ResQ Orchestrator

ResQ is a fault-tolerant, self-healing distributed job queue system built with Go and Redis that utilizes Groq (Llama 3) to automatically patch code errors and malformed payloads in real-time. click here

I’m honestly tired of people underestimating what this project does. This is ResQ Orchestrator, a deep-system engineering project that moves beyond simple message passing. It’s a distributed job queue that actually thinks. While standard systems like Celery or BullMQ simply retry failed jobs until they hit a dead-letter queue, ResQ implements an autonomous Self-Healing Loop. When a Go worker crashes due to a stack trace error or a type mismatch, the system doesn't just fail—it captures the execution context, sends the failure state to a Groq-powered Llama 3 model for sub-millisecond reasoning, patches the payload logic in real-time, and re-processes the job. This isn't just a flashy demo; it’s a showcase of AI-Ops and resilient backend architecture. If you want to see a system built for zero-downtime intention instead of just basic persistence, click here.

The Concept

ResQ was engineered to solve the most frustrating part of distributed systems: intermittent "poison pill" jobs that crash workers due to unexpected data structures. By integrating a high-speed Large Language Model directly into the worker’s error-handling middleware, ResQ transforms the standard "Fail-Retry" cycle into a "Fail-Analyze-Heal-Succeed" pipeline. By utilizing Groq’s LPU architecture, the self-healing process occurs almost instantaneously, allowing the system to maintain high throughput even when faced with adversarial or malformed inputs that would typically bring a worker cluster to a halt.

Technical Implementation

The Distributed Architecture

The system is architected as a decoupled microservice environment. The Producer (Go API) handles high-concurrency ingestion, pushing jobs into Redis which acts as a high-speed message broker. The Consumer (Go Workers) leverage Go’s native concurrency primitives and sync.Pool optimizations to process the queue with minimal overhead. The true innovation lies in the Self-Healing Engine: a dedicated interceptor that monitors the worker's lifecycle and triggers an automated recovery sequence whenever a panic or specific error code is detected, using Groq to perform real-time root-cause analysis on the serialized stack trace.

"ResQ redefines fault tolerance by shifting from passive error logging to active, ultra-low latency systemic recovery via Groq's inference engine."

The dashboard is a real-time monitoring suite built with HTML and WebSockets, providing a live stream of the queue’s health. It visualizes not just the successes and failures, but the "Healed" state—showing exactly how the AI agent modified a malformed payload to allow the worker to complete a previously failing task. This transparency ensures that while the system is autonomous, the engineering team retains full visibility into the AI's repair logic.

Critical Engineering Challenge: Sub-Second Error Contextualization The hardest part was engineering the "Healing Loop" to be fast enough that it didn't become a bottleneck for the entire queue. Using Groq was essential here; I had to architect a context-capture utility that could serialize a Go panic, extract the failing JSON segment, and get a patch back from Llama 3 in under 500ms. Mapping those AI-generated patches back into a strongly-typed Go environment required a robust "Dirty Payload" handler that could dynamically re-validate the healed data before allowing it back into the primary processing stream.

[ Producer (Go API) ] --- (JSON Payload) ---> [ Redis Broker ]
                                                   |
                                           [ Worker Cluster ]
                                            /      |       \
                             [ Success ]  <-- [ Logic ] --> [ Crash Detected ]
                                                                   |
                                                         [ Groq (Llama 3) ]
                                                                   |
                             [ Success ]  <-- [ Re-Process ] <-- [ Healed Data ]