chore(task): archive 09-07-api-performance-diagnosis
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yuxuanhui
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"""Task-local benchmark: isolated localhost PostgreSQL only; no production writes.
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Run from zhixing-server with DATABASE_URL pointing to a disposable test database.
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Set PYTHONPATH to the before/after source tree to compare the same persisted data.
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Use --seed once, then --run before.json / --run after.json (five reads per endpoint).
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"""
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import argparse
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import hashlib
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import json
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import os
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import runpy
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import statistics
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import time
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from dataclasses import replace
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from datetime import timedelta
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from decimal import Decimal
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from pathlib import Path
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from urllib.parse import urlsplit
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from unittest.mock import patch
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import psycopg
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from alembic import command
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from alembic.config import Config
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from zhixing_server.bootstrap.config import Settings, sqlalchemy_database_url
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from zhixing_server.modules.sector_radar.application.details import ReadRadarDetails
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from zhixing_server.modules.sector_radar.application.read import ReadSectorRadar, RadarQuery
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from zhixing_server.modules.sector_radar.domain.models import PublicationStatus, SectorType, MetricKind, MetricUnit
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from zhixing_server.modules.sector_radar.domain.metrics import AmountNetStrategy, RatioTurnoverStrategy, SwingEqualThreeToTenStrategy
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from zhixing_server.modules.sector_radar.domain.persistence import PublicationSourceGroup as Group, PublicationSourceRecord, RankingRecord
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from zhixing_server.modules.sector_radar.domain.ranking import rank_metric_observations
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from zhixing_server.modules.sector_radar.domain.source import build_source_snapshot
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from zhixing_server.modules.sector_radar.infrastructure.postgres import PostgresSectorRadarRepository
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from zhixing_server.modules.sector_radar.presentation.http import _detail_response, _history_response, _rankings_response
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parser = argparse.ArgumentParser()
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parser.add_argument('--seed', action='store_true')
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parser.add_argument('--run', type=Path)
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args = parser.parse_args()
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url = os.environ['DATABASE_URL']
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assert urlsplit(url).hostname in {'localhost', '127.0.0.1'}, 'disposable local database only'
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samples = runpy.run_path('tests/unit/sector_radar/test_detail_reads.py')
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target, now, code = samples['TARGET'], samples['NOW'], samples['CODE']
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repo = PostgresSectorRadarRepository(url)
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if args.seed:
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config = Config('alembic.ini')
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config.set_main_option('sqlalchemy.url', sqlalchemy_database_url(url).replace('%', '%%'))
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config.config_file_name = None
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with patch('zhixing_server.bootstrap.config.get_settings', return_value=Settings(database_url=url)):
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command.upgrade(config, 'head')
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samples['seed_detail'](repo)
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current = repo.get_successful_publication(target)
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template = repo.load_rankings(current.publication_id)[0].observation
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stocks = [f'{i:06d}.SZ' for i in range(10, 3010)]
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sectors = [f'PERF{i:04d}.DC' for i in range(500)]
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def save(group, rows, order, day=target):
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snapshot = build_source_snapshot(api_name=group.value, params={'batch': str(order)}, rows=rows,
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target_trade_date=day, observed_at=now)
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repo.save_source_snapshots((snapshot,))
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repo.save_publication_sources((PublicationSourceRecord(current.publication_id, group, order, snapshot),))
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save(Group.STOCK_BASICS, tuple({'ts_code': s, 'symbol': s[:6], 'name': s, 'exchange': 'SZSE',
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'list_status': 'L', 'list_date': '20200101'} for s in stocks), 1)
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save(Group.CONCEPT_INDICES, tuple({'ts_code': s, 'name': s, 'trade_date': str(target),
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'pct_change': '1.1234'} for s in sectors), 1)
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save(Group.MEMBERS, tuple({'ts_code': sector, 'con_code': stocks[(i*7+j)%len(stocks)],
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'name': stocks[(i*7+j)%len(stocks)], 'trade_date': str(target)}
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for i, sector in enumerate(sectors) for j in range(100)), 1)
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for offset in range(10):
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day = target-timedelta(days=offset)
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for group in (Group.DAILY, Group.MONEYFLOW_DC, Group.MONEYFLOW):
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rows = tuple({'ts_code': stock, 'trade_date': str(day), 'name': stock,
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'pct_chg': str(Decimal(i % 123)/100), 'amount': str(i*12),
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'net_amount': str(i-1500), 'net_mf_amount': str(i-500),
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'close': str(Decimal(i%1000)/10+1), 'vol': str(i*33)}
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for i, stock in enumerate(stocks))
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save(group, rows, offset+1, day)
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versions = ((MetricKind.AMOUNT, AmountNetStrategy.metric_version, MetricUnit.CNY_100M),
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(MetricKind.RATIO, RatioTurnoverStrategy.metric_version, MetricUnit.RATIO),
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(MetricKind.SWING, SwingEqualThreeToTenStrategy.metric_version, MetricUnit.RATIO))
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for offset in range(30):
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day = target-timedelta(days=offset)
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if offset < 2:
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publication = repo.get_successful_publication(day)
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else:
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running = replace(current, publication_id=f'bench-{offset}', target_trade_date=day,
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status=PublicationStatus.RUNNING, input_hash=None, finished_at=None)
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repo.create_publication(running)
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publication = replace(running, status=PublicationStatus.SUCCESS, input_hash='a'*64,
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finished_at=now+timedelta(seconds=1))
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repo.finish_publication(publication)
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rankings = []
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for kind, version, unit in versions:
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observations = tuple(replace(template, trade_date=day, sector_code=sector, sector_name=sector,
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metric_kind=kind, metric_version=version, unit=unit,
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value=Decimal(i+1)) for i, sector in enumerate(sectors))
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rankings.extend(RankingRecord(publication.publication_id, row)
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for row in rank_metric_observations(observations))
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repo.save_rankings(rankings)
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with psycopg.connect(url) as connection:
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connection.execute('ANALYZE')
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print('dataset', connection.execute('SELECT count(*), sum(row_count), sum(octet_length(payload::text)) FROM sector_radar_source_snapshot').fetchone(),
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'rankings', connection.execute('SELECT count(*) FROM sector_radar_ranking').fetchone())
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if args.run:
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repo.open()
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reads = {
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'detail': lambda: _detail_response(ReadRadarDetails(repo).detail(target, SectorType.CONCEPT, code)),
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'history': lambda: _history_response(ReadRadarDetails(repo).history(target, SectorType.CONCEPT, code)),
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'rankings': lambda: _rankings_response(ReadSectorRadar(repo).query(RadarQuery(trade_date=target, page_size=20))),
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}
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report = {}
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for label, read in reads.items():
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timings = []
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responses = []
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for _ in range(5):
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start = time.perf_counter()
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response = read().model_dump(mode='json')
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timings.append(time.perf_counter()-start)
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responses.append(response)
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assert all(item == responses[0] for item in responses)
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fingerprint = hashlib.sha256(json.dumps(responses[0], sort_keys=True).encode()).hexdigest()
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report[label] = {'seconds': timings, 'median_seconds': statistics.median(timings),
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'response_sha256': fingerprint, 'response': responses[0]}
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print(label, 'median', report[label]['median_seconds'], 'sha256', fingerprint, flush=True)
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args.run.write_text(json.dumps(report, ensure_ascii=False, indent=2))
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repo.close()
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+100
@@ -0,0 +1,100 @@
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{
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"environment": "isolated local PostgreSQL 16-alpine, Python 3.12.11, psycopg 3.3.4; same data, sequential requests, warm database/connection pool, no application cache",
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"baseline_commit": "e567e5f",
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"dataset": {
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"snapshot_count": 41,
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"source_rows": 143521,
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"source_json_bytes": 22785765,
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"ranking_rows": 45003
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},
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"samples_per_endpoint": 5,
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"measurements": {
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"detail": {
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"before_seconds": [
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1.7919143750004878,
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1.7288594170004217,
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1.7106771250000747,
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1.7057415830004174,
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1.6912810410012753
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],
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"after_seconds": [
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0.6212627920012892,
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0.4883682920008141,
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0.44150824999996985,
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0.43969708400072705,
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0.4841277909999917
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],
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"before_median_seconds": 1.7106771250000747,
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"after_median_seconds": 0.4841277909999917,
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"reduction_percent": 71.7,
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"responses_identical": true,
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"response_sha256": "d2d5693152e949767a2db8e807852fe6fd2cb6ec175664a5dab9f615cdda4467"
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},
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"history": {
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"before_seconds": [
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0.8467425830003776,
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0.8580366249989311,
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0.8756502500000352,
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0.8701953330000833,
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0.8388115420002578
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],
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"after_seconds": [
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0.026230833000226994,
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0.024217250000219792,
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0.025365250001414097,
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0.028609290999156656,
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0.026671499999793014
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],
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"before_median_seconds": 0.8580366249989311,
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"after_median_seconds": 0.026230833000226994,
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"reduction_percent": 96.9,
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"responses_identical": true,
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"response_sha256": "edf098e1f124a6d5526d9f202b67c6e97986095a6cb18fad3d0d6ba07d10d387"
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},
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"rankings": {
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"before_seconds": [
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1.3065002089988411,
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1.2318050410012802,
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1.2226990420003858,
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1.3149193330009439,
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1.2414632910004002
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],
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"after_seconds": [
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0.06522883400066348,
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0.06445087500105728,
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0.06437025000013818,
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0.06701250000151049,
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0.06281666699942434
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],
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"before_median_seconds": 1.2414632910004002,
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"after_median_seconds": 0.06445087500105728,
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"reduction_percent": 94.8,
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"responses_identical": true,
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"response_sha256": "dbe90217c0086e22a570990062e08583c44d72ca1b4cf87b39208b033ce12e62"
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}
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},
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"query_plan_samples": [
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{
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"query": "source_rows",
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"execution_ms": 31.641,
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"planning_ms": 1.534,
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"rows": 3
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},
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{
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"query": "rank_history",
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"execution_ms": 23.911,
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"planning_ms": 0.664,
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"rows": 90
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}
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],
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"validation": {
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"pytest_passed": 226,
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"pytest_failed_baseline": 1,
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"baseline_failure": "test_market_data_repository_pool.py:65 Connection.executemany AttributeError, reproduced before edits",
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"pyright_baseline_errors": 14,
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"pyright_new_errors": 0,
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"ruff": "passed",
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"review_agent": "/root/read_path_review complete: no blocking SQL/data correctness findings",
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"caveat": "Default integration-first ordering also disables caplog loggers through pre-existing Alembic fileConfig. Full tests were run unit/HTTP/integration order with both DB environment variables set; one baseline integration failure remained."
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}
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}
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