为了精确翻译SQL，DeepSeek采用了复杂的字典作为循环中列表的元素，以达到用列名访问的效果，这降低了效率。
让他把b的元素由字典改成列表，用索引0代替cnt,1代替bit,2代替bit2,去掉判断重复逻辑，代new_rows用生成式代替两重for循环，while循环每步结束若new_rows有值，用它覆盖b，而不是扩展，不做别的。
python
def main():
# with n as (select level n from dual connect by level<=5)
n_list = [i for i in range(1, 8)] # level从1开始
# A as (select n1.n x, n2.n y, power(2,rownum-1) bit from n n1,n n2)
A = []
bit_value = 1
for x in n_list:
for y in n_list:
A.append({'x': x, 'y': y, 'bit': bit_value})
bit_value *= 2
# C as (select a1.bit,a1.x,a1.y, sum(a2.bit) bit2 from a a1,a a2 where (a1.x-a2.x)*(a1.y-a2.y)<=0 group by a1.bit,a1.x,a1.y)
C = []
for a1 in A:
bit2_sum = 0
for a2 in A:
if (a1['x'] - a2['x']) * (a1['y'] - a2['y']) <= 0:
bit2_sum += a2['bit']
C.append({
'bit': a1['bit'],
'x': a1['x'],
'y': a1['y'],
'bit2': bit2_sum
})
# 递归CTE b(cnt,bit,bit2) - 用列表代替字典
# 初始部分: select 1,c.bit,c.bit2 from c
b = [[1, c_row['bit'], c_row['bit2']] for c_row in C]
# 递归部分: 模拟递归CTE
changed = True
while changed:
changed = False
# 用生成式代替两重for循环
new_rows = [
[b_row[0] + 1, b_row[1] + c_row['bit'], b_row[2] & c_row['bit2']]
for b_row in b
for c_row in C
if c_row['bit'] > b_row[1] and (b_row[2] & c_row['bit']) > 0
]
if new_rows:
b = new_rows # 覆盖而不是扩展
changed = True
# 找到cnt最大的行: select * from (select b.*,rank() over(order by cnt desc) rnk from b) where rnk=1
if not b:
return
max_cnt = max(row[0] for row in b)
max_rows = [row for row in b if row[0] == max_cnt]
# 为每个最大集合生成点坐标字符串
results = []
for r in max_rows:
points = []
for a_row in A:
# where bitand(r.bit, a.bit) > 0
if r[1] & a_row['bit'] > 0:
points.append(f"({a_row['x']},{a_row['y']})")
# listagg within group(order by r.bit) - 这里按bit值排序可能没有意义，改为按坐标排序
points.sort()
results.append(''.join(points))
# 输出结果
print(f"最大集合大小: {max_cnt}")
print(f"找到 {len(results)} 个最大集合:")
#for i, result in enumerate(results, 1):
# print(f"{i}: {result}")
if __name__ == "__main__":
main()执行结果又快了0.2秒，同时节省一半内存
C:\d>timer64 python sql2py5.py
最大集合大小: 13
找到 924 个最大集合:
Kernel Time = 0.015 = 0%
User Time = 2.468 = 98%
Process Time = 2.484 = 99% Virtual Memory = 96 MB
Global Time = 2.493 = 100% Physical Memory = 101 MB
C:\d>timer64 python sql2py4b.py
最大集合大小: 13
找到 924 个最大集合:
Kernel Time = 0.062 = 2%
User Time = 2.578 = 93%
Process Time = 2.640 = 95% Virtual Memory = 145 MB
Global Time = 2.756 = 100% Physical Memory = 149 MB用pypy运行则差距更大
C:\d>timer64 pypy/pypy sql2py5.py
鏈€澶ч泦鍚堝ぇ灏? 13
鎵惧埌 924 涓渶澶ч泦鍚?
Kernel Time = 0.031 = 3%
User Time = 0.718 = 71%
Process Time = 0.750 = 74% Virtual Memory = 114 MB
Global Time = 1.008 = 100% Physical Memory = 111 MB
C:\d>timer64 pypy/pypy sql2py4b.py
鏈€澶ч泦鍚堝ぇ灏? 13
鎵惧埌 924 涓渶澶ч泦鍚?
Kernel Time = 0.078 = 4%
User Time = 1.437 = 88%
Process Time = 1.515 = 92% Virtual Memory = 328 MB
Global Time = 1.630 = 100% Physical Memory = 322 MB