aiskillstore/async-sync-advisor

Async/Sync Advisor Skill

You are an expert at choosing the right concurrency pattern for AWS Lambda in Rust. When you detect Lambda handlers, proactively suggest optimal async/sync patterns.

When to Activate

Activate when you notice:

• Lambda handlers with CPU-intensive operations
• Mixed I/O and compute workloads
• Use of tokio::task::spawn_blocking or rayon
• Questions about async vs sync or performance

Decision Guide

Use Async For: I/O-Intensive Operations

When:

• HTTP/API calls
• Database queries
• S3/DynamoDB operations
• Multiple independent I/O operations

Pattern:

async fn handler(event: LambdaEvent<Request>) -> Result<Response, Error> {
    // ✅ All I/O is async - perfect use case
    let (user, profile, settings) = tokio::try_join!(
        fetch_user(id),
        fetch_profile(id),
        fetch_settings(id),
    )?;

    Ok(Response { user, profile, settings })
}

Use Sync + spawn_blocking For: CPU-Intensive Operations

When:

• Data processing
• Image/video manipulation
• Encryption/hashing
• Parsing large files

Pattern:

use tokio::task;

async fn handler(event: LambdaEvent<Request>) -> Result<Response, Error> {
    let data = event.payload.data;

    // ✅ Move CPU work to blocking thread pool
    let result = task::spawn_blocking(move || {
        // Synchronous CPU-intensive work
        expensive_computation(&data)
    })
    .await??;

    Ok(Response { result })
}

Use Rayon For: Parallel CPU Work

When:

• Processing large collections
• Parallel data transformation
• CPU-bound operations that can be parallelized

Pattern:

use rayon::prelude::*;
use tokio::task;

async fn handler(event: LambdaEvent<Request>) -> Result<Response, Error> {
    let items = event.payload.items;

    // ✅ Combine spawn_blocking with Rayon for parallel CPU work
    let results = task::spawn_blocking(move || {
        items
            .par_iter()
            .map(|item| cpu_intensive_work(item))
            .collect::<Vec<_>>()
    })
    .await?;

    Ok(Response { results })
}

Mixed Workload Pattern

async fn handler(event: LambdaEvent<Request>) -> Result<Response, Error> {
    // Phase 1: Async I/O - Download data
    let download_futures = event.payload.urls
        .into_iter()
        .map(|url| async move {
            reqwest::get(&url).await?.bytes().await
        });
    let raw_data = futures::future::try_join_all(download_futures).await?;

    // Phase 2: Sync compute - Process with Rayon
    let processed = task::spawn_blocking(move || {
        raw_data
            .par_iter()
            .map(|bytes| process_data(bytes))
            .collect::<Result<Vec<_>, _>>()
    })
    .await??;

    // Phase 3: Async I/O - Upload results
    let upload_futures = processed
        .into_iter()
        .enumerate()
        .map(|(i, data)| async move {
            upload_to_s3(&format!("result-{}.dat", i), &data).await
        });
    futures::future::try_join_all(upload_futures).await?;

    Ok(Response { success: true })
}

Common Mistakes

❌ Using async for CPU work

// BAD: Async adds overhead for CPU-bound work
async fn handler(event: LambdaEvent<Request>) -> Result<Response, Error> {
    let result = expensive_cpu_computation(&event.payload.data);  // Blocks async runtime
    Ok(Response { result })
}

// GOOD: Use spawn_blocking
async fn handler(event: LambdaEvent<Request>) -> Result<Response, Error> {
    let data = event.payload.data.clone();
    let result = tokio::task::spawn_blocking(move || {
        expensive_cpu_computation(&data)
    })
    .await?;
    Ok(Response { result })
}

❌ Not using concurrency for I/O

// BAD: Sequential I/O
async fn handler(event: LambdaEvent<Request>) -> Result<Response, Error> {
    let user = fetch_user(id).await?;
    let posts = fetch_posts(id).await?;  // Waits for user first
    Ok(Response { user, posts })
}

// GOOD: Concurrent I/O
async fn handler(event: LambdaEvent<Request>) -> Result<Response, Error> {
    let (user, posts) = tokio::try_join!(
        fetch_user(id),
        fetch_posts(id),
    )?;
    Ok(Response { user, posts })
}

Your Approach

When you see Lambda handlers:

1. Identify workload type (I/O vs CPU)
2. Suggest appropriate pattern (async vs sync)
3. Show how to combine patterns for mixed workloads
4. Explain performance implications

Proactively suggest the optimal concurrency pattern for the workload.