合并排序因其可靠性和可预测的性能而在排序算法中脱颖而出。让我们通过清晰的示例和实际应用来分解它在 Python 中的工作原理。
了解归并排序的核心概念
归并排序遵循分而治之的方法：
1. 将列表一分为二
2. 递归地对每一半进行排序
3. 合并已排序的两半
下面是演示这些步骤的基本实现：
def merge_sort(arr):
# Base case: lists of length 0 or 1 are already sorted
if len(arr) <= 1:
return arr
# Divide the array into two halves
mid = len(arr) // 2
left = arr[:mid]
right = arr[mid:]
# Recursively sort each half
left = merge_sort(left)
right = merge_sort(right)
# Merge the sorted halves
return merge(left, right)
def merge(left, right):
result = []
left_idx, right_idx = 0, 0
# Compare elements from both lists and merge in sorted order
while left_idx < len(left) and right_idx < len(right):
if left[left_idx] <= right[right_idx]:
result.append(left[left_idx])
left_idx += 1
else:
result.append(right[right_idx])
right_idx += 1
# Add remaining elements from either list
result.extend(left[left_idx:])
result.extend(right[right_idx:])
return result
# Example usage
numbers = [64, 34, 25, 12, 22, 11, 90]
sorted_numbers = merge_sort(numbers)
print(f"Original: {numbers}")
print(f"Sorted: {sorted_numbers}")添加视觉进度跟踪
让我们修改我们的实现，逐步展示算法的工作原理：
def merge_sort_with_steps(arr, depth=0):
print(" " * depth + f"Splitting: {arr}")
if len(arr) <= 1:
return arr
mid = len(arr) // 2
left = arr[:mid]
right = arr[mid:]
left = merge_sort_with_steps(left, depth + 1)
right = merge_sort_with_steps(right, depth + 1)
result = merge(left, right)
print(" " * depth + f"Merged: {result}")
return result
# Example with smaller list for clarity
numbers = [38, 27, 43, 3, 9, 82, 10]
print("Starting merge sort...")
sorted_numbers = merge_sort_with_steps(numbers)
print(f"Final result: {sorted_numbers}")此版本显示了每个拆分和合并作，可帮助您了解算法的进展情况。
处理自定义对象
实际应用程序通常需要对更复杂的数据进行排序。以下是使用归并排序对对象进行排序的方法：
class Student:
def __init__(self, name, grade):
self.name = name
self.grade = grade
def __repr__(self):
return f"Student(name='{self.name}', grade={self.grade})"
def merge_sort_objects(arr, key_func):
if len(arr) <= 1:
return arr
mid = len(arr) // 2
left = arr[:mid]
right = arr[mid:]
left = merge_sort_objects(left, key_func)
right = merge_sort_objects(right, key_func)
return merge_objects(left, right, key_func)
def merge_objects(left, right, key_func):
result = []
left_idx, right_idx = 0, 0
while left_idx < len(left) and right_idx < len(right):
if key_func(left[left_idx]) <= key_func(right[right_idx]):
result.append(left[left_idx])
left_idx += 1
else:
result.append(right[right_idx])
right_idx += 1
result.extend(left[left_idx:])
result.extend(right[right_idx:])
return result
# Example usage with Student objects
students = [
Student("Alice", 85),
Student("Bob", 92),
Student("Charlie", 78),
Student("Diana", 95),
Student("Eve", 88)
]
# Sort by grade
sorted_by_grade = merge_sort_objects(students, lambda x: x.grade)
print("\nSorted by grade:")
for student in sorted_by_grade:
print(f"{student.name}: {student.grade}")
# Sort by name
sorted_by_name = merge_sort_objects(students, lambda x: x.name)
print("\nSorted by name:")
for student in sorted_by_name:
print(f"{student.name}: {student.grade}")优化内存使用情况
对于大型列表，我们可以实现使用较少内存的就地版本：
def merge_sort_inplace(arr, start=0, end=None):
if end is None:
end = len(arr)
if end - start <= 1:
return
mid = (start + end) // 2
merge_sort_inplace(arr, start, mid)
merge_sort_inplace(arr, mid, end)
merge_inplace(arr, start, mid, end)
def merge_inplace(arr, start, mid, end):
# Create temporary arrays
left = arr[start:mid]
right = arr[mid:end]
i = start # Index for main array
l = 0 # Index for left array
r = 0 # Index for right array
# Merge elements back into main array
while l < len(left) and r < len(right):
if left[l] <= right[r]:
arr[i] = left[l]
l += 1
else:
arr[i] = right[r]
r += 1
i += 1
# Add remaining elements
while l < len(left):
arr[i] = left[l]
l += 1
i += 1
while r < len(right):
arr[i] = right[r]
r += 1
i += 1
# Example usage with memory tracking
import tracemalloc
def demonstrate_memory_usage():
# Generate a larger list for testing
import random
data = [random.randint(1, 1000) for _ in range(10000)]
# Track memory for regular merge sort
tracemalloc.start()
sorted_data1 = merge_sort(data.copy())
snapshot1 = tracemalloc.get_snapshot()
total1 = sum(stat.size for stat in snapshot1.statistics('lineno'))
tracemalloc.stop()
# Track memory for in-place merge sort
tracemalloc.start()
data_copy = data.copy()
merge_sort_inplace(data_copy)
snapshot2 = tracemalloc.get_snapshot()
total2 = sum(stat.size for stat in snapshot2.statistics('lineno'))
tracemalloc.stop()
print(f"Regular merge sort memory usage: {total1 / 1024:.2f} KB")
print(f"In-place merge sort memory usage: {total2 / 1024:.2f} KB")
demonstrate_memory_usage()实际应用程序：处理日志文件
下面是使用合并排序处理服务器日志条目的实际示例：
from datetime import datetime
class LogEntry:
def __init__(self, timestamp, level, message):
self.timestamp = timestamp
self.level = level
self.message = message
def __repr__(self):
return f"{self.timestamp.strftime('%Y-%m-%d %H:%M:%S')} [{self.level}] {self.message}"
def process_logs(log_files):
"""Merge and sort logs from multiple files."""
all_logs = []
# Read logs from each file
for file_name in log_files:
with open(file_name) as f:
for line in f:
# Parse log line (simplified format)
parts = line.strip().split(' ', 2)
if len(parts) == 3:
timestamp = datetime.strptime(parts[0], '%Y-%m-%d')
level = parts[1]
message = parts[2]
all_logs.append(LogEntry(timestamp, level, message))
# Sort logs by timestamp
sorted_logs = merge_sort_objects(all_logs, lambda x: x.timestamp)
return sorted_logs
# Example usage:
"""
Sample log files content format:
2024-01-15 ERROR Database connection failed
2024-01-15 INFO Server started
2024-01-15 WARN High memory usage
"""
log_files = ['server1.log', 'server2.log', 'server3.log']
# sorted_logs = process_logs(log_files) # Commented out as files don't exist性能注意事项
归并排序具有几个特性，使其适用于不同的方案：
1. 时间复杂度： O（n log n） — 始终如一，与输入无关
2. 空间复杂度：O（n） 为普通版本，O（1） 为原位
3. 稳定性：保持相等元素的相对顺序
这是一个简单的基准测试函数：
import time
import random
def benchmark_sorting(size=10000, runs=5):
times = []
for _ in range(runs):
# Generate random data
data = [random.randint(1, 10000) for _ in range(size)]
# Time the sort
start = time.perf_counter()
merge_sort(data.copy())
end = time.perf_counter()
times.append(end - start)
avg_time = sum(times) / len(times)
print(f"Average time for {size} elements: {avg_time:.4f} seconds")
print(f"Best time: {min(times):.4f} seconds")
print(f"Worst time: {max(times):.4f} seconds")
# Run benchmark
benchmark_sorting()结论
当您需要时，合并排序是一个很好的选择：
- 可预测的性能（始终为 O（n log n））
- 稳定性（保持相等元素的顺序）
- 外部排序（处理大文件时）
此处提供的代码示例为在您自己的项目中实现归并排序奠定了坚实的基础。从基本实施开始，然后随着需求的增长迁移到优化版本。
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