Skip to main content

SqlAlchemyExecutionEngine

class great_expectations.execution_engine.SqlAlchemyExecutionEngine(name: Optional[str] = None, credentials: Optional[dict] = None, data_context: Optional[Any] = None, engine: Optional[SaEngine] = None, connection_string: Optional[str] = None, url: Optional[str] = None, batch_data_dict: Optional[dict] = None, create_temp_table: bool = True, concurrency: Optional[ConcurrencyConfig] = None, **kwargs)#

SparkDFExecutionEngine instantiates the ExecutionEngine API to support computations using Spark platform.

Constructor builds a SqlAlchemyExecutionEngine, using a provided connection string/url/engine/credentials to access the desired database.

Also initializes the dialect to be used.

Parameters:

  • name (str) – The name of the SqlAlchemyExecutionEngine

  • credentials – If the Execution Engine is not provided, the credentials can be used to build the ExecutionEngine object. If the Engine is provided, it will be used instead.

  • data_context (DataContext) – An object representing a Great Expectations project that can be used to access ExpectationSuite objects and the Project Data itself.

  • engine (Engine) – A SqlAlchemy Engine used to set the SqlAlchemyExecutionEngine being configured, useful if an Engine has already been configured and should be reused. Will override Credentials if provided.

  • connection_string (string) – If neither the engines nor the credentials have been provided, a connection string can be used to access the data. This will be overridden by both the engine and credentials if those are provided.

  • url (string) – If neither the engines, the credentials, nor the connection_string have been provided, a URL can be used to access the data. This will be overridden by all other configuration options if any are provided.

  • concurrency (ConcurrencyConfig) – Concurrency config used to configure the sqlalchemy engine.

For example:

    execution_engine: ExecutionEngine = SqlAlchemyExecutionEngine(connection_string="dbmstype://user:password@host:5432/database_name")

get_compute_domain(domain_kwargs: dict, domain_type: Union[str, great_expectations.core.metric_domain_types.MetricDomainTypes], accessor_keys: Optional[Iterable[str]] = None) → Tuple[sqlalchemy.sql.selectable.Selectable, dict, dict]#

Uses a given batch dictionary and Domain kwargs to obtain a SqlAlchemy column object.

Parameters:

  • domain_kwargs (dict) – a dictionary consisting of the Domain kwargs specifying which data to obtain

  • domain_type (str or MetricDomainTypes) – an Enum value indicating which metric Domain the user would like to be using, or a corresponding string value representing it. String types include "identity", "column", "column_pair", "table" and "other". Enum types include capitalized versions of these from the class MetricDomainTypes.

  • accessor_keys (str iterable) – keys that are part of the compute Domain but should be ignored when describing the Domain and simply transferred with their associated values into accessor_domain_kwargs.

Returns:

SqlAlchemy column

get_domain_records(domain_kwargs: dict) → sqlalchemy.sql.selectable.Selectable#

Uses the given Domain kwargs (which include row_condition, condition_parser, and ignore_row_if directives) to obtain and/or query a Batch of data.

Parameters:

domain_kwargs (dict) –

Returns:

An SqlAlchemy table/column(s) (the selectable object for obtaining data on which to compute returned in the format of an SqlAlchemy table/column(s) object)