Can I use Rayon with Tokio inside a Lambda function?

Yes, this skill provides specific patterns for wrapping Rayon parallel iterators within tokio::task::spawn_blocking to safely bridge parallel CPU work with an async runtime.

When does the Async/Sync Advisor activate?

It activates automatically when Claude detects Lambda handlers involving mixed workloads, heavy data processing, or the use of concurrency primitives like spawn_blocking.

Why shouldn't I use async for CPU-bound tasks in Rust Lambda?

Asynchronous tasks are designed for I/O. CPU-bound tasks block the async executor, preventing other tasks from progressing and significantly hurting the throughput and responsiveness of your handler.

How does this skill help reduce AWS costs?

By optimizing concurrency patterns, your Lambda functions execute faster and utilize resources more efficiently, leading to shorter billing durations and lower overall costs.

Rust Lambda Async/Sync Advisor

Name: Rust Lambda Async/Sync Advisor
Author: EmilLindfors

byEmilLindfors

クラウドインフラストラクチャ

Optimizes Rust AWS Lambda performance by guiding developers through the selection of asynchronous and synchronous concurrency patterns.

概要

The Async/Sync Advisor is a specialized skill designed to improve the efficiency and reliability of Rust-based AWS Lambda functions. It monitors handler implementations to identify mixed I/O and CPU-bound workloads, providing proactive recommendations on when to utilize Tokio's async runtime versus offloading intensive computations to blocking thread pools with Rayon or spawn_blocking. By preventing common mistakes like blocking the async runtime with heavy CPU tasks, this skill ensures your serverless functions remain responsive, scalable, and cost-efficient.

主な機能

Workload identification for I/O vs. CPU-intensive operations
0 GitHub stars
Pattern templates for mixed-workload handler optimization
Performance analysis for concurrent vs. sequential execution
Best practices for implementing tokio::task::spawn_blocking
Parallel processing patterns using Rayon within Lambda environments

ユースケース

Optimizing a Lambda that performs both database lookups and complex data hashing
Refactoring sequential API calls into concurrent tokio::try_join patterns
Offloading image processing or file parsing to a dedicated thread pool

概要

主な機能

Workload identification for I/O vs. CPU-intensive operations
0 GitHub stars
Pattern templates for mixed-workload handler optimization
Performance analysis for concurrent vs. sequential execution
Best practices for implementing tokio::task::spawn_blocking
Parallel processing patterns using Rayon within Lambda environments

ユースケース

Optimizing a Lambda that performs both database lookups and complex data hashing
Refactoring sequential API calls into concurrent tokio::try_join patterns
Offloading image processing or file parsing to a dedicated thread pool