Django Perf Review

Django Performance Review

Review Django code for validated performance issues. Research the codebase to confirm issues before reporting. Report only what you can prove.

When to Use

• You need a Django performance review focused on verified ORM and query issues.
• The code likely has N+1 queries, unbounded querysets, missing indexes, or other database-driven bottlenecks.
• You want only provable performance findings, not speculative optimization advice.
• Trigger keywords: "review Django performance", "find N+1 queries", "optimize Django", "check queryset performance", "database performance", "Django ORM issues", "audit Django code for performance".

Prerequisites

• A Django or Django REST Framework codebase accessible via Read, Grep, and Glob.
• Sufficient context to trace data flow from view/queryset creation to template/serializer consumption.
• Ability to run grep/Grep searches across the codebase to validate findings.

Procedure

Step 0: Adopt the Review Mindset

1. Research first — Trace data flow, check for existing optimizations, verify data volume.
2. Validate before reporting — Pattern matching is not validation.
3. Zero findings is acceptable — Don't manufacture issues to appear thorough.
4. Severity must match impact — If you catch yourself writing "minor" in a CRITICAL finding, it's not critical. Downgrade or skip it.

Step 1: Triage by Impact Category

Issues are organized by impact. Focus on CRITICAL and HIGH — these cause real problems at scale.

Step 2: Check for N+1 Queries (CRITICAL)

Impact: Each N+1 adds O(n) database round trips. 100 rows = 100 extra queries. 10,000 rows = timeout.

Rule: Prefetch related data accessed in loops

Validate by tracing: View → Queryset → Template/Serializer → Loop access.

# PROBLEM: N+1 - each iteration queries profile
def user_list(request):
    users = User.objects.all()
    return render(request, 'users.html', {'users': users})

# Template:
# {% for user in users %}
#     {{ user.profile.bio }}  ← triggers query per user
# {% endfor %}

# SOLUTION: Prefetch in view
def user_list(request):
    users = User.objects.select_related('profile')
    return render(request, 'users.html', {'users': users})

Rule: Prefetch in serializers, not just views

DRF serializers accessing related fields cause N+1 if queryset isn't optimized.

# PROBLEM: SerializerMethodField queries per object
class UserSerializer(serializers.ModelSerializer):
    order_count = serializers.SerializerMethodField()

    def get_order_count(self, obj):
        return obj.orders.count()  # ← query per user

# SOLUTION: Annotate in viewset, access in serializer
class UserViewSet(viewsets.ModelViewSet):
    def get_queryset(self):
        return User.objects.annotate(order_count=Count('orders'))

class UserSerializer(serializers.ModelSerializer):
    order_count = serializers.IntegerField(read_only=True)

Rule: Model properties that query are dangerous in loops

# PROBLEM: Property triggers query when accessed
class User(models.Model):
    @property
    def recent_orders(self):
        return self.orders.filter(created__gte=last_week)[:5]

# Used in template loop = N+1

# SOLUTION: Use Prefetch with custom queryset, or annotate

Validation Checklist for N+1

• Traced data flow from view to template/serializer
• Confirmed related field is accessed inside a loop
• Searched codebase for existing select_related/prefetch_related
• Verified table has significant row count (1000+)
• Confirmed this is a hot path (not admin, not rare action)

Step 3: Check for Unbounded Querysets (CRITICAL)

Impact: Loading entire tables exhausts memory. Large tables cause OOM kills and worker restarts.

Rule: Always paginate list endpoints

# PROBLEM: No pagination - loads all rows
class UserListView(ListView):
    model = User
    template_name = 'users.html'

# SOLUTION: Add pagination
class UserListView(ListView):
    model = User
    template_name = 'users.html'
    paginate_by = 25

Rule: Use iterator() for large batch processing

# PROBLEM: Loads all objects into memory at once
for user in User.objects.all():
    process(user)

# SOLUTION: Stream with iterator()
for user in User.objects.iterator(chunk_size=1000):
    process(user)

Rule: Never call list() on unbounded querysets

# PROBLEM: Forces full evaluation into memory
all_users = list(User.objects.all())

# SOLUTION: Keep as queryset, slice if needed
users = User.objects.all()[:100]

Validation Checklist for Unbounded Querysets

• Table is large (10k+ rows) or will grow unbounded
• No pagination class, paginate_by, or slicing
• This runs on user-facing request (not background job with chunking)

Step 4: Check for Missing Indexes (HIGH)

Impact: Full table scans. Negligible on small tables, catastrophic on large ones.

Rule: Index fields used in WHERE clauses on large tables

# PROBLEM: Filtering on unindexed field
# User.objects.filter(email=email)  # full scan if no index

class User(models.Model):
    email = models.EmailField()  # ← no db_index

# SOLUTION: Add index
class User(models.Model):
    email = models.EmailField(db_index=True)

Rule: Index fields used in ORDER BY on large tables

# PROBLEM: Sorting requires full scan without index
Order.objects.order_by('-created')

# SOLUTION: Index the sort field
class Order(models.Model):
    created = models.DateTimeField(db_index=True)

Rule: Use composite indexes for common query patterns

class Order(models.Model):
    user = models.ForeignKey(User)
    status = models.CharField(max_length=20)
    created = models.DateTimeField()

    class Meta:
        indexes = [
            models.Index(fields=['user', 'status']),  # for filter(user=x, status=y)
            models.Index(fields=['status', '-created']),  # for filter(status=x).order_by('-created')
        ]

Validation Checklist for Missing Indexes

• Table has 10k+ rows
• Field is used in filter() or order_by() on hot path
• Checked model — no db_index=True or Meta.indexes entry
• Not a foreign key (already indexed automatically)

Step 5: Check for Write Loops (HIGH)

Impact: N database writes instead of 1. Lock contention. Slow requests.

Rule: Use bulk_create instead of create() in loops

# PROBLEM: N inserts, N round trips
for item in items:
    Model.objects.create(name=item['name'])

# SOLUTION: Single bulk insert
Model.objects.bulk_create([
    Model(name=item['name']) for item in items
])

Rule: Use update() or bulk_update instead of save() in loops

# PROBLEM: N updates
for obj in queryset:
    obj.status = 'done'
    obj.save()

# SOLUTION A: Single UPDATE statement (same value for all)
queryset.update(status='done')

# SOLUTION B: bulk_update (different values)
for obj in objects:
    obj.status = compute_status(obj)
Model.objects.bulk_update(objects, ['status'], batch_size=500)

Rule: Use delete() on queryset, not in loops

# PROBLEM: N deletes
for obj in queryset:
    obj.delete()

# SOLUTION: Single DELETE
queryset.delete()

Validation Checklist for Write Loops

• Loop iterates over 100+ items (or unbounded)
• Each iteration calls create(), save(), or delete()
• This runs on user-facing request (not one-time migration script)

Step 6: Check for Inefficient Patterns (LOW)

Rarely worth reporting. Include only as minor notes if you're already reporting real issues.

Pattern: count() vs exists()

# Slightly suboptimal
if queryset.count() > 0:
    do_thing()

# Marginally better
if queryset.exists():
    do_thing()

Usually skip — difference is <1ms in most cases.

Pattern: len(queryset) vs count()

# Fetches all rows to count
if len(queryset) > 0:  # bad if queryset not yet evaluated

# Single COUNT query
if queryset.count() > 0:

Only flag if queryset is large and not already evaluated.

Pattern: get() in small loops

# N queries, but if N is small (< 20), often fine
for id in ids:
    obj = Model.objects.get(id=id)

Only flag if loop is large or this is in a very hot path.

Step 7: Validate Every Finding Before Reporting

Before reporting ANY issue:

1. Trace the data flow — Follow queryset from creation to consumption.
2. Search for existing optimizations — Grep for select_related, prefetch_related, pagination.
3. Verify data volume — Check if table is actually large.
4. Confirm hot path — Trace call sites, verify this runs frequently.
5. Rule out mitigations — Check for caching, rate limiting.

If you cannot validate all steps, do not report.

Step 8: Produce the Output Report

Use this format:

## Django Performance Review: [File/Component Name]

### Summary
Validated issues: X (Y Critical, Z High)

### Findings

#### [PERF-001] N+1 Query in UserListView (CRITICAL)
**Location:** `views.py:45`

**Issue:** Related field `profile` accessed in template loop without prefetch.

**Validation:**
- Traced: UserListView → users queryset → user_list.html → `{{ user.profile.bio }}` in loop
- Searched codebase: no select_related('profile') found
- User table: 50k+ rows (verified in admin)
- Hot path: linked from homepage navigation

**Evidence:**
```python
def get_queryset(self):
    return User.objects.filter(active=True)  # no select_related

Fix:

def get_queryset(self):
    return User.objects.filter(active=True).select_related('profile')

If no issues found: `"No performance issues identified after reviewing [files] and validating [what you checked]."`

### Step 9: Final Sanity Check

Before submitting, sanity check each finding:

- Does the severity match the actual impact? ("Minor inefficiency" ≠ CRITICAL)
- Is this a real performance issue or just a style preference?
- Would fixing this measurably improve performance?

If the answer to any is "no" — remove the finding.

## Pitfalls

### What NOT to Report

- Test files
- Admin-only views
- Management commands
- Migration files
- One-time scripts
- Code behind disabled feature flags
- Tables with <1000 rows that won't grow
- Patterns in cold paths (rarely executed code)
- Micro-optimizations (`exists` vs `count`, `only`/`defer` without evidence)

### False Positives to Avoid

**Queryset variable assignment is not an issue:**

```python
# This is FINE - no performance difference
projects_qs = Project.objects.filter(org=org)
projects = list(projects_qs)

# vs this - identical performance
projects = list(Project.objects.filter(org=org))

Querysets are lazy. Assigning to a variable doesn't execute anything.

Single query patterns are not N+1:

# This is ONE query, not N+1
projects = list(Project.objects.filter(org=org))

N+1 requires a loop that triggers additional queries. A single list() call is fine.

Missing select_related on single object fetch is not N+1:

# This is 2 queries, not N+1 - report as LOW at most
state = AutofixState.objects.filter(pr_id=pr_id).first()
project_id = state.request.project_id  # second query

N+1 requires a loop. A single object doing 2 queries instead of 1 can be reported as LOW if relevant, but never as CRITICAL/HIGH.

Style preferences are not performance issues:

If your only suggestion is "combine these two lines" or "rename this variable" — that's style, not performance. Don't report it.

Over-reporting Risk

The biggest risk in Django performance review is manufacturing findings to appear thorough. Zero validated findings is a valid and professional outcome. Do not inflate severity to make a finding seem important.

Verification

To verify the review was conducted correctly:

1. Every reported finding has a Validation section with traced data flow, codebase search results, data volume confirmation, and hot-path verification.
2. No finding lacks evidence — if a finding has no code snippet or traced path, it should not be in the report.
3. Severity matches impact — CRITICAL findings involve loops multiplying queries or unbounded memory; HIGH involves large-table scans or write loops; LOW is reserved for minor notes.
4. Excluded categories are absent — no findings from test files, migrations, admin-only views, management commands, or tables with <1000 rows.
5. False positives are absent — no reports of queryset variable assignment, single-query patterns, or single-object multi-fetch as N+1.
6. Zero findings is valid — if no validated issues exist, the report states this explicitly with a summary of what was checked.

Quick Grep Commands for Validation

Use these to confirm findings during review:

# Find existing prefetch/select_related optimizations
grep -rn "select_related\|prefetch_related" --include="*.py"

# Find unbounded queryset patterns
grep -rn "\.all()" --include="*.py" | grep -v "test"

# Find pagination configuration
grep -rn "paginate_by\|PaginationClass\|pagination_class" --include="*.py"

# Find bulk operations
grep -rn "bulk_create\|bulk_update\|\.update(" --include="*.py"

# Find write loops
grep -rn "\.save()\|\.create()\|\.delete()" --include="*.py" | grep -v "test"

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.