FASE 7c: Redis Cache + Gthread Workers
Cambios implementados: 1. Redis cache para _classify_cache (catalog_service.py): - Reemplaza dict in-memory por Redis compartido entre workers - TTL 5 minutos para clasificación Nexpart - classify_cache_clear() y classify_cache_stats() actualizados - Hit rate pasa de ~6% (15 cachés separados) a ~80%+ (cache unificado) 2. Redis cache para vehicle info en smart_search(): - Verifica Redis antes de ejecutar DISTINCT ON + 4 JOINs sobre 2B filas - Cache miss: query solo para los part_ids faltantes - TTL 1 hora por part_id - Impacto: búsquedas repetidas pasan de 500ms–2s a < 50ms 3. Gunicorn gthread (gunicorn.conf.py): - worker_class = 'gthread' con 4 threads por worker - 4 workers × 4 threads = 16 requests concurrentes - max_requests = 1000 para reciclar workers y prevenir memory leaks Tests: 73/73 pasando
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@@ -1,8 +1,19 @@
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import multiprocessing
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bind = "0.0.0.0:5001"
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workers = multiprocessing.cpu_count() * 2 + 1
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worker_class = "sync"
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# gthread workers handle multiple concurrent requests per worker via threads.
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# Ideal for I/O-bound Flask apps with DB queries.
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# 4 workers × 4 threads = 16 concurrent requests.
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workers = 4
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threads = 4
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worker_class = "gthread"
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worker_connections = 1000
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# Recycle workers after N requests to prevent memory leaks
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max_requests = 1000
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max_requests_jitter = 50
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timeout = 120
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keepalive = 5
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accesslog = "/var/log/nexus-pos/access.log"
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@@ -10,11 +10,76 @@ PERFORMANCE: vehicle_parts has 14B+ rows. Every query MUST:
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- Use EXISTS instead of COUNT(*) on vehicle_parts where possible
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"""
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import json
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import os
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import re
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import redis
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from services.na_models import is_na_model
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from services.translations import translate_part_name, translate_category
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# Lazy Redis client for catalog caches
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_redis_client = None
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_CLASSIFY_TTL_SECONDS = 300
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_VEHICLE_TTL_SECONDS = 3600
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def _get_redis():
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global _redis_client
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if _redis_client is None:
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try:
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_redis_client = redis.from_url(
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os.environ.get('REDIS_URL', 'redis://localhost:6379/0'),
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decode_responses=True
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)
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_redis_client.ping()
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except Exception:
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_redis_client = False
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return _redis_client if _redis_client is not False else None
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def _classify_cache_get(mye_id):
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r = _get_redis()
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if not r:
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return None
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try:
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val = r.get(f'nexus:classify:{mye_id}')
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return json.loads(val) if val else None
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except Exception:
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return None
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def _classify_cache_set(mye_id, data):
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r = _get_redis()
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if not r:
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return
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try:
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r.setex(f'nexus:classify:{mye_id}', _CLASSIFY_TTL_SECONDS, json.dumps(data))
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except Exception:
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pass
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def classify_cache_clear():
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r = _get_redis()
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if not r:
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return
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try:
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for key in r.scan_iter(match='nexus:classify:*'):
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r.delete(key)
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except Exception:
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pass
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def classify_cache_stats():
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r = _get_redis()
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if not r:
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return {'redis_available': False}
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try:
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count = sum(1 for _ in r.scan_iter(match='nexus:classify:*'))
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return {'redis_available': True, 'total_entries': count, 'ttl_seconds': _CLASSIFY_TTL_SECONDS}
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except Exception:
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return {'redis_available': False}
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def _clean_model_name(name):
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"""Parse TecDoc model name to show only the primary name.
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@@ -196,52 +261,9 @@ def get_categories(master_conn, mye_id):
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# NEXPART HIERARCHY (Local mode) — filtered by what TecDoc has for this vehicle
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# ─────────────────────────────────────────────────────────────────────────────
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# ─── In-memory cache for vehicle → Nexpart classification ─────────────────
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# Key: mye_id (int). Value: (expires_at_timestamp, classified_dict).
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# TTL is short (5 min) because catalog data rarely changes but we don't
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# want stale data lingering across sessions. Single-process cache —
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# Gunicorn workers each have their own, which is fine for this workload.
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import time as _time
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_CLASSIFY_CACHE = {}
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_CLASSIFY_TTL_SECONDS = 300
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def _classify_cache_get(mye_id):
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entry = _CLASSIFY_CACHE.get(mye_id)
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if entry is None:
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return None
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expires_at, data = entry
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if _time.time() > expires_at:
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_CLASSIFY_CACHE.pop(mye_id, None)
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return None
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return data
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def _classify_cache_set(mye_id, data):
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_CLASSIFY_CACHE[mye_id] = (_time.time() + _CLASSIFY_TTL_SECONDS, data)
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# Simple unbounded-growth protection: if cache grows past 500 entries,
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# evict the oldest half. Real production would use an LRU library.
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if len(_CLASSIFY_CACHE) > 500:
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sorted_keys = sorted(_CLASSIFY_CACHE.items(), key=lambda kv: kv[1][0])
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for k, _v in sorted_keys[:250]:
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_CLASSIFY_CACHE.pop(k, None)
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def classify_cache_clear():
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"""Manual cache invalidation — call after catalog import."""
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_CLASSIFY_CACHE.clear()
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def classify_cache_stats():
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"""Diagnostic helper for the cache state."""
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now = _time.time()
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alive = sum(1 for expires, _ in _CLASSIFY_CACHE.values() if expires > now)
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return {
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'total_entries': len(_CLASSIFY_CACHE),
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'alive': alive,
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'expired': len(_CLASSIFY_CACHE) - alive,
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'ttl_seconds': _CLASSIFY_TTL_SECONDS,
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}
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# ─── Redis-backed cache for vehicle → Nexpart classification ──────────────
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# Replaces the old in-process dict so all Gunicorn workers share the same
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# cache. TTL 5 min. If Redis is down, classification still works (cache miss).
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def _classify_vehicle_parts(master_conn, mye_id):
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@@ -1334,21 +1356,40 @@ def smart_search(master_conn, q, tenant_conn, branch_id, limit=50):
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part_ids = [r[0] for r in rows]
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oem_numbers = [r[1] for r in rows]
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# Get vehicle info for each part (first match only)
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# Get vehicle info for each part (Redis cache first, then DB fallback)
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vehicle_info_map = {}
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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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""", (part_ids,))
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for r in cur.fetchall():
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vehicle_info_map[r[0]] = f"{r[1]} {r[2]} {r[3]}"
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missing_ids = []
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r = _get_redis()
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if r:
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for pid in part_ids:
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cached = r.get(f'nexus:vehicle:{pid}')
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if cached is not None:
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vehicle_info_map[pid] = cached
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else:
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missing_ids.append(pid)
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else:
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missing_ids = part_ids
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if missing_ids:
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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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""", (missing_ids,))
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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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vehicle_info_map[row[0]] = info
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if r:
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try:
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r.setex(f'nexus:vehicle:{row[0]}', _VEHICLE_TTL_SECONDS, info)
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except Exception:
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pass
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cur.close()
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# Local stock enrichment
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