在这种特殊情况下，最好降到 DB-API 级别，因为您需要一些即使 SQLAlchemy Core 也不会直接公开的工具，例如copy_expert()。这可以使用raw_connection() 来完成。如果您的源数据是 CSV 文件，那么在这种情况下您根本不需要 pandas。首先创建一个临时临时表，将数据复制到临时表，然后插入到目标表中并进行冲突处理：
conn = engine.raw_connection()
try:
with conn.cursor() as cur:
cur.execute("""CREATE TEMPORARY TABLE TEST_STAGING ( LIKE TEST_TABLE )
ON COMMIT DROP""")
with open("your_source.csv") as data:
cur.copy_expert("""COPY TEST_STAGING ( itemid, title, street, pincode )
FROM STDIN WITH CSV""", data)
cur.execute("""INSERT INTO TEST_TABLE ( itemid, title, street, pincode )
SELECT itemid, title, street, pincode
FROM TEST_STAGING
ON CONFLICT ( itemid )
DO UPDATE SET title = EXCLUDED.title
, street = EXCLUDED.street
, pincode = EXCLUDED.pincode""")
except:
conn.rollback()
raise
else:
conn.commit()
finally:
conn.close()
另一方面，如果您的源数据是DataFrame，您仍然可以通过passing a function as method= to to_sql() 使用COPY。该函数甚至可以隐藏所有上述逻辑：
import csv
from io import StringIO
from psycopg2 import sql
def psql_upsert_copy(table, conn, keys, data_iter):
dbapi_conn = conn.connection
buf = StringIO()
writer = csv.writer(buf)
writer.writerows(data_iter)
buf.seek(0)
if table.schema:
table_name = sql.SQL("{}.{}").format(
sql.Identifier(table.schema), sql.Identifier(table.name))
else:
table_name = sql.Identifier(table.name)
tmp_table_name = sql.Identifier(table.name + "_staging")
columns = sql.SQL(", ").join(map(sql.Identifier, keys))
with dbapi_conn.cursor() as cur:
# Create the staging table
stmt = "CREATE TEMPORARY TABLE {} ( LIKE {} ) ON COMMIT DROP"
stmt = sql.SQL(stmt).format(tmp_table_name, table_name)
cur.execute(stmt)
# Populate the staging table
stmt = "COPY {} ( {} ) FROM STDIN WITH CSV"
stmt = sql.SQL(stmt).format(tmp_table_name, columns)
cur.copy_expert(stmt, buf)
# Upsert from the staging table to the destination. First find
# out what the primary key columns are.
stmt = """
SELECT kcu.column_name
FROM information_schema.table_constraints tco
JOIN information_schema.key_column_usage kcu
ON kcu.constraint_name = tco.constraint_name
AND kcu.constraint_schema = tco.constraint_schema
WHERE tco.constraint_type = 'PRIMARY KEY'
AND tco.table_name = %s
"""
args = (table.name,)
if table.schema:
stmt += "AND tco.table_schema = %s"
args += (table.schema,)
cur.execute(stmt, args)
pk_columns = {row[0] for row in cur.fetchall()}
# Separate "data" columns from (primary) key columns
data_columns = [k for k in keys if k not in pk_columns]
# Build conflict_target
pk_columns = sql.SQL(", ").join(map(sql.Identifier, pk_columns))
set_ = sql.SQL(", ").join([
sql.SQL("{} = EXCLUDED.{}").format(k, k)
for k in map(sql.Identifier, data_columns)])
stmt = """
INSERT INTO {} ( {} )
SELECT {}
FROM {}
ON CONFLICT ( {} )
DO UPDATE SET {}
"""
stmt = sql.SQL(stmt).format(
table_name, columns, columns, tmp_table_name, pk_columns, set_)
cur.execute(stmt)
然后您将插入新的DataFrame 使用
df.to_sql("test_table", engine,
method=psql_upsert_copy,
index=False,
if_exists="append")
在这台带有本地数据库的机器上使用这种方法更新插入约 1,000,000 行大约需要 16 秒。