以下是Python 3的复制源的二进制类似功能:

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()

Update

我重写了上面的方法来编写文件以供复制.我在Python中的数据是NumPy数组，所以使用这些数组是有意义的.下面是一个data行7列的例子:

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

在我的数据库中，有两个表:

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);

和另一个类似的表名为num_data_text.

下面是一些简单的帮助函数，可以使用NumPy记录数组中的信息为复制(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)

以下是我如何使用helper函数对两种复制格式方法进行基准测试:

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)

以下是最后两个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

所以两个小矮人→ 文件和文件→ 使用二进制方法，数据库步骤要快得多.明显的区别在于Python如何准备拷贝文件，这对于文本来说非常慢.一般来说，二进制格式作为该模式的文本格式在2/3的时间内加载到数据库中.

最后，我比较了数据库中两个表中的值，看看数字是否不同.对于第s0列，大约1.46%的行具有不同的值，而对于第s6列，这一比例增加到6.17%(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在9.3132257e-010和7.6293945e-006之间.文本加载方法和二进制加载方法之间的这些微小差异是由于浮点的精度损失造成的→ 文本→ 文本格式方法所需的浮点转换.