性能可能是一个合理的问题。如果数据库模式未更改，则每次运行脚本时反映数据库可能会很耗时。这在开发过程中更多是一个问题，而不是启动一个长时间运行的应用程序。如果您的数据库位于远程服务器上（同样，尤其是在开发期间），这也可以节省大量时间。
我使用类似于答案here 的代码（如@ACV 所述）。一般的计划是第一次执行反射，然后pickle元数据对象。下次运行脚本时，它会查找 pickle 文件并使用它。该文件可以在任何地方，但我将我的放在~/.sqlalchemy_cache。这是一个基于您的代码的示例。
import os
from sqlalchemy.ext.declarative import declarative_base
self.engine = create_engine(
'mysql://%s:%s@%s/%s?charset=utf8mb4' % (
config.DB_USER, config.DB_PASSWD, config.DB_HOST, config.DB_NAME
), echo=False
)
metadata_pickle_filename = "mydb_metadata"
cache_path = os.path.join(os.path.expanduser("~"), ".sqlalchemy_cache")
cached_metadata = None
if os.path.exists(cache_path):
try:
with open(os.path.join(cache_path, metadata_pickle_filename), 'rb') as cache_file:
cached_metadata = pickle.load(file=cache_file)
except IOError:
# cache file not found - no problem, reflect as usual
pass
if cached_metadata:
base = declarative_base(bind=self.engine, metadata=cached_metadata)
else:
base = automap_base()
base.prepare(self.engine, reflect=True) # reflect the tables
# save the metadata for future runs
try:
if not os.path.exists(cache_path):
os.makedirs(cache_path)
# make sure to open in binary mode - we're writing bytes, not str
with open(os.path.join(cache_path, metadata_pickle_filename), 'wb') as cache_file:
pickle.dump(Base.metadata, cache_file)
except:
# couldn't write the file for some reason
pass
self.TableName = base.classes.table_name
对于使用声明性表类定义的任何人，假设 Base 对象定义为例如
Base = declarative_base(bind=engine)
metadata_pickle_filename = "ModelClasses_trilliandb_trillian.pickle"
# ------------------------------------------
# Load the cached metadata if it's available
# ------------------------------------------
# NOTE: delete the cached file if the database schema changes!!
cache_path = os.path.join(os.path.expanduser("~"), ".sqlalchemy_cache")
cached_metadata = None
if os.path.exists(cache_path):
try:
with open(os.path.join(cache_path, metadata_pickle_filename), 'rb') as cache_file:
cached_metadata = pickle.load(file=cache_file)
except IOError:
# cache file not found - no problem
pass
# ------------------------------------------
# define all tables
#
class MyTable(Base):
if cached_metadata:
__table__ = cached_metadata.tables['my_schema.my_table']
else:
__tablename__ = 'my_table'
__table_args__ = {'autoload':True, 'schema':'my_schema'}
...
# ----------------------------------------
# If no cached metadata was found, save it
# ----------------------------------------
if cached_metadata is None:
# cache the metadata for future loading
# - MUST DELETE IF THE DATABASE SCHEMA HAS CHANGED
try:
if not os.path.exists(cache_path):
os.makedirs(cache_path)
# make sure to open in binary mode - we're writing bytes, not str
with open(os.path.join(cache_path, metadata_pickle_filename), 'wb') as cache_file:
pickle.dump(Base.metadata, cache_file)
except:
# couldn't write the file for some reason
pass
重要提示！！如果数据库架构发生变化，您必须删除缓存文件以强制代码自动加载并创建新缓存。如果不这样做，更改将反映在代码中。这很容易忘记。