apiVersion: v1 kind: ConfigMap metadata: name: spark-analytics-webview labels: app.kubernetes.io/name: spark-analytics app.kubernetes.io/part-of: nowchess data: serve.py: | #!/usr/bin/env python3 """Spark analytics results viewer โ€” queries PostgreSQL analytics tables, serves HTML tables.""" import html import os import re import psycopg2 import psycopg2.extras from http.server import BaseHTTPRequestHandler, HTTPServer PORT = int(os.environ.get("PORT", "8080")) _url = os.environ.get("NOWCHESS_JDBC_URL", "jdbc:postgresql://localhost:5432/nowchess") _m = re.match(r"jdbc:postgresql://([^:/]+):(\d+)/(.+)", _url) DB_HOST = _m.group(1) if _m else "localhost" DB_PORT = int(_m.group(2)) if _m else 5432 DB_NAME = _m.group(3) if _m else "nowchess" DB_USER = os.environ.get("NOWCHESS_DB_USER", "nowchess") DB_PASS = os.environ.get("NOWCHESS_DB_PASS", "") # Each tuple: (url-slug, display label, postgres table name) DATASETS = [ ("opening-book", "Opening Book (Top 1000)", "analytics_opening_stats"), ("player-stats", "Player Statistics", "analytics_player_stats"), ("cluster-archetypes", "Cluster Archetypes", "analytics_cluster_archetypes"), ("component-sizes", "Graph Component Sizes", "analytics_component_sizes"), ("game-length", "Game Length Distribution", "analytics_game_length_distribution"), ("game-length-by-result", "Game Length by Result", "analytics_game_length_by_result"), ("color-advantage", "Color Advantage", "analytics_color_advantage"), ("elo-distribution", "ELO Distribution", "analytics_elo_distribution"), ("time-control", "Time Control Analysis", "analytics_time_control_stats"), ("hourly-activity", "Hourly Activity", "analytics_hourly_activity"), ("weekly-activity", "Weekly Activity", "analytics_weekly_activity"), ("rating-mismatch", "Rating Mismatch (Upsets)", "analytics_rating_mismatch"), ("termination-stats", "Termination Types", "analytics_termination_stats"), ] CSS = """ * { box-sizing: border-box; } body { font-family: 'Segoe UI', sans-serif; margin: 0; background: #0d1117; color: #c9d1d9; } header { background: #161b22; border-bottom: 1px solid #30363d; padding: 1rem 2rem; } header h1 { margin: 0; color: #58a6ff; font-size: 1.25rem; font-weight: 600; } header span { color: #8b949e; font-size: 0.875rem; margin-left: 0.5rem; } main { padding: 1.5rem 2rem; } h2 { color: #e6edf3; font-size: 1rem; font-weight: 600; margin: 0 0 1rem; } .cards { display: grid; grid-template-columns: repeat(auto-fill, minmax(260px, 1fr)); gap: 1rem; } .card { background: #161b22; border: 1px solid #30363d; border-radius: 8px; padding: 1rem; } .card h3 { margin: 0 0 0.5rem; font-size: 0.9rem; color: #58a6ff; } .card a { text-decoration: none; color: inherit; display: block; } .card a:hover .card-label { text-decoration: underline; } .badge { display: inline-block; background: #21262d; border: 1px solid #30363d; border-radius: 12px; padding: 1px 8px; font-size: 0.75rem; color: #8b949e; } .badge.ready { border-color: #238636; color: #3fb950; } .back { color: #58a6ff; text-decoration: none; font-size: 0.875rem; } .back:hover { text-decoration: underline; } .meta { color: #8b949e; font-size: 0.8rem; margin: 0.5rem 0 1rem; } table { width: 100%; border-collapse: collapse; font-size: 0.8rem; } thead th { background: #161b22; position: sticky; top: 0; padding: 6px 12px; text-align: left; color: #8b949e; border-bottom: 1px solid #30363d; font-weight: 600; white-space: nowrap; } tbody td { padding: 5px 12px; border-bottom: 1px solid #21262d; } tbody tr:hover td { background: #161b22; } .table-wrap { overflow-x: auto; border: 1px solid #30363d; border-radius: 6px; } .notice { background: #161b22; border: 1px solid #30363d; border-radius: 6px; padding: 1.5rem; color: #8b949e; } """ def get_conn(): return psycopg2.connect(host=DB_HOST, port=DB_PORT, dbname=DB_NAME, user=DB_USER, password=DB_PASS) def table_row_count(table: str) -> int: try: with get_conn() as conn: with conn.cursor() as cur: cur.execute(f"SELECT COUNT(*) FROM {table}") return cur.fetchone()[0] except Exception: return -1 def fetch_rows(table: str, limit: int = 10_000): with get_conn() as conn: with conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor) as cur: cur.execute(f"SELECT * FROM {table} LIMIT %s", (limit,)) rows = cur.fetchall() if not rows: return [], [] headers = list(rows[0].keys()) data = [ [str(row[h]) if row[h] is not None else "" for h in headers] for row in rows ] return headers, data def page(title: str, body: str) -> str: return ( f"" f"" f"{html.escape(title)} โ€” Spark Analytics" f"" f"

Spark Analytics

NowChess ยท staging
" f"
{body}
" ) def index_html() -> str: cards = "" for slug, label, table in DATASETS: count = table_row_count(table) if count > 0: badge = f'{count} rows' elif count == 0: badge = 'no data yet' else: badge = 'table missing' cards += ( f'
' f'

{html.escape(label)}

' f"{badge}
" ) return page("Results", f"

Datasets

{cards}
") def table_html(slug: str, label: str, table: str) -> str: back = 'โ† All datasets' try: headers, rows = fetch_rows(table) except Exception as e: return page( label, f"{back}

{html.escape(label)}

" f"
Error querying {html.escape(table)}: {html.escape(str(e))}
", ) if not headers: return page( label, f"{back}

{html.escape(label)}

" "
No data yet. Run the CronJob first, then check back.
", ) ths = "".join(f"{html.escape(h)}" for h in headers) trs = "".join( "" + "".join(f"{html.escape(str(c))}" for c in row) + "" for row in rows[:10_000] ) truncated = f" (showing first 10 000 of {len(rows)})" if len(rows) > 10_000 else "" return page( label, f"{back}

{html.escape(label)}

" f"

{len(rows)} rows{truncated}

" f"
{ths}" f"{trs}
", ) 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 _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() self.wfile.write(data) if __name__ == "__main__": server = HTTPServer(("0.0.0.0", PORT), Handler) print(f"Listening on :{PORT} DB={DB_HOST}:{DB_PORT}/{DB_NAME}", 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", "HOME=/tmp pip install psycopg2-binary --quiet --no-cache-dir && 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