CODE REVIEW / Functional Software, Inc. dba Sentry
Django performance review
Review Django code and report only performance issues the reviewer traced. Zero findings is an allowed result.
“Django performance code review. Use when asked to "review Django performance", "find N+1 queries", "optimize Django", "check queryset performance", "database performance", "Django ORM issues", or audit Django code for performance problems.”
01 / THE REASONING
Why this made the selection.
- N+1 and unbounded queries are the critical items, and they must be traced to a real loop and a large table.
- Micro-patterns such as count versus exists are usually not reported.
02 / THE REVIEW RECORD
What we actually inspected.
Source review has boundaries.
A clear record is more useful than a “safe” badge.
Material inspected
- skills/django-perf-review/SKILL.md
- LICENSE
- README.md
- vercel-labs/skills@48dc9e8eb8aa19040e91cb90ba0b642c9cbad57e:README.md
Our findings
- The pinned frontmatter allows Read, Grep, Glob, Bash, and Task, and the body says to research before reporting.
- Priority 1 is N+1 from views, querysets, templates, or serializers, and the checklist requires an existing select_related or prefetch_related search.
- Unbounded querysets and write loops are critical or high only on user-facing paths with large or unbounded data, not on chunked jobs or one-time migrations.
- Inefficient patterns such as len on a queryset are low and included only as notes beside a real issue.
- The review checklist demands hot-path and volume evidence and permits zero findings instead of equating a code pattern with a proven incident.
- Validation requires ruling out caching and rate-limiting mitigations after tracing the data flow, existing optimizations, table volume, and call sites; the file says to omit findings if any validation step is incomplete.
Not established by this review
- The skill was not executed inside a host agent.
- Not executed: it was not installed or run.
The review applies to the material and revision named here. A newer upstream release can change its behavior.
03 / PUT IT TO WORK
Add a skill to your workflow.
DOCUMENTED COMMAND
npx skills add getsentry/skills/skills/django-perf-review Run in your terminal to choose your AI tools, project or global scope, and installation method. This short command follows the current upstream version; the review covers the revision shown above.
Optional: install the reviewed revision
Keeps the original source and installer version where recorded, while leaving your AI tools and installation scope for you to choose.
npx skills add https://github.com/getsentry/skills/tree/d18b7aa8ba878354e5c348310230e652f7690f9c/skills/django-perf-review - The command pins only this review skill at the reviewed commit. The installer was not executed.
- Do not use a global yes-to-all install of the repository. That would install skills this review did not select.
- For manual installation instead, download the complete bundle from https://undominated.ai/resources/skills/getsentry-django-perf-review/bundle.zip and extract it into a new directory. It holds SKILL.md unchanged at revision d18b7aa8, the upstream licence and an attribution file.
- Place the extracted directory in your host's documented skills directory, named django-perf-review. Keep SKILL.md and the licence together.
Before you start
- Node.js and npm
- A Django tree you are allowed to read
THE COMPLETE REVIEWED DEFINITION
Read it before you reuse it.
Original source bytes, with attribution.
Review the host-specific setup notes above.
---
name: django-perf-review
description: Django performance code review. Use when asked to "review Django performance", "find N+1 queries", "optimize Django", "check queryset performance", "database performance", "Django ORM issues", or audit Django code for performance problems.
allowed-tools: Read Grep Glob Bash Task
license: LICENSE
---
# Django Performance Review
Review Django code for **validated** performance issues. Research the codebase to confirm issues before reporting. Report only what you can prove.
## Review Approach
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.
## Impact Categories
Issues are organized by impact. Focus on CRITICAL and HIGH - these cause real problems at scale.
| Priority | Category | Impact |
|----------|----------|--------|
| 1 | N+1 Queries | **CRITICAL** - Multiplies with data, causes timeouts |
| 2 | Unbounded Querysets | **CRITICAL** - Memory exhaustion, OOM kills |
| 3 | Missing Indexes | **HIGH** - Full table scans on large tables |
| 4 | Write Loops | **HIGH** - Lock contention, slow requests |
| 5 | Inefficient Patterns | **LOW** - Rarely worth reporting |
---
## Priority 1: 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
```python
# 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.
```python
# 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
```python
# 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)
---
## Priority 2: Unbounded Querysets (CRITICAL)
**Impact:** Loading entire tables exhausts memory. Large tables cause OOM kills and worker restarts.
### Rule: Always paginate list endpoints
```python
# 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
```python
# 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
```python
# 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)
---
## Priority 3: 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
```python
# 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
```python
# 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
```python
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)
---
## Priority 4: Write Loops (HIGH)
**Impact:** N database writes instead of 1. Lock contention. Slow requests.
### Rule: Use bulk_create instead of create() in loops
```python
# 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
```python
# 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
```python
# 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)
---
## Priority 5: Inefficient Patterns (LOW)
**Rarely worth reporting.** Include only as minor notes if you're already reporting real issues.
### Pattern: count() vs exists()
```python
# 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()
```python
# 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
```python
# 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.
---
## Validation Requirements
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.**
---
## Output Format
```markdown
## 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:**
```python
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]."
**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.
---
## 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:**
```python
# 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:**
```python
# 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.
The download contains SKILL.md. Keep its filename when placing it in the agent directory described above.
By Functional Software, Inc. dba Sentry. Exact upstream source ↗ · Licence · Attribution
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04 / FOLLOW THE EVIDENCE
The source trail.
Our notes are separate from the original resource.
Check upstream before adopting a new version.
https://github.com/getsentry/skills/blob/d18b7aa8ba878354e5c348310230e652f7690f9c/skills/django-perf-review/SKILL.md
Supports: Trace before report, Zero findings allowed
- Apache-2.0 licence ↗Checked
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Supports: Copyright 2025 Functional Software, Inc. dba Sentry, Redistribution under Apache-2.0
- Directory licence pointer ↗Checked
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Supports: Points at the repository root licence
https://raw.githubusercontent.com/getsentry/skills/d18b7aa8ba878354e5c348310230e652f7690f9c/skills/django-perf-review/SKILL.md
Supports: review.findings, bestFor