Janis Eccarius
587ea19e0e
70B model at 30-50 tok/s was exceeding the 30s urllib timeout for larger outputs. Raise server timeout to 60s, add browser AbortController at 65s so the UI fails gracefully. Trim max_tokens (row: 300, dataset: 512) to keep generation well inside the window. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
681 lines
28 KiB
YAML
Executable File
681 lines
28 KiB
YAML
Executable File
apiVersion: v1
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kind: ConfigMap
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metadata:
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name: spark-analytics-webview
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labels:
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app.kubernetes.io/name: spark-analytics
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app.kubernetes.io/part-of: nowchess
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data:
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serve.py: |
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#!/usr/bin/env python3
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"""Spark analytics results viewer — queries PostgreSQL analytics tables, serves HTML tables."""
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import html
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import json
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import os
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import re
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import urllib.error
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import urllib.request
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import psycopg2
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import psycopg2.extras
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from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
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PORT = int(os.environ.get("PORT", "8080"))
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_url = os.environ.get("NOWCHESS_JDBC_URL", "")
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_m = re.match(r"jdbc:postgresql://([^:/]+)(?::(\d+))?/([^?]+)", _url)
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DB_HOST = _m.group(1) if _m else "localhost"
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DB_PORT = int(_m.group(2)) if (_m and _m.group(2)) else 5432
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DB_NAME = _m.group(3) if _m else "nowchess"
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DB_USER = os.environ.get("NOWCHESS_DB_USER", "nowchess")
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DB_PASS = os.environ.get("NOWCHESS_DB_PASS", "")
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# Each tuple: (url-slug, display label, postgres table name)
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DATASETS = [
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("opening-book", "Opening Book (Top 1000)", "analytics_opening_stats"),
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("player-stats", "Player Statistics", "analytics_player_stats"),
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("cluster-archetypes", "Cluster Archetypes", "analytics_cluster_archetypes"),
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("component-sizes", "Graph Component Sizes", "analytics_component_sizes"),
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("game-length", "Game Length Distribution", "analytics_game_length_distribution"),
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("game-length-by-result", "Game Length by Result", "analytics_game_length_by_result"),
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("color-advantage", "Color Advantage", "analytics_color_advantage"),
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("elo-distribution", "ELO Distribution", "analytics_elo_distribution"),
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("time-control", "Time Control Analysis", "analytics_time_control_stats"),
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("hourly-activity", "Hourly Activity", "analytics_hourly_activity"),
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("weekly-activity", "Weekly Activity", "analytics_weekly_activity"),
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("rating-mismatch", "Rating Mismatch (Upsets)", "analytics_rating_mismatch"),
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("termination-stats", "Termination Types", "analytics_termination_stats"),
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]
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CSS = """
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* { box-sizing: border-box; }
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body { font-family: 'Segoe UI', sans-serif; margin: 0; background: #0d1117; color: #c9d1d9; }
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header { background: #161b22; border-bottom: 1px solid #30363d; padding: 1rem 2rem; display: flex; align-items: center; gap: 1rem; }
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header h1 { margin: 0; color: #58a6ff; font-size: 1.25rem; font-weight: 600; flex: 1; }
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header span { color: #8b949e; font-size: 0.875rem; }
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.settings-btn { background: none; border: 1px solid #30363d; border-radius: 6px; color: #8b949e;
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cursor: pointer; padding: 4px 8px; font-size: 0.8rem; display: flex; align-items: center; gap: 4px; }
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.settings-btn:hover { border-color: #58a6ff; color: #58a6ff; }
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.settings-btn.active { border-color: #3fb950; color: #3fb950; }
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main { padding: 1.5rem 2rem; }
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h2 { color: #e6edf3; font-size: 1rem; font-weight: 600; margin: 0 0 1rem; }
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.cards { display: grid; grid-template-columns: repeat(auto-fill, minmax(260px, 1fr)); gap: 1rem; }
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.card { background: #161b22; border: 1px solid #30363d; border-radius: 8px; padding: 1rem; }
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.card h3 { margin: 0 0 0.5rem; font-size: 0.9rem; color: #58a6ff; }
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.card a { text-decoration: none; color: inherit; display: block; }
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.card a:hover .card-label { text-decoration: underline; }
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.badge { display: inline-block; background: #21262d; border: 1px solid #30363d;
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border-radius: 12px; padding: 1px 8px; font-size: 0.75rem; color: #8b949e; }
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.badge.ready { border-color: #238636; color: #3fb950; }
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.back { color: #58a6ff; text-decoration: none; font-size: 0.875rem; }
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.back:hover { text-decoration: underline; }
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.meta { color: #8b949e; font-size: 0.8rem; margin: 0.5rem 0 1rem; }
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table { width: 100%; border-collapse: collapse; font-size: 0.8rem; }
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thead th { background: #161b22; position: sticky; top: 0; padding: 6px 12px;
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text-align: left; color: #8b949e; border-bottom: 1px solid #30363d;
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font-weight: 600; white-space: nowrap; }
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tbody td { padding: 5px 12px; border-bottom: 1px solid #21262d; }
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tbody tr:hover td { background: #161b22; }
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.table-wrap { overflow-x: auto; border: 1px solid #30363d; border-radius: 6px; }
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.notice { background: #161b22; border: 1px solid #30363d; border-radius: 6px;
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padding: 1.5rem; color: #8b949e; }
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/* Settings modal */
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.modal-overlay { display: none; position: fixed; inset: 0; background: rgba(0,0,0,0.6);
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z-index: 100; align-items: center; justify-content: center; }
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.modal-overlay.open { display: flex; }
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.modal { background: #161b22; border: 1px solid #30363d; border-radius: 10px;
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padding: 1.5rem; width: 420px; max-width: 95vw; }
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.modal h3 { margin: 0 0 0.25rem; color: #e6edf3; font-size: 0.95rem; }
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.modal p { margin: 0 0 1rem; color: #8b949e; font-size: 0.8rem; }
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.modal input { width: 100%; background: #0d1117; border: 1px solid #30363d; border-radius: 6px;
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color: #c9d1d9; font-size: 0.85rem; padding: 8px 10px; margin-bottom: 0.75rem;
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font-family: monospace; }
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.modal input:focus { outline: none; border-color: #58a6ff; }
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.modal-actions { display: flex; gap: 0.5rem; justify-content: flex-end; }
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.btn { border-radius: 6px; padding: 6px 14px; font-size: 0.8rem; cursor: pointer; border: 1px solid transparent; }
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.btn-primary { background: #238636; color: #fff; border-color: #2ea043; }
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.btn-primary:hover { background: #2ea043; }
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.btn-danger { background: none; color: #f85149; border-color: #f85149; }
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.btn-danger:hover { background: rgba(248,81,73,0.1); }
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.btn-ghost { background: none; color: #8b949e; border-color: #30363d; }
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.btn-ghost:hover { color: #c9d1d9; border-color: #8b949e; }
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/* Explain panel */
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.explain-bar { display: flex; align-items: center; gap: 0.75rem; margin: 1rem 0; }
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.explain-btn { background: #1f3a5f; border: 1px solid #388bfd; color: #58a6ff; border-radius: 6px;
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padding: 6px 14px; font-size: 0.8rem; cursor: pointer; display: flex; align-items: center; gap: 6px; }
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.explain-btn:hover { background: #263d6a; }
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.explain-btn:disabled { opacity: 0.5; cursor: not-allowed; }
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.no-key-hint { color: #8b949e; font-size: 0.78rem; }
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.explain-panel { background: #161b22; border: 1px solid #388bfd; border-radius: 8px;
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padding: 1.25rem; margin-bottom: 1rem; }
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.explain-panel h4 { margin: 0 0 0.5rem; color: #58a6ff; font-size: 0.85rem; font-weight: 600; }
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.explain-panel .explain-text { color: #c9d1d9; font-size: 0.85rem; line-height: 1.6; white-space: pre-wrap; }
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.explain-panel .explain-error { color: #f85149; font-size: 0.82rem; }
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.spinner { display: inline-block; width: 12px; height: 12px; border: 2px solid #388bfd;
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border-top-color: transparent; border-radius: 50%; animation: spin 0.7s linear infinite; }
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@keyframes spin { to { transform: rotate(360deg); } }
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/* Per-row AI */
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.row-ai-btn { background: none; border: 1px solid #30363d; border-radius: 4px; color: #8b949e;
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cursor: pointer; padding: 1px 5px; font-size: 0.72rem; line-height: 1.4; }
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.row-ai-btn:hover { border-color: #388bfd; color: #58a6ff; }
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.row-ai-btn:disabled { opacity: 0.4; cursor: not-allowed; }
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.row-ai-btn.done { border-color: #238636; color: #3fb950; }
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.row-expand-tr td { background: #0a0e14; padding: 0.6rem 1rem 0.6rem 2rem;
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font-size: 0.8rem; line-height: 1.6; border-bottom: 1px solid #30363d; }
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.row-explain-text { color: #c9d1d9; white-space: pre-wrap; }
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.row-explain-error { color: #f85149; }
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"""
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JS = """
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const NIM_KEY_STORAGE = 'nim_api_key';
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const rowCache = {};
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let nimQueue = Promise.resolve();
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function getKey() { return localStorage.getItem(NIM_KEY_STORAGE) || ''; }
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function hasKey() { return !!getKey(); }
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function updateSettingsBtn() {
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const btn = document.getElementById('settings-btn');
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if (!btn) return;
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if (hasKey()) {
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btn.classList.add('active');
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btn.title = 'NIM API key set — click to change';
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} else {
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btn.classList.remove('active');
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btn.title = 'Configure NVIDIA NIM API key for AI explanations';
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}
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}
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function openSettings() {
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document.getElementById('nim-modal').classList.add('open');
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const inp = document.getElementById('nim-key-input');
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inp.value = getKey();
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inp.focus();
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updateNoKeyHint();
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}
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function closeSettings() {
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document.getElementById('nim-modal').classList.remove('open');
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}
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function saveSettings() {
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const val = document.getElementById('nim-key-input').value.trim();
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if (val) localStorage.setItem(NIM_KEY_STORAGE, val);
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else localStorage.removeItem(NIM_KEY_STORAGE);
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closeSettings();
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updateSettingsBtn();
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updateNoKeyHint();
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}
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function clearKey() {
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localStorage.removeItem(NIM_KEY_STORAGE);
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document.getElementById('nim-key-input').value = '';
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updateSettingsBtn();
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updateNoKeyHint();
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}
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function updateNoKeyHint() {
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const hint = document.getElementById('no-key-hint');
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if (!hint) return;
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hint.style.display = hasKey() ? 'none' : 'inline';
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const btn = document.getElementById('explain-btn');
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if (btn) btn.disabled = !hasKey();
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}
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async function nimFetch(payload) {
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const ctrl = new AbortController();
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const tid = setTimeout(() => ctrl.abort(), 65000);
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let resp;
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try {
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resp = await fetch('/explain', {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify(payload),
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signal: ctrl.signal,
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});
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} finally {
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clearTimeout(tid);
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}
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if (resp.status === 429) {
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const retryAfter = parseInt(resp.headers.get('Retry-After') || '10', 10);
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const err = new Error('Rate limited — retry in ' + retryAfter + 's');
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err.retryAfter = retryAfter;
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throw err;
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}
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if (!resp.ok) {
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const msg = await resp.text();
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throw new Error('Proxy ' + resp.status + ': ' + msg);
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}
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const data = await resp.json();
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if (data.error) throw new Error(data.error);
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return data.text || '(no response)';
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}
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function queueNim(payload) {
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const next = nimQueue.then(() => nimFetch(payload));
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nimQueue = next.catch(() => {});
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return next;
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}
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async function explainDataset() {
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const key = getKey();
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if (!key) { openSettings(); return; }
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const btn = document.getElementById('explain-btn');
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const panel = document.getElementById('explain-panel');
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const textEl = document.getElementById('explain-text');
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btn.disabled = true;
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btn.innerHTML = '<span class="spinner"></span> Thinking…';
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panel.style.display = 'block';
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textEl.className = 'explain-text';
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textEl.textContent = '';
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try {
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const text = await queueNim({ key: key, mode: 'dataset', dataset: window.DATASET });
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textEl.textContent = text;
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} catch (e) {
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textEl.className = 'explain-error';
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textEl.textContent = 'Error: ' + e.message;
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} finally {
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btn.disabled = false;
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btn.innerHTML = '✦ Analyse top 10 rows';
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updateNoKeyHint();
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}
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}
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async function analyzeRow(idx) {
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const key = getKey();
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if (!key) { openSettings(); return; }
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if (rowCache[idx] !== undefined) return;
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const btn = document.getElementById('row-btn-' + idx);
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const expandRow = document.getElementById('row-expand-' + idx);
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const textEl = document.getElementById('row-explain-' + idx);
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if (!btn || !expandRow || !textEl) return;
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const dataset = window.DATASET;
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const row = dataset.rows && dataset.rows[idx];
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if (!row) {
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expandRow.style.display = 'table-row';
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textEl.className = 'row-explain-error';
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textEl.textContent = 'Row data not available.';
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return;
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}
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btn.disabled = true;
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btn.innerHTML = '<span class="spinner"></span>';
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expandRow.style.display = 'table-row';
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textEl.className = 'row-explain-text';
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textEl.textContent = 'Analyzing…';
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try {
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const text = await queueNim({
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key: key, mode: 'row',
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dataset: { label: dataset.label, headers: dataset.headers, row: row },
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});
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rowCache[idx] = text;
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textEl.textContent = text;
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btn.innerHTML = '✓';
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btn.classList.add('done');
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} catch (e) {
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textEl.className = 'row-explain-error';
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textEl.textContent = e.message;
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btn.disabled = false;
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btn.innerHTML = '⚡';
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}
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}
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document.addEventListener('DOMContentLoaded', () => {
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updateSettingsBtn();
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updateNoKeyHint();
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document.getElementById('nim-modal')?.addEventListener('click', e => {
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if (e.target === e.currentTarget) closeSettings();
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});
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document.getElementById('nim-key-input')?.addEventListener('keydown', e => {
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if (e.key === 'Enter') saveSettings();
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if (e.key === 'Escape') closeSettings();
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});
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});
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"""
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SETTINGS_MODAL = """
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<div class="modal-overlay" id="nim-modal">
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<div class="modal">
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<h3>NVIDIA NIM API Key</h3>
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<p>Used client-side only — stored in your browser's localStorage, never sent to this server.</p>
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<input type="password" id="nim-key-input" placeholder="nvapi-…" autocomplete="off" spellcheck="false" />
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<div class="modal-actions">
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<button class="btn btn-danger" onclick="clearKey()">Clear</button>
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<button class="btn btn-ghost" onclick="closeSettings()">Cancel</button>
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<button class="btn btn-primary" onclick="saveSettings()">Save</button>
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</div>
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</div>
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</div>
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"""
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def get_conn():
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return psycopg2.connect(host=DB_HOST, port=DB_PORT, dbname=DB_NAME, user=DB_USER, password=DB_PASS)
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def all_row_counts() -> dict:
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counts = {}
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try:
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with get_conn() as conn:
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with conn.cursor() as cur:
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for _, _, table in DATASETS:
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try:
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cur.execute(f"SELECT COUNT(*) FROM {table}")
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counts[table] = cur.fetchone()[0]
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except Exception:
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conn.rollback()
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counts[table] = -1
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except Exception:
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for _, _, table in DATASETS:
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counts.setdefault(table, -1)
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return counts
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def fetch_rows(table: str, limit: int = 10_000):
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with get_conn() as conn:
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with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur:
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cur.execute(f"SELECT * FROM {table} LIMIT %s", (limit,))
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rows = cur.fetchall()
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if not rows:
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return [], []
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headers = list(rows[0].keys())
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data = [
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[str(row[h]) if row[h] is not None else "" for h in headers]
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for row in rows
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]
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return headers, data
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def page(title: str, body: str, dataset_json: str = "null") -> str:
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return (
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f"<!doctype html><html lang=en><head><meta charset=utf-8>"
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f"<meta name=viewport content='width=device-width,initial-scale=1'>"
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f"<title>{html.escape(title)} — Spark Analytics</title>"
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f"<style>{CSS}</style></head><body>"
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f"<header>"
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f"<h1>Spark Analytics</h1><span>NowChess · staging</span>"
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f"<button class='settings-btn' id='settings-btn' onclick='openSettings()' title='Configure NIM API key'>⚙ NIM Key</button>"
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f"</header>"
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f"<main>{body}</main>"
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f"{SETTINGS_MODAL}"
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f"<script>window.DATASET={dataset_json};\n{JS}</script>"
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f"</body></html>"
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)
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def index_html() -> str:
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counts = all_row_counts()
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cards = ""
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for slug, label, table in DATASETS:
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count = counts.get(table, -1)
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if count > 0:
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badge = f'<span class="badge ready">{count} rows</span>'
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elif count == 0:
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badge = '<span class="badge">no data yet</span>'
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else:
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badge = '<span class="badge">table missing</span>'
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cards += (
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f'<div class="card"><a href="/{slug}">'
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f'<div class="card-label"><h3>{html.escape(label)}</h3></div>'
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f"{badge}</a></div>"
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)
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return page("Results", f"<h2>Datasets</h2><div class='cards'>{cards}</div>")
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def table_html(slug: str, label: str, table: str) -> str:
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back = '<a class="back" href="/">← All datasets</a>'
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try:
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headers, rows = fetch_rows(table)
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except Exception as e:
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return page(
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label,
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f"{back}<h2>{html.escape(label)}</h2>"
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f"<div class='notice'>Error querying {html.escape(table)} on {html.escape(DB_HOST)}:{DB_PORT}/{html.escape(DB_NAME)}: {html.escape(str(e))}</div>",
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)
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if not headers:
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return page(
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label,
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f"{back}<h2>{html.escape(label)}</h2>"
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"<div class='notice'>No data yet. Run the CronJob first, then check back.</div>",
|
|
)
|
|
|
|
ai_rows = rows[:200]
|
|
dataset_json = json.dumps({
|
|
"label": label,
|
|
"headers": headers,
|
|
"sample": rows[:10],
|
|
"rows": ai_rows,
|
|
"total_rows": len(rows),
|
|
})
|
|
|
|
explain_bar = (
|
|
"<div class='explain-bar'>"
|
|
"<button class='explain-btn' id='explain-btn' onclick='explainDataset()'>✦ Analyse top 10 rows</button>"
|
|
"<span class='no-key-hint' id='no-key-hint'>— <a href='#' onclick='openSettings();return false;' style='color:#58a6ff'>add NIM key</a> to enable AI explanations</span>"
|
|
"</div>"
|
|
"<div class='explain-panel' id='explain-panel' style='display:none'>"
|
|
"<h4>AI Explanation</h4>"
|
|
"<div class='explain-text' id='explain-text'></div>"
|
|
"</div>"
|
|
)
|
|
|
|
num_cols = len(headers)
|
|
ths = "".join(f"<th>{html.escape(h)}</th>" for h in headers) + "<th></th>"
|
|
trs = ""
|
|
for i, row in enumerate(rows[:10_000]):
|
|
cells = "".join(f"<td>{html.escape(str(c))}</td>" for c in row)
|
|
if i < 200:
|
|
ai_cell = (
|
|
f"<td><button class='row-ai-btn' id='row-btn-{i}' "
|
|
f"onclick='analyzeRow({i})' title='Analyse this row with AI'>⚡</button></td>"
|
|
)
|
|
expand = (
|
|
f"<tr class='row-expand-tr' id='row-expand-{i}' style='display:none'>"
|
|
f"<td colspan='{num_cols + 1}'>"
|
|
f"<span id='row-explain-{i}'></span>"
|
|
f"</td></tr>"
|
|
)
|
|
else:
|
|
ai_cell = "<td></td>"
|
|
expand = ""
|
|
trs += f"<tr>{cells}{ai_cell}</tr>{expand}"
|
|
|
|
truncated = f" (showing first 10 000 of {len(rows)})" if len(rows) > 10_000 else ""
|
|
return page(
|
|
label,
|
|
f"{back}<h2>{html.escape(label)}</h2>"
|
|
f"<p class='meta'>{len(rows)} rows{truncated}</p>"
|
|
f"{explain_bar}"
|
|
f"<div class='table-wrap'><table><thead><tr>{ths}</tr></thead>"
|
|
f"<tbody>{trs}</tbody></table></div>",
|
|
dataset_json=dataset_json,
|
|
)
|
|
|
|
SLUG_MAP = {s: (label, table) for s, label, table in DATASETS}
|
|
|
|
class Handler(BaseHTTPRequestHandler):
|
|
def log_message(self, fmt, *args):
|
|
pass
|
|
|
|
def do_GET(self):
|
|
path = self.path.split("?")[0].lstrip("/")
|
|
if path == "" or path == "index.html":
|
|
self._send(index_html())
|
|
elif path in SLUG_MAP:
|
|
label, table = SLUG_MAP[path]
|
|
self._send(table_html(path, label, table))
|
|
else:
|
|
self.send_response(404)
|
|
self.end_headers()
|
|
|
|
def do_POST(self):
|
|
path = self.path.split("?")[0].lstrip("/")
|
|
if path != "explain":
|
|
self.send_response(404)
|
|
self.end_headers()
|
|
return
|
|
length = int(self.headers.get("Content-Length", 0))
|
|
try:
|
|
body = json.loads(self.rfile.read(length))
|
|
api_key = body.get("key", "")
|
|
mode = body.get("mode", "dataset")
|
|
dataset = body.get("dataset", {})
|
|
if not api_key:
|
|
raise ValueError("missing key")
|
|
|
|
label = dataset.get("label", "")
|
|
headers = dataset.get("headers", [])
|
|
|
|
if mode == "row":
|
|
row = dataset.get("row", [])
|
|
row_desc = ", ".join(f"{h}: {v}" for h, v in zip(headers, row))
|
|
system_prompt = (
|
|
"You are a chess analytics expert. Analyze a single data entry from a chess analytics dataset. "
|
|
"Be specific and insightful — 3-4 sentences. "
|
|
"If the entry involves a chess opening (ECO code or opening name present), explain the opening's "
|
|
"strategic ideas, strengths and weaknesses, and why players choose it. "
|
|
"For player data, explain what the stats reveal about their playing style. "
|
|
"For other types, explain what makes this entry notable."
|
|
)
|
|
user_prompt = (
|
|
f"Dataset: \"{label}\"\n"
|
|
f"Columns: {', '.join(headers)}\n\n"
|
|
f"Entry to analyze: {row_desc}\n\n"
|
|
"Provide a detailed, chess-specific analysis of this entry."
|
|
)
|
|
max_tokens = 300
|
|
else:
|
|
sample = dataset.get("sample", [])
|
|
rows_text = "\n".join(
|
|
f"Row {i + 1}: " + ", ".join(f"{h}: {v}" for h, v in zip(headers, row))
|
|
for i, row in enumerate(sample)
|
|
)
|
|
system_prompt = (
|
|
"You are a chess analytics expert. Analyze the top 10 entries from a chess analytics dataset. "
|
|
"For each entry write 1-2 specific sentences about what is notable. "
|
|
"If entries are chess openings (ECO codes or opening names present), name each opening properly "
|
|
"and explain its strategic character and why it ranks here. "
|
|
"Format your response as 'Row N: [analysis]' for each row."
|
|
)
|
|
user_prompt = (
|
|
f"Dataset: \"{label}\" ({dataset.get('total_rows', 0)} total rows)\n"
|
|
f"Columns: {', '.join(headers)}\n\n"
|
|
f"Top 10 entries:\n{rows_text}\n\n"
|
|
"Analyze each entry."
|
|
)
|
|
max_tokens = 512
|
|
|
|
payload = json.dumps({
|
|
"model": "meta/llama-3.3-70b-instruct",
|
|
"messages": [
|
|
{"role": "system", "content": system_prompt},
|
|
{"role": "user", "content": user_prompt},
|
|
],
|
|
"max_tokens": max_tokens,
|
|
"temperature": 0.4,
|
|
"stream": False,
|
|
}).encode("utf-8")
|
|
req = urllib.request.Request(
|
|
"https://integrate.api.nvidia.com/v1/chat/completions",
|
|
data=payload,
|
|
headers={
|
|
"Authorization": f"Bearer {api_key}",
|
|
"Content-Type": "application/json",
|
|
},
|
|
method="POST",
|
|
)
|
|
try:
|
|
with urllib.request.urlopen(req, timeout=60) as r:
|
|
nim_data = json.loads(r.read())
|
|
text = nim_data.get("choices", [{}])[0].get("message", {}).get("content", "(no response)")
|
|
self._send_json({"text": text})
|
|
except urllib.error.HTTPError as he:
|
|
if he.code == 429:
|
|
retry_after = he.headers.get("Retry-After", "10")
|
|
resp_body = json.dumps({"error": f"Rate limited — retry in {retry_after}s"}).encode("utf-8")
|
|
self.send_response(429)
|
|
self.send_header("Content-Type", "application/json")
|
|
self.send_header("Content-Length", str(len(resp_body)))
|
|
self.send_header("Retry-After", str(retry_after))
|
|
self.end_headers()
|
|
try:
|
|
self.wfile.write(resp_body)
|
|
except BrokenPipeError:
|
|
pass
|
|
else:
|
|
self._send_json({"error": f"NIM API error {he.code}: {he.reason}"})
|
|
except Exception as e:
|
|
self._send_json({"error": str(e)})
|
|
|
|
def _send(self, body: str):
|
|
data = body.encode("utf-8")
|
|
self.send_response(200)
|
|
self.send_header("Content-Type", "text/html; charset=utf-8")
|
|
self.send_header("Content-Length", str(len(data)))
|
|
self.end_headers()
|
|
try:
|
|
self.wfile.write(data)
|
|
except BrokenPipeError:
|
|
pass
|
|
|
|
def _send_json(self, obj: dict):
|
|
data = json.dumps(obj).encode("utf-8")
|
|
self.send_response(200)
|
|
self.send_header("Content-Type", "application/json")
|
|
self.send_header("Content-Length", str(len(data)))
|
|
self.end_headers()
|
|
try:
|
|
self.wfile.write(data)
|
|
except BrokenPipeError:
|
|
pass
|
|
|
|
if __name__ == "__main__":
|
|
server = ThreadingHTTPServer(("0.0.0.0", PORT), Handler)
|
|
print(f"Listening on :{PORT} DB={DB_HOST}:{DB_PORT}/{DB_NAME} JDBC_URL={_url!r}", flush=True)
|
|
server.serve_forever()
|
|
---
|
|
apiVersion: apps/v1
|
|
kind: Deployment
|
|
metadata:
|
|
name: spark-analytics-webview
|
|
labels:
|
|
app.kubernetes.io/name: spark-analytics
|
|
app.kubernetes.io/part-of: nowchess
|
|
spec:
|
|
replicas: 0
|
|
strategy:
|
|
type: Recreate
|
|
selector:
|
|
matchLabels:
|
|
app: spark-analytics-webview
|
|
template:
|
|
metadata:
|
|
labels:
|
|
app: spark-analytics-webview
|
|
app.kubernetes.io/name: spark-analytics
|
|
app.kubernetes.io/part-of: nowchess
|
|
spec:
|
|
securityContext:
|
|
runAsNonRoot: true
|
|
runAsUser: 65534
|
|
fsGroup: 65534
|
|
containers:
|
|
- name: webview
|
|
image: python:3.12-slim
|
|
command: ["sh", "-c", "pip install psycopg2-binary --quiet --no-cache-dir --target=/tmp/pkg && PYTHONPATH=/tmp/pkg python /scripts/serve.py"]
|
|
ports:
|
|
- containerPort: 8080
|
|
env:
|
|
- name: PORT
|
|
value: "8080"
|
|
- name: NOWCHESS_JDBC_URL
|
|
valueFrom:
|
|
secretKeyRef:
|
|
name: ncs-db-secrets
|
|
key: STORE_DB_URL
|
|
- name: NOWCHESS_DB_USER
|
|
valueFrom:
|
|
secretKeyRef:
|
|
name: ncs-db-secrets
|
|
key: STORE_DB_USER
|
|
- name: NOWCHESS_DB_PASS
|
|
valueFrom:
|
|
secretKeyRef:
|
|
name: ncs-db-secrets
|
|
key: STORE_DB_PASSWORD
|
|
volumeMounts:
|
|
- name: scripts
|
|
mountPath: /scripts
|
|
readinessProbe:
|
|
httpGet:
|
|
path: /
|
|
port: 8080
|
|
initialDelaySeconds: 15
|
|
periodSeconds: 10
|
|
resources:
|
|
requests:
|
|
cpu: 10m
|
|
memory: 64Mi
|
|
limits:
|
|
cpu: 200m
|
|
memory: 256Mi
|
|
volumes:
|
|
- name: scripts
|
|
configMap:
|
|
name: spark-analytics-webview
|
|
defaultMode: 0755
|
|
---
|
|
apiVersion: v1
|
|
kind: Service
|
|
metadata:
|
|
name: spark-analytics-webview
|
|
labels:
|
|
app.kubernetes.io/name: spark-analytics
|
|
app.kubernetes.io/part-of: nowchess
|
|
spec:
|
|
selector:
|
|
app: spark-analytics-webview
|
|
ports:
|
|
- name: http
|
|
port: 8080
|
|
targetPort: 8080
|