这是Python 3的COPY FROM的二进制等效文件：
from io import BytesIO
from struct import pack
import psycopg2
# Two rows of data; "id" is not in the upstream data source
# Columns: node, ts, val1, val2
data = [(23253, 342, -15.336734, 2494627.949375),
(23256, 348, 43.23524, 2494827.949375)]
conn = psycopg2.connect("dbname=mydb user=postgres")
curs = conn.cursor()
# Determine starting value for sequence
curs.execute("SELECT nextval('num_data_id_seq')")
id_seq = curs.fetchone()[0]
# Make a binary file object for COPY FROM
cpy = BytesIO()
# 11-byte signature, no flags, no header extension
cpy.write(pack('!11sii', b'PGCOPY\n\377\r\n\0', 0, 0))
# Columns: id, node, ts, val1, val2
# Zip: (column position, format, size)
row_format = list(zip(range(-1, 4),
('i', 'i', 'h', 'f', 'd'),
( 4, 4, 2, 4, 8 )))
for row in data:
# Number of columns/fields (always 5)
cpy.write(pack('!h', 5))
for col, fmt, size in row_format:
value = (id_seq if col == -1 else row[col])
cpy.write(pack('!i' + fmt, size, value))
id_seq += 1 # manually increment sequence outside of database
# File trailer
cpy.write(pack('!h', -1))
# Copy data to database
cpy.seek(0)
curs.copy_expert("COPY num_data FROM STDIN WITH BINARY", cpy)
# Update sequence on database
curs.execute("SELECT setval('num_data_id_seq', %s, false)", (id_seq,))
conn.commit()
我改写了上面的方法来为COPY编写文件。我在Python中的数据位于NumPy数组中，因此使用它们很有意义。这是一个data具有1M行，7列的示例：
import psycopg2
import numpy as np
from struct import pack
from io import BytesIO
from datetime import datetime
conn = psycopg2.connect("dbname=mydb user=postgres")
curs = conn.cursor()
# NumPy record array
shape = (7, 2000, 500)
print('Generating data with %i rows, %i columns' % (shape[1]*shape[2], shape[0]))
dtype = ([('id', 'i4'), ('node', 'i4'), ('ts', 'i2')] +
[('s' + str(x), 'f4') for x in range(shape[0])])
data = np.empty(shape[1]*shape[2], dtype)
data['id'] = np.arange(shape[1]*shape[2]) + 1
data['node'] = np.tile(np.arange(shape[1]) + 1, shape[2])
data['ts'] = np.repeat(np.arange(shape[2]) + 1, shape[1])
data['s0'] = np.random.rand(shape[1]*shape[2]) * 100
prv = 's0'
for nxt in data.dtype.names[4:]:
data[nxt] = data[prv] + np.random.rand(shape[1]*shape[2]) * 10
prv = nxt
CREATE TABLE num_data_binary
(
id integer PRIMARY KEY,
node integer NOT NULL,
ts smallint NOT NULL,
s0 real,
s1 real,
s2 real,
s3 real,
s4 real,
s5 real,
s6 real
) WITH (OIDS=FALSE);
另一个类似的表名为num_data_text。
以下是一些简单的辅助函数，它们通过使用NumPy记录数组中的信息为COPY（文本和二进制格式）准备数据：
def prepare_text(dat):
cpy = BytesIO()
for row in dat:
cpy.write('\t'.join([repr(x) for x in row]) + '\n')
return(cpy)
def prepare_binary(dat):
pgcopy_dtype = [('num_fields','>i2')]
for field, dtype in dat.dtype.descr:
pgcopy_dtype += [(field + '_length', '>i4'),
(field, dtype.replace('<', '>'))]
pgcopy = np.empty(dat.shape, pgcopy_dtype)
pgcopy['num_fields'] = len(dat.dtype)
for i in range(len(dat.dtype)):
field = dat.dtype.names[i]
pgcopy[field + '_length'] = dat.dtype[i].alignment
pgcopy[field] = dat[field]
cpy = BytesIO()
cpy.write(pack('!11sii', b'PGCOPY\n\377\r\n\0', 0, 0))
cpy.write(pgcopy.tostring()) # all rows
cpy.write(pack('!h', -1)) # file trailer
return(cpy)
这就是我使用帮助程序函数对两种COPY格式方法进行基准测试的方式：
def time_pgcopy(dat, table, binary):
print('Processing copy object for ' + table)
tstart = datetime.Now()
if binary:
cpy = prepare_binary(dat)
else: # text
cpy = prepare_text(dat)
tendw = datetime.Now()
print('Copy object prepared in ' + str(tendw - tstart) + '; ' +
str(cpy.tell()) + ' bytes; transfering to database')
cpy.seek(0)
if binary:
curs.copy_expert('COPY ' + table + ' FROM STDIN WITH BINARY', cpy)
else: # text
curs.copy_from(cpy, table)
conn.commit()
tend = datetime.Now()
print('Database copy time: ' + str(tend - tendw))
print(' Total time: ' + str(tend - tstart))
return
time_pgcopy(data, 'num_data_text', binary=False)
time_pgcopy(data, 'num_data_binary', binary=True)
这是最后两个time_pgcopy命令的输出：
Processing copy object for num_data_text
Copy object prepared in 0:01:15.288695; 84355016 bytes; transfering to database
Database copy time: 0:00:37.929166
Total time: 0:01:53.217861
Processing copy object for num_data_binary
Copy object prepared in 0:00:01.296143; 80000021 bytes; transfering to database
Database copy time: 0:00:23.325952
Total time: 0:00:24.622095
因此，使用二进制方法，NumPy→文件和File→数据库步骤都更快。明显的区别是Python如何准备COPY文件，这对于文本来说确实很慢。通常，二进制格式会以这种格式的文本格式在2/3的时间内将其加载到数据库中。
最后，我比较了数据库中两个表中的值，以查看数字是否不同。大约1.46％的行的column值不同s0，并且该比例的值增加到6.17％s6（可能与我使用的随机方法有关）。所有70M 32位浮点值之间的非零绝对差值介于9.3132257e-010和7.6293945e-006之间。文本和二进制加载方法之间的这些细微差别是由于文本格式方法所需的float→text→float转换而导致精度损失。
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