PyPI 官网下载 | microcosm-sqlite-0.22.0.tar.gz

# microcosm-sqlite Opinionated data loading with SQLite. While most distributed application runtimes will use a networked data store for mutable state, the usage patterns of data that is read-only at runtime are great fit for SQLite. In particular, `microcosm-sqlite` assumes that applications will - Build data sets in advance and ship them as static artifacts (e.g. in source control) - Load data immutable sets at runtime without loading entire data sets into memory ## Writing Models Persistent data is expected to use SQLAlchemy's declarative base classes. Because different data sets may be shipped in different SQLite databases, each declarative base class needs to have a **unique** name and a separate engine configuration, which is achieved by adding `DataSet` as the base of the declarative base class: Base = DataSet.create("some_name") class SomeModel(Base): __tablename__ = "sometable" id = Column(Integer, primary_key=True) ## Using Stores Basic persistence operations are abstracted through a store: class SomeStore(Store): @property def model_class(self): return SomeModel store = SomeStore() results = store.search() ## Configuring SQLite Each `DataSet` defaults to using `:memory:` storage, but can be customized in two ways: 1. The `SQLiteBindFactory` can be configured with custom paths: loader = load_from_dict( sqlite=dict( paths={ "some_name": "/path/to/database", }, ), ) graph = create_object_graph("example", loader=loader) 2. The `microcosm.sqlite` entrypoint can contain a mapping from a data set name to a function that returns a path.