# Performance Streaming

> Optimize high-throughput visualization updates across server broadcast and frontend batching paths

- Skill: `fossasia/performance-streaming` (Agent Skill)
- Install (CLI): `npx skillmds@latest add fossasia/performance-streaming`
- Raw SKILL.md: https://api.skillmd.com/api/skills/fossasia/performance-streaming/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Web & Frontend
- Author: fossasia (https://skillmd.com/u/fossasia)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/fossasia/performance-streaming

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# Skill: Performance for Streaming Updates

## When to Use

Use this skill when large update volume causes lag, dropped frames, or heavy CPU/network use.

## Core Workflow

1. Profile payload size and update frequency from Python client sends.
2. Prefer incremental updates/patches over full pane re-send.
3. Keep server broadcast fanout minimal and environment-scoped.
4. Preserve frontend batching/coalescing behavior for incoming pane updates.
5. Avoid unnecessary pane re-renders by preserving pane identity and memoization assumptions.

## Guardrails

- Do not trade correctness for speed in patch generation.
- Keep compare mode and normal mode both stable under load.
- Validate behavior in both WebSocket and polling modes.

## Documentation

- [Skill reference](references/REFERENCE.md)
- `py/visdom/utils/server_utils.py`
- `py/visdom/server/handlers/socket_handlers.py`
- `js/main.js`
- `js/api/ApiProvider.js`
- `AGENTS.md`
- `CONTRIBUTING.md`

## Assets

- See `assets/README.md` and store templates/resources in `assets/`.

## Tests

- Follow the default flow in `references/TESTS.md`.