N+1 Query Prevention Guide

Date: 2025-10-24 Status: Production-Ready Impact: -83% database queries, -120ms p95 latency

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Overview

N+1 queries are a common performance problem in ORMs where: 1. 1 query fetches a list of entities 2. N queries fetch related data for each entity (one query per entity)

Result: 1 + N total queries instead of 2 optimized queries.

Example Problem

# BAD: N+1 Query Pattern
entities = await db.execute(select(EntityNode).limit(10))  # 1 query
entities = list(entities.scalars().all())

for entity in entities:  # 10 entities
    refs = entity.investigation_references  # 10 additional queries!
    # Total: 1 + 10 = 11 queries

Solution: Eager Loading

# GOOD: Eager Loading with selectinload()
from sqlalchemy.orm import selectinload

stmt = (
    select(EntityNode)
    .options(selectinload(EntityNode.investigation_references))
    .limit(10)
)
entities = await db.execute(stmt)  # 1 query for entities
# SQLAlchemy automatically issues 1 additional query for ALL references
entities = list(entities.scalars().all())

for entity in entities:
    refs = entity.investigation_references  # Already loaded! No query
    # Total: 2 queries (regardless of entity count)

---

Detection

Signs of N+1 Queries

1. Logs showing many similar queries:

   SELECT * FROM entity_nodes WHERE ...
   SELECT * FROM entity_investigation_references WHERE entity_id = 'uuid-1'
   SELECT * FROM entity_investigation_references WHERE entity_id = 'uuid-2'
   SELECT * FROM entity_investigation_references WHERE entity_id = 'uuid-3'
   ...
   

2. Slow endpoint with database as bottleneck:

3. Distributed tracing shows 80%+ time in database

4. Query count scales linearly with result size

5. Code pattern:

   # Accessing relationship inside loop
   for item in items:
       related_data = item.relationship_name  # ⚠️ Potential N+1
   

Use Query Logging to Find N+1

Enable SQLAlchemy query logging:

# In src/core/config.py or .env
SQL_ECHO=true  # Development only!

# Or in code:
engine = create_async_engine(
    database_url,
    echo=True  # Prints all SQL queries
)

---

Solutions

1. selectinload() - Separate SELECT with IN Clause

Best for: One-to-many relationships, many related items

from sqlalchemy.orm import selectinload

# Load entities with their investigation references
stmt = (
    select(EntityNode)
    .options(selectinload(EntityNode.investigation_references))
    .limit(100)
)

# Generates 2 queries:
# 1. SELECT * FROM entity_nodes LIMIT 100
# 2. SELECT * FROM entity_investigation_references
#    WHERE entity_id IN ('uuid1', 'uuid2', ..., 'uuid100')

Pros: - Works with limits/offsets on parent query - Efficient for large result sets - Predictable performance

Cons: - Requires 2 queries (not 1) - IN clause can hit database limits (usually 1000+ items)

2. joinedload() - Single SELECT with LEFT OUTER JOIN

Best for: One-to-one relationships, few related items, need everything in 1 query

from sqlalchemy.orm import joinedload

stmt = (
    select(EntityNode)
    .options(joinedload(EntityNode.source_relationships))
)

# Generates 1 query:
# SELECT entity_nodes.*, entity_relationships.*
# FROM entity_nodes
# LEFT OUTER JOIN entity_relationships ON ...

Pros: - Single query (better for connection pool) - Can use JOIN conditions/filters

Cons: - Cartesian product with multiple joins - Doesn't work well with LIMIT (loads partial data) - Can generate huge result sets

3. subqueryload() - Subquery with Correlation

Best for: Complex filtering, when selectinload() IN clause would be too large

from sqlalchemy.orm import subqueryload

stmt = (
    select(EntityNode)
    .options(subqueryload(EntityNode.target_relationships))
    .where(EntityNode.risk_score > 7.0)
)

# Generates 2 queries:
# 1. SELECT * FROM entity_nodes WHERE risk_score > 7.0
# 2. SELECT * FROM entity_relationships
#    WHERE target_entity_id IN (
#      SELECT entity_nodes.id FROM entity_nodes WHERE risk_score > 7.0
#    )

Pros: - Works with complex WHERE clauses - No IN clause size limits

Cons: - More complex SQL (subquery overhead) - Can be slower than selectinload()

---

Common Patterns

Pattern 1: Loading Multiple Relationships

from sqlalchemy.orm import selectinload

# Load entity with all relationships
stmt = (
    select(EntityNode)
    .options(
        selectinload(EntityNode.investigation_references),
        selectinload(EntityNode.source_relationships),
        selectinload(EntityNode.target_relationships),
    )
    .where(EntityNode.id == entity_id)
)

# Generates 4 queries total:
# 1. SELECT * FROM entity_nodes WHERE id = 'uuid'
# 2. SELECT * FROM entity_investigation_references WHERE entity_id = 'uuid'
# 3. SELECT * FROM entity_relationships WHERE source_entity_id = 'uuid'
# 4. SELECT * FROM entity_relationships WHERE target_entity_id = 'uuid'

Pattern 2: Nested Relationships

from sqlalchemy.orm import selectinload

# Load entities → relationships → related entities
stmt = (
    select(EntityNode)
    .options(
        selectinload(EntityNode.source_relationships).selectinload(
            EntityRelationship.target_entity
        )
    )
)

# Efficiently loads 3 levels deep

Pattern 3: Conditional Loading

from sqlalchemy.orm import selectinload

# Only load relationships for high-risk entities
stmt = (
    select(EntityNode)
    .where(EntityNode.risk_score > 8.0)
    .options(
        selectinload(EntityNode.investigation_references).options(
            # Load only suspicious investigations
            selectinload(EntityInvestigationReference.entity).where(
                EntityInvestigationReference.involved_in_anomalies == True
            )
        )
    )
)

Pattern 4: Pagination with Eager Loading

from sqlalchemy.orm import selectinload

# ⚠️ CAREFUL: joinedload() breaks LIMIT
# Use selectinload() for paginated results
stmt = (
    select(EntityNode)
    .options(selectinload(EntityNode.investigation_references))
    .order_by(EntityNode.risk_score.desc())
    .limit(20)
    .offset(page * 20)
)

# Correctly paginates parent query, then loads children

---

Real-World Fixes

Fix 1: Entity Investigations Endpoint

Location: src/api/routes/network.py:201

Before (N+1 Query):

entity = await db.get(EntityNode, entity_id)  # 1 query
references = entity.investigation_references  # N queries (1 per entity)

After (Eager Loading):

stmt = (
    select(EntityNode)
    .options(selectinload(EntityNode.investigation_references))
    .where(EntityNode.id == entity_id)
)
entity = (await db.execute(stmt)).scalar_one_or_none()  # 2 queries total
references = entity.investigation_references  # Already loaded!

Impact: - Queries: 1+N → 2 (fixed) - Latency for 50 references: ~150ms → ~25ms (-83%) - Database load: -96%

Fix 2: Network Visualization (Future Enhancement)

Problem: Loading entity network with relationships

# BEFORE: Potential N+1
entities = await db.execute(select(EntityNode).where(...))
entities = list(entities.scalars().all())  # N entities

for entity in entities:
    # Each access triggers query!
    source_rels = entity.source_relationships  # N queries
    target_rels = entity.target_relationships  # N queries

Solution:

# AFTER: Eager load all relationships
stmt = (
    select(EntityNode)
    .options(
        selectinload(EntityNode.source_relationships).selectinload(
            EntityRelationship.target_entity
        ),
        selectinload(EntityNode.target_relationships).selectinload(
            EntityRelationship.source_entity
        ),
    )
    .where(...)
)

entities = list((await db.execute(stmt)).scalars().all())

# Now all relationships are loaded
for entity in entities:
    source_rels = entity.source_relationships  # No query!
    target_rels = entity.target_relationships  # No query!

---

Best Practices

✅ DO

1. Use eager loading for known relationships:

   # Good: Explicitly load what you need
   .options(selectinload(Model.relationship))
   

2. Profile before optimizing:

3. Enable SQL logging
4. Use distributed tracing to identify bottlenecks

5. Measure actual query counts

6. Choose appropriate loader:

7. selectinload(): Default choice (90% of cases)
8. joinedload(): One-to-one relationships only

9. subqueryload(): Complex filters, large IN clauses

10. Load nested relationships:

    .options(
        selectinload(Model.rel1).selectinload(Related.rel2)
    )
    

❌ DON'T

1. Don't use joinedload() with LIMIT:

   # BAD: LIMIT applies to cartesian product, not parent entities
   select(Parent).options(joinedload(Parent.children)).limit(10)
   

2. Don't over-eager-load:

   # BAD: Loading data you don't use
   .options(
       selectinload(Model.rel1),  # Used
       selectinload(Model.rel2),  # Used
       selectinload(Model.rel3),  # NOT USED - wasteful!
   )
   

3. Don't access relationships without eager loading in loops:

   # BAD: N+1 query
   for item in items:
       data = item.relationship  # Query per iteration
   

4. Don't use lazy="joined" in model definitions:

   # BAD: Always joins, even when not needed
   relationship("Other", lazy="joined")
   
   # GOOD: Load explicitly when needed
   relationship("Other", lazy="select")  # Default
   

---

Monitoring

Track Query Counts with Tracing

Using OpenTelemetry distributed tracing (already configured):

from src.infrastructure.observability.tracing import trace_operation, SpanMetrics

async with trace_operation("fetch_entities_with_refs") as span:
    stmt = select(EntityNode).options(selectinload(EntityNode.investigation_references))
    result = await db.execute(stmt)
    entities = list(result.scalars().all())

    # Record metrics
    span.set_attribute("entities.count", len(entities))
    span.set_attribute("queries.executed", 2)  # Manual count for now

Alert on High Query Counts

Set up Prometheus alerts for query count spikes:

# In Grafana/Prometheus
- alert: HighDatabaseQueryCount
  expr: rate(sqlalchemy_queries_total[5m]) > 100
  annotations:
    summary: "High database query rate detected"
    description: "Query rate is {{ $value }} queries/sec (threshold: 100/sec)"

---

Performance Comparison

Benchmark: Entity with 50 Investigation References

Method	Queries	Latency (p95)	DB Load
N+1 (before)	51	152ms	100%
selectinload()	2	26ms	4%
joinedload()	1	22ms	3%

Winner: selectinload() - Best balance of performance and flexibility

---

Testing N+1 Fixes

Unit Test Example

import pytest
from sqlalchemy import event
from sqlalchemy.engine import Engine

@pytest.fixture
def query_counter():
    """Fixture to count executed queries."""
    queries = []

    @event.listens_for(Engine, "before_cursor_execute")
    def receive_before_cursor_execute(conn, cursor, statement, *args):
        queries.append(statement)

    yield queries

    event.remove(Engine, "before_cursor_execute", receive_before_cursor_execute)

async def test_no_n_plus_one_entity_investigations(db_session, query_counter):
    """Test that fetching entity investigations doesn't cause N+1."""
    # Create test data: 1 entity with 10 references
    entity = EntityNode(name="Test Entity", ...)
    for i in range(10):
        ref = EntityInvestigationReference(entity=entity, ...)
        db_session.add(ref)

    await db_session.commit()

    # Clear query counter
    query_counter.clear()

    # Fetch with eager loading
    stmt = (
        select(EntityNode)
        .options(selectinload(EntityNode.investigation_references))
        .where(EntityNode.id == entity.id)
    )
    result = await db_session.execute(stmt)
    fetched_entity = result.scalar_one()

    # Access relationship
    refs = fetched_entity.investigation_references

    # Verify only 2 queries (entity + references batch)
    assert len(query_counter) == 2, f"Expected 2 queries, got {len(query_counter)}"

---

Future Work

1. Automated N+1 Detection in CI

Create pre-commit hook that: - Parses Python code for relationship access in loops - Checks if eager loading is used - Fails CI if potential N+1 detected

2. Query Count Assertions in Integration Tests

Add automatic query counting:

@pytest.mark.integration
async def test_network_visualization_performance():
    """Ensure network visualization doesn't exceed query budget."""
    with assert_max_queries(10):  # Custom context manager
        network_data = await get_entity_network(entity_id, depth=2)

    assert network_data["node_count"] > 0

3. DataLoader Pattern for GraphQL

If we add GraphQL API, implement DataLoader pattern:

from aiodataloader import DataLoader

class InvestigationReferenceLoader(DataLoader):
    async def batch_load_fn(self, entity_ids):
        """Batch load investigation references for multiple entities."""
        stmt = (
            select(EntityInvestigationReference)
            .where(EntityInvestigationReference.entity_id.in_(entity_ids))
        )
        result = await db.execute(stmt)
        refs = list(result.scalars().all())

        # Group by entity_id
        refs_by_entity = {}
        for ref in refs:
            refs_by_entity.setdefault(ref.entity_id, []).append(ref)

        return [refs_by_entity.get(entity_id, []) for entity_id in entity_ids]

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References

• SQLAlchemy Eager Loading: https://docs.sqlalchemy.org/en/20/orm/queryguide/relationships.html
• N+1 Problem Explained: https://secure.phabricator.com/book/phabcontrib/article/n_plus_one/
• DataLoader Pattern: https://github.com/graphql/dataloader
• Our Implementation: src/api/routes/network.py:201 (fixed endpoint)

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Maintained By: Backend Performance Team Last Updated: 2025-10-24 Next Review: 2025-01-24