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`microcosm-sqlite` assumes that applications will\n\n -  Build data sets in advance and ship them as static artifacts (e.g. in source control)\n -  Load data immutable sets at runtime without loading entire data sets into memory\n\n\n## Writing Models\n\nPersistent data is expected to use SQLAlchemy's declarative base classes. Because different data sets\nmay be shipped in different SQLite databases, each declarative base class needs to have a **unique**\nname and a separate engine configuration, which is achieved by adding `DataSet` as the base of the\ndeclarative base class:\n\n    Base = DataSet.create(\"some_name\")\n\n\n    class SomeModel(Base):\n        __tablename__ = \"sometable\"\n\n        id = Column(Integer, primary_key=True)\n\n\n## Using Stores\n\nBasic persistence operations are abstracted through a store:\n\n    class SomeStore(Store):\n\n        @property\n        def model_class(self):\n            return SomeModel\n\n\n     store = SomeStore()\n     results = store.search()\n\n\n## Configuring SQLite\n\nEach `DataSet` defaults to using `:memory:` storage, but can be customized in two ways:\n\n 1. The `SQLiteBindFactory` can be configured with custom paths:\n\n        loader = load_from_dict(\n            sqlite=dict(\n                paths={\n                    \"some_name\": \"/path/to/database\",\n                },\n            ),\n        )\n        graph = create_object_graph(\"example\", loader=loader)\n\n 2. The `microcosm.sqlite` entrypoint can contain a mapping from a data set name to a\n    function that returns a path.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fglobality-corp%2Fmicrocosm-sqlite","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fglobality-corp%2Fmicrocosm-sqlite","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fglobality-corp%2Fmicrocosm-sqlite/lists"}