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Version: devel

sources.sql_database

Source that loads tables form any SQLAlchemy supported database, supports batching requests and incremental loads.

sql_database​

@decorators.source
def sql_database(
    credentials: Union[ConnectionStringCredentials, Engine,
                       str] = dlt.secrets.value,
    schema: Optional[str] = dlt.config.value,
    metadata: Optional[MetaData] = None,
    table_names: Optional[List[str]] = dlt.config.value,
    chunk_size: int = 50000,
    backend: TableBackend = "sqlalchemy",
    detect_precision_hints: Optional[bool] = False,
    reflection_level: Optional[ReflectionLevel] = "full",
    defer_table_reflect: Optional[bool] = None,
    table_adapter_callback: Callable[[Table], None] = None,
    backend_kwargs: Dict[str, Any] = None,
    include_views: bool = False,
    type_adapter_callback: Optional[TTypeAdapter] = None,
    query_adapter_callback: Optional[TQueryAdapter] = None
) -> Iterable[DltResource]

[view_source]

A dlt source which loads data from an SQL database using SQLAlchemy. Resources are automatically created for each table in the schema or from the given list of tables.

Arguments:

  • credentials Union[ConnectionStringCredentials, Engine, str] - Database credentials or an sqlalchemy.Engine instance.
  • schema Optional[str] - Name of the database schema to load (if different from default).
  • metadata Optional[MetaData] - Optional sqlalchemy.MetaData instance. schema argument is ignored when this is used.
  • table_names Optional[List[str]] - A list of table names to load. By default, all tables in the schema are loaded.
  • chunk_size int - Number of rows yielded in one batch. SQL Alchemy will create additional internal rows buffer twice the chunk size.
  • backend TableBackend - Type of backend to generate table data. One of: "sqlalchemy", "pyarrow", "pandas" and "connectorx". "sqlalchemy" yields batches as lists of Python dictionaries, "pyarrow" and "connectorx" yield batches as arrow tables, "pandas" yields panda frames. "sqlalchemy" is the default and does not require additional dependencies, "pyarrow" creates stable destination schemas with correct data types, "connectorx" is typically the fastest but ignores the "chunk_size" so you must deal with large tables yourself.
  • detect_precision_hints bool - Deprecated. Use reflection_level. Set column precision and scale hints for supported data types in the target schema based on the columns in the source tables. This is disabled by default.
  • reflection_level - (ReflectionLevel): Specifies how much information should be reflected from the source database schema.
  • "minimal" - Only table names, nullability and primary keys are reflected. Data types are inferred from the data.
  • "full" - Data types will be reflected on top of "minimal". dlt will coerce the data into reflected types if necessary. This is the default option.
  • "full_with_precision" - Sets precision and scale on supported data types (ie. decimal, text, binary). Creates big and regular integer types.
  • defer_table_reflect bool - Will connect and reflect table schema only when yielding data. Requires table_names to be explicitly passed. Enable this option when running on Airflow. Available on dlt 0.4.4 and later.
  • table_adapter_callback - (Callable): Receives each reflected table. May be used to modify the list of columns that will be selected.
  • backend_kwargs **kwargs - kwargs passed to table backend ie. "conn" is used to pass specialized connection string to connectorx.
  • include_views bool - Reflect views as well as tables. Note view names included in table_names are always included regardless of this setting.
  • type_adapter_callback(Optional[Callable]) - Callable to override type inference when reflecting columns. Argument is a single sqlalchemy data type (TypeEngine instance) and it should return another sqlalchemy data type, or None (type will be inferred from data) query_adapter_callback(Optional[Callable[Select, Table], Select]): Callable to override the SELECT query used to fetch data from the table. The callback receives the sqlalchemy Select and corresponding Table objects and should return the modified Select.

Returns:

  • Iterable[DltResource] - A list of DLT resources for each table to be loaded.

sql_table​

@decorators.resource(name=lambda args: args["table"],
                     standalone=True,
                     spec=SqlTableResourceConfiguration)
def sql_table(
        credentials: Union[ConnectionStringCredentials, Engine,
                           str] = dlt.secrets.value,
        table: str = dlt.config.value,
        schema: Optional[str] = dlt.config.value,
        metadata: Optional[MetaData] = None,
        incremental: Optional[Incremental[Any]] = None,
        chunk_size: int = 50000,
        backend: TableBackend = "sqlalchemy",
        detect_precision_hints: Optional[bool] = None,
        reflection_level: Optional[ReflectionLevel] = "full",
        defer_table_reflect: Optional[bool] = None,
        table_adapter_callback: Callable[[Table], None] = None,
        backend_kwargs: Dict[str, Any] = None,
        type_adapter_callback: Optional[TTypeAdapter] = None,
        included_columns: Optional[List[str]] = None,
        query_adapter_callback: Optional[TQueryAdapter] = None) -> DltResource

[view_source]

A dlt resource which loads data from an SQL database table using SQLAlchemy.

Arguments:

  • credentials Union[ConnectionStringCredentials, Engine, str] - Database credentials or an Engine instance representing the database connection.
  • table str - Name of the table or view to load.
  • schema Optional[str] - Optional name of the schema the table belongs to.
  • metadata Optional[MetaData] - Optional sqlalchemy.MetaData instance. If provided, the schema argument is ignored.
  • incremental Optional[dlt.sources.incremental[Any]] - Option to enable incremental loading for the table. E.g., incremental=dlt.sources.incremental('updated_at', pendulum.parse('2022-01-01T00:00:00Z'))
  • chunk_size int - Number of rows yielded in one batch. SQL Alchemy will create additional internal rows buffer twice the chunk size.
  • backend TableBackend - Type of backend to generate table data. One of: "sqlalchemy", "pyarrow", "pandas" and "connectorx". "sqlalchemy" yields batches as lists of Python dictionaries, "pyarrow" and "connectorx" yield batches as arrow tables, "pandas" yields panda frames. "sqlalchemy" is the default and does not require additional dependencies, "pyarrow" creates stable destination schemas with correct data types, "connectorx" is typically the fastest but ignores the "chunk_size" so you must deal with large tables yourself.
  • reflection_level - (ReflectionLevel): Specifies how much information should be reflected from the source database schema.
  • "minimal" - Only table names, nullability and primary keys are reflected. Data types are inferred from the data.
  • "full" - Data types will be reflected on top of "minimal". dlt will coerce the data into reflected types if necessary. This is the default option.
  • "full_with_precision" - Sets precision and scale on supported data types (ie. decimal, text, binary). Creates big and regular integer types.
  • detect_precision_hints bool - Deprecated. Use reflection_level. Set column precision and scale hints for supported data types in the target schema based on the columns in the source tables. This is disabled by default.
  • defer_table_reflect bool - Will connect and reflect table schema only when yielding data. Enable this option when running on Airflow. Available on dlt 0.4.4 and later
  • table_adapter_callback - (Callable): Receives each reflected table. May be used to modify the list of columns that will be selected.
  • backend_kwargs **kwargs - kwargs passed to table backend ie. "conn" is used to pass specialized connection string to connectorx.
  • type_adapter_callback(Optional[Callable]) - Callable to override type inference when reflecting columns. Argument is a single sqlalchemy data type (TypeEngine instance) and it should return another sqlalchemy data type, or None (type will be inferred from data)
  • included_columns Optional[List[str] - List of column names to select from the table. If not provided, all columns are loaded. query_adapter_callback(Optional[Callable[Select, Table], Select]): Callable to override the SELECT query used to fetch data from the table. The callback receives the sqlalchemy Select and corresponding Table objects and should return the modified Select.

Returns:

  • DltResource - The dlt resource for loading data from the SQL database table.

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!

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