Django performance monitoring with django-silk

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                  INSTALLED_APPS += ['silk']

MIDDLEWARE += ['silk.middleware.SilkyMiddleware']

# Silk configuration
SILKY_PYTHON_PROFILER = True
SILKY_PYTHON_PROFILER_BINARY = True
SILKY_MAX_REQUEST_BODY_SIZE = -1  # Silk takes anything
SILKY_MAX_RESPONSE_BODY_SIZE = -1
SILKY_INTERCEPT_PERCENT = 100  # Profile all requests in dev

                  if settings.DEBUG:
    urlpatterns += [path('silk/', include('silk.urls', namespace='silk'))]

                  from silk.profiling.profiler import silk_profile


@silk_profile(name='Complex Calculation')
def complex_calculation(data):
    # Expensive operation
    result = process(data)
    return result

Django Silk profiles SQL queries, HTTP requests, and Python code. I install it in development to identify bottlenecks. It shows query counts, execution times, and duplicate queries per request. The web UI visualizes performance data. I use @silk_profile() decorator for specific functions. Silk helps find N+1 queries and slow endpoints. For production, I use APM tools like New Relic or Datadog. Silk's detailed insights make optimization straightforward. I remove it or disable in production to avoid overhead.

Priya Sharma

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Django performance monitoring with django-silk

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