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gh-christianlouis-docuelevate/benchmark_notifications.py
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copilot-swe-agent[bot] 651b48658c fix: resolve merge conflicts with main branch
Merge origin/main into feature branch, resolving 3 conflicts:
- app/api/__init__.py: add classification_rules_router alongside new
  routers from main (audit_logs, i18n, mobile, compliance, translation)
- app/models.py: keep ClassificationRuleModel alongside new models from
  main (MobileDevice, ComplianceTemplate, PipelineRoutingRule)
- tests/conftest.py: import both ClassificationRuleModel and new models
  from main (AuditLog, ComplianceTemplate)

Also renumber migration from 027 to 037 to chain from the latest
migration on main (036_add_document_translation_fields).

Co-authored-by: christianlouis <361235+christianlouis@users.noreply.github.com>
2026-03-16 22:33:59 +00:00

51 lines
1.4 KiB
Python

import json
import time
import pytest
from app.database import get_db
from app.models import UserNotificationTarget, UserNotificationPreference
from app.main import app
from tests.test_notifications_api import _make_client, _OWNER, _cleanup
import statistics
def run_benchmark(notif_engine, notif_session, client, items_count, iterations=5):
# Setup
target = UserNotificationTarget(
owner_id=_OWNER,
channel_type="webhook",
name="My Webhook",
config=json.dumps({"url": "https://x.com"}),
)
notif_session.add(target)
notif_session.commit()
notif_session.refresh(target)
# Generate big payload
preferences = []
for i in range(items_count):
preferences.append({
"event_type": f"event.type.{i}",
"channel_type": "webhook",
"is_enabled": True,
"target_id": target.id,
})
payload = {"preferences": preferences}
# Warm up
client.put("/api/user-notifications/preferences", json=payload)
times = []
for _ in range(iterations):
# Alter the values a bit so it's a real update
for p in payload["preferences"]:
p["is_enabled"] = not p["is_enabled"]
start = time.time()
resp = client.put("/api/user-notifications/preferences", json=payload)
end = time.time()
assert resp.status_code == 200
times.append(end - start)
return statistics.mean(times)