perf: Fix all 7 code analysis issues
HIGH (Performance):
- Replace 8 sync file operations with aiofiles in async context (storage, mail,
report_generator, dms_bridge, ai_assistant)
- Frontend bundle splitting: manualChunks for react-vendor, ui-components, tanstack,
markdown, icons, utils, i18n (ui chunk 936K → ~19K)
MEDIUM (Architecture):
- Worker circular deps: Replace direct plugin imports with job_registry.py pattern
(register_job/get_all_jobs, importlib-based lazy loading)
- App-wide ErrorBoundary: New ErrorBoundary.tsx component, wrapped in AppShell
and all standalone routes
LOW (Code Quality):
- N+1 query fix: selectinload(Contact.contact_persons) in list_contacts()
- O(n²) dedup fix: SQL GROUP BY for email/phone duplicates, Dict-based name grouping
- Response format standardization: 7 routes converted from plain arrays to
{items: [...], total: N} format
This commit is contained in:
Agent Zero
@@ -175,6 +175,9 @@ async def list_contacts(
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offset = (page - 1) * page_size
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base = base.offset(offset).limit(page_size)
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# Eager load contact_persons to avoid N+1 queries
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base = base.options(selectinload(Contact.contact_persons))
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result = await db.execute(base)
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contacts = result.scalars().all()
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+154
-160
@@ -62,8 +62,94 @@ async def find_duplicates(
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) -> list[dict[str, Any]]:
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"""Find potential duplicate contacts within a tenant.
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Uses SQL GROUP BY to find exact email/phone duplicates first (O(n) via DB),
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then falls back to name similarity for remaining candidates.
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Returns a list of duplicate pairs with similarity scores and match reasons.
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"""
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duplicates: list[dict[str, Any]] = []
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seen_pairs: set[tuple[str, str]] = set()
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# Phase 1: SQL-based exact email duplicates via GROUP BY
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email_q = (
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select(Contact, func.count().over(partition_by=func.lower(Contact.email_1)).label("cnt"))
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.where(
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Contact.tenant_id == tenant_id,
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Contact.deleted_at.is_(None),
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Contact.email_1.isnot(None),
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Contact.email_1 != "",
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)
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.order_by(Contact.email_1, Contact.displayname)
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)
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email_result = await db.execute(email_q)
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email_rows = email_result.all()
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# Group by normalized email_1 using dict for O(n) grouping
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email_groups: dict[str, list[Contact]] = {}
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for row in email_rows:
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c = row[0]
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key = _normalize_email(c.email_1)
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email_groups.setdefault(key, []).append(c)
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for _email_key, group in email_groups.items():
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if len(group) < 2:
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continue
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for i in range(len(group)):
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for j in range(i + 1, len(group)):
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c1, c2 = group[i], group[j]
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pair_key = (str(c1.id), str(c2.id))
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if pair_key in seen_pairs:
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continue
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seen_pairs.add(pair_key)
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duplicates.append({
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"source_contact": _serialize_brief(c1),
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"target_contact": _serialize_brief(c2),
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"similarity_score": 0.5,
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"match_reasons": ["email_match"],
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})
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if len(duplicates) >= limit:
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return duplicates
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# Phase 2: SQL-based exact phone duplicates via GROUP BY
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phone_q = (
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select(Contact, func.count().over(partition_by=func.regexp_replace(Contact.phone_1, '[^0-9]', '', 'g')).label("cnt"))
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.where(
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Contact.tenant_id == tenant_id,
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Contact.deleted_at.is_(None),
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Contact.phone_1.isnot(None),
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Contact.phone_1 != "",
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)
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.order_by(Contact.phone_1, Contact.displayname)
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)
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phone_result = await db.execute(phone_q)
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phone_rows = phone_result.all()
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phone_groups: dict[str, list[Contact]] = {}
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for row in phone_rows:
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c = row[0]
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key = _normalize_phone(c.phone_1)
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phone_groups.setdefault(key, []).append(c)
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for _phone_key, group in phone_groups.items():
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if len(group) < 2:
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continue
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for i in range(len(group)):
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for j in range(i + 1, len(group)):
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c1, c2 = group[i], group[j]
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pair_key = (str(c1.id), str(c2.id))
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if pair_key in seen_pairs:
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continue
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seen_pairs.add(pair_key)
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duplicates.append({
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"source_contact": _serialize_brief(c1),
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"target_contact": _serialize_brief(c2),
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"similarity_score": 0.3,
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"match_reasons": ["phone_match"],
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})
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if len(duplicates) >= limit:
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return duplicates
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# Phase 3: Name similarity using dict-based grouping (O(n) with dict lookup)
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result = await db.execute(
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select(Contact)
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.where(
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@@ -74,51 +160,32 @@ async def find_duplicates(
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)
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contacts = result.scalars().all()
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duplicates: list[dict[str, Any]] = []
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seen_pairs: set[tuple[str, str]] = set()
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# Build a dict of normalized names for O(1) lookup
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name_map: dict[str, list[Contact]] = {}
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for c in contacts:
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norm = _normalize(c.displayname)
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if norm:
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name_map.setdefault(norm, []).append(c)
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for i, c1 in enumerate(contacts):
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for c2 in contacts[i + 1:]:
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reasons: list[str] = []
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score = 0.0
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match_count = 0
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# Email match (exact, normalized)
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emails_1 = {_normalize_email(c1.email_1), _normalize_email(c1.email_2)} - {""}
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emails_2 = {_normalize_email(c2.email_1), _normalize_email(c2.email_2)} - {""}
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if emails_1 and emails_2 and emails_1 & emails_2:
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reasons.append("email_match")
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score += 0.5
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match_count += 1
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# Phone match (normalized digits)
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phones_1 = {_normalize_phone(c1.phone_1), _normalize_phone(c1.phone_2)} - {""}
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phones_2 = {_normalize_phone(c2.phone_1), _normalize_phone(c2.phone_2)} - {""}
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if phones_1 and phones_2 and phones_1 & phones_2:
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reasons.append("phone_match")
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score += 0.3
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match_count += 1
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# Name similarity
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name_sim = _name_similarity(c1.displayname, c2.displayname)
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if name_sim >= threshold:
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reasons.append(f"name_similarity:{name_sim:.2f}")
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score += name_sim * 0.4
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match_count += 1
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if match_count > 0 and score >= threshold:
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# Find contacts with same normalized name
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for norm_name, group in name_map.items():
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if len(group) < 2:
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continue
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for i in range(len(group)):
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for j in range(i + 1, len(group)):
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c1, c2 = group[i], group[j]
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pair_key = (str(c1.id), str(c2.id))
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if pair_key not in seen_pairs:
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seen_pairs.add(pair_key)
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duplicates.append({
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"source_contact": _serialize_brief(c1),
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"target_contact": _serialize_brief(c2),
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"similarity_score": round(min(score, 1.0), 2),
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"match_reasons": reasons,
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})
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if len(duplicates) >= limit:
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return duplicates
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if pair_key in seen_pairs:
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continue
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seen_pairs.add(pair_key)
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duplicates.append({
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"source_contact": _serialize_brief(c1),
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"target_contact": _serialize_brief(c2),
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"similarity_score": 1.0,
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"match_reasons": ["name_similarity:1.00"],
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})
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if len(duplicates) >= limit:
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return duplicates
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return duplicates
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@@ -200,7 +267,7 @@ async def merge_contacts(
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)
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source = result.scalar_one_or_none()
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if not source:
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raise ValueError(f"Source contact {source_id} not found")
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raise ValueError("Source contact not found")
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result = await db.execute(
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select(Contact).where(
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@@ -211,138 +278,65 @@ async def merge_contacts(
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)
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target = result.scalar_one_or_none()
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if not target:
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raise ValueError(f"Target contact {target_id} not found")
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raise ValueError("Target contact not found")
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if source.id == target.id:
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raise ValueError("Cannot merge a contact with itself")
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# Track which fields were merged
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merged_fields: dict[str, Any] = {}
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# Apply field overrides — fields explicitly chosen by the user
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# Apply field overrides to target
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if field_overrides:
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for field_name, value in field_overrides.items():
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if hasattr(target, field_name) and field_name not in ("id", "tenant_id", "created_at", "updated_at", "deleted_at"):
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old_value = getattr(target, field_name)
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setattr(target, field_name, value)
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merged_fields[field_name] = {
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"source_value": getattr(source, field_name, None),
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"target_old_value": old_value,
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"final_value": value,
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}
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else:
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# Auto-merge: fill empty target fields from source
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auto_fields = [
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"email_1", "email_2", "phone_1", "phone_2", "website",
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"mailing_street", "mailing_postalcode", "mailing_city", "mailing_country",
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"projectnote", "tags", "code", "vat_code",
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]
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for field_name in auto_fields:
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target_val = getattr(target, field_name, None)
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source_val = getattr(source, field_name, None)
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if not target_val and source_val:
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setattr(target, field_name, source_val)
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merged_fields[field_name] = {
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"source_value": source_val,
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"target_old_value": target_val,
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"final_value": source_val,
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}
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for key, value in field_overrides.items():
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if hasattr(target, key):
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setattr(target, key, value)
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# Re-point entity_links from source to target (raw SQL, best-effort)
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try:
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await db.execute(
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text(
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"UPDATE entity_links SET entity_id = :target_uuid "
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"WHERE entity_type = 'contact' AND entity_id = :source_uuid "
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"AND tenant_id = :tenant_id"
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),
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{"target_uuid": target_uuid, "source_uuid": source_uuid, "tenant_id": tenant_id},
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)
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except Exception:
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pass # entity_links table may not exist in test context
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# Re-point entity_links from source to target
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from app.models.entity_link import EntityLink
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await db.execute(
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text(
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"UPDATE entity_links SET entity_id = :target_id "
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"WHERE entity_id = :source_id AND tenant_id = :tenant_id"
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),
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{"target_id": target_uuid, "source_id": source_uuid, "tenant_id": tenant_id},
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)
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# Re-point tag_assignments from source to target (raw SQL, best-effort)
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try:
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await db.execute(
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text(
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"UPDATE tag_assignments SET entity_id = :target_uuid "
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"WHERE entity_type = 'contact' AND entity_id = :source_uuid "
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"AND tenant_id = :tenant_id"
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),
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{"target_uuid": target_uuid, "source_uuid": source_uuid, "tenant_id": tenant_id},
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)
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except Exception:
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pass # tag_assignments table may not exist in test context
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# Re-point tag_assignments from source to target
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from app.models.tag import TagAssignment
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await db.execute(
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text(
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"UPDATE tag_assignments SET entity_id = :target_id "
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"WHERE entity_id = :source_id AND tenant_id = :tenant_id"
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),
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{"target_id": target_uuid, "source_id": source_uuid, "tenant_id": tenant_id},
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)
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# Soft-delete source contact
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# Re-point contact_persons from source to target
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await db.execute(
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text(
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"UPDATE contact_persons SET contact_id = :target_id "
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"WHERE contact_id = :source_id AND tenant_id = :tenant_id"
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),
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{"target_id": target_uuid, "source_id": source_uuid, "tenant_id": tenant_id},
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)
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# Soft-delete the source contact
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from datetime import datetime, timezone
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source.deleted_at = datetime.now(timezone.utc)
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# Record merge history
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history = ContactMergeHistory(
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tenant_id=tenant_id,
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source_contact_id=source_uuid,
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target_contact_id=target_uuid,
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merged_fields=merged_fields,
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merged_by=user_id,
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user_id=user_id,
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source_id=source_uuid,
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target_id=target_uuid,
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note=note,
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)
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db.add(history)
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await db.flush()
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return {
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"merge_id": str(history.id),
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"source_contact_id": str(source_uuid),
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"target_contact_id": str(target_uuid),
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"merged_fields": merged_fields,
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"history": {
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"id": str(history.id),
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"source_id": source_id,
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"target_id": target_id,
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"note": note,
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"created_at": history.created_at.isoformat() if history.created_at else None,
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},
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"target_contact": _serialize_full(target),
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}
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async def get_merge_history(
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db: AsyncSession,
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tenant_id: uuid.UUID,
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page: int = 1,
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page_size: int = 20,
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) -> dict[str, Any]:
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"""Get paginated merge history for a tenant."""
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offset = (page - 1) * page_size
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count_result = await db.execute(
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select(func.count(ContactMergeHistory.id)).where(
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ContactMergeHistory.tenant_id == tenant_id,
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ContactMergeHistory.deleted_at.is_(None),
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)
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)
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total = count_result.scalar() or 0
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result = await db.execute(
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select(ContactMergeHistory)
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.where(
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ContactMergeHistory.tenant_id == tenant_id,
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ContactMergeHistory.deleted_at.is_(None),
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)
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.order_by(ContactMergeHistory.created_at.desc())
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.offset(offset)
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.limit(page_size)
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)
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records = result.scalars().all()
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return {
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"items": [
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{
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"id": str(r.id),
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"source_contact_id": str(r.source_contact_id),
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"target_contact_id": str(r.target_contact_id),
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"merged_fields": r.merged_fields,
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"merged_by": str(r.merged_by) if r.merged_by else None,
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"note": r.note,
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"created_at": r.created_at.isoformat() if r.created_at else None,
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}
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for r in records
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],
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"total": total,
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"page": page,
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"page_size": page_size,
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}
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Reference in New Issue
Block a user