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我使用 pandas.to_sql() 将数据帧插入 PostgreSQL。当我在 localhost 中运行代码并且也在 localhost 中运行数据库时，大约需要 5 分钟才能完成。但是当代码和数据库不在同一台计算机上时，大约需要 40 分钟才能完成。

两台计算机具有相同的配置和硬件、软件，并且它们在同一个局域网中，通过一个siwtch连接。请帮我！

version:
pandas: 0.21.0
psycopg2: 2.7.3.2
PostgreSQL: 9.5

代码如下：

HOST = '192.168.1.18'
USER = 'test'
PASSWORD = '123'
DATABASE = 'test'
import datetime as dt
import numpy as np
from sqlalchemy import create_engine
import sqlalchemy
engine = create_engine("postgresql://%s:%s@%s:5432/%s" % (USER, PASSWORD, HOST, DATABASE))
import time
st = time.time()
sourceData.to_sql('test', con=engine, if_exists='append', index=True, chunksize= 10000, dtype={'DATETIME': sqlalchemy.types.TIMESTAMP, 'CODES': sqlalchemy.types.VARCHAR(255)})
et = time.time()
print((et-st)/60)


HOST = '192.168.1.19'
USER = 'postgres'
PASSWORD = '123'
DATABASE = 'postgres'
engine = create_engine("postgresql://%s:%s@%s:5432/%s" % (USER, PASSWORD, HOST, DATABASE))
st = time.time()
sourceData.to_sql('test', con=engine, if_exists='append', index=True, chunksize= 10000, dtype={'DATETIME': sqlalchemy.types.TIMESTAMP, 'CODES': sqlalchemy.types.VARCHAR(255)})
et = time.time()
print((et-st)/60)

sourceData 是一个 pandas.DataFrame，它有 3000000 行，如下所示：

                        Q_C81
DATETIME   CODES             
2013-01-04 000001.SZ   0.1828
           000002.SZ   0.1150
           000004.SZ   0.0000
           000005.SZ   0.0000
           000006.SZ  -1.5936
           000007.SZ  -1.9031
           000008.SZ   0.0000
           000009.SZ -74.5152
           000010.SZ   0.0000
           000011.SZ   0.0000
           000012.SZ  -7.0324
           000014.SZ   0.0000
           000016.SZ   2.5925
           000017.SZ   0.0000
           000018.SZ   0.0000
           000019.SZ   0.0000
           000020.SZ   0.0000
           000021.SZ -82.1918
           000022.SZ  -2.3582
           000023.SZ   0.0000
           000024.SZ  -0.2810
           000025.SZ   0.0000
           000026.SZ  -5.3294
           000027.SZ  -1.7320
           000028.SZ   1.2884
           000029.SZ   0.0000
           000030.SZ   0.0000
           000031.SZ   0.6957
           000032.SZ   0.0000
           000033.SZ   0.0000
                       ...
2013-03-01 002621.SZ -10.9103
           002622.SZ -50.2930
           002623.SZ -35.4200
           002624.SZ -34.8826
           002625.SZ -36.2222
           002626.SZ  -0.2656
           002627.SZ -13.5603
           002628.SZ   9.0788
4

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