homelab-design/decisions/0061-go-handler-refactor.md

# Refactor NATS Handler Services from Python to Go

* Status: proposed
* Date: 2026-02-19
* Deciders: Billy
* Technical Story: Reduce container image sizes and resource consumption for non-ML handler services by rewriting them in Go

## Context and Problem Statement

The AI pipeline's non-inference services — `chat-handler`, `voice-assistant`, `pipeline-bridge`, `tts-module`, and the HTTP-forwarding variant of `stt-module` — are Python applications built on the `handler-base` shared library. None of these services perform local ML inference; they orchestrate calls to external Ray Serve endpoints over HTTP and route messages via NATS with MessagePack encoding.

Despite doing only lightweight I/O orchestration, each service inherits the full Python runtime and its dependency tree through `handler-base` (which pulls in `numpy`, `pymilvus`, `redis`, `httpx`, `pydantic`, `opentelemetry-*`, `mlflow`, and `psycopg2-binary`). This results in container images of **500–700 MB each** — five services totalling **~3 GB** of registry storage — for workloads that are fundamentally HTTP/NATS glue code.

The homelab already has two production Go services (`companions-frontend` and `ntfy-discord`) that prove the NATS + MessagePack + OpenTelemetry pattern works well in Go with images under 30 MB.

How do we reduce the image footprint and resource consumption of the non-ML handler services without disrupting the ML inference layer?

## Decision Drivers

* Container images for glue services are 500–700 MB despite doing no ML work
* Go produces static binaries yielding images of ~15–30 MB (scratch/distroless base)
* Go services start in milliseconds vs. seconds for Python, improving pod scheduling
* Go's memory footprint is ~10× lower for equivalent I/O-bound workloads
* The NATS + msgpack + OTel pattern is already proven in `companions-frontend`
* Go has first-class Kubernetes client support (`client-go`) — relevant for `pipeline-bridge`
* ML inference services (Ray Serve, kuberay-images) must remain Python — only orchestration moves
* Five services share a common base (`handler-base`) — a single Go module replaces it for all

## Considered Options

1. **Rewrite handler services in Go with a shared Go module**
2. **Optimise Python images (multi-stage builds, slim deps, compiled wheels)**
3. **Keep current Python stack unchanged**

## Decision Outcome

Chosen option: **Option 1 — Rewrite handler services in Go**, because the services are pure I/O orchestration with no ML dependencies, the Go pattern is already proven in-cluster, and the image + resource savings are an order of magnitude improvement that Python optimisation cannot match.

### Positive Consequences

* Five container images shrink from ~3 GB total to ~100–150 MB total
* Sub-second cold start enables faster rollouts and autoscaling via KEDA
* Lower memory footprint frees cluster resources for ML workloads
* Eliminates Python runtime CVE surface area from non-ML services
* Single `handler-go` module provides shared NATS, health, OTel, and client code
* `pipeline-bridge` gains `client-go` — the canonical Kubernetes client library
* Go's type system catches message schema drift at compile time

### Negative Consequences

* One-time rewrite effort across five services
* Team must maintain Go **and** Python codebases (Python remains for Ray Serve, Kubeflow pipelines, Gradio UIs)
* `handler-go` needs feature parity with `handler-base` for the orchestration subset (NATS client, health server, OTel, HTTP clients, Milvus client)
* Audio handling in `stt-module` (VAD) requires a Go webrtcvad binding or equivalent

## Pros and Cons of the Options

### Option 1 — Rewrite in Go

* Good, because images shrink from ~600 MB → ~20 MB per service
* Good, because memory usage drops from ~150 MB → ~15 MB per service
* Good, because startup time drops from ~3 s → <100 ms
* Good, because Go has mature libraries for every dependency (nats.go, client-go, otel-go, milvus-sdk-go)
* Good, because two existing Go services in the cluster prove the pattern
* Bad, because one-time engineering effort to rewrite five services
* Bad, because two language ecosystems to maintain

### Option 2 — Optimise Python images

* Good, because no rewrite needed
* Good, because multi-stage builds and dependency trimming can reduce images by 30–50%
* Bad, because Python runtime + interpreter overhead remains (~200 MB floor)
* Bad, because memory and startup improvements are marginal
* Bad, because `handler-base` dependency tree is difficult to slim without breaking shared code

### Option 3 — Keep current stack

* Good, because zero effort
* Bad, because images remain 500–700 MB for glue code
* Bad, because resource waste reduces headroom for ML workloads
* Bad, because slow cold starts limit KEDA autoscaling effectiveness

## Implementation Plan

### Phase 1: `handler-go` Shared Module

Create `git.daviestechlabs.io/daviestechlabs/handler-go` as a Go module with:

| Package | Purpose | Python Equivalent |
|---------|---------|-------------------|
| `nats/` | NATS/JetStream client with msgpack encoding | `handler_base.nats_client` |
| `health/` | HTTP health + readiness server | `handler_base.health` |
| `telemetry/` | OTel traces + metrics setup | `handler_base.telemetry` |
| `config/` | Env-based configuration (struct tags) | `handler_base.config` (pydantic-settings) |
| `clients/` | HTTP clients for LLM, embeddings, reranker, STT, TTS | `handler_base.clients` |
| `milvus/` | Milvus vector search client | `pymilvus` wrapper in handler_base |

Reference implementations: `companions-frontend/internal/` (NATS, msgpack, OTel), `ntfy-discord/internal/` (health, config, metrics).

### Phase 2: Service Ports (in order of complexity)

| Order | Service | Rationale |
|-------|---------|-----------|
| 1 | `pipeline-bridge` | Simplest — NATS + HTTP + k8s API calls. Validates `handler-go` module. |
| 2 | `tts-module` | Tiny NATS ↔ HTTP bridge to external Coqui API |
| 3 | `chat-handler` | Core text pipeline — NATS + Milvus + HTTP calls |
| 4 | `voice-assistant` | Same pattern as chat-handler with audio base64 handling |
| 5 | `stt-module` (streaming) | Requires Go VAD bindings for the HTTP-forwarding variant |

### Phase 3: Cleanup

* Archive Python versions of ported services
* Update Flux manifests for new Go images
* Update CI pipelines (Gitea Actions) for Go build/test/lint
* Update CODING-CONVENTIONS.md with Go section

### What Stays in Python

| Repository | Reason |
|------------|--------|
| `ray-serve` | PyTorch, vLLM, sentence-transformers — core ML inference |
| `kuberay-images` | GPU runtime Docker images (ROCm, CUDA, IPEX) |
| `gradio-ui` | Gradio is Python-only; dev/testing tool, not production |
| `kubeflow/` | Kubeflow Pipelines SDK is Python-only |
| `mlflow/` | MLflow SDK integration (tracking + model registry) |
| `stt-module` (local Whisper variant) | PyTorch + openai-whisper on GPU |
| `spark-analytics-jobs` | PySpark (being replaced by Flink anyway) |

## Links

* Related: [ADR-0003](0003-use-nats-for-messaging.md) — NATS as messaging backbone
* Related: [ADR-0004](0004-use-messagepack-for-nats.md) — MessagePack binary encoding
* Related: [ADR-0011](0011-kuberay-unified-gpu-backend.md) — KubeRay unified GPU backend
* Related: [ADR-0013](0013-gitea-actions-for-ci.md) — Gitea Actions CI
* Related: [ADR-0014](0014-docker-build-best-practices.md) — Docker build best practices
* Related: [ADR-0019](0019-handler-deployment-strategy.md) — Handler deployment strategy
* Related: [ADR-0024](0024-ray-repository-structure.md) — Ray repository structure
* Related: [ADR-0046](0046-companions-frontend-architecture.md) — Companions frontend (Go reference)
* Related: [ADR-0051](0051-keda-event-driven-autoscaling.md) — KEDA autoscaling