Performance Considerations¶
This document outlines performance considerations when using python-newtype and provides guidance on optimizing your code.
Memory Usage¶
python-newtype is designed to be memory-efficient, with minimal overhead compared to the base types being wrapped. However, there are a few things to keep in mind:
- Each wrapped instance maintains a reference to its base class
- Additional attributes and methods will consume extra memory
- Method interception adds a small overhead for intercepted calls
Performance Impact¶
Method Call Overhead¶
When using NewType, there is a small performance overhead for method calls due to the interception mechanism:
from newtype import NewType
import time
# Regular string
regular_str = "hello" * 1000
start = time.time()
for _ in range(10000):
regular_str.upper()
regular_time = time.time() - start
# NewType string
class EnhancedStr(NewType(str)):
pass
enhanced_str = EnhancedStr("hello" * 1000)
start = time.time()
for _ in range(10000):
enhanced_str.upper()
newtype_time = time.time() - start
print(f"Regular string time: {regular_time:.4f}s")
print(f"NewType string time: {newtype_time:.4f}s")
The overhead is typically negligible for most applications but may become noticeable in performance-critical loops.
Optimization Tips¶
1. Minimize Method Interception¶
2. Avoid Unnecessary Wrapping¶
Don't wrap objects unnecessarily:
# Less efficient
class SimpleList(NewType(list)):
pass
# More efficient - use regular list if no new functionality is needed
regular_list = []
Benchmarking¶
Always benchmark your specific use case to ensure performance meets your requirements. Use Python's timeit
module or a profiling tool like cProfile for accurate measurements.
Example benchmarking code:
import timeit
def benchmark_regular_vs_newtype():
setup = """
from newtype import NewType
class EnhancedStr(NewType(str)):
def reverse(self):
return self[::-1]
regular_str = "hello" * 100
enhanced_str = EnhancedStr("hello" * 100)
"""
regular_code = "regular_str.upper()"
enhanced_code = "enhanced_str.upper()"
regular_time = timeit.timeit(regular_code, setup=setup, number=100000)
enhanced_time = timeit.timeit(enhanced_code, setup=setup, number=100000)
print(f"Regular string: {regular_time:.4f}s")
print(f"Enhanced string: {enhanced_time:.4f}s")
Memory Profiling¶
For memory-critical applications, use memory profiling tools: