使用aiopg -下面的代码段工作得非常好

    # items = [10, 11, 12, 13]
    # group = 1
    tup = [(gid, pid) for pid in items]
    args_str = ",".join([str(s) for s in tup])
    # insert into group values (1, 10), (1, 11), (1, 12), (1, 13)
    yield from cur.execute("INSERT INTO group VALUES " + args_str)

Execute_batch在这个问题发布后已经添加到psycopg2。

它比execute_values快。

安全漏洞

截至2022-11-16，@Clodoaldo Neto (Psycopg 2.6)， @Joseph Sheedy， @J。J， @Bart Jonk， @kevo Njoki， @TKoutny和@Nihal Sharma包含SQL注入漏洞，不应使用。

目前为止最快的建议(copy_from)也不应该使用，因为它很难正确地转义数据。当尝试插入'，"，\n， \， \t或\n这样的字符时，这很容易看出。

psycopg2的作者也建议不要使用copy_from:

Copy_from()和copy_to()实际上只是古老且不完整的方法

最快的方法

最快的方法是游标。copy_expert，它可以直接从CSV文件插入数据。

with open("mydata.csv") as f:
    cursor.copy_expert("COPY mytable (my_id, a, b) FROM STDIN WITH csv", f)

copy_expert也是动态生成CSV文件时最快的方法。作为参考，请参阅下面的CSVFile类，该类注意限制内存使用。

import io, csv

class CSVFile(io.TextIOBase):
    # Create a CSV file from rows. Can only be read once.
    def __init__(self, rows, size=8192):
        self.row_iter = iter(rows)
        self.buf = io.StringIO()
        self.available = 0
        self.size = size

    def read(self, n):
        # Buffer new CSV rows until enough data is available
        buf = self.buf
        writer = csv.writer(buf)
        while self.available < n:
            try:
                row_length = writer.writerow(next(self.row_iter))
                self.available += row_length
                self.size = max(self.size, row_length)
            except StopIteration:
                break

        # Read requested amount of data from buffer
        write_pos = buf.tell()
        read_pos = write_pos - self.available
        buf.seek(read_pos)
        data = buf.read(n)
        self.available -= len(data)

        # Shrink buffer if it grew very large
        if read_pos > 2 * self.size:
            remaining = buf.read()
            buf.seek(0)
            buf.write(remaining)
            buf.truncate()
        else:
            buf.seek(write_pos)

        return data

这个类可以这样使用:

rows = [(1, "a", "b"), (2, "c", "d")]
cursor.copy_expert("COPY mytable (my_id, a, b) FROM STDIN WITH csv", CSVFile(rows))

如果所有数据都适合内存，您也可以直接生成整个CSV数据，而不需要使用CSVFile类，但是如果您不知道将来要插入多少数据，则可能不应该这样做。

f = io.StringIO()
writer = csv.writer(f)
for row in rows:
    writer.writerow(row)
f.seek(0)
cursor.copy_expert("COPY mytable (my_id, a, b) FROM STDIN WITH csv", f)

基准测试结果

914毫秒——多次调用cursor.execute 846毫秒——cursor.executemany 362毫秒- psycopg2.extras.execute_batch 346毫秒——execute_batch with page_size=1000 265毫秒——execute_batch带有预处理语句 161毫秒- psycopg2.extras.execute_values 127毫秒——游标。使用字符串连接的值执行 39毫秒- copy_expert一次生成整个CSV文件 32毫秒- copy_expert with CSVFile

所有这些技术在Postgres术语中都被称为“扩展插入”，截至2016年11月24日，它仍然比psychopg2的executemany()和这个线程中列出的所有其他方法快得多(在得到这个答案之前我尝试过)。

下面是一些不使用cur.mogrify的代码，很好，很简单:

valueSQL = [ '%s', '%s', '%s', ... ] # as many as you have columns.
sqlrows = []
rowsPerInsert = 3 # more means faster, but with diminishing returns..
for row in getSomeData:
        # row == [1, 'a', 'yolo', ... ]
        sqlrows += row
        if ( len(sqlrows)/len(valueSQL) ) % rowsPerInsert == 0:
                # sqlrows == [ 1, 'a', 'yolo', 2, 'b', 'swag', 3, 'c', 'selfie' ]
                insertSQL = 'INSERT INTO "twitter" VALUES ' + ','.join(['(' + ','.join(valueSQL) + ')']*rowsPerInsert)
                cur.execute(insertSQL, sqlrows)
                con.commit()
                sqlrows = []
insertSQL = 'INSERT INTO "twitter" VALUES ' + ','.join(['(' + ','.join(valueSQL) + ')']*len(sqlrows))
cur.execute(insertSQL, sqlrows)
con.commit()

但需要注意的是，如果可以使用copy_from()，则应该使用copy_from;)

执行任意接受数组的元组

https://www.postgresqltutorial.com/postgresql-python/insert/

    """ array of tuples """
    vendor_list = [(value1,)]

    """ insert multiple vendors into the vendors table  """
    sql = "INSERT INTO vendors(vendor_name) VALUES(%s)"
    conn = None
    try:
        # read database configuration
        params = config()
        # connect to the PostgreSQL database
        conn = psycopg2.connect(**params)
        # create a new cursor
        cur = conn.cursor()
        # execute the INSERT statement
        cur.executemany(sql,vendor_list)
        # commit the changes to the database
        conn.commit()
        # close communication with the database
        cur.close()
    except (Exception, psycopg2.DatabaseError) as error:
        print(error)
    finally:
        if conn is not None:
            conn.close()

从@ant32

def myInsertManyTuples(connection, table, tuple_of_tuples):
    cursor = connection.cursor()
    try:
        insert_len = len(tuple_of_tuples[0])
        insert_template = "("
        for i in range(insert_len):
            insert_template += "%s,"
        insert_template = insert_template[:-1] + ")"

        args_str = ",".join(
            cursor.mogrify(insert_template, x).decode("utf-8")
            for x in tuple_of_tuples
        )
        cursor.execute("INSERT INTO " + table + " VALUES " + args_str)
        connection.commit()

    except psycopg2.Error as e:
        print(f"psycopg2.Error in myInsertMany = {e}")
        connection.rollback()