Understanding Async Request Starvation in Actix Web

Background and Problem Statement

Actix Web applications rely heavily on async programming with the Tokio runtime. Under high load, developers often report endpoints that become unresponsive, even though CPU and memory usage appear normal. This behavior typically stems from async task starvation—where long-running tasks block the async executor, preventing new requests from being scheduled.

Symptoms

• Endpoints intermittently hang without error logs.
• CPU usage stays low despite heavy request load.
• Request latencies spike unpredictably.
• Timeouts from load balancers or API gateways.

Minimal Example Demonstrating Starvation

#[actix_web::main]
async fn main() -> std::io::Result<()> {
    HttpServer::new(|| {
        App::new().route("/slow", web::get().to(slow_handler))
    })
    .bind("127.0.0.1:8080")?
    .run()
    .await
}

async fn slow_handler() -> impl Responder {
    // Simulating long CPU-bound task in async context
    let result = heavy_computation().await;
    HttpResponse::Ok().body(result)
}

async fn heavy_computation() -> String {
    // BAD: Blocks the executor thread
    std::thread::sleep(std::time::Duration::from_secs(5));
    "done".to_string()
}

This handler blocks the executor thread instead of yielding to the runtime, starving other tasks.

Root Causes of Starvation

Blocking Code in Async Handlers

Calling blocking functions like std::thread::sleep or heavy computations inside async handlers prevents the Tokio runtime from scheduling other futures, causing starvation.

Insufficient Worker Threads

Actix Web uses a limited thread pool based on the number of CPU cores. If all threads are blocked or occupied by long-running tasks, incoming requests queue up indefinitely.

Missing Bounded Executors

Async runtimes like Tokio can spawn tasks unboundedly unless constrained. Without proper control, background tasks can overwhelm the scheduler.

Diagnosis Strategy

Use Tokio Console

Install and run tokio-console to visualize task execution and identify bottlenecks:

[dependencies]
console-subscriber = "0.1"

#[tokio::main]
async fn main() {
    console_subscriber::init();
    // Start Actix server
}

This tool helps detect long-lived or blocked tasks in real time.

Enable Logging

RUST_LOG=actix_web=debug,tokio=trace cargo run

Detailed logs help trace stalled request flows and async task lifecycle events.

Monitor Thread Usage

Track runtime thread activity using OS-level tools like htop, perf, or dtrace to see if threads are idling or stuck.

Step-by-Step Remediation

1. Offload Blocking Work

Use web::block or tokio::task::spawn_blocking for CPU-bound or blocking IO operations:

async fn slow_handler() -> impl Responder {
    let result = web::block(move || heavy_computation_sync()).await.unwrap();
    HttpResponse::Ok().body(result)
}

fn heavy_computation_sync() -> String {
    std::thread::sleep(std::time::Duration::from_secs(5));
    "done".to_string()
}

2. Increase Worker Threads

Configure the number of worker threads explicitly to match concurrency needs:

HttpServer::new(|| App::new().route("/", web::get().to(index)))
    .workers(8)
    .bind("127.0.0.1:8080")?
    .run()
    .await

3. Implement Backpressure

Use bounded channels or semaphores to limit concurrent processing of expensive tasks:

let semaphore = Arc::new(Semaphore::new(100)); // max 100 concurrent ops

4. Benchmark Under Load

Use tools like wrk or k6 to simulate real-world traffic and evaluate how your service behaves under concurrency spikes.

Architectural Best Practices

• Always isolate blocking IO and CPU-bound work from the async runtime.
• Use structured logging and observability tools to monitor latency.
• Set worker counts based on benchmarking, not defaults.
• Favor spawn_blocking or background services for computational pipelines.
• Introduce health probes and timeouts to avoid upstream stalls.

Conclusion

Actix Web is an efficient framework for building fast web APIs in Rust, but it requires careful resource management in asynchronous contexts. Request starvation under load is typically caused by blocking tasks, poor concurrency configuration, or runtime misuse. By profiling task execution, isolating blocking code, and configuring the runtime appropriately, you can avoid these pitfalls and build resilient, high-performance web services. Understanding the low-level details of the async runtime is crucial when deploying Actix Web at scale.

FAQs

1. Why does Actix Web hang under high load?

It often hangs because blocking code prevents the async executor from progressing. All threads get saturated, causing starvation.

2. How do I detect blocking operations in async handlers?

Use tokio-console or trace logs to spot long-lived tasks. Blocking operations will appear as stalled futures.

3. Should I increase Actix Web workers for performance?

Yes, but only after profiling. Increasing workers without isolating blocking tasks can worsen contention.

4. Is it safe to use thread::sleep in Actix?

No. It blocks the entire executor thread. Use tokio::time::sleep or spawn_blocking for such delays.

5. What is the best way to handle CPU-heavy tasks in Actix?

Use spawn_blocking or delegate work to a background thread pool or task queue system outside the async runtime.