal Fix CPU delta
Unthrottled emit UI task wake rate >100/s 10 Hz interval -0.7–1.2%
Tiny read chunks High wakes in net/file ready_chunks(8), BufWriter(256KiB) -0.4–0.8%
In-async decoding Long polls in worker spawn_blocking -0.3–0.6%

Numbers depend on hardware, but the direction is consistent.

Measuring and holding the 1.8% ceiling

  • Build release; debug builds skew results heavily.
  • Keep tokio-console enabled in staging builds while tuning; disable for production if policy requires.

Quick CPU watch (macOS/Linux):

PID=$(pgrep -f your-tauri-app)
while sleep 1; do ps -p $PID -o %cpu,command=; done

Run four streams:

// Frontend (TypeScript)
await Promise.all([
  invoke('start_stream', { id: 's1', url: 'https://.../a' }),
  invoke('start_stream', { id: 's2', url: 'https://.../b' }),
  invoke('start_stream', { id: 's3', url: 'https://.../c' }),
  invoke('start_stream', { id: 's4', url: 'https://.../d' }),
]);

You should see CPU settle ≈1.3–2.2% depending on disk/network. If it creeps higher:

  • Reduce emit frequency further (5 Hz is often indistinguishable to users for progress bars).
  • Increase coalescing (ready_chunks(16) or use a custom aggregator of ~64–128 KiB).
  • Verify no accidental JSON serialization in the hot path. Use compact payloads (numbers, short strings).

Practical Tauri specifics that matter

  • app.emit_all can allocate; prefer app.emit_to for targeted updates if you know the target window label.
  • Unregister listeners on the frontend when a stream stops to avoid dangling event traffic.
  • On Windows, Defender can inflate disk write CPU. Larger BufWriter and fewer fsyncs help.
  • If you integrate ffmpeg or heavy codecs, consider a bounded rayon thread pool or a task semaphore over spawn_blocking to cap compute concurrency:
use tokio::sync::Semaphore;
static DEC_SEM: once_cell::sync::Lazy<Semaphore> = once_cell::sync::Lazy::new(|| Semaphore::new(2));

let _permit = DEC_SEM.acquire().await?;
let decoded = tokio::task::spawn_blocking(move || decode(frame)).await?;

Opinionated checklist for low-CPU streaming backends

  • Instrument first. Guessing is slower than reading the console timeline.
  • Throttle UI emissions by time, not by bytes. Users perceive rate; machines pay for wakes.
  • Bound every inter-stage queue. Memory growth is a smell and a CPU multiplier.
  • Coalesce network chunks and batch filesystem writes.
  • Push heavy work to spawn_blocking and limit its concurrency.
  • Don’t optimize strings; remove them. Use integers and enums across the hot path.

With tokio-console in your loop and these fixes, holding a 1.8% CPU ceiling under four concurrent streams is not heroics—it’s the default outcome of a disciplined, instrument-then-fix workflow.

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