Hello!
I want to cache query results in a distributed cache (Redis), so I need to
serialize/deserialize fetched entities very fast.
However, it turned that SQLAlchemy entities are very heavy for pickle to
dump/load. Regular classes and dicts with the same structure takes
significantly less amount of time to reconstruct from bytes.
It's even slower than fetching and mapping the entities from a database.
Here are benchmark results, loading 10000 simple entities:
class Entity(Base):
__tablename__ = 'test_entity'
id = sa.Column(sa.Integer, primary_key=True, autoincrement=True)
field1 = sa.Column(sa.String)
field2 = sa.Column(sa.Integer)
Name (time in ms) Median StdDev Dump
bytes
--------------------------------------------------------------------------------------------------------
test_unpickle_dicts 2.3764 (1.0) 0.0797 (1.09)
337806
test_unpickle_dataclasses 9.2546 (3.89) 0.0734 (1.0)
437861
test_unpickle_classes 9.4188 (3.96) 0.1230 (1.68)
437862
test_load_from_database 91.3482 (38.44) 0.6686 (9.10)
test_unpickle_unsaved_entities 108.6726 (45.73) 0.5154 (7.02)
1448169
test_unpickle_persisted_entities 166.4531 (70.04) 0.3787 (5.16)
1697849
---------------------------------------------------------------------------------------------------------
*Environment*:
OS: Ubuntu 16.04
Python: CPython 3.6.3
SQLAlchemy: 1.3.0
Database: in-memory SQLite, PostgreSQL (all gives similar results)
The full benchmark code is in a single attachment file.
To run:
pytest test_ser.py --benchmark-columns=median,stddev --benchmark-disable-gc
Requirements:
pip install sqlalchemy==1.3.0 pytest==3.6.0 pytest-benchmark==3.1.1
dataclasses==0.6
*Questions:*
1) What strategy for caching I should consider while using SQLAlchemy?
Currently, the only option I see is to have a duplicated declaration of
entities in a form of simple classes and use it when I don't need
modification. Needles to say, it's a lot of code duplication.
2) Is it possible to have a read-only loader in SQLAlchemy? I can imagine
mapping loaded data to an entity class but without instrumentation overhead
which is not needed to read the data.
If continue the idea further, I can imagine a situation when I load the
entity for modification, and it has a read-only relationship to another
entity. So that I don't pay for things I don't use.
Probably I'm making some crazy things and all wrong, feel free to point me
out. My ultimate goal is to manage caching with SQLAlchemy and understand
SQLAlchemy more in this direction.
Thank you in advance!
--
SQLAlchemy -
The Python SQL Toolkit and Object Relational Mapper
http://www.sqlalchemy.org/
To post example code, please provide an MCVE: Minimal, Complete, and Verifiable
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import pytest
from dataclasses import dataclass
from sqlalchemy.ext.declarative import declarative_base
import pickle
import sqlalchemy as sa
Base = declarative_base()
class Entity(Base):
__tablename__ = 'test_entity'
id = sa.Column(sa.Integer, primary_key=True, autoincrement=True)
field1 = sa.Column(sa.String)
field2 = sa.Column(sa.Integer)
def __init__(self, id_: int, field1: str, field2: int):
self.id = id_
self.field1 = field1
self.field2 = field2
def some_method(self) -> str:
return self.field1 + str(self.field2)
@pytest.fixture(scope='session')
def dbengine():
engine = sa.create_engine('sqlite://')
Base.metadata.create_all(engine)
yield engine
@pytest.fixture(scope='function')
def session(dbengine):
session_factory = sa.orm.sessionmaker(bind=dbengine)
Session = sa.orm.scoped_session(session_factory)
yield Session
Session.query(Entity).delete()
Session.remove()
@dataclass
class EntityData:
id: int
field1: str
field2: int
def some_method(self) -> str:
return self.field1 + str(self.field2)
class EntityClass:
def __init__(self, id_: int, field1: str, field2: int):
self.id = id_
self.field1 = field1
self.field2 = field2
def some_method(self) -> str:
return self.field1 + str(self.field2)
def generate_data():
for i in range(10000):
yield i + 1, str(i), i * 2
def test_unpickle_unsaved_entities(benchmark):
entities = [Entity(*data) for data in generate_data()]
dump_unsaved_entities = pickle.dumps(entities)
print(f'\nDump unsaved entities bytes {len(dump_unsaved_entities)}')
benchmark(pickle.loads, dump_unsaved_entities)
def test_unpickle_persisted_entities(session, benchmark):
entities = [Entity(*data) for data in generate_data()]
session.add_all(entities)
session.flush()
session.expunge_all()
entities = session.query(Entity).all()
dump_persisted_entities = pickle.dumps(entities)
print(f'\nDump persisted entities bytes {len(dump_persisted_entities)}')
benchmark(pickle.loads, dump_persisted_entities)
def test_unpickle_dicts(benchmark):
dicts = [
{'id': id_, 'field1': field1, 'field2': field2}
for id_, field1, field2 in generate_data()
]
dump_dicts = pickle.dumps(dicts)
print(f'\nDump dicts bytes {len(dump_dicts)}')
benchmark(pickle.loads, dump_dicts)
def test_unpickle_dataclasses(benchmark):
dataclasses = [EntityData(*data) for data in generate_data()]
dump_dataclasses = pickle.dumps(dataclasses)
print(f'\nDump dataclasses bytes {len(dump_dataclasses)}')
benchmark(pickle.loads, dump_dataclasses)
def test_unpickle_classes(benchmark):
instances = [EntityClass(*data) for data in generate_data()]
dump_classes = pickle.dumps(instances)
print(f'\nDump classes bytes {len(dump_classes)}')
benchmark(pickle.loads, dump_classes)
def test_load_from_database(session, benchmark):
entities = [Entity(*data) for data in generate_data()]
session.add_all(entities)
session.flush()
def load_from_db():
session.expunge_all()
loaded_entities = session.query(Entity).all()
assert len(loaded_entities) == len(entities)
benchmark(load_from_db)
