使用SQLAlchemy ORM批量插入

Is there any way to get SQLAlchemy to do a bulk insert rather than inserting each individual object. i.e.,

doing:

INSERT INTO `foo` (`bar`) VALUES (1), (2), (3)

rather than:

INSERT INTO `foo` (`bar`) VALUES (1)
INSERT INTO `foo` (`bar`) VALUES (2)
INSERT INTO `foo` (`bar`) VALUES (3)

I’ve just converted some code to use sqlalchemy rather than raw sql and although it is now much nicer to work with it seems to be slower now (up to a factor of 10), I’m wondering if this is the reason.

May be I could improve the situation using sessions more efficiently. At the moment I have autoCommit=False and do a session.commit() after I’ve added some stuff. Although this seems to cause the data to go stale if the DB is changed elsewhere, like even if I do a new query I still get old results back?

Thanks for your help!

SQLAlchemy introduced that in version 1.0.0:

Bulk operations – SQLAlchemy docs

With these operations, you can now do bulk inserts or updates!

For instance, you can do:

s = Session()
objects = [
    User(name="u1"),
    User(name="u2"),
    User(name="u3")
]
s.bulk_save_objects(objects)
s.commit()

Here, a bulk insert will be made.

The sqlalchemy docs have a writeup on the performance of various techniques that can be used for bulk inserts:

ORMs are basically not intended for high-performance bulk inserts – this is the whole reason SQLAlchemy offers the Core in addition to the ORM as a first-class component.

For the use case of fast bulk inserts, the SQL generation and execution system that the ORM builds on top of is part of the Core. Using this system directly, we can produce an INSERT that is competitive with using the raw database API directly.

Alternatively, the SQLAlchemy ORM offers the Bulk Operations suite of methods, which provide hooks into subsections of the unit of work process in order to emit Core-level INSERT and UPDATE constructs with a small degree of ORM-based automation.

The example below illustrates time-based tests for several different methods of inserting rows, going from the most automated to the least. With cPython 2.7, runtimes observed:

classics-MacBook-Pro:sqlalchemy classic$ python test.py
SQLAlchemy ORM: Total time for 100000 records 12.0471920967 secs
SQLAlchemy ORM pk given: Total time for 100000 records 7.06283402443 secs
SQLAlchemy ORM bulk_save_objects(): Total time for 100000 records 0.856323003769 secs
SQLAlchemy Core: Total time for 100000 records 0.485800027847 secs
sqlite3: Total time for 100000 records 0.487842082977 sec

Script:

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

Base = declarative_base()
DBSession = scoped_session(sessionmaker())
engine = None


class Customer(Base):
    __tablename__ = "customer"
    id = Column(Integer, primary_key=True)
    name = Column(String(255))


def init_sqlalchemy(dbname='sqlite:///sqlalchemy.db'):
    global engine
    engine = create_engine(dbname, echo=False)
    DBSession.remove()
    DBSession.configure(bind=engine, autoflush=False, expire_on_commit=False)
    Base.metadata.drop_all(engine)
    Base.metadata.create_all(engine)


def test_sqlalchemy_orm(n=100000):
    init_sqlalchemy()
    t0 = time.time()
    for i in xrange(n):
        customer = Customer()
        customer.name = 'NAME ' + str(i)
        DBSession.add(customer)
        if i % 1000 == 0:
            DBSession.flush()
    DBSession.commit()
    print(
        "SQLAlchemy ORM: Total time for " + str(n) +
        " records " + str(time.time() - t0) + " secs")


def test_sqlalchemy_orm_pk_given(n=100000):
    init_sqlalchemy()
    t0 = time.time()
    for i in xrange(n):
        customer = Customer(id=i+1, name="NAME " + str(i))
        DBSession.add(customer)
        if i % 1000 == 0:
            DBSession.flush()
    DBSession.commit()
    print(
        "SQLAlchemy ORM pk given: Total time for " + str(n) +
        " records " + str(time.time() - t0) + " secs")


def test_sqlalchemy_orm_bulk_insert(n=100000):
    init_sqlalchemy()
    t0 = time.time()
    n1 = n
    while n1 > 0:
        n1 = n1 - 10000
        DBSession.bulk_insert_mappings(
            Customer,
            [
                dict(name="NAME " + str(i))
                for i in xrange(min(10000, n1))
            ]
        )
    DBSession.commit()
    print(
        "SQLAlchemy ORM bulk_save_objects(): Total time for " + str(n) +
        " records " + str(time.time() - t0) + " secs")


def test_sqlalchemy_core(n=100000):
    init_sqlalchemy()
    t0 = time.time()
    engine.execute(
        Customer.__table__.insert(),
        [{"name": 'NAME ' + str(i)} for i in xrange(n)]
    )
    print(
        "SQLAlchemy Core: Total time for " + str(n) +
        " records " + str(time.time() - t0) + " secs")


def init_sqlite3(dbname):
    conn = sqlite3.connect(dbname)
    c = conn.cursor()
    c.execute("DROP TABLE IF EXISTS customer")
    c.execute(
        "CREATE TABLE customer (id INTEGER NOT NULL, "
        "name VARCHAR(255), PRIMARY KEY(id))")
    conn.commit()
    return conn


def test_sqlite3(n=100000, dbname='sqlite3.db'):
    conn = init_sqlite3(dbname)
    c = conn.cursor()
    t0 = time.time()
    for i in xrange(n):
        row = ('NAME ' + str(i),)
        c.execute("INSERT INTO customer (name) VALUES (?)", row)
    conn.commit()
    print(
        "sqlite3: Total time for " + str(n) +
        " records " + str(time.time() - t0) + " sec")

if __name__ == '__main__':
    test_sqlalchemy_orm(100000)
    test_sqlalchemy_orm_pk_given(100000)
    test_sqlalchemy_orm_bulk_insert(100000)
    test_sqlalchemy_core(100000)
    test_sqlite3(100000)

As far as I know, there is no way to get the ORM to issue bulk inserts. I believe the underlying reason is that SQLAlchemy needs to keep track of each object’s identity (i.e., new primary keys), and bulk inserts interfere with that. For example, assuming your foo table contains an id column and is mapped to a Foo class:

x = Foo(bar=1)
print x.id
# None
session.add(x)
session.flush()
# BEGIN
# INSERT INTO foo (bar) VALUES(1)
# COMMIT
print x.id
# 1

Since SQLAlchemy picked up the value for x.id without issuing another query, we can infer that it got the value directly from the INSERT statement. If you don’t need subsequent access to the created objects via the same instances, you can skip the ORM layer for your insert:

Foo.__table__.insert().execute([{'bar': 1}, {'bar': 2}, {'bar': 3}])
# INSERT INTO foo (bar) VALUES ((1,), (2,), (3,))

SQLAlchemy can’t match these new rows with any existing objects, so you’ll have to query them anew for any subsequent operations.

As far as stale data is concerned, it’s helpful to remember that the session has no built-in way to know when the database is changed outside of the session. In order to access externally modified data through existing instances, the instances must be marked as expired. This happens by default on session.commit(), but can be done manually by calling session.expire_all() or session.expire(instance). An example (SQL omitted):

x = Foo(bar=1)
session.add(x)
session.commit()
print x.bar
# 1
foo.update().execute(bar=42)
print x.bar
# 1
session.expire(x)
print x.bar
# 42

session.commit() expires x, so the first print statement implicitly opens a new transaction and re-queries x‘s attributes. If you comment out the first print statement, you’ll notice that the second one now picks up the correct value, because the new query isn’t emitted until after the update.

This makes sense from the point of view of transactional isolation – you should only pick up external modifications between transactions. If this is causing you trouble, I’d suggest clarifying or re-thinking your application’s transaction boundaries instead of immediately reaching for session.expire_all().

I usually do it using add_all.

from app import session
from models import User

objects = [User(name="u1"), User(name="u2"), User(name="u3")]
session.add_all(objects)
session.commit()

Direct support was added to SQLAlchemy as of version 0.8

As per the docs, connection.execute(table.insert().values(data)) should do the trick. (Note that this is not the same as connection.execute(table.insert(), data) which results in many individual row inserts via a call to executemany). On anything but a local connection the difference in performance can be enormous.

SQLAlchemy introduced that in version 1.0.0:

Bulk operations – SQLAlchemy docs

With these operations, you can now do bulk inserts or updates!

For instance (if you want the lowest overhead for simple table INSERTs), you can use Session.bulk_insert_mappings():

loadme = [(1, 'a'),
          (2, 'b'),
          (3, 'c')]
dicts = [dict(bar=t[0], fly=t[1]) for t in loadme]

s = Session()
s.bulk_insert_mappings(Foo, dicts)
s.commit()

Or, if you want, skip the loadme tuples and write the dictionaries directly into dicts (but I find it easier to leave all the wordiness out of the data and load up a list of dictionaries in a loop).

Piere’s answer is correct but one issue is that bulk_save_objects by default does not return the primary keys of the objects, if that is of concern to you. Set return_defaults to True to get this behavior.

The documentation is here.

foos = [Foo(bar='a',), Foo(bar='b'), Foo(bar='c')]
session.bulk_save_objects(foos, return_defaults=True)
for foo in foos:
    assert foo.id is not None
session.commit()

All Roads Lead to Rome, but some of them crosses mountains, requires ferries but if you want to get there quickly just take the motorway.

In this case the motorway is to use the execute_batch() feature of psycopg2. The documentation says it the best:

The current implementation of executemany() is (using an extremely charitable understatement) not particularly performing. These functions can be used to speed up the repeated execution of a statement against a set of parameters. By reducing the number of server roundtrips the performance can be orders of magnitude better than using executemany().

In my own test execute_batch() is approximately twice as fast as executemany(), and gives the option to configure the page_size for further tweaking (if you want to squeeze the last 2-3% of performance out of the driver).

The same feature can easily be enabled if you are using SQLAlchemy by setting use_batch_mode=True as a parameter when you instantiate the engine with create_engine()

This is a way:

values = [1, 2, 3]
Foo.__table__.insert().execute([{'bar': x} for x in values])

This will insert like this:

INSERT INTO `foo` (`bar`) VALUES (1), (2), (3)

Reference: The SQLAlchemy FAQ includes benchmarks for various commit methods.

The best answer I found so far was in sqlalchemy documentation:

http://docs.sqlalchemy.org/en/latest/faq/performance.html#i-m-inserting-400-000-rows-with-the-orm-and-it-s-really-slow

There is a complete example of a benchmark of possible solutions.

As shown in the documentation:

bulk_save_objects is not the best solution but it performance are correct.

The second best implementation in terms of readability I think was with the SQLAlchemy Core:

def test_sqlalchemy_core(n=100000):
    init_sqlalchemy()
    t0 = time.time()
    engine.execute(
        Customer.__table__.insert(),
            [{"name": 'NAME ' + str(i)} for i in xrange(n)]
    )

The context of this function is given in the documentation article.