7efd1368d1
Domain papers distilled from python-numbers-everyone-should-know: - async-overhead: 1,400x sync vs async overhead - collection-membership: 200x set vs list at 1000 items - json-serialization: 8x orjson vs stdlib - exception-flow: 6.5x exception overhead (try/except free) - string-formatting: f-strings > % > .format() - memory-slots: 69% memory reduction with __slots__ - import-optimization: 100ms+ for heavy packages - database-patterns: 98% commit overhead in SQLite RULEBOOK.md: ~200 token distillation for coding subagents 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
112 lines
3.0 KiB
Markdown
112 lines
3.0 KiB
Markdown
# Exception Flow: Performance Patterns
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**Domain:** Exception handling overhead
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**Source:** python-numbers-everyone-should-know benchmarks (Python 3.14.2, Apple Silicon)
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---
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## TL;DR
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- **try/except with no exception**: Nearly free (1.1 ns overhead)
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- **Raising an exception**: 6.5x slower than the happy path (139 ns vs 21.5 ns)
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- **EAFP is fine when exceptions are rare** (<5% of calls)
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- **Use LBYL for expected failures** (dict key lookup, file existence)
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- **Never use exceptions for normal control flow**
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---
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## The Numbers
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### Happy Path (No Exception Raised)
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| Operation | Time | Overhead vs Baseline |
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|-----------|------|---------------------|
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| Function call (no try/except) | 20.4 ns | baseline |
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| try/except (no exception raised) | 21.5 ns | +1.1 ns (+5%) |
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| try/except ValueError (specific) | 22.9 ns | +2.5 ns (+12%) |
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| try/except/finally | 22.1 ns | +1.7 ns (+8%) |
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**Key insight:** The try block itself is essentially free.
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### Sad Path (Exception Raised)
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| Operation | Time | Slowdown vs Happy Path |
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|-----------|------|----------------------|
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| raise + catch ValueError | 139 ns | **6.5x slower** |
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| raise + catch (base Exception) | 140 ns | 6.5x slower |
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| raise + catch custom exception | 146 ns | 6.8x slower |
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| raise + catch with `as e` | 148 ns | 6.9x slower |
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**Key insight:** The 6.5x overhead comes from:
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1. Creating the exception object (~40 ns)
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2. Capturing the traceback (~70 ns)
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3. Stack unwinding and handler lookup (~30 ns)
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---
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## EAFP vs LBYL: When to Use Which
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### EAFP (Easier to Ask Forgiveness than Permission)
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```python
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try:
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value = data[key]
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except KeyError:
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value = default
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```
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**Use when:** Exceptions are rare (<5% of calls)
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### LBYL (Look Before You Leap)
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```python
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if key in data:
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value = data[key]
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else:
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value = default
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```
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**Use when:** The failure case is common (>15% of calls)
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### Crossover Point
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**Rule of thumb:** If exceptions occur more than 15% of the time, use LBYL.
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### dict.get() Beats Both
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```python
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# Best: Use .get() - 26.3 ns, no exception possible
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config = settings.get('database', {})
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```
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---
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## Practical Rules for Coding Agents
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1. **try/except blocks are free** - don't avoid them for performance
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2. **Raising exceptions costs 6.5x** - only raise for truly exceptional cases
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3. **Use .get() for dicts** - beats both EAFP and LBYL
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4. **Return Optional for expected missing** - not exceptions
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5. **EAFP for file ops** - TOCTOU protection matters more than perf
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6. **LBYL when failures are common** (>15% of calls)
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7. **Never use exceptions for control flow**
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---
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## Summary
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| Scenario | Recommendation |
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|----------|----------------|
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| Exception rate <5% | EAFP (try/except) |
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| Exception rate >15% | LBYL (check first) |
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| Dict key lookup | Use `.get()` |
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| Optional return value | Return `None`, not exception |
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| File operations | EAFP (TOCTOU protection) |
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| Control flow | Never use exceptions |
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**The core insight:** try/except is free; raising is not. Design APIs to minimize raises, not to avoid try blocks.
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---
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*Benchmark source: python-numbers-everyone-should-know*
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