Async Task List

Problem

In distributed systems with microservices, complex processes can create control and reliability challenges. One solution is to divide them into atomic tasks, enabling more granular flow control, improved observability, ensured idempotency, and easier recovery from failures.

The challenge is to design a pattern that allows for controlled degradation, so that a single task failure doesn’t affect the entire flow, guaranteeing resilience and fault tolerance in production environments.

Solution

Create a task executor that orchestrates asynchronous tasks.
The execute function wraps each task, manages errors with do-catch as a circuit breaker, updates state on success, logs and persists failures, and ensures an atomic transaction to maintain data consistency.

func execute(
    status: StatusEnum,
    process: ProcessModel,
    _ work: () async throws -> ProcessModel
) async throws {
    do {
        let job = try await work()
        process.setStatus(job.status)
    } catch {
        process.setError(type: status, message: "\(error)")
    }
    try await repo.updateProcess(process)
}

Result

This implementation provides a high-level abstraction that enables precise orchestration of complex asynchronous processes with atomicity and durability guarantees.

🎯 Separation of Concerns: each task isolates its execution context and error handling.

🔄 Workflow Orchestration: enables task chaining through the pipeline pattern.

📊 Observability: generates a complete audit trail for debugging and monitoring.

⚡ Performance: maintains high concurrency without compromising data consistency.

🛡️ Resilience: incorporates automatic fail-fast and recovery patterns.

try await execute(status: .loadImages, process: process) {
    // Your implementation
}

Keep coding, keep running 🏃‍♂️

Async Task List

Table of contents

Problem

Solution

Result