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Always measure performance impact before implementing optimizations and focus on changes with meaningful benefits. Python’s performance characteristics can be unintuitive - optimizations that seem beneficial might have minimal impact, while simple changes can yield significant improvements.

Key optimization patterns to consider:

1. Cache attribute lookups in loops: ```python

   Inefficient - repeated attribute lookups

   missing_instances = [i for i in instances if not self.field.is_cached(i)]

Optimized - single lookup cached in local variable

is_cached = self.field.is_cached missing_instances = [i for i in instances if not is_cached(i)]

2. **Avoid expensive operations** like materializing entire stack traces with `inspect.stack()`. Use targeted approaches instead:
```python
# Expensive - materializes all stack frames
if "aggregate" in {frame.function for frame in inspect.stack()}:
    # ...

# More efficient - only access needed frames
frame = inspect.currentframe()
for _ in range(5):
    try:
        frame = frame.f_back
    except AttributeError:
        break
else:
    is_aggregate = 'aggregate' in frame.function
del frame  # Avoid reference cycles

1. Know your standard library operations. For dictionary filtering, copy() and pop() can be more efficient than comprehensions: ```python

   ~110ns

   p = params.copy() p.pop(“q”, None)

~240ns

{k: v for k, v in params.items() if k != “q”} ```

Always confirm your optimizations with timings (using timeit or similar tools) on representative data volumes to ensure the changes provide meaningful benefits.