Observability Skill

Comprehensive guide to logging, debugging, profiling, and performance monitoring in Python applications.

When This Skill Activates

• Adding logging to code
• Debugging production issues
• Profiling performance bottlenecks
• Monitoring application metrics
• Analyzing stack traces
• Performance optimization
• Keywords: "logging", "debug", "profiling", "performance", "monitoring"

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Core Concepts

1. Structured Logging

Structured logging with JSON format for machine-readable logs and rich context.

Why Structured Logging?

• Machine-parseable (easy to search, filter, aggregate)
• Context-rich (attach metadata to log entries)
• Consistent format across services

Key Features:

• JSON-formatted logs
• Log levels (DEBUG, INFO, WARNING, ERROR, CRITICAL)
• Context logging with extra metadata
• Best practices for meaningful logs

Example:

import logging
import json

logger = logging.getLogger(__name__)
logger.info("User action", extra={
    "user_id": 123,
    "action": "login",
    "ip": "192.168.1.1"
})

See: docs/structured-logging.md for Python logging setup and patterns

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2. Debugging Techniques

Interactive debugging with pdb/ipdb and effective debugging strategies.

Tools:

• Print debugging - Quick and simple
• pdb - Python's built-in debugger
• ipdb - IPython-enhanced debugger
• Post-mortem debugging - Debug after crash

pdb Commands:

• n (next) - Execute current line
• s (step) - Step into function
• c (continue) - Continue execution
• p variable - Print variable value
• l - List source code
• q - Quit debugger

Example:

import pdb; pdb.set_trace()  # Debugger starts here

See: docs/debugging.md for interactive debugging patterns

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3. Profiling

CPU and memory profiling to identify performance bottlenecks.

Tools:

• cProfile - CPU profiling (built-in)
• line_profiler - Line-by-line CPU profiling
• memory_profiler - Memory usage analysis
• py-spy - Sampling profiler (no code changes)

cProfile Example:

python -m cProfile -s cumulative script.py

Profile Decorator:

import cProfile
import pstats

def profile(func):
    def wrapper(*args, **kwargs):
        profiler = cProfile.Profile()
        profiler.enable()
        result = func(*args, **kwargs)
        profiler.disable()
        stats = pstats.Stats(profiler)
        stats.sort_stats('cumulative')
        stats.print_stats(10)  # Top 10 functions
        return result
    return wrapper

@profile
def slow_function():
    # Your code here
    pass

See: docs/profiling.md for comprehensive profiling techniques

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4. Monitoring & Metrics

Performance monitoring, timing decorators, and simple metrics.

Timing Patterns:

• Timing decorator - Measure function execution time
• Context manager timer - Measure code block duration
• Performance assertions - Fail if too slow

Simple Metrics:

• Counters - Track event occurrences
• Histograms - Track value distributions

Example:

import time
from functools import wraps

def timer(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
        start = time.time()
        result = func(*args, **kwargs)
        duration = time.time() - start
        print(f"{func.__name__} took {duration:.2f}s")
        return result
    return wrapper

@timer
def process_data():
    # Your code here
    pass

See: docs/monitoring-metrics.md for stack traces, timers, and metrics

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5. Best Practices & Anti-Patterns

Debugging strategies and logging anti-patterns to avoid.

Debugging Best Practices:

1. Binary Search Debugging - Narrow down the problem area
2. Rubber Duck Debugging - Explain the problem to someone (or something)
3. Add Assertions - Catch bugs early
4. Simplify and Isolate - Reproduce with minimal code

Logging Anti-Patterns to Avoid:

• Logging sensitive data (passwords, tokens)
• Logging in loops (use counters instead)
• No context in error logs
• Inconsistent log formats
• Too verbose logging (noise)

See: docs/best-practices-antipatterns.md for detailed strategies

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Quick Reference

Tool	Use Case	Details
Structured Logging	Production logs	docs/structured-logging.md
pdb/ipdb	Interactive debugging	docs/debugging.md
cProfile	CPU profiling	docs/profiling.md
line_profiler	Line-by-line profiling	docs/profiling.md
memory_profiler	Memory analysis	docs/profiling.md
Timer decorator	Function timing	docs/monitoring-metrics.md
Context timer	Code block timing	docs/monitoring-metrics.md

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Logging Cheat Sheet

import logging

# Setup
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

# Usage
logger.debug("Debug message")       # Detailed diagnostic
logger.info("Info message")         # General information
logger.warning("Warning message")   # Warning (recoverable)
logger.error("Error message")       # Error (handled)
logger.critical("Critical message") # Critical (unrecoverable)

# With context
logger.info("User action", extra={"user_id": 123, "action": "login"})

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Debugging Cheat Sheet

# pdb
import pdb; pdb.set_trace()

# ipdb (enhanced)
import ipdb; ipdb.set_trace()

# Post-mortem (debug after crash)
import pdb, sys
try:
    # Your code
    pass
except Exception:
    pdb.post_mortem(sys.exc_info()[2])

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Profiling Cheat Sheet

# CPU profiling
python -m cProfile -s cumulative script.py

# Line profiling
kernprof -l -v script.py

# Memory profiling
python -m memory_profiler script.py

# Sampling profiler (no code changes)
py-spy top --pid 12345

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Progressive Disclosure

This skill uses progressive disclosure to prevent context bloat:

• Index (this file): High-level concepts and quick reference (<500 lines)
• Detailed docs: docs/*.md files with implementation details (loaded on-demand)

Available Documentation:

• docs/structured-logging.md - Logging setup, levels, JSON format, best practices
• docs/debugging.md - Print debugging, pdb/ipdb, post-mortem debugging
• docs/profiling.md - cProfile, line_profiler, memory_profiler, py-spy
• docs/monitoring-metrics.md - Stack traces, timing patterns, simple metrics
• docs/best-practices-antipatterns.md - Debugging strategies and logging anti-patterns

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Cross-References

Related Skills:

• error-handling-patterns - Error handling best practices
• python-standards - Python coding conventions
• testing-guide - Testing and debugging strategies
• performance-optimization - Performance tuning techniques

Related Tools:

• Python logging - Standard library logging module
• pdb/ipdb - Interactive debuggers
• cProfile - CPU profiling
• memory_profiler - Memory analysis
• py-spy - Sampling profiler

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Key Takeaways

1. Use structured logging - JSON format for machine-readable logs
2. Log at appropriate levels - DEBUG < INFO < WARNING < ERROR < CRITICAL
3. Include context - Add metadata to logs (user_id, request_id, etc.)
4. Don't log sensitive data - Passwords, tokens, PII
5. Use pdb/ipdb for debugging - Interactive debugging is powerful
6. Profile before optimizing - Measure to find real bottlenecks
7. Use cProfile for CPU profiling - Identify slow functions
8. Use line_profiler for line-level profiling - Fine-grained analysis
9. Use memory_profiler for memory leaks - Track memory usage
10. Time critical sections - Decorator or context manager
11. Binary search debugging - Narrow down problem area
12. Simplify and isolate - Reproduce with minimal code

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Hard Rules

FORBIDDEN:

• Logging sensitive data (passwords, tokens, API keys) at any level
• Using print() for production logging (MUST use structured logging)
• Swallowing exceptions silently without logging

REQUIRED:

• All errors MUST be logged with context (what failed, input summary, stack trace)
• Log levels MUST be used correctly: DEBUG for dev, INFO for operations, WARNING for recoverable issues, ERROR for failures
• Performance-critical paths MUST have timing instrumentation
• All external calls MUST log duration and status