https://github.com/globality-corp/microcosm-sqlite

https://github.com/globality-corp/microcosm-sqlite

microcosm sqlite

Last synced: 5 months ago
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• Host: GitHub
• URL: https://github.com/globality-corp/microcosm-sqlite
• Owner: globality-corp
• License: apache-2.0
• Archived: true
• Created: 2018-01-08T07:51:31.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2025-05-14T16:51:19.000Z (about 1 year ago)
• Last Synced: 2025-08-31T01:25:30.056Z (10 months ago)
• Topics: microcosm, sqlite
• Language: Python
• Homepage:
• Size: 154 KB
• Stars: 0
• Watchers: 23
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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README

          
# 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.