htlee
a68dfb21b2
- prediction/: FastAPI 7단계 분류 파이프라인 + 6개 탐지 알고리즘 - snpdb 궤적 조회 → 인메모리 캐시(13K척) → 분류 → kcgdb 저장 - APScheduler 5분 주기, Python 3.9 호환 - 버그 수정: @property last_bucket, SQL INTERVAL 바인딩, rollback, None 가드 - 보안: DB 비밀번호 하드코딩 제거 → env 환경변수 필수 - deploy/kcg-prediction.service: systemd 서비스 (redis-211, 포트 8001) - deploy.yml: prediction CI/CD 배포 단계 추가 (192.168.1.18:32023) - backend: PredictionProxyController (health/status/trigger 프록시) - backend: AppProperties predictionBaseUrl + AuthFilter 인증 예외 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
67 lines
1.7 KiB
Python
67 lines
1.7 KiB
Python
import logging
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import sys
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from contextlib import asynccontextmanager
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from fastapi import BackgroundTasks, FastAPI
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from config import settings
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from db import kcgdb, snpdb
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from scheduler import get_last_run, run_analysis_cycle, start_scheduler, stop_scheduler
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logging.basicConfig(
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level=getattr(logging, settings.LOG_LEVEL, logging.INFO),
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format='%(asctime)s [%(levelname)s] %(name)s: %(message)s',
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stream=sys.stdout,
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)
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logger = logging.getLogger(__name__)
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@asynccontextmanager
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async def lifespan(application: FastAPI):
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from cache.vessel_store import vessel_store
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logger.info('starting KCG Prediction Service')
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snpdb.init_pool()
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kcgdb.init_pool()
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# 인메모리 캐시 초기 로드 (24시간)
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logger.info('loading initial vessel data (%dh)...', settings.INITIAL_LOAD_HOURS)
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vessel_store.load_initial(settings.INITIAL_LOAD_HOURS)
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logger.info('initial load complete: %s', vessel_store.stats())
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start_scheduler()
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yield
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stop_scheduler()
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snpdb.close_pool()
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kcgdb.close_pool()
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logger.info('KCG Prediction Service stopped')
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app = FastAPI(
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title='KCG Prediction Service',
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version='2.0.0',
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lifespan=lifespan,
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)
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@app.get('/health')
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def health_check():
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from cache.vessel_store import vessel_store
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return {
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'status': 'ok',
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'snpdb': snpdb.check_health(),
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'kcgdb': kcgdb.check_health(),
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'store': vessel_store.stats(),
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}
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@app.get('/api/v1/analysis/status')
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def analysis_status():
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return get_last_run()
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@app.post('/api/v1/analysis/trigger')
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def trigger_analysis(background_tasks: BackgroundTasks):
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background_tasks.add_task(run_analysis_cycle)
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return {'message': 'analysis cycle triggered'}
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