Juq097 May 2026
"collectors": ["type":"udp","port":8125],
"batch": "max_size_bytes":65536,"max_interval_ms":5000,
"persistence": "enabled":true,"path":"/var/lib/juq097/queue",
"exporter": "type":"http","endpoint":"https://ingest.example.com/v1","tls":true
"type": "line",
"data": "ws://api.example.com/stream",
"encoding":
"x": "field": "timestamp", "type": "temporal", "scale": "time" ,
"y": "field": "temperature", "type": "quantitative", "aggregate": "avg"
,
"interactive": true,
"theme": "dark"
Published on April 12 2026
# npm (recommended)
npm i juq097
# Or via CDN for quick prototyping
<script type="module" src="https://cdn.jsdelivr.net/npm/juq097@latest/dist/juq097.esm.js"></script>
The data‑visualization landscape is crowded: D3, Chart.js, Plotly, ECharts, and a growing list of specialized tools. Yet developers keep hitting three pain points: juq097
| Pain point | Traditional solutions | How juq097 solves it | |------------|-----------------------|----------------------| | Performance bottlenecks – Large datasets (> 100 k points) cause frame drops. | CPU‑centric SVG/Canvas pipelines, occasional WebGL wrappers. | Native WebGPU rendering + WebAssembly math kernels keep 60 fps even with millions of points. | | Framework lock‑in – Most libs are tightly coupled to React, Vue, or Angular. | You need wrappers or extra boilerplate. | Framework‑agnostic core; tiny adapters for any UI stack, even vanilla JS. | | Complex API surface – Custom visual tricks require deep D3 knowledge. | Verbose chaining, low‑level DOM manipulations. | Declarative schema (JSON/YAML) lets you describe a chart in < 30 lines; the imperative API is only a few dozen functions. | "type": "line", "data": "ws://api
If you’ve ever stared at a blinking cursor while trying to get a 3‑D scatter plot to stay smooth on a mobile phone, you’ll know why juq097 feels like a breath of fresh air. Published on April 12 2026
All tests ran on a 2024 MacBook Pro (M2 Pro, 16 GB RAM), Chrome 120, no extensions.
| Dataset | Points Rendered | Frame Rate (FPS) | Memory (MB) | |---------|----------------|------------------|-------------| | 100 k 2‑D points (scatter) | 100 k | 60 | 45 | | 1 M 2‑D points (down‑sampled) | 1 M (visible 30 k) | 58 | 62 | | 5 M 3‑D points (instanced) | 5 M | 55 | 78 | | 250 k streaming rows (line) | 250 k (rolling window) | 60 | 40 | | 10 k network nodes (force layout) | 10 k | 57 | 52 |
Compared with Plotly.js (WebGL) the same datasets dropped to ~30 FPS, and with Chart.js they capped at ~15 FPS.




