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?

Screenshots

Analytics dashboard — activity timeline, status donut, method bars, RT histogram, latency percentiles, queries-per-request histogram	Requests list — inline filter bar with multi-select method, status, path, and view filters; multi-column sort chips
N+1 detection — banner with detected pattern, query count badge, and per-query cost; offending rows highlighted	Profile detail — source file, function code highlighted, and associated SQL queries
cProfile output — raw cProfile dump with ncalls, tottime, cumtime, and filename:function	Settings — colour scheme picker (Light / Dark / Midnight / High Contrast) and selective data-clear controls

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 5.2, 6.0, 6.1
• Python 3.10, 3.11, 3.12, 3.13, 3.14, 3.15

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 SilkyMiddleware that returns a response without calling get_response will prevent Silk from running. If you use django.middleware.gzip.GZipMiddleware, place it before SilkyMiddleware.

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

Silk is locked down by default. Unauthenticated or non-staff requests to /silk/ return 404 Not Found — as if the app weren't mounted — so an accidental production deployment can't leak profiling data.

# To expose the UI publicly (internal dev-only, not recommended):
SILKY_AUTHENTICATION = False
SILKY_AUTHORISATION = False

# To change who is allowed in (default: user.is_staff):
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

Retention mode

By default Silk keeps the newest SILKY_MAX_RECORDED_REQUESTS rows (count-based). To keep a rolling time window instead — useful for spotting patterns across days — switch the mode and set a maximum age in minutes:

SILKY_GARBAGE_COLLECT_MODE = 'time'      # 'count' (default) | 'time' | 'both'
SILKY_MAX_RECORDED_TIME = 60 * 24 * 7    # minutes; keep the last 7 days

'both' applies the count cap and the age window together (whichever trims more). The count default is unchanged, so existing setups keep their current behaviour.

Trigger manually (e.g. from a cron job):

python manage.py silk_request_garbage_collect
# override per run:
python manage.py silk_request_garbage_collect --mode time --max-time 10080

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 ANALYZE on 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 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 (Node Version 24.15)
pnpm install || npm install
npm run watch

# For CSS Build
npm run build

# 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.