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How to Use __slots__ in Python Classes for Memory Efficiency
Defines classes with __slots__ to prevent dynamic attribute creation and reduce memory usage, including inheritance with additional slots.
```python
class Person:
__slots__ = ("name", "age")
def __init__(self, name: str, age: int):
self.name = name
self.age = age
def greet(self) -> str:
return f"Hi, I'm {self.name} and I'm {self.age} years old."
class Employee(Person):
__slots__ = ("role",)
def __init__(se…How to Implement Slowly Changing Dimension Type 2 History in Python
Build a type-2 slowly changing dimension pipeline that closes old records and opens new ones when customer data changes.
from datetime import datetime, timedelta
def apply_scd_type2(records, current_date):
"""Returns active records after inserting new records with type-2 history."""
history = []
active = {}
for record in records:
key = record["customer_id"]
if key in active:
active[key]["end…How to Reduce Instance Memory with __slots__ in Python
Demonstrates that classes with __slots__ use less memory per instance than regular classes because they skip the instance __dict__.
class SlottedPoint:
__slots__ = ('x', 'y', 'z')
def __init__(self, x, y, z):
self.x = x
self.y = y
self.z = z
class RegularPoint:
def __init__(self, x, y, z):
self.x = x
self.y = y
self.z = z
if __name__ == "__main__":
regular = RegularPoint(1, 2, 3)…Implement a Multi-Level Cache with L1 Memory and L2 Redis in Python
This code implements a simple multi-level cache with an in-process L1 cache (via functools.lru_cache) and a mock Redis L2 cache with TTL, falling back to a slow computation on misses.
import time
from functools import lru_cache
class MockRedis:
def __init__(self):
self.store = {}
def get(self, key):
return self.store.get(key, None)
def set(self, key, value, ttl=5):
self.store[key] = (value, time.time() + ttl)
def get_ttl(self, key):
value, expiry…How to Implement an Adaptive Rate Limiter in Python
Build an adaptive rate limiter that adjusts request intervals dynamically based on recent error rates, slowing down when failures spike.
import time
import random
class AdaptiveRateLimiter:
"""Simple adaptive rate limiter that reduces requests when error rate is high."""
def __init__(self, min_interval=0.1, max_interval=2.0, error_threshold=0.3):
self.min_interval = min_interval
self.max_interval = max_interval
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