consultoria-as
21959f1b37
Cambios implementados: 1. Lazy loading de imágenes: - catalog.js: loading="lazy" decoding="async" en part cards y detail panel - inventory.js: lazy loading en imagen de detalle de item 2. Minificación de assets: - scripts/minify-assets.sh: minifica JS (terser) y CSS para POS y Dashboard - 25 archivos .min.js + 5 .min.css generados en pos/static/ - 14 archivos .min.js + 8 .min.css generados en dashboard/ 3. Nginx auto-serve minified: - try_files $1.min.js antes de servir .js original - try_files $1.min.css antes de servir .css original - Transparente para los templates HTML (cero cambios en HTML) 4. Cache warming script: - scripts/warm_vehicle_cache.py: pobla Redis con vehicle info por batches - Mitiga DISTINCT ON + 4 JOINs sobre 2B filas - Corre en background, procesa ~1.5M parts Tests: 73/73 pasando
85 lines
2.6 KiB
Python
Executable File
85 lines
2.6 KiB
Python
Executable File
#!/usr/bin/env python3
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"""Warm Redis cache for vehicle info (part_vehicle_preview alternative).
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Runs in batches over all parts in the catalog, populating
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nexus:vehicle:{part_id} keys in Redis. This eliminates the
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DISTINCT ON + 4 JOINs query on vehicle_parts (2B rows) for
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cached parts.
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Usage:
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export MASTER_DB_URL="postgresql://..."
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export REDIS_URL="redis://localhost:6379/0"
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python3 warm_vehicle_cache.py
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"""
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import os, sys, json, time
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'pos'))
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import psycopg2
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import redis
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def _fix_dsn(dsn):
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if dsn and 'host=' not in dsn and '@/' in dsn:
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dsn = dsn.replace('@/', '@localhost/')
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return dsn
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MASTER_DB_URL = _fix_dsn(os.environ.get('MASTER_DB_URL', 'postgresql://postgres@localhost/nexus_autoparts'))
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REDIS_URL = os.environ.get('REDIS_URL', 'redis://localhost:6379/0')
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BATCH_SIZE = 5000
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TTL_SECONDS = 3600
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def main():
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print("Connecting to master DB and Redis...")
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conn = psycopg2.connect(MASTER_DB_URL)
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cur = conn.cursor()
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r = redis.from_url(REDIS_URL, decode_responses=True)
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r.ping()
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# Get all part_ids
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cur.execute("SELECT id_part FROM parts WHERE oem_part_number IS NOT NULL ORDER BY id_part")
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all_ids = [r[0] for r in cur.fetchall()]
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total = len(all_ids)
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print(f"Total parts to warm: {total}")
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processed = 0
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cached = 0
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start = time.time()
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for i in range(0, total, BATCH_SIZE):
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batch = all_ids[i:i + BATCH_SIZE]
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cur.execute("""
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SELECT DISTINCT ON (vp.part_id)
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vp.part_id, b.name_brand, m.name_model, y.year_car
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FROM vehicle_parts vp
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JOIN model_year_engine mye ON mye.id_mye = vp.model_year_engine_id
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JOIN models m ON m.id_model = mye.model_id
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JOIN brands b ON b.id_brand = m.brand_id
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JOIN years y ON y.id_year = mye.year_id
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WHERE vp.part_id = ANY(%s)
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ORDER BY vp.part_id, y.year_car DESC
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""", (batch,))
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pipe = r.pipeline()
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batch_cached = 0
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for row in cur.fetchall():
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info = f"{row[1]} {row[2]} {row[3]}"
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pipe.setex(f'nexus:vehicle:{row[0]}', TTL_SECONDS, info)
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batch_cached += 1
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pipe.execute()
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processed += len(batch)
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cached += batch_cached
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elapsed = time.time() - start
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rate = processed / elapsed if elapsed > 0 else 0
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print(f" [{processed}/{total}] cached={batch_cached} ({rate:.0f}/s)")
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cur.close()
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conn.close()
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print(f"\nDone. Cached {cached} vehicle entries in {elapsed:.0f}s")
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if __name__ == '__main__':
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main()
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