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Version: 1.0 prerelease

SparkDFExecutionEngine

class great_expectations.execution_engine.SparkDFExecutionEngine(*args, persist: bool = True, spark_config: typing.Optional[dict] = None, spark: typing.Optional[<great_expectations.compatibility.not_imported.NotImported object at 0x7fe4aa26f610>] = None, force_reuse_spark_context: typing.Optional[bool] = None, **kwargs)#

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

This class holds an attribute spark_df which is a spark.sql.DataFrame.

Constructor builds a SparkDFExecutionEngine, using provided configuration parameters.

Parameters:

  • *args – Positional arguments for configuring SparkDFExecutionEngine

  • persist – If True (default), then creation of the Spark DataFrame is done outside this class

  • spark_config – Dictionary of Spark configuration options. If there is an existing Spark context, the spark_config will be used to update that context in environments that allow it. In local environments the Spark context will be stopped and restarted with the new spark_config.

  • spark – A PySpark Session used to set the SparkDFExecutionEngine being configured. Will override spark_config if provided.

  • force_reuse_spark_context –

    If True then utilize existing SparkSession if it exists and is active

    Deprecated since version 1.0: The force_reuse_spark_context attribute is no longer part of any Spark Datasource classes. The existing Spark context will be reused if possible. If a spark_config is passed that doesn’t match the existing config, the context will be stopped and restarted in local environments only.

  • **kwargs – Keyword arguments for configuring SparkDFExecutionEngine

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

Uses a DataFrame and Domain kwargs (which include a row condition and a condition parser) to obtain and/or query a Batch of data.

Returns in the format of a Spark DataFrame along with Domain arguments required for computing. If the Domain is a single column, this is added to ‘accessor Domain kwargs’ and used for later access.

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:

  • a DataFrame (the data on which to compute)

  • a dictionary of compute_domain_kwargs, describing the DataFrame

  • a dictionary of accessor_domain_kwargs, describing any accessors needed to identify the Domain within the compute domain

Return type:

A tuple including

get_domain_records(domain_kwargs: dict)→ pyspark.DataFrame#

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

Parameters:

domain_kwargs (dict) –

Returns:

A DataFrame (the data on which to compute returned in the format of a Spark DataFrame)