Implementation of Python memory pool mechanism

1. What is Python memory pool

Python Memory Pool is a memory management mechanism designed by the Python interpreter to improve memory allocation efficiency. It reduces the performance overhead caused by frequent calls to the system malloc/free by pre-allocating and maintaining a certain number of memory blocks.

# Simple example to show the memory pool effectimport sys

a = 1
b = 1
print(a is b)  # True, small integers use memory pool
c = 1000
d = 1000
print(c is d)  # It may be False in Python 3.7+, and large integers may not use memory pools

2. How Python memory pool works

2.1 The hierarchical structure of memory pools

Python memory management is divided into 3 levels:

• Level 0: Operating system native memory allocator (malloc/free)
• Level 1: Python's own memory allocator (PyMem_API)
• Level 2: Object-specific allocator (int/dict, etc.)

2.2 Small object memory pool

For small objects (default <= 512 bytes), Python uses memory pooling mechanism:

• block: The smallest unit in the memory pool, the size is fixed to 8 bytes
• pool: consists of multiple blocks, each pool is usually 4KB
• arena: consists of multiple pools, usually 256KB

# View object memory usageimport sys

lst = [1, 2, 3]
print((lst))  # The size of the list object itselfprint((lst) + sum((x) for x in lst))  # Total occupancy

3. Specific implementation of Python memory pool

3.1 Integer Object Pool

Python preallocates small integers (-5 to 256):

# Small integer pool examplea = 100
b = 100
print(id(a) == id(b))  # True

x = 1000
y = 1000
print(id(x) == id(y))  # False is usually found in Python 3.7+

3.2 String residency mechanism

Python will reside (interning) on ​​strings that meet the criteria:

# String residency examples1 = "hello"
s2 = "hello"
print(s1 is s2)  # True

s3 = "hello world!"
s4 = "hello world!"
print(s3 is s4)  # False, length exceeds limit and does not reside

3.3 Empty tuple multiplexing

Python reuses empty tuple objects:

t1 = ()
t2 = ()
print(t1 is t2)  # True

4. Performance impact of memory pool

4.1 Advantages

• Reduce memory fragmentation: Allocated by fixed-size block
• Improve distribution speed: Avoid frequent calls to the system malloc
• Reduce the risk of memory leaks: The life cycle of the object is more controllable

4.2 Disadvantages

• Memory may be wasted: Allocated memory may not be fully utilized
• Not suitable for large objects: Large objects will use the system allocator directly

# Memory pool performance testimport time

def test_allocation():
    start = ()
    for _ in range(1000000):
        _ = {}
    print(f"time consuming: {() - start:.4f}Second")

test_allocation()

5. Optimization suggestions in actual development

5.1 Utilizing object reuse

# Bad writingdef process_data(data):
    temp = []
    for item in data:
        (process_item(item))
    return temp

# Good writing - Pre-allocated listdef process_data_optimized(data):
    result = [None] * len(data)  # Pre-allocated    for i, item in enumerate(data):
        result[i] = process_item(item)
    return result

5.2 Avoid unnecessary object creation

# Bad writingdef concatenate_strings(words):
    result = ""
    for word in words:
        result += word  # Create a new string every time    return result

# Good writing - use joindef concatenate_strings_optimized(words):
    return "".join(words)

5.3 Using appropriate data structures

# Use deque to insert and delete a large number of dequesfrom collections import deque

dq = deque()
(1)  #Efficiency(2)  #Efficiency

6. Memory pool related tools

6.1 Memory Analysis Tool

# Use tracemalloc to analyze memoryimport tracemalloc

()

# Execute some codedata = [x for x in range(10000)]

snapshot = tracemalloc.take_snapshot()
top_stats = ('lineno')

for stat in top_stats[:5]:
    print(stat)

6.2 Garbage recycling control

import gc

# Manually trigger garbage collection()

# Disable/enable GC()
# Execute critical performance code()

7. Summary

Python memory pools are a key component of Python's efficient memory management, and understanding how it works can help:

• Writing more efficient Python code
• Avoid common memory usage traps
• Better diagnose memory-related performance issues
• Design more reasonable data structures and algorithms

In actual development, data structures and algorithms should be designed reasonably in combination with memory pool characteristics to achieve optimal performance.

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