我正在尝试使用SQLAlchemy模块在python中编写批量upsert（不在SQL中！）。
我在SQLAlchemy add上收到以下错误：
sqlalchemy.exc.IntegrityError: (IntegrityError) duplicate key value violates unique constraint "posts_pkey"
DETAIL: Key (id)=(TEST1234) already exists.
我有一个名为posts的表格，id列上有一个主键。
在这个例子中，我已经在数据库中有一行id=TEST1234。当我尝试db.session.add() id设置为TEST1234的新帖子对象时，我收到上述错误。我的印象是，如果主键已经存在，记录将会更新。
如何仅基于主键使用Flask-SQLAlchemy进行升级？有简单的解决方案吗？
如果没有，我总是可以检查并删除任何具有匹配ID的记录，然后插入新记录，但这对我的情况来说似乎很昂贵，我不希望有很多更新。
答案 0 :(得分：21)
SQLAlchemy中有一个upsert-esque操作：
db.session.merge()
在我找到这个命令之后，我能够执行upserts，但是值得一提的是这个操作对于批量＆＃34; upsert＆＃34;来说很慢。
另一种方法是获取您想要插入的主键列表，并在数据库中查询任何匹配的ID：
# Imagine that post1, post5, and post1000 are posts objects with ids 1, 5 and 1000 respectively
# The goal is to "upsert" these posts.
# we initialize a dict which maps id to the post object
my_new_posts = {1: post1, 5: post5, 1000: post1000}
for each in posts.query.filter(posts.id.in_(my_new_posts.keys())).all():
# Only merge those posts which already exist in the database
db.session.merge(my_new_posts.pop(each.id))
# Only add those posts which did not exist in the database
db.session.add_all(my_new_posts.values())
# Now we commit our modifications (merges) and inserts (adds) to the database!
db.session.commit()
答案 1 :(得分：4)
您可以利用on_conflict_do_update变体。一个简单的示例如下：
from sqlalchemy.dialects.postgresql import insert
class Post(Base):
"""
A simple class for demonstration
"""
id = Column(Integer, primary_key=True)
title = Column(Unicode)
# Prepare all the values that should be "upserted" to the DB
values = [
{"id": 1, "title": "mytitle 1"},
{"id": 2, "title": "mytitle 2"},
{"id": 3, "title": "mytitle 3"},
{"id": 4, "title": "mytitle 4"},
]
stmt = insert(Post).values(values)
stmt = stmt.on_conflict_do_update(
# Let's use the constraint name which was visible in the original posts error msg
constraint="post_pkey",
# The columns that should be updated on conflict
set_={
"title": stmt.excluded.title
}
)
session.execute(stmt)
有关更多详细信息，请参见the PG docs（例如“排除”一词的来源）。
以上代码在values列表和set_的参数中都使用列名作为dict键。如果在类定义中更改了列名，则需要在任何地方进行更改，否则它将中断。可以通过访问列定义来避免这种情况，使代码更难看，但更可靠：
coldefs = Post.__table__.c
values = [
{coldefs.id.name: 1, coldefs.title.name: "mytitlte 1"},
...
]
stmt = stmt.on_conflict_do_update(
...
set_={
coldefs.title.name: stmt.excluded.title
...
}
)
答案 2 :(得分：3)
使用编译扩展（https://docs.sqlalchemy.org/en/13/core/compiler.html）的另一种方法：
from sqlalchemy.ext.compiler import compiles
from sqlalchemy.sql.expression import Insert
@compiles(Insert)
def compile_upsert(insert_stmt, compiler, **kwargs):
"""
converts every SQL insert to an upsert i.e;
INSERT INTO test (foo, bar) VALUES (1, 'a')
becomes:
INSERT INTO test (foo, bar) VALUES (1, 'a') ON CONFLICT(foo) DO UPDATE SET (bar = EXCLUDED.bar)
(assuming foo is a primary key)
:param insert_stmt: Original insert statement
:param compiler: SQL Compiler
:param kwargs: optional arguments
:return: upsert statement
"""
pk = insert_stmt.table.primary_key
insert = compiler.visit_insert(insert_stmt, **kwargs)
ondup = f'ON CONFLICT ({",".join(c.name for c in pk)}) DO UPDATE SET'
updates = ', '.join(f"{c.name}=EXCLUDED.{c.name}" for c in insert_stmt.table.columns)
upsert = ' '.join((insert, ondup, updates))
return upsert
这应该确保所有insert语句都表现为upsert。此实现采用Postgres方言，但对于MySQL方言，应该相当容易进行修改。
答案 3 :(得分：0)
这不是最安全的方法，但是它非常简单且非常快速。我只是试图有选择地覆盖表的一部分。我删除了我知道会发生冲突的已知行，然后从pandas数据框中追加了新行。您的pandas数据框列名称将需要与sql表列名称匹配。
eng = create_engine('postgresql://...')
conn = eng.connect()
conn.execute("DELETE FROM my_table WHERE col = %s", val)
df.to_sql('my_table', con=eng, if_exists='append')
答案 4 :(得分：0)
我开始研究这个，我认为我已经找到了一种在 sqlalchemy 中使用 bulk_insert_mappings 和 bulk_update_mappings 而不是 merge 的混合进行 upsert 的非常有效的方法。
import time
import sqlite3
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, Integer, String, create_engine
from sqlalchemy.orm import scoped_session, sessionmaker
from contextlib import contextmanager
engine = None
Session = sessionmaker()
Base = declarative_base()
def creat_new_database(db_name="sqlite:///bulk_upsert_sqlalchemy.db"):
global engine
engine = create_engine(db_name, echo=False)
local_session = scoped_session(Session)
local_session.remove()
local_session.configure(bind=engine, autoflush=False, expire_on_commit=False)
Base.metadata.drop_all(engine)
Base.metadata.create_all(engine)
@contextmanager
def db_session():
local_session = scoped_session(Session)
session = local_session()
session.expire_on_commit = False
try:
yield session
except BaseException:
session.rollback()
raise
finally:
session.close()
class Customer(Base):
__tablename__ = "customer"
id = Column(Integer, primary_key=True)
name = Column(String(255))
def bulk_upsert_mappings(customers):
entries_to_update = []
entries_to_put = []
with db_session() as sess:
t0 = time.time()
# Find all customers that needs to be updated and build mappings
for each in (
sess.query(Customer.id).filter(Customer.id.in_(customers.keys())).all()
):
customer = customers.pop(each.id)
entries_to_update.append({"id": customer["id"], "name": customer["name"]})
# Bulk mappings for everything that needs to be inserted
for customer in customers.values():
entries_to_put.append({"id": customer["id"], "name": customer["name"]})
sess.bulk_insert_mappings(Customer, entries_to_put)
sess.bulk_update_mappings(Customer, entries_to_update)
sess.commit()
print(
"Total time for upsert with MAPPING update "
+ str(len(customers))
+ " records "
+ str(time.time() - t0)
+ " sec"
+ " inserted : "
+ str(len(entries_to_put))
+ " - updated : "
+ str(len(entries_to_update))
)
def bulk_upsert_merge(customers):
entries_to_update = 0
entries_to_put = []
with db_session() as sess:
t0 = time.time()
# Find all customers that needs to be updated and merge
for each in (
sess.query(Customer.id).filter(Customer.id.in_(customers.keys())).all()
):
values = customers.pop(each.id)
sess.merge(Customer(id=values["id"], name=values["name"]))
entries_to_update += 1
# Bulk mappings for everything that needs to be inserted
for customer in customers.values():
entries_to_put.append({"id": customer["id"], "name": customer["name"]})
sess.bulk_insert_mappings(Customer, entries_to_put)
sess.commit()
print(
"Total time for upsert with MERGE update "
+ str(len(customers))
+ " records "
+ str(time.time() - t0)
+ " sec"
+ " inserted : "
+ str(len(entries_to_put))
+ " - updated : "
+ str(entries_to_update)
)
if __name__ == "__main__":
batch_size = 10000
# Only inserts
customers_insert = {
i: {"id": i, "name": "customer_" + str(i)} for i in range(batch_size)
}
# 50/50 inserts update
customers_upsert = {
i: {"id": i, "name": "customer_2_" + str(i)}
for i in range(int(batch_size / 2), batch_size + int(batch_size / 2))
}
creat_new_database()
bulk_upsert_mappings(customers_insert.copy())
bulk_upsert_mappings(customers_upsert.copy())
bulk_upsert_mappings(customers_insert.copy())
creat_new_database()
bulk_upsert_merge(customers_insert.copy())
bulk_upsert_merge(customers_upsert.copy())
bulk_upsert_merge(customers_insert.copy())
基准测试结果：
Total time for upsert with MAPPING: 0.17138004302978516 sec inserted : 10000 - updated : 0
Total time for upsert with MAPPING: 0.22074174880981445 sec inserted : 5000 - updated : 5000
Total time for upsert with MAPPING: 0.22307634353637695 sec inserted : 0 - updated : 10000
Total time for upsert with MERGE: 0.1724097728729248 sec inserted : 10000 - updated : 0
Total time for upsert with MERGE: 7.852903842926025 sec inserted : 5000 - updated : 5000
Total time for upsert with MERGE: 15.11970829963684 sec inserted : 0 - updated : 10000