使用numba的姿势不正确反而导致性能下降
numba能够极大的提高python在计算方面的性能。是不是所有的python代码上，都可以加上numba.jit装饰器？答案是否定的。
示例
环境
python3.6 fedora pymysql
示例
很常见的例子，从数据库从查询一千条数据，再进行简单的格式转换。
#coding=utf-8
import time
from numba import jit
import pymysql
conn = pymysql.connect("192.168.10.125", "username", "password", "db_name")
#@jit #位置1
def test_for():
c =conn.cursor()
c.execute("select * from user limit 1000")
start = time.time()
for a in c.fetchall():
to_dict(a, c.description)
end = time.time()
print("cost_seconds:", end-start)
#@jit #位置2
def to_dict(row, description):
"""
记录转为字典
"""
item = dict()
for i, field in enumerate(description):
field_name = field[0]
item[field_name] = row[i]
return item
test_for()
结果
python3 0.0033788681030273438 python3 +位置1jit 0.08921098709106445 python3 + 位置1jit+位置2jit 3.846426010131836 pypy 0.0065860748291015625
对比结果，发现如果是非计算问题，引入即时编译，反而导致性能下降。实际python在非计算问题上性能很好。
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