silk-debug

name silk-debug description Analyze Django Silk profiling data to debug slow requests, detect N+1 queries, and optimize database performance. Use when analyzing request IDs, investigating slow endpoints, or optimizing query performance. allowed-tools Bash(python:*), Bash(.venv/bin/python:*), Read, Grep Silk Debug Tool A CLI tool for analyzing Django Silk profiling data to debug slow requests, detect N+1 queries, and optimize database performance. Tool Location .venv/bin/python scripts/silk_debug.py Quick Reference Analyze a Specific Request When given a Silk request ID (UUID), use --full for comprehensive analysis: .venv/bin/python scripts/silk_debug.py <request_id> --full This shows: Request info (path, method, status, time, query count) Duplicate/similar queries (N+1 detection) Slow queries (>5ms by default) Queries grouped by table Query execution timeline Python cProfile data (if enabled) List and Filter Requests # List recent requests .venv/bin/python scripts/silk_debug.py --list # Sort by different criteria .venv/bin/python scripts/silk_debug.py --list -- sort queries # Most queries .venv/bin/python scripts/silk_debug.py --list -- sort duration # Slowest total time .venv/bin/python scripts/silk_debug.py --list -- sort db_time # Most DB time # Filter requests .venv/bin/python scripts/silk_debug.py --list --path /api/events --min-queries 20 .venv/bin/python scripts/silk_debug.py --list --method POST --min-time 100 Aggregate Analysis # Overall statistics .venv/bin/python scripts/silk_debug.py --stats # Endpoint summary (grouped by path pattern, shows P95) .venv/bin/python scripts/silk_debug.py --endpoints # Find slow endpoints .venv/bin/python scripts/silk_debug.py --slow-endpoints --slow-endpoint-threshold 100 Interpreting Results N+1 Query Detection When you see duplicate queries like: 🔴 15x similar queries: SELECT "events_ticket"."id" FROM "events_ticket" WHERE "events_ticket"."event_id" = '<UUID>' This indicates an N+1 problem. Fix with: select_related() for ForeignKey fields prefetch_related() for reverse relations or M2M fields Slow Queries Common causes of slow queries: COUNT on complex DISTINCT : Pagination wrapping complex visibility subqueries Fix: Materialize IDs in Python first, then filter with simple IN clause Missing indexes : Full table scans Fix: Add database indexes on filtered/joined columns Complex JOINs : Multiple related tables Fix: Optimize query structure or denormalize if appropriate Timeline Analysis The timeline shows query execution order with visual bars: 1. + 0.0ms [ 2.5ms] █ "accounts_reveluser" 7. + 60.2ms [ 2.8ms] █ "__count" Look for: Large gaps between queries (indicates Python processing time) Queries that could run in parallel but are sequential Expensive queries that block subsequent operations Common Optimization Patterns Expensive COUNT with DISTINCT When you see: SELECT COUNT ( * ) FROM ( SELECT DISTINCT ... complex subquery ...) Fix by materializing IDs: # Before (slow COUNT) qs = Event.objects.for_user(user). filter (...).distinct() # After (fast COUNT) event_ids = list (Event.objects.for_user(user).values_list( "id" , flat= True ).distinct()) qs = Event.objects.full(). filter (id__in=event_ids) Redundant Visibility Checks When the same for_user() query appears multiple times: Create a method that accepts already-checked objects Cache visibility results within the request Batch Operations When creating multiple objects: Use bulk_create() instead of individual .save() calls Fetch shared data (settings, related objects) once before the loop Send notifications in batches, not per-item CLI Options Reference Single Request Analysis --full, -f : Run all analyses --duplicates, -d : Show N+1 candidates --slow, -s : Show slow queries --slow-threshold N : Slow query threshold in ms (default: 5) --tables, -t : Group queries by table --timeline : Show execution timeline --traceback, -tb : Show code locations for duplicates --profile, -prof : Show Python cProfile data Listing and Filtering --list, -l : List requests --limit N : Number of results (default: 20) --sort {recent,queries,duration,db_time} : Sort order --path, -p : Filter by path (contains) --method, -m : Filter by HTTP method --status : Filter by status code --min-queries N : Minimum query count --min-time N : Minimum response time (ms) --min-db-time N : Minimum DB time (ms) Aggregate Views --stats : Show aggregate statistics --endpoints : Show endpoint summary with P95 --slow-endpoints : Group slow requests by endpoint --slow-endpoint-threshold N : Threshold in ms (default: 200) --min-count N : Minimum requests for endpoint summary