Silky smooth profiling for the Django Framework — modernized UI fork of django-silk
Project description
django-silky
django-silky is a modernized-UI fork of django-silk — a live profiling and inspection tool for the Django framework.
It keeps 100 % of the original functionality (request/response recording, SQL inspection, code profiling, dynamic profiling) while shipping a fully redesigned interface built on CSS custom properties (light and dark mode), Lucide icons, an inline filter bar, multi-column sort chips, and proper pagination.
What's different from django-silk?
| Feature | django-silk | django-silky |
|---|---|---|
| Theme | Fixed dark nav, light body | Full light/dark toggle, persisted in localStorage |
| Filter UI | 300 px slide-out drawer | Inline collapsible filter bar |
| Sort | Single column, GET param | Multi-column sort chips (session-persisted) |
| Pagination | Query-slice (LIMIT N) |
Real Django Paginator with prev/next/page numbers |
| Detail pages | Plain tables | Hero bar + metric pills + section cards |
| Icons | Font Awesome (CDN) | Lucide (self-hosted, no external requests) |
| URL sharing | State lost on reload | Sort + per-page encoded in URL; filter bar state in localStorage |
Migrating from django-silk
django-silky is a drop-in replacement — same app label (silk), same
database schema (migrations 0001 – 0008), all your existing data is retained.
pip uninstall django-silk
pip install django-silky
# No manage.py migrate needed — schema is identical
For full instructions, version compatibility details, and rollback steps see MIGRATING.md.
Requirements
- Django 4.2, 5.1, 5.2, 6.0
- Python 3.10, 3.11, 3.12, 3.13, 3.14
Installation
pip install django-silky
With optional request body formatting:
pip install django-silky[formatting]
settings.py
MIDDLEWARE = [
...
'silk.middleware.SilkyMiddleware',
...
]
TEMPLATES = [{
...
'OPTIONS': {
'context_processors': [
...
'django.template.context_processors.request',
],
},
}]
INSTALLED_APPS = [
...
'silk',
]
Middleware order: Any middleware placed before
SilkyMiddlewarethat returns a response without callingget_responsewill prevent Silk from running. If you usedjango.middleware.gzip.GZipMiddleware, place it beforeSilkyMiddleware.
urls.py
from django.urls import include, path
urlpatterns += [
path('silk/', include('silk.urls', namespace='silk')),
]
Migrate and collect static
python manage.py migrate
python manage.py collectstatic
The UI is now available at /silk/.
Features
Request Inspection
Silk's middleware records every HTTP request and response — method, status code, path, timing, SQL query count, and headers/bodies — and presents them in a filterable, sortable, paginated table.
SQL Inspection
Every SQL query executed during a request is captured with its execution time, tables involved, number of joins, and a full stack trace so you can see exactly where in your code it was triggered.
Code Profiling
Decorator / context manager
from silk.profiling.profiler import silk_profile
@silk_profile(name='View Blog Post')
def post(request, post_id):
p = Post.objects.get(pk=post_id)
return render(request, 'post.html', {'post': p})
def post(request, post_id):
with silk_profile(name='View Blog Post #%d' % post_id):
p = Post.objects.get(pk=post_id)
return render(request, 'post.html', {'post': p})
cProfile integration
SILKY_PYTHON_PROFILER = True
SILKY_PYTHON_PROFILER_BINARY = True # also save .prof files
When enabled, a call-graph coloured by time is shown on the profile detail page.
Dynamic profiling
Profile third-party code without touching its source:
SILKY_DYNAMIC_PROFILING = [{
'module': 'path.to.module',
'function': 'MyClass.bar',
}]
Code Generation
Silk generates a curl command and a Django test-client snippet for every request, making it easy to replay a captured request from the terminal or a unit test.
Configuration
Authentication
SILKY_AUTHENTICATION = True # user must be logged in
SILKY_AUTHORISATION = True # user must have is_staff=True (default)
# Custom permission check:
SILKY_PERMISSIONS = lambda user: user.is_superuser
Request / response body limits
SILKY_MAX_REQUEST_BODY_SIZE = -1 # -1 = no limit
SILKY_MAX_RESPONSE_BODY_SIZE = 1024 # bytes; larger bodies are discarded
Sampling (high-traffic sites)
SILKY_INTERCEPT_PERCENT = 50 # record only 50 % of requests
# or
SILKY_INTERCEPT_FUNC = lambda request: 'profile' in request.session
Garbage collection
SILKY_MAX_RECORDED_REQUESTS = 10_000
SILKY_MAX_RECORDED_REQUESTS_CHECK_PERCENT = 10 # GC runs on 10 % of requests
Trigger manually (e.g. from a cron job):
python manage.py silk_request_garbage_collect
Clear all data immediately:
python manage.py silk_clear_request_log
Query analysis
SILKY_ANALYZE_QUERIES = True
SILKY_EXPLAIN_FLAGS = {'format': 'JSON', 'costs': True}
Warning:
EXPLAIN ANALYZEon PostgreSQL actually executes the query, which may cause unintended side effects. Use with caution.
Meta-profiling
SILKY_META = True # shows how long Silk itself takes per request
Sensitive data masking
# Default set — case insensitive
SILKY_SENSITIVE_KEYS = {'username', 'api', 'token', 'key', 'secret', 'password', 'signature'}
Custom profiler storage
# Django >= 4.2
STORAGES = {
'SILKY_STORAGE': {
'BACKEND': 'path.to.StorageClass',
},
}
SILKY_PYTHON_PROFILER_RESULT_PATH = '/path/to/profiles/'
Development
git clone https://github.com/VaishnavGhenge/django-silky.git
cd django-silky
python -m venv .venv && source .venv/bin/activate
pip install -e ".[formatting]"
pip install -r project/requirements.txt
# Run the example project
DB_ENGINE=sqlite3 python project/manage.py migrate
DB_ENGINE=sqlite3 python project/manage.py runserver
# Visit http://127.0.0.1:8000/silk/ (login: admin / admin)
# Watch SCSS while editing UI
npx gulp watch
# Run tests
DB_ENGINE=sqlite3 python -m pytest project/tests/ -q
Credits
django-silky is a fork of django-silk, originally created by Michael Ford and maintained by Jazzband. All core profiling functionality comes from the upstream project; this fork focuses solely on UI improvements.
License
MIT — same as the upstream django-silk.
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